18 July 2021

Tensorflow datavalidation

by Hasan

This is the tutorial of tensorflow_data_validation. This tutorial is a part of coursera course Machine learning Data Lifecycle

import tensorflow as tf
import tensorflow_data_validation as tfdv
import pandas as pd
import numpy as np

from tensorflow_metadata.proto.v0 import schema_pb2

print(f'tensorflow version = {tf.__version__}')
print(f'tensorflow data validation version = {tfdv.__version__}')

tensorflow version = 2.5.0
tensorflow data validation version = 1.1.0

In this notebook we will look at the __tensorflow_data_validation__ module. We will see

Visulaize training data statistics
Infer data schema
Generate and visualize eval dataset
Calculate and display evaluation anomalies
Revising the schema
Fix anomalies in the Schema
Examining dataset slices

We will be using Census Income Dataset

from pathlib import Path
Path.ls = lambda x:list(x.iterdir())
Path.cwd()

WindowsPath('D:/Online_courses/coursera/ML_production/Machine Learning Data Lifecycle in Production/week1')

df = pd.read_csv('../data/adult.data')
df.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country	label
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	40	Cuba	<=50K

df = pd.read_csv('../data/adult.data', skipinitialspace=True)
df.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country	label
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	40	Cuba	<=50K

So at first we will be creating evaluation set

from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2, shuffle=False)

train.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country	label
0	39	State-gov	77516	Bachelors	13	Never-married	Adm-clerical	Not-in-family	White	Male	2174	40	United-States	<=50K
1	50	Self-emp-not-inc	83311	Bachelors	13	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	13	United-States	<=50K
2	38	Private	215646	HS-grad	9	Divorced	Handlers-cleaners	Not-in-family	White	Male	0	40	United-States	<=50K
3	53	Private	234721	11th	7	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0	40	United-States	<=50K
4	28	Private	338409	Bachelors	13	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0	40	Cuba	<=50K

test.head()

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	hours-per-week	native-country	label
26048	30	Private	270886	Some-college	10	Never-married	Other-service	Own-child	White	Female	40	United-States	<=50K
26049	21	Private	216129	HS-grad	9	Never-married	Other-service	Own-child	White	Male	35	United-States	<=50K
26050	33	Private	189368	Some-college	10	Married-civ-spouse	Transport-moving	Husband	Black	Male	40	United-States	>50K
26051	19	?	141418	Some-college	10	Never-married	?	Own-child	White	Male	15	United-States	<=50K
26052	19	Private	306225	HS-grad	9	Never-married	Handlers-cleaners	Own-child	White	Male	25	United-States	<=50K

So now we will see the first part. See train data statistics

train_stats = tfdv.generate_statistics_from_dataframe(train)
tfdv.visualize_statistics(train_stats)

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    facets_iframe.srcdoc = facets_html;
     facets_iframe.id = "";
     setTimeout(() => {
       facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')
     }, 1500)
     </script>

So first point is done. From here we can see the statistics and also visualized the train data

2. Infer data Schema

Infer schema actually created from train statistics
It will just show you expected data type
Let see it in action

schema = tfdv.infer_schema(statistics=train_stats)
tfdv.display_schema(schema)

	Type	Presence	Domain
Feature name
'age'	INT	required	-
'workclass'	STRING	required	'workclass'
'fnlwgt'	INT	required	-
'education'	STRING	required	'education'
'education-num'	INT	required	-
'marital-status'	STRING	required	'marital-status'
'occupation'	STRING	required	'occupation'
'relationship'	STRING	required	'relationship'
'race'	STRING	required	'race'
'sex'	STRING	required	'sex'
'capital-gain'	INT	required	-
'capital-loss'	INT	required	-
'hours-per-week'	INT	required	-
'native-country'	STRING	required	'native-country'
'label'	STRING	required	'label'

C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\display_util.py:180: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

	Values
Domain
'workclass'	'?', 'Federal-gov', 'Local-gov', 'Never-worked', 'Private', 'Self-emp-inc', 'Self-emp-not-inc', 'State-gov', 'Without-pay'
'education'	'10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Assoc-acdm', 'Assoc-voc', 'Bachelors', 'Doctorate', 'HS-grad', 'Masters', 'Preschool', 'Prof-school', 'Some-college'
'marital-status'	'Divorced', 'Married-AF-spouse', 'Married-civ-spouse', 'Married-spouse-absent', 'Never-married', 'Separated', 'Widowed'
'occupation'	'?', 'Adm-clerical', 'Armed-Forces', 'Craft-repair', 'Exec-managerial', 'Farming-fishing', 'Handlers-cleaners', 'Machine-op-inspct', 'Other-service', 'Priv-house-serv', 'Prof-specialty', 'Protective-serv', 'Sales', 'Tech-support', 'Transport-moving'
'relationship'	'Husband', 'Not-in-family', 'Other-relative', 'Own-child', 'Unmarried', 'Wife'
'race'	'Amer-Indian-Eskimo', 'Asian-Pac-Islander', 'Black', 'Other', 'White'
'sex'	'Female', 'Male'
'native-country'	'?', 'Cambodia', 'Canada', 'China', 'Columbia', 'Cuba', 'Dominican-Republic', 'Ecuador', 'El-Salvador', 'England', 'France', 'Germany', 'Greece', 'Guatemala', 'Haiti', 'Holand-Netherlands', 'Honduras', 'Hong', 'Hungary', 'India', 'Iran', 'Ireland', 'Italy', 'Jamaica', 'Japan', 'Laos', 'Mexico', 'Nicaragua', 'Outlying-US(Guam-USVI-etc)', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto-Rico', 'Scotland', 'South', 'Taiwan', 'Thailand', 'Trinadad&Tobago', 'United-States', 'Vietnam', 'Yugoslavia'
'label'	'<=50K', '>50K'

3. Generate and visualize evaluation dataset statistics

So we already used and saw train statistics what is special here to show.
Actually tfdv give you to compare it with training and evaluation set together.
- lhs_statistics
- rhs_statistics
- lhs_name
- rhs_name

test_stats = tfdv.generate_statistics_from_dataframe(test)
tfdv.visualize_statistics(lhs_statistics=test_stats,
                          rhs_statistics=train_stats,
                          lhs_name='TEST_DATASET',
                          rhs_name='TRAIN_DATASET')

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4. Calcualte and display evaluation anomalies

anomalies = tfdv.validate_statistics(statistics=test_stats,
                                     schema=schema)
tfdv.display_anomalies(anomalies)

C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\display_util.py:217: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

No anomalies found.

Nice We have no anomaly found. But main point whether we can detect any anomly if there is anomaly. let’s check. Now we will add some rows to check whether anomaly detection can detect it or not.

def add_extra_rows(df):
    rows = [
        {
            'age': 46, 
            'fnlwgt': 257473, 
            'education': 'Bachelors', 
            'education-num': 8,
            'marital-status': 'Married-civ-spouse', 
            'occupation': 'Plumber', 
            'relationship': 'Husband', 
            'race': 'Other', 
            'sex': 'Male',
            'capital-gain': 1000, 
            'capital-loss': 0, 
            'hours-per-week': 41, 
            'native-country': 'Australia',
            'label': '>50K'
        },
        {
            'age': 0, 
            'workclass': 'Private', 
            'fnlwgt': 257473, 
            'education': 'Masters', 
            'education-num': 8,
            'marital-status': 'Married-civ-spouse', 
            'occupation': 'Adm-clerical', 
            'relationship': 'Wife', 
            'race': 'Asian', 
            'sex': 'Female',
            'capital-gain': 0, 
            'capital-loss': 0, 
            'hours-per-week': 40, 
            'native-country': 'Pakistan',
            'label': '>50K'
        },
        {
            'age': 1000, 
            'workclass': 'Private', 
            'fnlwgt': 257473, 
            'education': 'Masters', 
            'education-num': 8,
            'marital-status': 'Married-civ-spouse', 
            'occupation': 'Prof-specialty', 
            'relationship': 'Husband', 
            'race': 'Black', 
            'sex': 'Male',
            'capital-gain': 0, 
            'capital-loss': 0, 
            'hours-per-week': 20, 
            'native-country': 'Cameroon',
            'label': '<=50K'
        },
        {
            'age': 25, 
            'workclass': '?', 
            'fnlwgt': 257473, 
            'education': 'Masters', 
            'education-num': 8,
            'marital-status': 'Married-civ-spouse', 
            'occupation': 'gamer', 
            'relationship': 'Husband', 
            'race': 'Asian', 
            'sex': 'Female',
            'capital-gain': 0, 
            'capital-loss': 0, 
            'hours-per-week': 50, 
            'native-country': 'Mongolia',
            'label': '<=50K'
        }
    ]
    
    df = df.append(rows, ignore_index=True)
    
    return df

test = add_extra_rows(test)

test_stats = tfdv.generate_statistics_from_dataframe(test)
anomalies = tfdv.validate_statistics(statistics=test_stats, schema=schema)
tfdv.display_anomalies(anomalies)

	Anomaly short description	Anomaly long description
Feature name
'race'	Unexpected string values	Examples contain values missing from the schema: Asian (<1%).
'native-country'	Unexpected string values	Examples contain values missing from the schema: Australia (<1%), Cameroon (<1%), Mongolia (<1%), Pakistan (<1%).
'occupation'	Unexpected string values	Examples contain values missing from the schema: Plumber (<1%), gamer (<1%).
'workclass'	Multiple errors	The feature has a shape, but it's not always present (if the feature is nested, then it should always be present at each nested level) or its value lengths vary. The feature was present in fewer examples than expected: minimum fraction = 1.000000, actual = 0.999847

As anomaly is very much specific to problems, sometimes anomaly shows something important to the data. However TFDV provides some useful functionality for anomaly removal
TFDV provides a set of utility methods and parameters that you can use for revising the inferred schema. This reference lists down the type of anomalies and the parameters that you can edit but we’ll focus only on a couple here.

You can relax the minimum fraction of values that must come from the domain of a particular feature (as described by ENUM_TYPE_UNEXPECTED_STRING_VALUES in the reference):

tfdv.get_feature(schema, 'feature_column_name').distribution_constraints.min_domain_mass = <float: 0.0 to 1.0>

You can add a new value to the domain of a particular feature:

tfdv.get_domain(schema, 'feature_column_name').value.append('string')

We will try here to fix anomaly in this schema

# filter the age range
test = test [test['age'] > 16]
test = test[test['age'] < 91]

# drop missing values
test.dropna(inplace=True)

test_stats = tfdv.generate_statistics_from_dataframe(test)
anomalies = tfdv.validate_statistics(statistics=test_stats, schema=schema)
tfdv.display_anomalies(anomalies)

C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\display_util.py:217: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

	Anomaly short description	Anomaly long description
Feature name
'race'	Unexpected string values	Examples contain values missing from the schema: Asian (<1%).
'native-country'	Unexpected string values	Examples contain values missing from the schema: Mongolia (<1%).
'occupation'	Unexpected string values	Examples contain values missing from the schema: gamer (<1%).

So something is changed. We did it manually, however let see we can change it

# Relax the minimum fraction of values that must come from the domain for the feature `native-country`
country_feature = tfdv.get_feature(schema, 'native-country')
country_feature.distribution_constraints.min_domain_mass = 0.9

# Relax the minimum fraction of values that must come from the domain for the feature occupation
occupation_feature = tfdv.get_feature(schema, 'occupation')
occupation_feature.distribution_constraints.min_domain_mass = 0.9

If more rigid needs to be expected

race_domain = tfdv.get_domain(schema, 'race')
race_domain.value.append('Asian')

If some numerical values range needs to be restricted, then can be done also

tfdv.set_domain(schema, 'age', schema_pb2.IntDomain(name='age', min=17, max=90))
# Display the modified schema, Notice the domain column of age
tfdv.display_schema(schema)

	Type	Presence	Domain
Feature name
'age'	INT	required	min: 17; max: 90
'workclass'	STRING	required	'workclass'
'fnlwgt'	INT	required	-
'education'	STRING	required	'education'
'education-num'	INT	required	-
'marital-status'	STRING	required	'marital-status'
'occupation'	STRING	required	'occupation'
'relationship'	STRING	required	'relationship'
'race'	STRING	required	'race'
'sex'	STRING	required	'sex'
'capital-gain'	INT	required	-
'capital-loss'	INT	required	-
'hours-per-week'	INT	required	-
'native-country'	STRING	required	'native-country'
'label'	STRING	required	'label'

C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\display_util.py:180: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

	Values
Domain
'workclass'	'?', 'Federal-gov', 'Local-gov', 'Never-worked', 'Private', 'Self-emp-inc', 'Self-emp-not-inc', 'State-gov', 'Without-pay'
'education'	'10th', '11th', '12th', '1st-4th', '5th-6th', '7th-8th', '9th', 'Assoc-acdm', 'Assoc-voc', 'Bachelors', 'Doctorate', 'HS-grad', 'Masters', 'Preschool', 'Prof-school', 'Some-college'
'marital-status'	'Divorced', 'Married-AF-spouse', 'Married-civ-spouse', 'Married-spouse-absent', 'Never-married', 'Separated', 'Widowed'
'occupation'	'?', 'Adm-clerical', 'Armed-Forces', 'Craft-repair', 'Exec-managerial', 'Farming-fishing', 'Handlers-cleaners', 'Machine-op-inspct', 'Other-service', 'Priv-house-serv', 'Prof-specialty', 'Protective-serv', 'Sales', 'Tech-support', 'Transport-moving'
'relationship'	'Husband', 'Not-in-family', 'Other-relative', 'Own-child', 'Unmarried', 'Wife'
'race'	'Amer-Indian-Eskimo', 'Asian-Pac-Islander', 'Black', 'Other', 'White', 'Asian'
'sex'	'Female', 'Male'
'native-country'	'?', 'Cambodia', 'Canada', 'China', 'Columbia', 'Cuba', 'Dominican-Republic', 'Ecuador', 'El-Salvador', 'England', 'France', 'Germany', 'Greece', 'Guatemala', 'Haiti', 'Holand-Netherlands', 'Honduras', 'Hong', 'Hungary', 'India', 'Iran', 'Ireland', 'Italy', 'Jamaica', 'Japan', 'Laos', 'Mexico', 'Nicaragua', 'Outlying-US(Guam-USVI-etc)', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto-Rico', 'Scotland', 'South', 'Taiwan', 'Thailand', 'Trinadad&Tobago', 'United-States', 'Vietnam', 'Yugoslavia'
'label'	'<=50K', '>50K'

updated_anomalies = tfdv.validate_statistics(test_stats, schema)
tfdv.display_anomalies(updated_anomalies)

C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\display_util.py:217: FutureWarning: Passing a negative integer is deprecated in version 1.0 and will not be supported in future version. Instead, use None to not limit the column width.
  pd.set_option('max_colwidth', -1)

No anomalies found.

We can actually go further can remove all anomalies, but we will not do it further. Let’s consider now last part.

7. Examining dataset slices

get_feature_value_slicer from slicing_util, Normally dictionary features argument. If we want full domain, then we can just tell None, means if we use None, then both male and female in this case, if we consider the sex feature

from tensorflow_data_validation.utils import slicing_util
slice_fn = slicing_util.get_feature_value_slicer(features={'sex':None})

Now we can see the statistics

slice_stats_options = tfdv.StatsOptions(schema=schema,
                                        slice_functions=[slice_fn],
                                        infer_type_from_schema=True)

# Convert dataframe to CSV since `slice_functions` works only with `tfdv.generate_statistics_from_csv`
CSV_PATH = 'slice_sample.csv'
train.to_csv(CSV_PATH)

# Calculate statistics for the sliced dataset
sliced_stats = tfdv.generate_statistics_from_csv(CSV_PATH, stats_options=slice_stats_options)

WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features.

WARNING:root:Make sure that locally built Python SDK docker image has Python 3.7 interpreter.
WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.

WARNING:tensorflow:From C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

WARNING:tensorflow:From C:\Users\hasan\.conda\envs\tensorflow_validation\lib\site-packages\tensorflow_data_validation\utils\stats_util.py:247: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

print(f'Datasets generated: {[sliced.name for sliced in sliced_stats.datasets]}')

Datasets generated: ['All Examples', 'sex_Male', 'sex_Female']

print(f'Type of sliced_stats elements: {type(sliced_stats.datasets[0])}')

Type of sliced_stats elements: <class 'tensorflow_metadata.proto.v0.statistics_pb2.DatasetFeatureStatistics'>

from tensorflow_metadata.proto.v0.statistics_pb2 import DatasetFeatureStatisticsList

# Convert `Male` statistics (index=1) to the correct type and get the dataset name
male_stats_list = DatasetFeatureStatisticsList()
male_stats_list.datasets.extend([sliced_stats.datasets[1]])
male_stats_name = sliced_stats.datasets[1].name

# Convert `Female` statistics (index=2) to the correct type and get the dataset name
female_stats_list = DatasetFeatureStatisticsList()
female_stats_list.datasets.extend([sliced_stats.datasets[2]])
female_stats_name = sliced_stats.datasets[2].name

# Visualize the two slices side by side
tfdv.visualize_statistics(
    lhs_statistics=male_stats_list,
    rhs_statistics=female_stats_list,
    lhs_name=male_stats_name,
    rhs_name=female_stats_name
)

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If we want to be more specific, then we can map the specific value to the feature name. For example, if we want just Male, then we can declare it as features={'sex': [b'Male']}. Notice that the string literal needs to be passed in as bytes with the b' prefix.
We can also pass in several features if we want. For example, if we want to slice through both the sex and race features, then we can do features={'sex': None, 'race': None}.

tags:

Hasan's Post

Tutorial repository

Tensorflow datavalidation

2. Infer data Schema

3. Generate and visualize evaluation dataset statistics

4. Calcualte and display evaluation anomalies

No anomalies found.

We will try here to fix anomaly in this schema

No anomalies found.

7. Examining dataset slices