Library for exploring and validating machine learning data

Last update: Jan 3, 2023

Related tags

Data Visualization data-validation

Overview

TensorFlow Data Validation

TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX).

TF Data Validation includes:

Scalable calculation of summary statistics of training and test data.
Integration with a viewer for data distributions and statistics, as well as faceted comparison of pairs of features (Facets)
Automated data-schema generation to describe expectations about data like required values, ranges, and vocabularies
A schema viewer to help you inspect the schema.
Anomaly detection to identify anomalies, such as missing features, out-of-range values, or wrong feature types, to name a few.
An anomalies viewer so that you can see what features have anomalies and learn more in order to correct them.

For instructions on using TFDV, see the get started guide and try out the example notebook. Some of the techniques implemented in TFDV are described in a technical paper published in SysML'19.

Caution: TFDV may be backwards incompatible before version 1.0.

Installing from PyPI

The recommended way to install TFDV is using the PyPI package:

pip install tensorflow-data-validation

Nightly Packages

TFDV also hosts nightly packages at https://pypi-nightly.tensorflow.org on Google Cloud. To install the latest nightly package, please use the following command:

pip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-data-validation

This will install the nightly packages for the major dependencies of TFDV such as TFX Basic Shared Libraries (TFX-BSL) and TensorFlow Metadata (TFMD).

Build with Docker

This is the recommended way to build TFDV under Linux, and is continuously tested at Google.

1. Install Docker

Please first install docker and docker-compose by following the directions: docker; docker-compose.

2. Clone the TFDV repository

git clone https://github.com/tensorflow/data-validation
cd data-validation

Note that these instructions will install the latest master branch of TensorFlow Data Validation. If you want to install a specific branch (such as a release branch), pass -b <branchname> to the git clone command.

3. Build the pip package

Then, run the following at the project root:

sudo docker-compose build manylinux2010
sudo docker-compose run -e PYTHON_VERSION=${PYTHON_VERSION} manylinux2010

where PYTHON_VERSION is one of {35, 36, 37, 38}.

A wheel will be produced under dist/.

4. Install the pip package

pip install dist/*.whl

Build from source

1. Prerequisites

To compile and use TFDV, you need to set up some prerequisites.

Install NumPy

If NumPy is not installed on your system, install it now by following these directions.

Install Bazel

If Bazel is not installed on your system, install it now by following these directions.

2. Clone the TFDV repository

git clone https://github.com/tensorflow/data-validation
cd data-validation

3. Build the pip package

TFDV wheel is Python version dependent -- to build the pip package that works for a specific Python version, use that Python binary to run:

python setup.py bdist_wheel

You can find the generated .whl file in the dist subdirectory.

4. Install the pip package

pip install dist/*.whl

Supported platforms

TFDV is tested on the following 64-bit operating systems:

macOS 10.14.6 (Mojave) or later.
Ubuntu 16.04 or later.
Windows 7 or later.

Notable Dependencies

TensorFlow is required.

Apache Beam is required; it's the way that efficient distributed computation is supported. By default, Apache Beam runs in local mode but can also run in distributed mode using Google Cloud Dataflow and other Apache Beam runners.

Apache Arrow is also required. TFDV uses Arrow to represent data internally in order to make use of vectorized numpy functions.

Compatible versions

The following table shows the package versions that are compatible with each other. This is determined by our testing framework, but other untested combinations may also work.

tensorflow-data-validation	apache-beam[gcp]	pyarrow	tensorflow	tensorflow-metadata	tensorflow-transform	tfx-bsl
GitHub master	2.27.0	2.0.0	nightly (1.x/2.x)	0.27.0	n/a	0.27.0
0.27.0	2.27.0	2.0.0	1.15 / 2.4	0.27.0	n/a	0.27.0
0.26.0	2.25.0	0.17.0	1.15 / 2.3	0.26.0	0.26.0	0.26.0
0.25.0	2.25.0	0.17.0	1.15 / 2.3	0.25.0	0.25.0	0.25.0
0.24.1	2.24.0	0.17.0	1.15 / 2.3	0.24.0	0.24.1	0.24.1
0.24.0	2.23.0	0.17.0	1.15 / 2.3	0.24.0	0.24.0	0.24.0
0.23.1	2.24.0	0.17.0	1.15 / 2.3	0.23.0	0.23.0	0.23.0
0.23.0	2.23.0	0.17.0	1.15 / 2.3	0.23.0	0.23.0	0.23.0
0.22.2	2.20.0	0.16.0	1.15 / 2.2	0.22.0	0.22.0	0.22.1
0.22.1	2.20.0	0.16.0	1.15 / 2.2	0.22.0	0.22.0	0.22.1
0.22.0	2.20.0	0.16.0	1.15 / 2.2	0.22.0	0.22.0	0.22.0
0.21.5	2.17.0	0.15.0	1.15 / 2.1	0.21.0	0.21.1	0.21.3
0.21.4	2.17.0	0.15.0	1.15 / 2.1	0.21.0	0.21.1	0.21.3
0.21.2	2.17.0	0.15.0	1.15 / 2.1	0.21.0	0.21.0	0.21.0
0.21.1	2.17.0	0.15.0	1.15 / 2.1	0.21.0	0.21.0	0.21.0
0.21.0	2.17.0	0.15.0	1.15 / 2.1	0.21.0	0.21.0	0.21.0
0.15.0	2.16.0	0.14.0	1.15 / 2.0	0.15.0	0.15.0	0.15.0
0.14.1	2.14.0	0.14.0	1.14	0.14.0	0.14.0	n/a
0.14.0	2.14.0	0.14.0	1.14	0.14.0	0.14.0	n/a
0.13.1	2.11.0	n/a	1.13	0.12.1	0.13.0	n/a
0.13.0	2.11.0	n/a	1.13	0.12.1	0.13.0	n/a
0.12.0	2.10.0	n/a	1.12	0.12.1	0.12.0	n/a
0.11.0	2.8.0	n/a	1.11	0.9.0	0.11.0	n/a
0.9.0	2.6.0	n/a	1.9	n/a	n/a	n/a

Questions

Please direct any questions about working with TF Data Validation to Stack Overflow using the tensorflow-data-validation tag.

Links

Comments

Slow performance when computing stats for moderately large data set
Hi,

I like this project a lot, and thanks for releasing it! I see the potential to save a lot of time when I first receive new datasets. However, I have issues with performance.

OS: the notebook is running in a Docker container based on https://hub.docker.com/r/tensorflow/tensorflow/.

Hardware:

GPUs 16X NVIDIA® Tesla V100 GPU Memory 512GB total CPU Dual Intel Xeon Platinum 8168, 2.7 GHz, 24-cores System Memory 1.5TB

It takes me >8 hours (30900 s) to compute the statistics for a ~100 dataset, with file sizes from 0.5 MB to 300 MB, with a median of 70 MB. It's true that the Docker container introduces some overhead, but given the specs of my hardware, I think it's too much. Any tips on how to speed up computations, w/o changing hardware (i.e., no cloud)? For example, if there was an option to compute statistics in a dataframe, rather than in a protocol buffer, one could use Modin to speedup pandas computations with minimal changes to code.

PS if I use a GPU container instead, with

$ docker run --runtime=nvidia -it -p 8888:8888 tensorflow/tensorflow:latest-gpu should I see a speedup?
stat:awaiting response type:bug
opened by AndreaPi 12
Statistics visualization doesn't work in Firefox

tfdv.visualize_statistics(train_stats) display nothing except a line. I print 'train_stats', it does contain the stats i wanted. so i think the steps before "tfdv.visualize_statistics(train_stats)" is all normal. My browser is firefox. where the problem is?
stat:awaiting response type:support

opened by advancera 12
TFDV does not catch out-of-domain values for categorical ints

The domain of a categorical int feature is included in my schema as a string_domain (generated by using feature.int_domain.is_categorical = True).

However, when I try to run tfdv.validate_instance() on an example with an out-of-domain value for a categorical int, TFDV doesn't generate any anomalies.

Here's a Colab to reproduce.
stat:awaiting response type:bug

opened by kennysong 11
Python 3 Support

Currently, TFDV requires Python 2.7 due to dependency on Apache Beam, which is not compatible with Python 3 yet.

But Python 3 support should be available very soon, as Apache Beam is almost Python 3 ready.
Announcement

opened by paulgc 11
pip install tensorflow-data-validation fails on OS X El Capitan (10.11.6)

pip install tensorflow-data-validation Collecting tensorflow-data-validation Could not find a version that satisfies the requirement tensorflow-data-validation (from versions: ) No matching distribution found for tensorflow-data-validation
type:build/install

opened by wjarek2 10

[DataflowRuntimeException] ImportError: No module named tfdv.statistics.stats_impl

Context

When running tfdv.generate_statistics_from_tfrecord on Dataflow, the job gets submitted successfully to the cluster but I get a: ImportError: No module named tensorflow_data_validation.statistics.stats_impl during the job unpickling phase in the Dataflow worker

Error trace

---------------------------------------------------------------------------
DataflowRuntimeException                  Traceback (most recent call last)
<ipython-input-23-8f1147effd88> in <module>()
     16 # for more options about stats, run `?tfdv.generate_statistics_from_tfrecord`
     17 tfdv.generate_statistics_from_tfrecord(TFRECORDS_PATH, 
---> 18                                        pipeline_options=pipeline_options)

/Users/romain/dev/venv/lib/python2.7/site-packages/tensorflow_data_validation/utils/stats_gen_lib.pyc in generate_statistics_from_tfrecord(data_location, output_path, stats_options, pipeline_options)
     86             shard_name_template='',
     87             coder=beam.coders.ProtoCoder(
---> 88                 statistics_pb2.DatasetFeatureStatisticsList)))
     89   return load_statistics(output_path)
     90 

/Users/romain/dev/venv/lib/python2.7/site-packages/apache_beam/pipeline.pyc in __exit__(self, exc_type, exc_val, exc_tb)
    421   def __exit__(self, exc_type, exc_val, exc_tb):
    422     if not exc_type:
--> 423       self.run().wait_until_finish()
    424 
    425   def visit(self, visitor):

/Users/romain/dev/venv/lib/python2.7/site-packages/apache_beam/runners/dataflow/dataflow_runner.pyc in wait_until_finish(self, duration)
   1164         raise DataflowRuntimeException(
   1165             'Dataflow pipeline failed. State: %s, Error:\n%s' %
-> 1166             (self.state, getattr(self._runner, 'last_error_msg', None)), self)
   1167     return self.state
   1168 

DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error:
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/dataflow_worker/batchworker.py", line 642, in do_work
    work_executor.execute()
  File "/usr/local/lib/python2.7/dist-packages/dataflow_worker/executor.py", line 130, in execute
    test_shuffle_sink=self._test_shuffle_sink)
  File "/usr/local/lib/python2.7/dist-packages/dataflow_worker/executor.py", line 104, in create_operation
    is_streaming=False)
  File "apache_beam/runners/worker/operations.py", line 636, in apache_beam.runners.worker.operations.create_operation
    op = create_pgbk_op(name_context, spec, counter_factory, state_sampler)
  File "apache_beam/runners/worker/operations.py", line 482, in apache_beam.runners.worker.operations.create_pgbk_op
    return PGBKCVOperation(step_name, spec, counter_factory, state_sampler)
  File "apache_beam/runners/worker/operations.py", line 538, in apache_beam.runners.worker.operations.PGBKCVOperation.__init__
    fn, args, kwargs = pickler.loads(self.spec.combine_fn)[:3]
  File "/usr/local/lib/python2.7/dist-packages/apache_beam/internal/pickler.py", line 246, in loads
    return dill.loads(s)
  File "/usr/local/lib/python2.7/dist-packages/dill/_dill.py", line 316, in loads
    return load(file, ignore)
  File "/usr/local/lib/python2.7/dist-packages/dill/_dill.py", line 304, in load
    obj = pik.load()
  File "/usr/lib/python2.7/pickle.py", line 864, in load
    dispatch[key](self)
  File "/usr/lib/python2.7/pickle.py", line 1096, in load_global
    klass = self.find_class(module, name)
  File "/usr/local/lib/python2.7/dist-packages/dill/_dill.py", line 465, in find_class
    return StockUnpickler.find_class(self, module, name)
  File "/usr/lib/python2.7/pickle.py", line 1130, in find_class
    __import__(module)
ImportError: No module named tensorflow_data_validation.statistics.stats_impl

What code did I run?

!pip install -U tensorflow \
                tensorflow-data-validation \
                apache-beam[gcp]

import tensorflow_data_validation as tfdv
from apache_beam.options.pipeline_options import PipelineOptions, GoogleCloudOptions, StandardOptions, SetupOptions

# Create and set your PipelineOptions.
pipeline_options = PipelineOptions()

# For Cloud execution, set the Cloud Platform project, job_name,
# staging location, temp_location and specify DataflowRunner.
google_cloud_options = pipeline_options.view_as(GoogleCloudOptions)
google_cloud_options.project = PROJECT_ID
google_cloud_options.job_name = JOB_NAME
google_cloud_options.staging_location = GCS_STAGING_LOCATION
google_cloud_options.temp_location = GCS_TMP_LOCATION
pipeline_options.view_as(StandardOptions).runner = 'DataflowRunner'
    
tfdv.generate_statistics_from_tfrecord(TFRECORDS_PATH, 
                                       pipeline_options=pipeline_options)

Pip trace

Requirement already up-to-date: tensorflow in /Users/romain/dev/venv/lib/python2.7/site-packages (1.12.0)
Requirement already up-to-date: tensorflow-data-validation in /Users/romain/dev/venv/lib/python2.7/site-packages (0.11.0)
Requirement already up-to-date: apache-beam[gcp] in /Users/romain/dev/venv/lib/python2.7/site-packages (2.8.0)
Requirement already satisfied, skipping upgrade: enum34>=1.1.6 in /Users/romain/dev/venv/lib/python2.7/site-packages (from tensorflow) (1.1.6)
Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in /Users/romain/dev/venv/lib/python2.7/site-packages (from tensorflow) (1.0.5)
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Requirement already satisfied, skipping upgrade: pyparsing>=2.1.4 in /Users/romain/dev/venv/lib/python2.7/site-packages (from pydot<1.3,>=1.2.0->apache-beam[gcp]) (2.1.10)
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stat:awaiting response type:support

opened by yonromai 9

infer_schema(..., infer_feature_shape=True) should parse VarLenFeature to SparseFeature

If I understand things correctly, the desired behavior of infer_schema(..., infer_feature_shape=True) would be to infer feature shapes for FixedLenFeatures while parsing VarLenFeatures to SparseFeatures in the schema. However, my VarLenFeature currently gets parsed as a Feature with dim=0. Am I understanding this properly / am I doing something wrong?
stat:awaiting response type:support

opened by schmidt-jake 8
TDFV==0.14.0 Wheel Fails Integrity Check
It appears TFDV==0.14.0 has a bad hash value and is failing the integrity check done by the resolver, pex:

Exception message: Bad hash for file 'tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so'.

It has been reported (https://github.com/pypa/pip/issues/4705) that pip does not check the file hashes for integrity but other resolvers such as Pex do which leads to failures. Thus, it appears there's some mutation after the wheel is built ?

Minimal Repo:

pip install pex pex tensorflow-data-validation==0.14.0 -o tfdv.pex
stat:awaiting response type:support
opened by jhamet93 8

tfdv manylinux pypi packages are built/linked on too new of a platform for general compatibility

when we attempt to use the current manylinux bdist from pypi (tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl) on a centos7 machine, we see the following ImportError:

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-2-1ad020593972> in <module>()
1 import pkg_resources, importlib
2 importlib.reload(pkg_resources)
----> 3 import tensorflow_data_validation as tfdv
 
/var/lib/mesos/slaves/8bfbe6e2-3bf7-4b49-90c3-15e4be759186-S218/frameworks/201104070004-0000002563-0000/executors/thermos-kwilson-devel-pycx-notebook-0-e9d46056-d500-4c44-9970-ebab4e39f006/runs/18847da2-8502-4393-a01d-c5bfc264f405/sandbox/.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/__init__.py in <module>()
19
20 # Import validation API.
---> 21 from tensorflow_data_validation.api.validation_api import infer_schema
22 from tensorflow_data_validation.api.validation_api import validate_instance
23 from tensorflow_data_validation.api.validation_api import validate_statistics
 
/var/lib/mesos/slaves/8bfbe6e2-3bf7-4b49-90c3-15e4be759186-S218/frameworks/201104070004-0000002563-0000/executors/thermos-kwilson-devel-pycx-notebook-0-e9d46056-d500-4c44-9970-ebab4e39f006/runs/18847da2-8502-4393-a01d-c5bfc264f405/sandbox/.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/api/validation_api.py in <module>()
26 import tensorflow as tf
27 from tensorflow_data_validation import types
---> 28 from tensorflow_data_validation.pywrap import pywrap_tensorflow_data_validation
29 from tensorflow_data_validation.statistics import stats_impl
30 from tensorflow_data_validation.statistics import stats_options
 
/var/lib/mesos/slaves/8bfbe6e2-3bf7-4b49-90c3-15e4be759186-S218/frameworks/201104070004-0000002563-0000/executors/thermos-kwilson-devel-pycx-notebook-0-e9d46056-d500-4c44-9970-ebab4e39f006/runs/18847da2-8502-4393-a01d-c5bfc264f405/sandbox/.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/pywrap_tensorflow_data_validation.py in <module>()
26                 fp.close()
27             return _mod
---> 28     _pywrap_tensorflow_data_validation = swig_import_helper()
29     del swig_import_helper
30 else:
 
/var/lib/mesos/slaves/8bfbe6e2-3bf7-4b49-90c3-15e4be759186-S218/frameworks/201104070004-0000002563-0000/executors/thermos-kwilson-devel-pycx-notebook-0-e9d46056-d500-4c44-9970-ebab4e39f006/runs/18847da2-8502-4393-a01d-c5bfc264f405/sandbox/.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/pywrap_tensorflow_data_validation.py in swig_import_helper()
22         if fp is not None:
23             try:
---> 24                 _mod = imp.load_module('_pywrap_tensorflow_data_validation', fp, pathname, description)
25             finally:
26                 fp.close()
 
/opt/ee/python/3.6/lib/python3.6/imp.py in load_module(name, file, filename, details)
241                 return load_dynamic(name, filename, opened_file)
242         else:
--> 243             return load_dynamic(name, filename, file)
244     elif type_ == PKG_DIRECTORY:
245         return load_package(name, filename)
 
/opt/ee/python/3.6/lib/python3.6/imp.py in load_dynamic(name, path, file)
341         spec = importlib.machinery.ModuleSpec(
342             name=name, loader=loader, origin=path)
--> 343         return _load(spec)
344
345 else:
 
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /var/lib/mesos/slaves/8bfbe6e2-3bf7-4b49-90c3-15e4be759186-S218/frameworks/201104070004-0000002563-0000/executors/thermos-kwilson-devel-pycx-notebook-0-e9d46056-d500-4c44-9970-ebab4e39f006/runs/18847da2-8502-4393-a01d-c5bfc264f405/sandbox/.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so)

ldd reveals a linking issue on the inner .so:

$ ldd .pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so
.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by .pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so)
.pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so: /lib64/libstdc++.so.6: version `CXXABI_1.3.8' not found (required by .pex/install/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl.840798a46d57eb5c1ed0f639d3f47149480121e9/tensorflow_data_validation-0.13.1-cp36-cp36m-manylinux1_x86_64.whl/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so)
        linux-vdso.so.1 =>  (0x00007ffff1ebc000)
        libdl.so.2 => /lib64/libdl.so.2 (0x00007f06a4570000)
        libpthread.so.0 => /lib64/libpthread.so.0 (0x00007f06a4354000)
        libm.so.6 => /lib64/libm.so.6 (0x00007f06a4052000)
        libstdc++.so.6 => /lib64/libstdc++.so.6 (0x00007f06a3d4b000)
        libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007f06a3b35000)
        libc.so.6 => /lib64/libc.so.6 (0x00007f06a3768000)
        /lib64/ld-linux-x86-64.so.2 (0x00007f06a4e42000)

which points to being built/linked on a newer system than is compatible with this configuration:

sh-4.2$ cat /etc/redhat-release
CentOS release 7.6.1810 (Core)
sh-4.2$ rpm -q glibc libstdc++
glibc-2.17-260.el7_6.3.x86_64
libstdc++-4.8.5-36.el7.x86_64

thus I'm fairly sure these binaries aren't actually manylinux (or even broadly centos7) compatible.

stat:awaiting tensorflower type:build/install

opened by kwlzn 8

Issue with importing TFDV 0.21.2 on MacOS 10.12.6(Sierra)

I am running into the following error, trying both on PyCharm and a virtualenv when I am trying to import tensorflow_data_validation.

Error importing tfx_bsl_extension.arrow.array_util. Some tfx_bsl functionalities are not availableError importing tfx_bsl_extension.arrow.table_util. Some tfx_bsl functionalities are not availableTraceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/__init__.py", line 27, in <module> from tensorflow_data_validation.api.validation_api import infer_schema File "/Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/api/validation_api.py", line 31, in <module> from tensorflow_data_validation.pywrap import pywrap_tensorflow_data_validation File "/Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/pywrap/pywrap_tensorflow_data_validation.py", line 28, in <module> _pywrap_tensorflow_data_validation = swig_import_helper() File "/Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/pywrap/pywrap_tensorflow_data_validation.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_data_validation', fp, pathname, description) File "/Users/ky/tfdvtest/lib/python3.7/imp.py", line 242, in load_module return load_dynamic(name, filename, file) File "/Users/ky/tfdvtest/lib/python3.7/imp.py", line 342, in load_dynamic return _load(spec) ImportError: dlopen(/Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so, 2): Symbol not found: ____chkstk_darwin Referenced from: /Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so Expected in: /usr/lib/libSystem.B.dylib in /Users/ky/tfdvtest/lib/python3.7/site-packages/tensorflow_data_validation/pywrap/_pywrap_tensorflow_data_validation.so

I have been using all the versions of tensorflow(2.1), apache-beam(2.17), and pyarrow(0.15.0) that are recommended as compatible. I am also using tfx-bsl(0.21.2). Has anyone run into this issue before?
stat:awaiting tensorflower type:build/install

opened by KevsProjects 7
List datatype (multiple category feature type) not supported
The tfdv.generate_statistics_from_dataframe(...) will fail with the following error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

when one of the columns contain list (e.g. list of strings)
type:support
opened by wsuchy 7

The latest numpy release 1.24.0 broke TFDV

TFDV allows 'numpy>=1.16,<2'. However, the latest numpy version 1.24.0 breaks TFDV. I encountered the following error in a TFDV-related component via TFX.

...
venv/lib/python3.8/site-packages/tfx/components/__init__.py:22: in <module>
    from tfx.components.example_validator.component import ExampleValidator
venv/lib/python3.8/site-packages/tfx/components/example_validator/component.py:20: in <module>
    from tfx.components.example_validator import executor
venv/lib/python3.8/site-packages/tfx/components/example_validator/executor.py:20: in <module>
    import tensorflow_data_validation as tfdv
venv/lib/python3.8/site-packages/tensorflow_data_validation/__init__.py:18: in <module>
    from tensorflow_data_validation.api.stats_api import GenerateStatistics
venv/lib/python3.8/site-packages/tensorflow_data_validation/api/stats_api.py:50: in <module>
    from tensorflow_data_validation.statistics import stats_impl
venv/lib/python3.8/site-packages/tensorflow_data_validation/statistics/stats_impl.py:28: in <module>
    from tensorflow_data_validation.statistics.generators import image_stats_generator
venv/lib/python3.8/site-packages/tensorflow_data_validation/statistics/generators/image_stats_generator.py:99: in <module>
    class TfImageDecoder(ImageDecoderInterface):
venv/lib/python3.8/site-packages/tensorflow_data_validation/statistics/generators/image_stats_generator.py:146: in TfImageDecoder
    def get_formats(self, values: List[np.object]) -> np.ndarray:
venv/lib/python3.8/site-packages/numpy/__init__.py:284: in __getattr__
    raise AttributeError("module {!r} has no attribute "
E   AttributeError: module 'numpy' has no attribute 'object'

Python version: 3.8.12 TFX version: 1.6.2 TFDV version: 1.6.0 numpy version: 1.24.0

stat:awaiting response type:feature

opened by daikeshi 3

Using tfdv to validate text based data

Hi,

After searching online whether tfdv could be used to validate data that contains text. For instance, for a dataset with sentences that have to be mapped to labels. I could not find any real useful tutorials, as the ones that I could find only go into numerical data regarding the dataset. For instance, height, weights, etc.

After looking around in the data-validation package I have found a couple of files that seem to be related to this. https://github.com/tensorflow/data-validation/blob/master/tensorflow_data_validation/statistics/generators/natural_language_stats_generator.py And https://github.com/tensorflow/data-validation/blob/master/tensorflow_data_validation/statistics/generators/natural_language_domain_inferring_stats_generator.py

Furthermore on the Tensorflow website about the StatsOptions class I found the following: https://www.tensorflow.org/tfx/data_validation/api_docs/python/tfdv/StatsOptions

Arguments | Description -- | -- enable_semantic_domain_stats | If True statistics for semantic domains are generated (e.g: image, text domains). semantic_domain_stats_sample_rate | An optional sampling rate for semantic domain statistics. If specified, semantic domain statistics is computed over a sample. vocab_paths | An optional dictionary mapping vocab names to paths. Used in the schema when specifying a NaturalLanguageDomain. The paths can either be to GZIP-compressed TF record files that have a tfrecord.gz suffix or to text files.

These arguments and files do indicate that tfdv can be used to analyze and validate data that would be used in NLP / Text classification type problems.

However, it is unclear to me how one would go about and use these features to validate text-based data? I have enabled the enable_semantic_domain_stats argument and this does give information like sequence length etc. However, how would one extend on this, and validate vocabularies for known/unknown word ratio's; etc.

Any tips or thoughts are highly appreciated! Kind Regards, Caspar
type:docs stat:awaiting tensorflower

opened by Capsar 0
Issue using `allowlist_features` and `denylist_features` in `visualize_statistics`
Overview

I'm having issues specifying the features to include/exclude when visualizing stats in TFDV. It seems like the allowlist_features and denylist_features require a tensorflow_data_validation.types.FeaturePath object, which took a bit to figure out how to construct. This doesn't seem that user friendly -- was it intended to allow a list of strings to be passed?

Code to reproduce

I can reproduce the problem in the public colab example. In the "Compute and Visualize Statistics" section of the above notebook, update the visualize_statistics call to be: tfdv.visualize_statistics(train_stats, denylist_features=['pickup_community_area']). The first feature shouldn't exist in the visualized example (if I'm calling this correctly).

Workaround code

To make this work, I have to manually construct a tensorflow_data_validation.types.FeaturePath object. Perhaps it would be better to do the filter comparison on each feature's path string?

# Show string name of feature first_feat = train_stats.datasets[0].features[0] print(first_feat.path) # Construct necessary object to make `allowlist_feature` filter work from tensorflow_data_validation import types print(types.FeaturePath.from_proto(first_feat.path)) # docs-infra: no-execute tfdv.visualize_statistics(train_stats, allowlist_features=[types.FeaturePath.from_proto(first_feat.path)])
stat:awaiting tensorflower type:bug
opened by wronk 0
Model Unit Testing feature

Hi, I recently checked the TensorFlow Data Validation paper (https://mlsys.org/Conferences/2019/doc/2019/167.pdf). First of all, thanks for the publishing the paper and open-sourcing this project.

But I cannot find similar features in this project with the Model Unit Testing module mentioned in section 5. Is there any plans to open-source model unit testing module or is it already open-sourced?
stat:awaiting tensorflower type:feature

opened by jeongukjae 0

Releases(v1.12.0)

v1.12.0(Dec 8, 2022)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

TFDV is now tested against macOS 12.5 (Monterey).

Known Issues

N/A

Breaking Changes

Depends on tensorflow>=2.11,<3

Depends on tfx-bsl>=1.12.0,<1.13.0.

Depends on tensorflow-metadata>=1.12.0,<1.13.0.

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.11.0(Nov 16, 2022)
Major Features and Improvements

This is the last version that supports TensorFlow 1.15.x. TF 1.15.x support will be removed in the next version. Please check the TF2 migration guide to migrate to TF2.

Add a custom_validate_statistics function to the validation API, and support passing custom validations to validate_statistics. Note that custom validation is not supported on Windows.

Bug Fixes and Other Changes

Fix bug in implementation of semantic_domain_stats_sample_rate.

Add beam metrics on string length

Determine whether to calculate string statistics based on the is_categorical field in the schema string domain.

Histograms counts should now be more accurate for distributions with few distinct values, or frequent individual values.

Nested list length histogram counts are no longer based on the number of values one up in the nested list hierarchy.

Support using jensen-shannon divergence to detect drift and skew for string and categorical features.

get_drift_skew_dataframe now includes a threshold column.

Adds support for NormalizedAbsoluteDifference comparator.

Depends on tensorflow>=1.15.5,<2 or tensorflow>=2.10,<3

Depends on joblib>=1.2.0.

Known Issues

N/A

Breaking Changes

Histogram semantics are slightly changed, so that buckets include their upper bound instead of their lower bound. STANDARD histograms will no longer generate buckets that contain infinite and finite endpoints together.

Introduces StatsOptions.use_sketch_based_topk_uniques replacing experimental_use_sketch_based_topk_uniques. The latter option can still be written, but not read.

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.10.0(Aug 29, 2022)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Skew pipeline supports counting pairs of feature values in base/test.

Depends on apache-beam[gcp]>=2.40,<3.

Depends on pyarrow>=6,<7.

Depends on tfx-bsl>=1.10.1,<1.11.0.

Depends on tensorflow-metadata>=1.10.0,<1.11.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.9.0(Jun 29, 2022)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on tensorflow>=1.15.5,<2 or tensorflow>=2.9,<3

Depends on tfx-bsl>=1.9.0,<1.10.0.

Depends on tensorflow-metadata>=1.9.0,<1.10.0.

Known Issues

N/A

Breaking Changes

Some fields in feature skew results proto changed names to be more generic.

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.8.0(May 16, 2022)
Major Features and Improvements

From this version we will be releasing python 3.9 wheels.

Bug Fixes and Other Changes

Adds get_statistics_html to the public API.

Fixes several incorrect type annotations.

Schema inference handles derived features.

StatsOptions.to_json now raises an error if it encounters unsupported options.

Depends on apache-beam[gcp]>=2.38,<3.

Depends on tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3.

Depends on tensorflow-metadata>=1.8.0,<1.9.0.

Depends on tfx-bsl>=1.8.0,<1.9.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.7.0(Mar 2, 2022)
Major Features and Improvements

Adds the DetectFeatureSkew PTransform to the public API, which can be used to detect feature skew between training and serving examples.

Uses sketch-based top-k/uniques in TFDV inmemory mode.

Bug Fixes and Other Changes

Fixes a bug in load_statistics that would cause failure when reading binary protos.

Depends on pyfarmhash>=0.2,<0.4.

Depends on tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,<3.

Depends on tensorflow-metadata>=1.7.0,<1.8.0.

Depends on tfx-bsl>=1.7.0,<1.8.0.

Depends on apache-beam[gcp]>=2.36,<3.

Updated the documentation for CombinerStatsGenerator to clarify that the first accumulator passed to merge_accumulators may be modified.

Added compression type detection when reading csv header.

Detection of invalid utf8 strings now works regardless of relative frequency.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.6.0(Jan 21, 2022)
Major Features and Improvements

Introduces a convenience wrapper for handling indexed access to statistics protos.

String features are checked for UTF-8 validity, and the number of invalid strings is reported as invalid_utf8_count.

Bug Fixes and Other Changes

Depends on numpy>=1.16,<2.

Depends on absl-py>=0.9,<2.0.0.

Depends on tensorflow>=1.15.5,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3.

Depends on tensorflow-metadata>=1.6.0,<1.7.0.

Depends on tfx-bsl>=1.6.0,<1.7.0.

Depends on apache-beam[gcp]>=2.35,<3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.5.0(Dec 1, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

BasicStatsGenerator is now responsible for setting the global num_examples. This field will no longer be populated at the DatasetFeatureStatistics level if default generators are disabled.

Depends on apache-beam[gcp]>=2.34,<3.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3.

Depends on tensorflow-metadata>=1.5.0,<1.6.0.

Depends on tfx-bsl>=1.5.0,<1.6.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.4.0(Oct 27, 2021)
Major Features and Improvements

Float features can now be analyzed as categorical for the purposes of top-k and unique count using experimental sketch based generators.

Support SQL based slicing in TFDV. This would enable slicing (using SQL) in TFX OSS and Dataflow environments. SQL based slicing is currently not supported on Windows.

Bug Fixes and Other Changes

Variance calculations have been updated to be more numerically stable for large datasets or large magnitude numeric data.

When running per-example validation against a schema, output of validate_examples_in_tfrecord and validate_examples_in_csv now optionally return samples of anomalous examples.

Changes to source code ensures that it can now work with pyarrow>=3.

Add load_anomalies_binary utility function.

Merge two accumulators at a time instead of batching.

BasicStatsGenerator is now responsible for setting FeatureNameStatistics.Type. Previously it was possible for a top-k generator and BasicStatsGenerator to set different types for categorical numeric features with physical type STRING.

Depends on pyarrow>=1,<6.

Depends on tensorflow-metadata>=1.4,<1.5.

Depends on tfx-bsl>=1.4,<1.5.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated python 3.6 support.

Source code(tar.gz)
Source code(zip)
v1.3.0(Sep 20, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Fixed bug in JensenShannonDivergence calculation affecting comparisons of histograms that each contain a single value.

Fixed bug in dataset constraints validation that caused failures with very large numbers of examples.

Fixed a bug wherein slicing on a feature missing from some batches could produce slice keys derived from a different feature.

Depends on apache-beam[gcp]>=2.32,<3.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,<3.

Depends on tfx-bsl>=1.3,<1.4.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.2.0(Jul 28, 2021)
Major Features and Improvements

Added statistics/generators/mutual_information.py. It estimates AMI using a knn estimation. It differs from sklearn_mutual_information.py in that this supports multivalent features/labels (by encoding) and multivariate features/labels. The plan is to deprecate sklearn_mutual_information.py in the future.

Fixed NonStreamingCustomStatsGenerator to respect max_batches_per_partition.

Bug Fixes and Other Changes

Depends on 'scikit-learn>=0.23,<0.24' ("mutual-information" extra only)

Depends on 'scipy>=1.5,<2' ("mutual-information" extra only)

Depends on apache-beam[gcp]>=2.31,<3.

Depends on tensorflow-metadata>=1.2,<1.3.

Depends on tfx-bsl>=1.2,<1.3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-1.2.0-cp36-cp36m-macosx_10_9_x86_64.whl(1.43 MB)
tensorflow_data_validation-1.2.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.1.whl(1.32 MB)
tensorflow_data_validation-1.2.0-cp36-cp36m-win_amd64.whl(1.11 MB)
tensorflow_data_validation-1.2.0-cp37-cp37m-macosx_10_9_x86_64.whl(1.43 MB)
tensorflow_data_validation-1.2.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.32 MB)
tensorflow_data_validation-1.2.0-cp37-cp37m-win_amd64.whl(1.11 MB)
tensorflow_data_validation-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl(1.43 MB)
tensorflow_data_validation-1.2.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.32 MB)
tensorflow_data_validation-1.2.0-cp38-cp38-win_amd64.whl(1.11 MB)
v1.1.1(Jul 26, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on google-cloud-bigquery>=1.28.0,<2.21.

Depends on tfx-bsl>=1.1.1,<1.2.

Fixes error when using tfdv.experimental_get_feature_value_slicer with

pandas==1.3.0.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v1.1.0(Jun 22, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Optimized certain stats generators that needs to materialize the input RecordBatches.

Depends on protobuf>=3.13,<4.

Depends on tensorflow-metadata>=1.1,<1.2.

Depends on tfx-bsl>=1.1,<1.2.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.1.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-1.1.0-cp36-cp36m-win_amd64.whl(1.09 MB)
tensorflow_data_validation-1.1.0-cp37-cp37m-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-1.1.0-cp37-cp37m-win_amd64.whl(1.09 MB)
tensorflow_data_validation-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.1.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.30 MB)
tensorflow_data_validation-1.1.0-cp38-cp38-win_amd64.whl(1.09 MB)
v1.0.0(May 24, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Increased the threshold beyond which a string feature value is considered "large" by the experimental sketch-based top-k/unique generator to 1024.

Added normalized AMI to sklearn mutual information generator.

Depends on apache-beam[gcp]>=2.29,<3.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,<3.

Depends on tensorflow-metadata>=1.0,<1.1.

Depends on tfx-bsl>=1.0,<1.1.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Removed the following deprecated symbols. Their deprecation was announced in 0.30.0.

tfdv.validate_instance

tfdv.lift_stats_generator

tfdv.partitioned_stats_generator

tfdv.get_feature_value_slicer

Removed parameter compression_type in tfdv.generate_statistics_from_tfrecord

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-1.0.0-cp36-cp36m-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.0.0-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-1.0.0-cp36-cp36m-win_amd64.whl(1.09 MB)
tensorflow_data_validation-1.0.0-cp37-cp37m-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.0.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-1.0.0-cp37-cp37m-win_amd64.whl(1.09 MB)
tensorflow_data_validation-1.0.0-cp38-cp38-macosx_10_9_x86_64.whl(1.41 MB)
tensorflow_data_validation-1.0.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-1.0.0-cp38-cp38-win_amd64.whl(1.09 MB)
v0.26.1(May 10, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.25,!=2.26.*,<2.29.

Known Issues

N/A

Breaking changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v0.30.0(Apr 21, 2021)
Major Features and Improvements

This version is the last version before TFDV 1.0. Once 1.0, all the TFDV public APIs (i.e. symbols in the root __init__.py) will be subject to semantic versioning. We are deprecating some public APIs in this version and they will be removed in 1.0.

Sketch-based top-k/unique stats generator now is able to detect invalid utf-8 sequences / large texts and replace them with a placeholder. It will not suffer from memory issue usually caused by image / large text features in the data. Note that this generator is not by default used yet.

Added StatsOptions.experimental_use_sketch_based_topk_uniques which enables the sketch-based top-k/unique stats generator.

Bug Fixes and Other Changes

Fixed bug in display_schema that caused domains not to be displayed.

Modified how get_schema_dataframe outputs numeric domains.

Anomalies previously (un)classified as UKNOWN_TYPE now trigger more specific anomaly types: INVALID_DOMAIN_SPECIFICATION and MULTIPLE_REASONS.

Depends on tensorflow-metadata>=0.30,<0.31.

Depends on tfx-bsl>=0.30,<0.31.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

tfdv.LiftStatsGenerator is going to be removed in the next version from the public API. To enable that generator, supply StatsOptions.label_feature

tfdv.NonStreamingCustomStatsGenerator is going to be removed in the next version from the public API. You may continue to import it from TFDV but it will not be subject to compatibility guarantees.

tfdv.validate_instance is going to be removed in the next version from the public API. You may continue to import it from TFDV but it will not be subject to compatibility guarantees.

Removed tfdv.DecodeCSV, tfdv.DecodeTFExample (deprecated in 0.27).

Removed feature_whitelist in tfdv.StatsOptions (deprecated in 0.28). Use feature_allowlist instead.

tfdv.get_feature_value_slicer is deprecated. tfdv.experimental_get_feature_value_slicer is introduced as a replacement. TFDV is likely to have a different slicing functionality post 1.0, which may not be compatible with the current slicers.

StatsOptions.slicing_functions is deprecated. StatsOptions.experimental_slicing_functions is introduced as a replacement.

tfdv.WriteStatisticsToText is removed (deprecated in 0.25.0).

Parameter compression_type in tfdv.generate_statistics_from_tfrecord is deprecated. The compression type is currently automatically determined.

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-0.30.0-cp36-cp36m-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.30.0-cp36-cp36m-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.30.0-cp36-cp36m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.30.0-cp37-cp37m-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.30.0-cp37-cp37m-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.30.0-cp37-cp37m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.30.0-cp38-cp38-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.30.0-cp38-cp38-manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-0.30.0-cp38-cp38-win_amd64.whl(1.08 MB)
v0.29.0(Mar 24, 2021)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Added check for invalid min and max values for values_counts for nested features.

Bumped the mininum bazel version required to build TFDV to 3.7.2.

Depends on absl-py>=0.9,<0.13.

Depends on tensorflow-metadata>=0.29,<0.30.

Depends on tfx-bsl>=0.29,<0.30.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-0.29.0-cp36-cp36m-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.29.0-cp36-cp36m-manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-0.29.0-cp36-cp36m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.29.0-cp37-cp37m-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.29.0-cp37-cp37m-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.29.0-cp37-cp37m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.29.0-cp38-cp38-macosx_10_9_x86_64.whl(1.40 MB)
tensorflow_data_validation-0.29.0-cp38-cp38-manylinux2010_x86_64.whl(1.29 MB)
tensorflow_data_validation-0.29.0-cp38-cp38-win_amd64.whl(1.08 MB)
v0.28.0(Feb 24, 2021)
Major Features and Improvements

Add anomaly detection for max bytes size for images.

Bug Fixes and Other Changes

Depends on numpy>=1.16,<1.20.

Fixed a bug that affected all CombinerFeatureStatsGenerators.

Allow for bytes type in get_feature_value_slicer in addition to Text and int.

Fixed a bug that caused TFDV to improperly infer a fixed shape when tfdv.infer_schema and tfdv.update_schema were called with infer_feature_shape=True.

Deprecated parameter infer_feature_shape of function tfdv.update_schema. If a schema feature has a pre-defined shape, tfdv.update_schema will always validate it. Otherwise, it will not try to add a shape.

Deprecated tfdv.StatsOptions.feature_whitelist and added feature_allowlist as a replacement. The former will be removed in the next release.

Added get_schema_dataframe and get_anomalies_dataframe utility functions.

Depends on apache-beam[gcp]>=2.28,<3.

Depends on tensorflow-metadata>=0.28,<0.29.

Depends on tfx-bsl>=0.28.1,<0.29.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-0.28.0-cp36-cp36m-macosx_10_9_x86_64.whl(2.93 MB)
tensorflow_data_validation-0.28.0-cp36-cp36m-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.28.0-cp36-cp36m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.28.0-cp37-cp37m-macosx_10_9_x86_64.whl(2.93 MB)
tensorflow_data_validation-0.28.0-cp37-cp37m-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.28.0-cp37-cp37m-win_amd64.whl(1.08 MB)
tensorflow_data_validation-0.28.0-cp38-cp38-macosx_10_9_x86_64.whl(2.93 MB)
tensorflow_data_validation-0.28.0-cp38-cp38-manylinux2010_x86_64.whl(1.28 MB)
tensorflow_data_validation-0.28.0-cp38-cp38-win_amd64.whl(1.08 MB)
v0.27.0(Jan 28, 2021)
Major Features and Improvements

Performance improvement to BasicStatsGenerator.

Bug Fixes and Other Changes

Added a compact() and setup() interface to CombinerStatsGenerator, CombinerFeatureStatsWrapperGenerator, BasicStatsGenerator, CompositeStatsGenerator, and ConstituentStatsGenerator.

Stopped depending on tensorflow-transform.

Depends on apache-beam[gcp]>=2.27,<3.

Depends on pyarrow>=1,<3.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3.

Depends on tensorflow-metadata>=0.27,<0.28.

Depends on tfx-bsl>=0.27,<0.28.

Known Issues

N/A

Breaking changes

N/A

Deprecations

tfdv.DecodeCSV and tfdv.DecodeTFExample are deprecated. Use tfx_bsl.public.tfxio.CsvTFXIO and tfx_bsl.public.tfxio.TFExampleRecord instead.

Source code(tar.gz)
Source code(zip)
v0.26.0(Dec 17, 2020)
Version 0.26.0

Major Features and Improvements

Added support for per-feature example weights which allows associating each column its specific weight column. See the per_feature_weight_override parameter in StatsOptions.__init__.

Bug Fixes and Other Changes

Newly added LifecycleStage.DISABLED is now exempt from validation (similar to LifecycleStage.DEPRECATED, etc).

Fixed a bug where TFDV blindly trusts the claim type in the provided schema. TFDV now computes the stats according to the actual type of the data, and only when the actual type matches the claim in the schema will it compute type-specific stats (e.g. categorical ints).

Added an option to control whether to add default stats generators when tfdv.GenerateStatistics().

Started using a new quantiles computation routine that does not depend on TF. This could potentially increase the performance of TFDV under certain workloads.

Extending schema_util to support sematic domains.

Moving natural_language_stats_generator to natural_language_domain_inferring_stats_generator.

Providing vocab_utils to assist in opening / loading vocabulary files.

A SchemaDiff will be reported upon J-S skew/drift.

Fixed a bug in FLOAT_TYPE_SMALL_FLOAT anomaly message.

Depends on apache-beam[gcp]>=2.25,!=2.26.*,<3.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.4.*,<3.

Depends on tensorflow-metadata>=0.26,<0.27.

Depends on tensorflow-transform>=0.26,<0.27.

Depends on tfx-bsl>=0.26,<0.27.

Known Issues

N/A

Breaking changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
tensorflow_data_validation-0.26.0-cp36-cp36m-macosx_10_9_x86_64.whl(2.92 MB)
tensorflow_data_validation-0.26.0-cp36-cp36m-manylinux2010_x86_64.whl(1.27 MB)
tensorflow_data_validation-0.26.0-cp36-cp36m-win_amd64.whl(1.07 MB)
tensorflow_data_validation-0.26.0-cp37-cp37m-macosx_10_9_x86_64.whl(2.92 MB)
tensorflow_data_validation-0.26.0-cp37-cp37m-manylinux2010_x86_64.whl(1.27 MB)
tensorflow_data_validation-0.26.0-cp37-cp37m-win_amd64.whl(1.06 MB)
tensorflow_data_validation-0.26.0-cp38-cp38-macosx_10_9_x86_64.whl(2.92 MB)
tensorflow_data_validation-0.26.0-cp38-cp38-manylinux2010_x86_64.whl(1.27 MB)
tensorflow_data_validation-0.26.0-cp38-cp38-win_amd64.whl(1.06 MB)
v0.25.0(Nov 5, 2020)
Version 0.25.0

Major Features and Improvements

Add support for detecting drift and distribution skew in numeric features.

tfdv.validate_statistics now also reports the raw measurements of distribution skew/drift (if any is done), regardless whether skew/drift is detected. The report is in the drift_skew_info of the Anomalies proto (return value of validate_statistics).

From this release TFDV will also be hosting nightly packages on https://pypi-nightly.tensorflow.org. To install the nightly package use the following command:

pip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-data-validation

Note: These nightly packages are unstable and breakages are likely to happen. The fix could often take a week or more depending on the complexity involved for the wheels to be available on the PyPI cloud service. You can always use the stable version of TFDV available on PyPI by running the command pip install tensorflow-data-validation .

Bug Fixes and Other Changes

Added tfdv.load_stats_binary to load stats what were written using tfdv.WriteStatisticsToText (now tfdv.WriteStatisticsToBinaryFile).

Anomalies previously (un)classified as UKNOWN_TYPE now trigger more specific anomaly types: DOMAIN_INVALID_FOR_TYPE, UNEXPECTED_DATA_TYPE, FEATURE_MISSING_NAME, FEATURE_MISSING_TYPE, INVALID_SCHEMA_SPECIFICATION

Fixed a bug that import tensorflow_data_validation would fail if IPython is not installed. IPython is an optional dependency of TFDV.

Depends on apache-beam[gcp]>=2.25,<3.

Depends on tensorflow-metadata>=0.25,<0.26.

Depends on tensorflow-transform>=0.25,<0.26.

Depends on tfx-bsl>=0.25,<0.26.

Known Issues

N/A

Breaking Changes

tfdv.WriteStatisticsToText is renamed as tfdv.WriteStatisticsToBinaryFile. The former is still available but will be removed in a future release.

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v0.24.1(Sep 24, 2020)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.24,<3.

Depends on tensorflow-transform>=0.24.1,<0.25.

Depends on tfx-bsl>=0.24.1,<0.25.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v0.23.1(Sep 24, 2020)
Major Features and Improvements

N/A

Bug Fixes and Other Changes

Depends on apache-beam[gcp]>=2.24,<3.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated python 3.5 support.

Source code(tar.gz)
Source code(zip)
v0.24.0(Sep 14, 2020)
Major Features and Improvements

You can now build the TFDV wheel with python setup.py bdist_wheel. Note:

If you want to build a manylinux2010 wheel you'll still need to use Docker.

Bazel is still required.

You can now build manylinux2010 TFDV wheel for Python 3.8.

Bug Fixes and Other Changes

Support allowlist and denylist features in tfdv.visualize_statistics method.

Depends on absl-py>=0.9,<0.11.

Depends on pandas>=1.0,<2.

Depends on protobuf>=3.9.2,<4.

Depends on tensorflow-metadata>=0.24,<0.25.

Depends on tensorflow-transform>=0.24,<0.25.

Depends on tfx-bsl>=0.24,<0.25.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

Deprecated Py3.5 support.

Deprecated sample_count option in tfdv.StatsOptions. Use sample_rate option instead.

Source code(tar.gz)
Source code(zip)
v0.23.0(Aug 14, 2020)
Major Features and Improvements

Data validation is now able to handle arbitrarily nested arrow List/LargeList types. Schema entries for features with multiple nest levels describe the value count at each level in the value_counts field.

Add combiner stats generator to estimate top-K and uniques using Misra-Gries and K-Minimum Values sketches.

Bug Fixes and Other Changes

Validate that enough supported images are present (if image_domain.minimum_supported_image_fraction is provided).

Stopped requiring avro-python3.

Depends on apache-beam[gcp]>=2.23,<3.

Depends on pyarrow>=0.17,<0.18.

Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,<3.

Depends on tensorflow-metadata>=0.23,<0.24.

Depends on tensorflow-transform>=0.23,<0.24.

Depends on tfx-bsl>=0.23,<0.24.

Known Issues

N/A

Breaking Changes

N/A

Deprecations

N/A

Source code(tar.gz)
Source code(zip)
v0.22.2(Jun 29, 2020)
Major Features and Improvements

Bug Fixes and Other Changes

Fixed a bug that affected tfx 0.22.0 to work with TFDV 0.22.1.

Depends on 'avro-python3>=1.8.1,<1.9.2' on Python 3.5 + MacOS

Known Issues

Breaking Changes

Deprecations
Source code(tar.gz)
Source code(zip)
v0.22.1(Jun 24, 2020)
Major Features and Improvements

Statistics generation is now able to handle arbitrarily nested arrow List/LargeList types. Stats about the list elements' presence and valency are computed at each nest level, and stored in a newly added field, valency_and_presence_stats in CommonStatistics.

Bug Fixes and Other Changes

Trigger DATASET_HIGH_NUM_EXAMPLES when a dataset has more than the specified limit on number of examples.

Fix bug in display_anomalies that prevented dataset-level anomalies from being displayed.

Trigger anomalies when a feature has a number of unique values that does not conform to the specified minimum/maximum.

Depends on pandas>=0.24,<2.

Depends on tensorflow-metadata>=0.22.2,<0.23.0.

Depends on tfx-bsl>=0.22.1,<0.23.0.

Known Issues

Breaking Changes

Deprecations
Source code(tar.gz)
Source code(zip)
v0.22.0(May 15, 2020)
Major Features and Improvements

Bug Fixes and Other Changes

Crop values in natural language stats generator.

Switch to using PyBind11 instead of SWIG for wrapping C++ libraries.

CSV decoder support for multivalent columns by using tfx_bsl's decoder.

When inferring a schema entry for a feature, do not add a shape with dim = 0 when min_num_values = 0.

Add utility methods tfdv.get_slice_stats to get statistics for a slice and tfdv.compare_slices to compare statistics of two slices using Facets.

Make tfdv.load_stats_text and tfdv.write_stats_text public.

Add PTransforms tfdv.WriteStatisticsToText and tfdv.WriteStatisticsToTFRecord to write statistics proto to text and tfrecord files respectively.

Modify tfdv.load_statistics to handle reading statistics from TFRecord and text files.

Added an extra requirement group mutual-information. As a result, barebone TFDV does not require scikit-learn any more.

Added an extra requirement group visualization. As a result, barebone TFDV does not require ipython any more.

Added an extra requirement group all that specifies all the extra dependencies TFDV needs. Use pip install tensorflow-data-validation[all] to pull in those dependencies.

Depends on pyarrow>=0.16,<0.17.

Depends on apache-beam[gcp]>=2.20,<3.

Depends on `ipython>=7,<8;python_version>="3"'.

Depends on `scikit-learn>=0.18,<0.24'.

Depends on tensorflow>=1.15,!=2.0.*,<3.

Depends on tensorflow-metadata>=0.22.0,<0.23.

Depends on tensorflow-transform>=0.22,<0.23.

Depends on tfx-bsl>=0.22,<0.23.

Known Issues

(Known issue resolution) It is no longer necessary to use Apache Beam 2.17 when running TFDV on Windows. The current release of Apache Beam will work.

Breaking Changes

tfdv.GenerateStatistics now accepts a PCollection of pa.RecordBatch instead of pa.Table.

All the TFDV coders now output a PCollection of pa.RecordBatch instead of a PCollection of pa.Table.

tfdv.validate_instances and tfdv.api.validation_api.IdentifyAnomalousExamples now takes pa.RecordBatch as input instead of pa.Table.

The StatsGenerator interface (and all its sub-classes) now takes pa.RecordBatch as the input data instead of pa.Table.

Custom slicing functions now accepts a pa.RecordBatch instead of pa.Table as input and should output a tuple (slice_key, record_batch).

Deprecations

Deprecating Py2 support.

Source code(tar.gz)
Source code(zip)
v0.21.5(Mar 6, 2020)
Release 0.21.5

Major Features and Improvements

Add label_feature to StatsOptions and enable LiftStatsGenerator when label_feature and schema are provided.

Add JSON serialization support for StatsOptions.

Bug Fixes and Other Changes

Only requires avro-python3>=1.8.1,!=1.9.2.*,<2.0.0 on Python 3.5 + MacOS

Breaking Changes

Deprecations
Source code(tar.gz)
Source code(zip)
v0.21.4(Mar 5, 2020)
Release 0.21.4

Major Features and Improvements

Support visualizing feature value lift in facets visualization.

Bug Fixes and Other Changes

Fix issue writing out string feature values in LiftStatsGenerator.

Requires 'apache-beam[gcp]>=2.17,<3'.

Requires 'tensorflow-transform>=0.21.1,<0.22'.

Requires 'tfx-bsl>=0.21.3,<0.22'.

Breaking Changes

Deprecations
Source code(tar.gz)
Source code(zip)

Library for exploring and validating machine learning data

Related tags

Overview

TensorFlow Data Validation

Installing from PyPI

Nightly Packages

Build with Docker

1. Install Docker

2. Clone the TFDV repository

3. Build the pip package

4. Install the pip package

Build from source

1. Prerequisites

Install NumPy

Install Bazel

2. Clone the TFDV repository

3. Build the pip package

4. Install the pip package

Supported platforms

Notable Dependencies

Compatible versions

Questions

Links

Comments

Context

Error trace

What code did I run?

Pip trace

Overview

Code to reproduce

Workaround code

Releases(v1.12.0)

v1.12.0(Dec 8, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.11.0(Nov 16, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.10.0(Aug 29, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.9.0(Jun 29, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.8.0(May 16, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.7.0(Mar 2, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.6.0(Jan 21, 2022)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations

v1.5.0(Dec 1, 2021)

Major Features and Improvements

Bug Fixes and Other Changes

Known Issues

Breaking Changes

Deprecations