Full Code of paypal/autosklearn-zeroconf for AI

Repository: paypal/autosklearn-zeroconf
Branch: master
Commit: c61ada6d354a
Files: 19
Total size: 7.6 MB

Directory structure:
gitextract_q2uq55sg/

├── .gitignore
├── .idea/
│   └── vcs.xml
├── LICENSE.txt
├── README.md
├── bin/
│   ├── dataTransformationProcessing.py
│   ├── evaluate-dataset-Adult.py
│   ├── load-dataset-Adult.py
│   ├── load-dataset-Titanic.py
│   ├── utility.py
│   └── zeroconf.py
├── data/
│   ├── Adult.h5
│   ├── adult.data
│   ├── adult.names
│   ├── adult.test
│   └── adult.test.withid
├── parameter/
│   ├── default.yml
│   ├── logger.yml
│   └── standard.yml
└── requirements.txt

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FILE CONTENTS
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FILE: .gitignore
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# Created by .ignore support plugin (hsz.mobi)
work
log
data/zeroconf-result.csv

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FILE: .idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
  <component name="VcsDirectoryMappings">
    <mapping directory="$PROJECT_DIR$" vcs="Git" />
  </component>
</project>

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FILE: LICENSE.txt
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Copyright 2017 PayPal

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


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FILE: README.md
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## What is autosklearn-zeroconf
The autosklearn-zeroconf file takes a dataframe of any size and trains [auto-sklearn](https://github.com/automl/auto-sklearn) binary classifier ensemble. No configuration is needed as the name suggests. Auto-sklearn is the recent [AutoML Challenge](http://www.kdnuggets.com/2016/08/winning-automl-challenge-auto-sklearn.html) winner [more @microsoft.com](https://www.microsoft.com/en-us/research/blog/automl-challenge-leap-forward-machine-learning-competitions/).

As a result of using automl-zeroconf running auto-sklearn becomes a "fire and forget" type of operation. It greatly increases the utility and decreases turnaround time for experiments.

The main value proposition is that a data analyst or a data savvy business user can quickly run the iterations on the data (actual sources and feature design) side and on the ML side not a bit has to be changed. So it's a great tool for people not doing hardcore data science full time. Up to 90% of (marketing) data analysts may fall into this target group currently. 

## How Does It Work
To keep the training time reasonable autosklearn-zeroconf samples the data and tests all the models from autosklearn library on it once. The results of the test (duration) is used to calculate the per_run_time_limit, time_left_for_this_task and number of seeds parameters for autosklearn. The code also converts the pandas dataframe into a form that autosklearn can handle (categorical and float datatypes).
<img src=https://github.com/paypal/autosklearn-zeroconf/blob/master/AutosklearnModellingLossOverTimeExample.png></img>

## Algoritms included
 bernoulli_nb,
 extra_trees,
 gaussian_nb,
 adaboost,
 gradient_boosting,
 k_nearest_neighbors,
 lda,
 liblinear_svc,
 multinomial_nb,
 passive_aggressive,
 random_forest,
 sgd

plus samplers, scalers, imputers (14 feature processing methods, and 3 data preprocessing
methods,  giving  rise  to  a  structured  hypothesis  space  with  100+  hyperparameters)

## Running autosklearn-zeroconf
To run autosklearn-zeroconf start <pre>python bin/zeroconf.py -d your_dataframe.h5</pre> from command line.
The script was tested on Ubuntu and RedHat. It won't work on any WindowsOS because auto-sklearn doesn't support Windows.

## Data Format
The code uses a pandas dataframe format to manage the data. It is stored in the HDF5 .h5 file for convenience. (Python module "tables")

## Example
As an example you can run autosklearn-zeroconf on a "Census Income" dataset https://archive.ics.uci.edu/ml/datasets/Adult.
<pre>python ./bin/zeroconf.py -d ./data/Adult.h5</pre>
And then to evaluate the prediction stored in zerconf-result.csv against the test dataset file adult.test.withid 
<pre>python ./bin/evaluate-dataset-Adult.py</pre>

## Installation
The script itself needs no installation, just copy it with the rest of the files in your working directory.
Alternatively you could use git clone
<pre>
sudo apt-get update && sudo apt-get install git && git clone https://github.com/paypal/autosklearn-zeroconf.git
</pre>

### Happy path installation on Ubuntu 18.04LTS
<pre>
sudo apt-get update && sudo apt-get install git gcc build-essential swig python-pip virtualenv python3-dev
git clone https://github.com/paypal/autosklearn-zeroconf.git
pip install virtualenv
virtualenv zeroconf -p /usr/bin/python3.6
source zeroconf/bin/activate
curl https://raw.githubusercontent.com/paypal/autosklearn-zeroconf/master/requirements.txt | xargs -n 1 -L 1 pip install
git clone https://github.com/paypal/autosklearn-zeroconf.git
cd autosklearn-zeroconf/ && python ./bin/zeroconf.py -d ./data/Adult.h5 2>/dev/null
</pre>

## License
autosklearn-zeroconf is licensed under the [BSD 3-Clause License (Revised)](LICENSE.txt)

## Example of the output
<pre>
python zeroconf.py -d ./data/Adult.h5 2>/dev/null | grep [ZEROCONF]

2017-10-11 10:52:15,893 - [ZEROCONF] - zeroconf.py - INFO - Program Call Parameter (Arguments and Parameter File Values):
2017-10-11 10:52:15,893 - [ZEROCONF] - zeroconf.py - INFO -    basedir: /home/ulrich/PycharmProjects/autosklearn-zeroconf
2017-10-11 10:52:15,893 - [ZEROCONF] - zeroconf.py - INFO -    data_file: /home/ulrich/PycharmProjects/autosklearn-zeroconf/data/Adult.h5
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    id_field: cust_id
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    max_classifier_time_budget: 1200
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    max_sample_size: 100000
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    memory_limit: 15000
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    parameter_file: /home/ulrich/PycharmProjects/autosklearn-zeroconf/parameter/default.yml
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    proc: zeroconf.py
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    resultfile: /home/ulrich/PycharmProjects/autosklearn-zeroconf/data/zeroconf-result.csv
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    runid: 20171011105215
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    runtype: Fresh Run Start
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    target_field: category
2017-10-11 10:52:15,894 - [ZEROCONF] - zeroconf.py - INFO -    workdir: /home/ulrich/PycharmProjects/autosklearn-zeroconf/work/20171011105215
2017-10-11 10:52:15,944 - [ZEROCONF] - zeroconf.py - INFO - Read dataset from the store
2017-10-11 10:52:15,945 - [ZEROCONF] - zeroconf.py - INFO - Values of y [  0.   1.  nan]
2017-10-11 10:52:15,945 - [ZEROCONF] - zeroconf.py - INFO - We need to protect NAs in y from the prediction dataset so we convert them to -1
2017-10-11 10:52:15,946 - [ZEROCONF] - zeroconf.py - INFO - New values of y [ 0.  1. -1.]
2017-10-11 10:52:15,946 - [ZEROCONF] - zeroconf.py - INFO - Filling missing values in X with the most frequent values
2017-10-11 10:52:16,043 - [ZEROCONF] - zeroconf.py - INFO - Factorizing the X
2017-10-11 10:52:16,176 - [ZEROCONF] - x_y_dataframe_split - INFO - Dataframe split into X and y
2017-10-11 10:52:16,178 - [ZEROCONF] - zeroconf.py - INFO - Preparing a sample to measure approx classifier run time and select features
2017-10-11 10:52:16,191 - [ZEROCONF] - zeroconf.py - INFO - train size:21815
2017-10-11 10:52:16,191 - [ZEROCONF] - zeroconf.py - INFO - test size:10746
2017-10-11 10:52:16,192 - [ZEROCONF] - zeroconf.py - INFO - Reserved 33% of the training dataset for validation (upto 33k rows)
2017-10-11 10:52:16,209 - [ZEROCONF] - max_estimators_fit_duration - INFO - Constructing preprocessor pipeline and transforming sample data
2017-10-11 10:52:18,712 - [ZEROCONF] - max_estimators_fit_duration - INFO - Running estimators on the sample
2017-10-11 10:52:18,729 - [ZEROCONF] - zeroconf.py - INFO - adaboost starting
2017-10-11 10:52:18,734 - [ZEROCONF] - zeroconf.py - INFO - bernoulli_nb starting
2017-10-11 10:52:18,761 - [ZEROCONF] - zeroconf.py - INFO - extra_trees starting
2017-10-11 10:52:18,769 - [ZEROCONF] - zeroconf.py - INFO - decision_tree starting
2017-10-11 10:52:18,780 - [ZEROCONF] - zeroconf.py - INFO - gaussian_nb starting
2017-10-11 10:52:18,800 - [ZEROCONF] - zeroconf.py - INFO - bernoulli_nb training time: 0.06455278396606445
2017-10-11 10:52:18,802 - [ZEROCONF] - zeroconf.py - INFO - gradient_boosting starting
2017-10-11 10:52:18,808 - [ZEROCONF] - zeroconf.py - INFO - k_nearest_neighbors starting
2017-10-11 10:52:18,809 - [ZEROCONF] - zeroconf.py - INFO - decision_tree training time: 0.03273773193359375
2017-10-11 10:52:18,826 - [ZEROCONF] - zeroconf.py - INFO - lda starting
2017-10-11 10:52:18,845 - [ZEROCONF] - zeroconf.py - INFO - liblinear_svc starting
2017-10-11 10:52:18,867 - [ZEROCONF] - zeroconf.py - INFO - gaussian_nb training time: 0.08569979667663574
2017-10-11 10:52:18,882 - [ZEROCONF] - zeroconf.py - INFO - multinomial_nb starting
2017-10-11 10:52:18,905 - [ZEROCONF] - zeroconf.py - INFO - passive_aggressive starting
2017-10-11 10:52:18,943 - [ZEROCONF] - zeroconf.py - INFO - random_forest starting
2017-10-11 10:52:18,971 - [ZEROCONF] - zeroconf.py - INFO - sgd starting
2017-10-11 10:52:19,012 - [ZEROCONF] - zeroconf.py - INFO - lda training time: 0.17656564712524414
2017-10-11 10:52:19,023 - [ZEROCONF] - zeroconf.py - INFO - multinomial_nb training time: 0.13777780532836914
2017-10-11 10:52:19,124 - [ZEROCONF] - zeroconf.py - INFO - liblinear_svc training time: 0.27405595779418945
2017-10-11 10:52:19,416 - [ZEROCONF] - zeroconf.py - INFO - passive_aggressive training time: 0.508676290512085
2017-10-11 10:52:19,473 - [ZEROCONF] - zeroconf.py - INFO - sgd training time: 0.49777913093566895
2017-10-11 10:52:20,471 - [ZEROCONF] - zeroconf.py - INFO - adaboost training time: 1.7392246723175049
2017-10-11 10:52:20,625 - [ZEROCONF] - zeroconf.py - INFO - k_nearest_neighbors training time: 1.8141863346099854
2017-10-11 10:52:22,258 - [ZEROCONF] - zeroconf.py - INFO - extra_trees training time: 3.4934401512145996
2017-10-11 10:52:22,696 - [ZEROCONF] - zeroconf.py - INFO - random_forest training time: 3.7496204376220703
2017-10-11 10:52:24,215 - [ZEROCONF] - zeroconf.py - INFO - gradient_boosting training time: 5.41023063659668
2017-10-11 10:52:24,230 - [ZEROCONF] - max_estimators_fit_duration - INFO - Test classifier fit completed
2017-10-11 10:52:24,239 - [ZEROCONF] - zeroconf.py - INFO - per_run_time_limit=5
2017-10-11 10:52:24,239 - [ZEROCONF] - zeroconf.py - INFO - Process pool size=2
2017-10-11 10:52:24,240 - [ZEROCONF] - zeroconf.py - INFO - Starting autosklearn classifiers fiting on a 67% sample up to 67k rows
2017-10-11 10:52:24,252 - [ZEROCONF] - train_multicore - INFO - Max time allowance for a model 1 minute(s)
2017-10-11 10:52:24,252 - [ZEROCONF] - train_multicore - INFO - Overal run time is about 10 minute(s)
2017-10-11 10:52:24,255 - [ZEROCONF] - train_multicore - INFO - Multicore process 2 started
2017-10-11 10:52:24,258 - [ZEROCONF] - train_multicore - INFO - Multicore process 3 started
2017-10-11 10:52:24,276 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Start AutoSklearnClassifier seed=2
2017-10-11 10:52:24,278 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Start AutoSklearnClassifier seed=3
2017-10-11 10:52:24,295 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Done AutoSklearnClassifier seed=3
2017-10-11 10:52:24,297 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Done AutoSklearnClassifier seed=2
2017-10-11 10:52:26,299 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Starting seed=2
2017-10-11 10:52:27,298 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - Starting seed=3
2017-10-11 10:56:30,949 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - ####### Finished seed=2
2017-10-11 10:56:31,600 - [ZEROCONF] - spawn_autosklearn_classifier - INFO - ####### Finished seed=3
2017-10-11 10:56:31,614 - [ZEROCONF] - train_multicore - INFO - Multicore fit completed
2017-10-11 10:56:31,626 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - Building ensemble
2017-10-11 10:56:31,626 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - Done AutoSklearnClassifier - seed:1
2017-10-11 10:56:54,017 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - Ensemble built - seed:1
2017-10-11 10:56:54,017 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - Show models - seed:1
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - [(0.400000, SimpleClassificationPipeline({'classifier:__choice__': 'adaboost', 'one_hot_encoding:use_minimum_fraction': 'True', 'preprocessor:select_percentile_classification:percentile': 85.5410729966473, 'classifier:adaboost:n_estimators': 88, 'one_hot_encoding:minimum_fraction': 0.01805038589303469, 'rescaling:__choice__': 'minmax', 'balancing:strategy': 'weighting', 'preprocessor:__choice__': 'select_percentile_classification', 'classifier:adaboost:max_depth': 1, 'classifier:adaboost:learning_rate': 0.10898092508755285, 'preprocessor:select_percentile_classification:score_func': 'chi2', 'imputation:strategy': 'most_frequent', 'classifier:adaboost:algorithm': 'SAMME.R'},
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - dataset_properties={
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'task': 1,
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'signed': False,
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'sparse': False,
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multiclass': False,
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'target_type': 'classification',
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multilabel': False})),
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - (0.300000, SimpleClassificationPipeline({'classifier:__choice__': 'random_forest', 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'one_hot_encoding:use_minimum_fraction': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:min_samples_leaf': 4, 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:min_samples_split': 16, 'classifier:random_forest:bootstrap': 'False', 'one_hot_encoding:minimum_fraction': 0.1453954841364777, 'rescaling:__choice__': 'none', 'balancing:strategy': 'none', 'preprocessor:__choice__': 'select_percentile_classification', 'preprocessor:select_percentile_classification:percentile': 96.35414862145892, 'preprocessor:select_percentile_classification:score_func': 'chi2', 'imputation:strategy': 'mean', 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:max_features': 3.342759426984195, 'classifier:random_forest:n_estimators': 100},
2017-10-11 10:56:54,596 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - dataset_properties={
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'task': 1,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'signed': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'sparse': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multiclass': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'target_type': 'classification',
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multilabel': False})),
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - (0.200000, SimpleClassificationPipeline({'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'classifier:__choice__': 'extra_trees', 'classifier:extra_trees:n_estimators': 100, 'classifier:extra_trees:bootstrap': 'True', 'preprocessor:extra_trees_preproc_for_classification:min_samples_split': 5, 'classifier:extra_trees:min_samples_leaf': 10, 'rescaling:__choice__': 'minmax', 'classifier:extra_trees:max_depth': 'None', 'preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'preprocessor:extra_trees_preproc_for_classification:criterion': 'gini', 'classifier:extra_trees:max_features': 4.413198608615693, 'classifier:extra_trees:criterion': 'gini', 'preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'classifier:extra_trees:min_samples_split': 16, 'one_hot_encoding:use_minimum_fraction': 'False', 'balancing:strategy': 'weighting', 'preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 1, 'preprocessor:extra_trees_preproc_for_classification:max_features': 1.4824479003506632, 'imputation:strategy': 'median', 'preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'preprocessor:extra_trees_preproc_for_classification:max_depth': 'None'},
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - dataset_properties={
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'task': 1,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'signed': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'sparse': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multiclass': False,
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'target_type': 'classification',
2017-10-11 10:56:54,597 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multilabel': False})),
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - (0.100000, SimpleClassificationPipeline({'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'classifier:__choice__': 'extra_trees', 'classifier:extra_trees:n_estimators': 100, 'classifier:extra_trees:bootstrap': 'True', 'preprocessor:extra_trees_preproc_for_classification:min_samples_split': 16, 'classifier:extra_trees:min_samples_leaf': 10, 'rescaling:__choice__': 'minmax', 'classifier:extra_trees:max_depth': 'None', 'preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'preprocessor:extra_trees_preproc_for_classification:criterion': 'gini', 'classifier:extra_trees:max_features': 4.16852017424403, 'classifier:extra_trees:criterion': 'gini', 'preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'classifier:extra_trees:min_samples_split': 16, 'one_hot_encoding:use_minimum_fraction': 'False', 'balancing:strategy': 'weighting', 'preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 1, 'preprocessor:extra_trees_preproc_for_classification:max_features': 1.5781770540350555, 'imputation:strategy': 'median', 'preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'preprocessor:extra_trees_preproc_for_classification:max_depth': 'None'},
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - dataset_properties={
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'task': 1,
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'signed': False,
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'sparse': False,
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multiclass': False,
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'target_type': 'classification',
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO -   'multilabel': False})),
2017-10-11 10:56:54,598 - [ZEROCONF] - zeroconf_fit_ensemble - INFO - ]
2017-10-11 10:56:54,613 - [ZEROCONF] - zeroconf.py - INFO - Validating
2017-10-11 10:56:54,613 - [ZEROCONF] - zeroconf.py - INFO - Predicting on validation set
2017-10-11 10:56:57,373 - [ZEROCONF] - zeroconf.py - INFO - ########################################################################
2017-10-11 10:56:57,374 - [ZEROCONF] - zeroconf.py - INFO - Accuracy score 84%
2017-10-11 10:56:57,374 - [ZEROCONF] - zeroconf.py - INFO - The below scores are calculated for predicting '1' category value
2017-10-11 10:56:57,379 - [ZEROCONF] - zeroconf.py - INFO - Precision: 64%, Recall: 77%, F1: 0.70
2017-10-11 10:56:57,379 - [ZEROCONF] - zeroconf.py - INFO - Confusion Matrix: https://en.wikipedia.org/wiki/Precision_and_recall
2017-10-11 10:56:57,386 - [ZEROCONF] - zeroconf.py - INFO - [7058 1100]
2017-10-11 10:56:57,386 - [ZEROCONF] - zeroconf.py - INFO - [ 603 1985]
2017-10-11 10:56:57,392 - [ZEROCONF] - zeroconf.py - INFO - Baseline 2588 positives from 10746 overall = 24.1%
2017-10-11 10:56:57,392 - [ZEROCONF] - zeroconf.py - INFO - ########################################################################
2017-10-11 10:56:57,404 - [ZEROCONF] - x_y_dataframe_split - INFO - Dataframe split into X and y
2017-10-11 10:56:57,405 - [ZEROCONF] - zeroconf.py - INFO - Re-fitting the model ensemble on full known dataset to prepare for prediciton. This can take a long time.
2017-10-11 10:58:39,836 - [ZEROCONF] - zeroconf.py - INFO - Predicting. This can take a long time for a large prediction set.
2017-10-11 10:58:45,221 - [ZEROCONF] - zeroconf.py - INFO - Prediction done
2017-10-11 10:58:45,223 - [ZEROCONF] - zeroconf.py - INFO - Exporting the data
2017-10-11 10:58:45,267 - [ZEROCONF] - zeroconf.py - INFO - ##### Zeroconf Script Completed! #####
2017-10-11 10:58:45,268 - [ZEROCONF] - zeroconf.py - INFO - Clean up / Delete work directory: /home/ulrich/PycharmProjects/autosklearn-zeroconf/work/20171011105215

Process finished with exit code 0
</pre>

<pre>
python evaluate-dataset-Adult.py 
[ZEROCONF]  # 00:37:43 #
[ZEROCONF] ######################################################################## # 00:37:43 #
[ZEROCONF] Accuracy score 85% # 00:37:43 #
[ZEROCONF] The below scores are calculated for predicting '1' category value # 00:37:43 #
[ZEROCONF] Precision: 65%, Recall: 78%, F1: 0.71 # 00:37:43 #
[ZEROCONF] Confusion Matrix: https://en.wikipedia.org/wiki/Precision_and_recall # 00:37:43 #
[ZEROCONF] [[10835  1600] # 00:37:43 #
[ZEROCONF]  [  860  2986]] # 00:37:43 #
[ZEROCONF] Baseline 3846 positives from 16281 overall = 23.6% # 00:37:43 #
[ZEROCONF] ######################################################################## # 00:37:43 #
[ZEROCONF]  # 00:37:43 #
</pre>
## Workarounds
these are not related to the autosklearn-zeroconf or auto-sklearn but rather general issues depending on your python and OS installation
### xgboost issues
#### complains about ELF header
<pre>pip uninstall xgboost; pip install --no-cache-dir -v xgboost==0.4a30</pre>
#### can not find libraries
<pre>conda install libgcc # for xgboost</pre>
alternatively search for them with 
<pre>sudo find / -name libgomp.so.1
/usr/lib/x86_64-linux-gnu/libgomp.so.1</pre> 
and explicitly add them to the libraries path
<pre>export LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libstdc++.so.6":"/usr/lib/x86_64-linux-gnu/libgomp.so.1"; python zeroconf.py Titanic.h5 2>/dev/null|grep ZEROCONF</pre>
Also see https://github.com/automl/auto-sklearn/issues/247

### Install auto-sklearn
<pre>
# A compiler (gcc) is needed to compile a few things the from auto-sklearn requirements.txt
# Chose just the line for your Linux flavor below

# On Ubuntu
sudo apt-get install gcc build-essential swig

# On CentOS 7-1611 http://www.osboxes.org/centos/ https://drive.google.com/file/d/0B_HAFnYs6Ur-bl8wUWZfcHVpMm8/view?usp=sharing
sudo yum -y update 
sudo reboot
sudo yum install epel-release python34 python34-devel python34-setuptools
sudo yum -y groupinstall 'Development Tools'

# auto-sklearn requires swig 3.0 
wget downloads.sourceforge.net/project/swig/swig/swig-3.0.12/swig-3.0.12.tar.gz -O swig-3.0.12.tar.gz
tar xf swig-3.0.12.tar.gz 
cd swig-3.0.12 
./configure --without-pcre
make
sudo make install
cd ..

sudo easy_install-3.4 pip
# if you want to use virtual environments
sudo pip3 install virtualenv
virtualenv zeroconf -p /usr/bin/python3.4
source zeroconf/bin/activate

curl https://raw.githubusercontent.com/paypal/autosklearn-zeroconf/master/requirements.txt | xargs -n 1 -L 1 pip install
</pre>

# Contributors
Egor Kobylkin, Ulrich Arndt


================================================
FILE: bin/dataTransformationProcessing.py
================================================
import inspect
import math
import multiprocessing
import time
import traceback
from time import sleep

import autosklearn.pipeline
import autosklearn.pipeline.components.classification
import utility as utl
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import psutil
from autosklearn.classification import AutoSklearnClassifier
from autosklearn.constants import *
from autosklearn.pipeline.classification import SimpleClassificationPipeline


def time_single_estimator(clf_name, clf_class, X, y, max_clf_time, logger):
    if ('libsvm_svc' == clf_name  # doesn't even scale to a 100k rows
        or 'qda' == clf_name):  # crashes
        return 0
    logger.info(clf_name + " starting")
    default = clf_class.get_hyperparameter_search_space().get_default_configuration()
    clf = clf_class(**default._values)
    t0 = time.time()
    try:
        clf.fit(X, y)
    except Exception as e:
        logger.info(e)
    classifier_time = time.time() - t0  # keep time even if classifier crashed
    logger.info(clf_name + " training time: " + str(classifier_time))
    if max_clf_time.value < int(classifier_time):
        max_clf_time.value = int(classifier_time)
        # no return statement here because max_clf_time is a managed object


def max_estimators_fit_duration(X, y, max_classifier_time_budget, logger, sample_factor=1):
    lo = utl.get_logger(inspect.stack()[0][3])

    lo.info("Constructing preprocessor pipeline and transforming sample data")
    # we don't care about the data here but need to preprocess, otherwise the classifiers crash

    pipeline = SimpleClassificationPipeline(
        include={'imputation': ['most_frequent'], 'rescaling': ['standardize']})
    default_cs = pipeline.get_hyperparameter_search_space().get_default_configuration()
    pipeline = pipeline.set_hyperparameters(default_cs)

    pipeline.fit(X, y)
    X_tr, dummy = pipeline.fit_transformer(X, y)

    lo.info("Running estimators on the sample")
    # going over all default classifiers used by auto-sklearn
    clfs = autosklearn.pipeline.components.classification._classifiers

    processes = []
    with multiprocessing.Manager() as manager:
        max_clf_time = manager.Value('i', 3)  # default 3 sec
        for clf_name, clf_class in clfs.items():
            pr = multiprocessing.Process(target=time_single_estimator, name=clf_name
                                         , args=(clf_name, clf_class, X_tr, y, max_clf_time, logger))
            pr.start()
            processes.append(pr)
        for pr in processes:
            pr.join(max_classifier_time_budget)  # will block for max_classifier_time_budget or
            # until the classifier fit process finishes. After max_classifier_time_budget 
            # we will terminate all still running processes here. 
            if pr.is_alive():
                logger.info("Terminating " + pr.name + " process due to timeout")
                pr.terminate()
        result_max_clf_time = max_clf_time.value

    lo.info("Test classifier fit completed")

    per_run_time_limit = int(sample_factor * result_max_clf_time)
    return max_classifier_time_budget if per_run_time_limit > max_classifier_time_budget else per_run_time_limit


def read_dataframe_h5(filename, logger):
    with pd.HDFStore(filename, mode='r') as store:
        df = store.select('data')
    logger.info("Read dataset from the store")
    return df


def x_y_dataframe_split(dataframe, parameter, id=False):
    lo = utl.get_logger(inspect.stack()[0][3])

    lo.info("Dataframe split into X and y")
    X = dataframe.drop([parameter["id_field"], parameter["target_field"]], axis=1)
    y = pd.np.array(dataframe[parameter["target_field"]], dtype='int')
    if id:
        row_id = dataframe[parameter["id_field"]]
        return X, y, row_id
    else:
        return X, y


def define_pool_size(memory_limit):
    # some classifiers can use more than one core - so keep this at half memory and cores
    max_pool_size = int(math.ceil(psutil.virtual_memory().total / (memory_limit * 1000000)))
    half_of_cores = int(math.ceil(psutil.cpu_count() / 2.0))
    
    lo = utl.get_logger(inspect.stack()[0][3])
    lo.info("Virtual Memory Size = " + str(psutil.virtual_memory().total) )
    lo.info("CPU Count =" + str(psutil.cpu_count()) )
    lo.info("Max CPU Pool Size by Memory = " + str(max_pool_size) )
    
    return half_of_cores if max_pool_size > half_of_cores else max_pool_size


def calculate_time_left_for_this_task(pool_size, per_run_time_limit):
    half_cpu_cores = pool_size
    queue_factor = 30
    if queue_factor * half_cpu_cores < 100:  # 100 models to test overall
        queue_factor = 100 / half_cpu_cores

    time_left_for_this_task = int(queue_factor * per_run_time_limit)
    return time_left_for_this_task


def spawn_autosklearn_classifier(X_train, y_train, seed, dataset_name, time_left_for_this_task, per_run_time_limit,
                                 feat_type, memory_limit, atsklrn_tempdir):
    lo = utl.get_logger(inspect.stack()[0][3])

    try:
        lo.info("Start AutoSklearnClassifier seed=" + str(seed))
        clf = AutoSklearnClassifier(time_left_for_this_task=time_left_for_this_task,
                                    per_run_time_limit=per_run_time_limit,
                                    ml_memory_limit=memory_limit,
                                    shared_mode=True,
                                    tmp_folder=atsklrn_tempdir,
                                    output_folder=atsklrn_tempdir,
                                    delete_tmp_folder_after_terminate=False,
                                    delete_output_folder_after_terminate=False,
                                    initial_configurations_via_metalearning=0,
                                    ensemble_size=0,
                                    seed=seed)
    except Exception:
        lo.exception("Exception AutoSklearnClassifier seed=" + str(seed))
        raise

    lo = utl.get_logger(inspect.stack()[0][3])
    lo.info("Done AutoSklearnClassifier seed=" + str(seed))

    sleep(seed)

    try:
        lo.info("Starting seed=" + str(seed))
        try:
            clf.fit(X_train, y_train, metric=autosklearn.metrics.f1, feat_type=feat_type, dataset_name=dataset_name)
        except Exception:
            lo = utl.get_logger(inspect.stack()[0][3])
            lo.exception("Error in clf.fit - seed:" + str(seed))
            raise
    except Exception:
        lo = utl.get_logger(inspect.stack()[0][3])
        lo.exception("Exception in seed=" + str(seed) + ".  ")
        traceback.print_exc()
        raise
    lo = utl.get_logger(inspect.stack()[0][3])
    lo.info("####### Finished seed=" + str(seed))
    return None


def train_multicore(X, y, feat_type, memory_limit, atsklrn_tempdir, pool_size=1, per_run_time_limit=60):
    lo = utl.get_logger(inspect.stack()[0][3])

    time_left_for_this_task = calculate_time_left_for_this_task(pool_size, per_run_time_limit)

    lo.info("Max time allowance for a model " + str(math.ceil(per_run_time_limit / 60.0)) + " minute(s)")
    lo.info("Overal run time is about " + str(2 * math.ceil(time_left_for_this_task / 60.0)) + " minute(s)")

    processes = []
    for i in range(2, pool_size + 2):  # reserve seed 1 for the ensemble building
        seed = i
        pr = multiprocessing.Process(target=spawn_autosklearn_classifier
                                     , args=(
            X, y, i, 'foobar', time_left_for_this_task, per_run_time_limit, feat_type, memory_limit, atsklrn_tempdir))
        pr.start()
        lo.info("Multicore process " + str(seed) + " started")
        processes.append(pr)
    for pr in processes:
        pr.join()

    lo.info("Multicore fit completed")


def zeroconf_fit_ensemble(y, atsklrn_tempdir):
    lo = utl.get_logger(inspect.stack()[0][3])

    lo.info("Building ensemble")

    seed = 1

    ensemble = AutoSklearnClassifier(
        time_left_for_this_task=300, per_run_time_limit=150, ml_memory_limit=20240, ensemble_size=50,
        ensemble_nbest=200,
        shared_mode=True, tmp_folder=atsklrn_tempdir, output_folder=atsklrn_tempdir,
        delete_tmp_folder_after_terminate=False, delete_output_folder_after_terminate=False,
        initial_configurations_via_metalearning=0,
        seed=seed)

    lo.info("Done AutoSklearnClassifier - seed:" + str(seed))

    try:
        lo.debug("Start ensemble.fit_ensemble - seed:" + str(seed))
        ensemble.fit_ensemble(
            task=BINARY_CLASSIFICATION
            , y=y
            , metric=autosklearn.metrics.f1
            , precision='32'
            , dataset_name='foobar'
            , ensemble_size=10
            , ensemble_nbest=15)
    except Exception:
        lo = utl.get_logger(inspect.stack()[0][3])
        lo.exception("Error in ensemble.fit_ensemble - seed:" + str(seed))
        raise

    lo = utl.get_logger(inspect.stack()[0][3])
    lo.debug("Done ensemble.fit_ensemble - seed:" + str(seed))

    sleep(20)
    lo.info("Ensemble built - seed:" + str(seed))

    lo.info("Show models - seed:" + str(seed))
    txtList = str(ensemble.show_models()).split("\n")
    for row in txtList:
        lo.info(row)

    return ensemble


================================================
FILE: bin/evaluate-dataset-Adult.py
================================================
# -*- coding: utf-8 -*-
"""
Copyright 2017 Egor Kobylkin 
Created on Sun Apr 23 11:52:59 2017
@author: ekobylkin
This is an example on how to prepare data for autosklearn-zeroconf.
It is using a well known Adult (Salary) dataset from UCI https://archive.ics.uci.edu/ml/datasets/Adult .
"""
import pandas as pd

test = pd.read_csv(filepath_or_buffer='./data/adult.test.withid',sep=',', error_bad_lines=False, index_col=False)
#print(test)

prediction = pd.read_csv(filepath_or_buffer='./data/zeroconf-result.csv',sep=',', error_bad_lines=False, index_col=False)
#print(prediction)

df=pd.merge(test, prediction, how='inner', on=['cust_id',])

y_test=df['category']
y_hat=df['prediction']

from sklearn.metrics import (confusion_matrix, precision_score
, recall_score, f1_score, accuracy_score)
from time import time,sleep,strftime
def p(text):
    for line in str(text).splitlines():
        print ('[ZEROCONF] '+line+" # "+strftime("%H:%M:%S")+" #")

p("\n")
p("#"*72)
p("Accuracy score {0:2.0%}".format(accuracy_score(y_test, y_hat)))
p("The below scores are calculated for predicting '1' category value")
p("Precision: {0:2.0%}, Recall: {1:2.0%}, F1: {2:.2f}".format(
precision_score(y_test, y_hat),recall_score(y_test, y_hat),f1_score(y_test, y_hat)))
p("Confusion Matrix: https://en.wikipedia.org/wiki/Precision_and_recall")
p(confusion_matrix(y_test, y_hat))   
baseline_1 = str(sum(a for a in y_test))
baseline_all = str(len(y_test))
baseline_prcnt = "{0:2.0%}".format( float(sum(a for a in y_test)/len(y_test))) 
p("Baseline %s positives from %s overall = %1.1f%%" %
(sum(a for a in y_test), len(y_test), 100*sum(a for a in y_test)/len(y_test)))
p("#"*72)
p("\n")


================================================
FILE: bin/load-dataset-Adult.py
================================================
# -*- coding: utf-8 -*-
"""
Copyright 2017 Egor Kobylkin 
Created on Sun Apr 23 11:52:59 2017
@author: ekobylkin
This is an example on how to prepare data for autosklearn-zeroconf.
It is using a well known Adult (Salary) dataset from UCI https://archive.ics.uci.edu/ml/datasets/Adult .
"""
import pandas as pd
# wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
# wget https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test
col_names=[
'age',
'workclass',
'fnlwgt',
'education',
'education-num',
'marital-status',
'occupation',
'relationship',
'race',
'sex',
'capital-gain',
'capital-loss',
'hours-per-week',
'native-country',
'category'
]

train = pd.read_csv(filepath_or_buffer='../data/adult.data',sep=',', error_bad_lines=False, index_col=False, names=col_names)
category_mapping={' >50K':1,' <=50K':0}
train['category']= train['category'].map(category_mapping)
#dataframe=train

test = pd.read_csv(filepath_or_buffer='../data/adult.test',sep=',', error_bad_lines=False, index_col=False, names=col_names, skiprows=1)
test['set_name']='test'
category_mapping={' >50K.':1,' <=50K.':0}
test['category']= test['category'].map(category_mapping) 

dataframe=train.append(test)

# autosklearn-zeroconf requires cust_id and category (target or "y" variable) columns, the rest is optional
dataframe['cust_id']=dataframe.index

# let's save the test with the cus_id and binarized category for the validation of the prediction afterwards
test_df=dataframe.loc[dataframe['set_name']=='test'].drop(['set_name'], axis=1)
test_df.to_csv('../data/adult.test.withid', index=False, header=True)

# We will use the test.csv data to make a prediction. You can compare the predicted values with the ground truth yourself.
dataframe.loc[dataframe['set_name']=='test','category']=None
dataframe=dataframe.drop(['set_name'], axis=1)

print(dataframe)

store = pd.HDFStore('../data/Adult.h5') # this is the file cache for the data
store['data'] = dataframe
store.close()
#Now run 'python zeroconf.py Adult.h5' (python >=3.5)


================================================
FILE: bin/load-dataset-Titanic.py
================================================
# -*- coding: utf-8 -*-
"""
Copyright 2017 PayPal
Created on Sun Oct 02 17:13:59 2016
@author: ekobylkin

This is an example on how to prepare data for autosklearn-zeroconf.
It is using a well known Titanic dataset from Kaggle https://www.kaggle.com/c/titanic .
"""
import pandas as pd
# Dowlnoad these files from Kaggle dataset
#https://www.kaggle.com/c/titanic/download/train.csv
#https://www.kaggle.com/c/titanic/download/test.csv
train = pd.read_csv(filepath_or_buffer='train.csv',sep=',', error_bad_lines=False, index_col=False)
test = pd.read_csv(filepath_or_buffer='test.csv',sep=',', error_bad_lines=False, index_col=False)

# We will use the test.csv data to make a prediction. You can compare the predicted values with the ground truth yourself.
test['Survived']=None # The empty target column tells autosklearn-zeroconf to use these cases for the prediction

dataframe=train.append(test)

# autosklearn-zeroconf requires cust_id and category (target or "y" variable) columns, the rest is optional
dataframe.rename(columns = {'PassengerId':'cust_id','Survived':'category'},inplace=True)

store = pd.HDFStore('Titanic.h5') # this is the file cache for the data
store['data'] = dataframe
store.close()
#Now run 'python zeroconf.py Titanic.h5' (python >=3.5)


================================================
FILE: bin/utility.py
================================================
import datetime
import logging

import os
import ruamel.yaml as yaml
import shutil


def init_process(file, basedir=''):
    absfile = os.path.abspath(file)
    if (basedir == ''):
        basedir = os.path.join(*splitall(absfile)[0:(len(splitall(absfile)) - 2)])
    proc = os.path.basename(absfile)
    if not os.path.isdir(basedir + '/work'):
        os.mkdir(basedir + '/work')
    runidfile = basedir + '/work/current_runid.txt'
    runid, runtype = get_runid(runidfile, basedir)

    parameter = {}
    parameter["runid"] = runid
    parameter["runtype"] = runtype
    parameter["proc"] = proc
    parameter["workdir"] = basedir + '/work/' + runid
    return parameter


def get_logger(name):
    ##################
    # the repeating setup of the logging is related to an issue in the sklearn package
    # this resulted in a lost of the logger...
    ##################
    setup_logging()
    logger = logging.getLogger(name)
    return logger


def handle_exception(exc_type, exc_value, exc_traceback):
    logger = get_logger(__file__)
    if issubclass(exc_type, KeyboardInterrupt):
        sys.__excepthook__(exc_type, exc_value, exc_traceback)
        return
    logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))

def merge_two_dicts(x, y):
    """Given two dicts, merge them into a new dict as a shallow copy."""
    z = x.copy()
    z.update(y)
    return z

def read_parameter(parameter_file, parameter):
    fr = open(parameter_file, "r")
    param = yaml.load(fr, yaml.RoundTripLoader)
    return merge_two_dicts(parameter,param)


def end_proc_success(parameter, logger):
    logger.info("Clean up / Delete work directory: " + parameter["basedir"] + "/work/" + parameter["runid"])
    shutil.rmtree(parameter["basedir"] + "/work/" + parameter["runid"])
    exit(0)


def setup_logging(
        default_path='./parameter/logger.yml',
        default_level=logging.INFO,
        env_key='LOG_CFG'
):
    """Setup logging configuration

    """
    path = os.path.abspath(default_path)
    value = os.getenv(env_key, None)
    if value:
        path = value
    if os.path.exists(os.path.abspath(path)):
        with open(path, 'rt') as f:
            config = yaml.safe_load(f.read())
        logging.config.dictConfig(config)
    else:
        logging.basicConfig(level=default_level)


def get_runid(runidfile, basedir):
    now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
    if os.path.isfile(runidfile):
        rf = open(runidfile, 'r')
        runid = rf.read().rstrip()
        rf.close()
        if os.path.isdir(basedir + '/work/' + runid):
            runtype = 'RESTART'
        else:
            runtype = 'Fresh Run Start'
            rf = open(runidfile, 'w')
            runid = now
            print(runid, file=rf)
            rf.close()
            os.mkdir(basedir + '/work/' + runid)
    else:
        runtype = 'Fresh Run Start - no current_runid file'
        rf = open(runidfile, 'w')
        runid = now
        print(runid, file=rf)
        rf.close()
        os.mkdir(basedir + '/work/' + runid)
    return runid, runtype


def splitall(path):
    allparts = []
    while 1:
        parts = os.path.split(path)
        if parts[0] == path:  # sentinel for absolute paths
            allparts.insert(0, parts[0])
            break
        elif parts[1] == path:  # sentinel for relative paths
            allparts.insert(0, parts[1])
            break
        else:
            path = parts[0]
            allparts.insert(0, parts[1])
    return allparts


================================================
FILE: bin/zeroconf.py
================================================
# -*- coding: utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
"""
Copyright 2017 PayPal
Created on Mon Feb 27 19:11:59 PST 2017
@author: ekobylkin
@version 0.2
@author: ulrich arndt - data2knowledge
@update: 2017-09-27
"""

import argparse
import numpy as np
import os
import pandas as pd
import shutil

from sklearn.model_selection import train_test_split
from sklearn.metrics import (confusion_matrix, precision_score,
                             recall_score, f1_score, accuracy_score)

import utility as utl
import dataTransformationProcessing as dt

parameter = utl.init_process(__file__)

###########################################################
# define the command line argument parser
###########################################################
# https://docs.python.org/2/howto/argparse.html
parser = argparse.ArgumentParser(
    description='zero configuration predictic modeling script. Requires a pandas HDFS dataframe file ' + \
                'and a yaml parameter file as input as input')
parser.add_argument('-d',
                    '--data_file',
                    nargs=1,
                    help='input pandas HDFS dataframe .h5 with an unique indentifier and a target column\n' +
                         'as well as additional data columns\n'
                         'default values are cust_id and category or need to be defined in an\n' +
                         'optional parameter file '
                    )
parser.add_argument('-p',
                    '--param_file',
                    help='input yaml parameter file'
                    )

args = parser.parse_args()
logger = utl.get_logger(os.path.basename(__file__))
logger.info("Program started with the following arguments:")
logger.info(args)

###########################################################
# set dir to project dir
###########################################################
abspath = os.path.abspath(__file__)
dname = os.path.dirname(os.path.dirname(abspath))
os.chdir(dname)

###########################################################
# file check for the parameter
###########################################################
param_file = ''
if args.param_file:
    param_file = args.param_file[0]
else:
    param_file = os.path.abspath("./parameter/default.yml")
    logger.info("Using the default parameter file: " + param_file)
if (not (os.path.isfile(param_file))):
    msg = 'the input parameter file: ' + param_file + ' does not exist!'
    logger.error(msg)
    exit(8)

data_file = ''
if args.data_file:
    data_file = args.data_file[0]
else:
    msg = "A data file is mandatory!"
    logger.error(msg)
    exit(8)
if (not (os.path.isfile(data_file))):
    msg = 'the input parameter file: ' + data_file + ' does not exist!'
    logger.error(msg)
    exit(8)

parameter = utl.read_parameter(param_file, parameter)

parameter["data_file"] = os.path.abspath(data_file)
parameter["basedir"] = os.path.abspath(parameter["basedir"])
parameter["parameter_file"] = os.path.abspath(param_file)
parameter["resultfile"] = os.path.abspath(parameter["resultfile"])


###########################################################
# set base dir
###########################################################
os.chdir(parameter["basedir"])
logger.info("Set basedir to: " + parameter["basedir"])

logger = utl.get_logger(os.path.basename(__file__))

logger.info("Program Call Parameter (Arguments and Parameter File Values):")
for key in sorted(parameter.keys()):
    logger.info("   " + key + ": " + str(parameter[key]))

work_dir = parameter["workdir"]
result_filename = parameter["resultfile"]
atsklrn_tempdir = os.path.join(work_dir, 'atsklrn_tmp')
shutil.rmtree(atsklrn_tempdir, ignore_errors=True)  # cleanup - remove temp directory


# if the memory limit is lower the model can fail and the whole process will crash
memory_limit = parameter["memory_limit"]  # MB
global max_classifier_time_budget
max_classifier_time_budget = parameter["max_classifier_time_budget"]  # but 10 minutes is usually more than enough
max_sample_size = parameter["max_sample_size"]  # so that the classifiers fit method completes in a reasonable time

dataframe = dt.read_dataframe_h5(data_file, logger)

logger.info("Values of y " + str(dataframe[parameter["target_field"]].unique()))
logger.info("We need to protect NAs in y from the prediction dataset so we convert them to -1")
dataframe[parameter["target_field"]] = dataframe[parameter["target_field"]].fillna(-1)
logger.info("New values of y " + str(dataframe[parameter["target_field"]].unique()))

logger.info("Filling missing values in X with the most frequent values")
dataframe = dataframe.fillna(dataframe.mode().iloc[0])

logger.info("Factorizing the X")
# we need this list of original dtypes for the Autosklearn fit, create it before categorisation or split
col_dtype_dict = {col: ('Numerical' if np.issubdtype(dataframe[col].dtype, np.number) else 'Categorical')
                  for col in dataframe.columns if col not in [parameter["id_field"], parameter["target_field"]]}

# http://stackoverflow.com/questions/25530504/encoding-column-labels-in-pandas-for-machine-learning
# http://stackoverflow.com/questions/24458645/label-encoding-across-multiple-columns-in-scikit-learn?rq=1
# https://github.com/automl/auto-sklearn/issues/121#issuecomment-251459036

for col in dataframe.select_dtypes(exclude=[np.number]).columns:
    if col not in [parameter["id_field"], parameter["target_field"]]:
        dataframe[col] = dataframe[col].astype('category').cat.codes

df_unknown = dataframe[dataframe[parameter["target_field"]] == -1]  # 'None' gets categorzized into -1
df_known = dataframe[dataframe[parameter["target_field"]] != -1]  # not [0,1] for multiclass labeling compartibility
logger.debug("Length of unknown dataframe:" + str(len(df_unknown)))
logger.debug("Length of known dataframe:" + str(len(df_known)))

del dataframe

X, y = dt.x_y_dataframe_split(df_known, parameter)

logger.info("Preparing a sample to measure approx classifier run time and select features")
dataset_size = df_known.shape[0]

if dataset_size > max_sample_size:
    sample_factor = dataset_size / float(max_sample_size)
    logger.info("Sample factor =" + str(sample_factor))
    X_sample, y_sample = dt.x_y_dataframe_split(df_known.sample(max_sample_size, random_state=42), parameter)
    X_train, X_test, y_train, y_test = train_test_split(X.copy(), y, stratify=y, test_size=33000,
                                                           random_state=42)  # no need for larger test
else:
    sample_factor = 1
    X_sample, y_sample = X.copy(), y
    X_train, X_test, y_train, y_test = train_test_split(X.copy(), y, stratify=y, test_size=0.33, random_state=42)
logger.info("train size:" + str(len(X_train)))
logger.info("test size:" + str(len(X_test)))
logger.info("Reserved 33% of the training dataset for validation (upto 33k rows)")

per_run_time_limit = dt.max_estimators_fit_duration(X_train.values, y_train, max_classifier_time_budget, logger,
                                                    sample_factor)
logger.info("per_run_time_limit=" + str(per_run_time_limit))
pool_size = dt.define_pool_size(int(memory_limit))
logger.info("Process pool size=" + str(pool_size))
feat_type = [col_dtype_dict[col] for col in X.columns]
logger.info("Starting autosklearn classifiers fiting on a 67% sample up to 67k rows")
dt.train_multicore(X_train.values, y_train, feat_type, int(memory_limit), atsklrn_tempdir, pool_size,
                   per_run_time_limit)

ensemble = dt.zeroconf_fit_ensemble(y_train, atsklrn_tempdir)

logger = utl.get_logger(os.path.basename(__file__))
logger.info("Validating")
logger.info("Predicting on validation set")
y_hat = ensemble.predict(X_test.values)

logger.info("#" * 72)
logger.info("Accuracy score {0:2.0%}".format(accuracy_score(y_test, y_hat)))
logger.info("The below scores are calculated for predicting '1' category value")
logger.info("Precision: {0:2.0%}, Recall: {1:2.0%}, F1: {2:.2f}".format(
    precision_score(y_test, y_hat), recall_score(y_test, y_hat), f1_score(y_test, y_hat)))
#############################
## Print COnfusion Matrix
#############################
logger.info("Confusion Matrix: https://en.wikipedia.org/wiki/Precision_and_recall")
cm = confusion_matrix(y_test, y_hat)
for row in cm:
    logger.info(row)

baseline_1 = str(sum(a for a in y_test))
baseline_all = str(len(y_test))
baseline_prcnt = "{0:2.0%}".format(float(sum(a for a in y_test) / len(y_test)))
logger.info("Baseline %s positives from %s overall = %1.1f%%" %
            (sum(a for a in y_test), len(y_test), 100 * sum(a for a in y_test) / len(y_test)))
logger.info("#" * 72)

if df_unknown.shape[0] == 0:  # if there is nothing to predict we can stop already
    logger.info("##### Nothing to predict. Prediction dataset is empty. #####")
    exit(0)

X_unknown, y_unknown, row_id_unknown = dt.x_y_dataframe_split(df_unknown, parameter, id=True)

logger.info("Re-fitting the model ensemble on full known dataset to prepare for prediciton. This can take a long time.")
try:
    ensemble.refit(X.copy().values, y)
except Exception as e:
    logger.info("Refit failed, reshuffling the rows, restarting")
    logger.info(e)
    try:
        X2 = X.copy().values
        indices = np.arange(X2.shape[0])
        np.random.shuffle(indices)  # a workaround to algoritm shortcomings
        X2 = X2[indices]
        y = y[indices]
        ensemble.refit(X2, y)
    except Exception as e:
        logger.info("Second refit failed")
        logger.info(e)
        logger.info(
            " WORKAROUND: because Refitting fails due to an upstream bug https://github.com/automl/auto-sklearn/issues/263")
        logger.info(" WORKAROUND: we are fitting autosklearn classifiers a second time, now on the full dataset")
        dt.train_multicore(X.values, y, feat_type, int(memory_limit), atsklrn_tempdir, pool_size, per_run_time_limit)
        ensemble = dt.zeroconf_fit_ensemble(y_train, atsklrn_tempdir)

logger.info("Predicting. This can take a long time for a large prediction set.")
try:
    y_pred = ensemble.predict(X_unknown.copy().values)
    logger.info("Prediction done")
except Exception as e:
    logger.info(e)
    logger.info(
        " WORKAROUND: because REfitting fails due to an upstream bug https://github.com/automl/auto-sklearn/issues/263")
    logger.info(" WORKAROUND: we are fitting autosklearn classifiers a second time, now on the full dataset")
    dt.train_multicore(X.values, y, feat_type, int(memory_limit), atsklrn_tempdir, pool_size, per_run_time_limit)
    ensemble = dt.zeroconf_fit_ensemble(y_train, atsklrn_tempdir)
    logger.info("Predicting. This can take a long time for a large prediction set.")
    try:
        y_pred = ensemble.predict(X_unknown.copy().values)
        logger.info("Prediction done")
    except Exception as e:
        logger.info("##### Prediction failed, exiting! #####")
        logger.info(e)
        exit(2)

result_df = pd.DataFrame(
    {parameter["id_field"]: row_id_unknown, 'prediction': pd.Series(y_pred, index=row_id_unknown.index)})
logger.info("Exporting the data")
result_df.to_csv(result_filename, index=False, header=True)
logger.info("##### Zeroconf Script Completed! #####")
utl.end_proc_success(parameter, logger)


================================================
FILE: data/adult.data
================================================
39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K
50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, <=50K
38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K
28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K
37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K
49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, <=50K
52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, >50K
42, Private, 159449, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5178, 0, 40, United-States, >50K
37, Private, 280464, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 80, United-States, >50K
30, State-gov, 141297, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, >50K
23, Private, 122272, Bachelors, 13, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 30, United-States, <=50K
32, Private, 205019, Assoc-acdm, 12, Never-married, Sales, Not-in-family, Black, Male, 0, 0, 50, United-States, <=50K
40, Private, 121772, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, ?, >50K
34, Private, 245487, 7th-8th, 4, Married-civ-spouse, Transport-moving, Husband, Amer-Indian-Eskimo, Male, 0, 0, 45, Mexico, <=50K
25, Self-emp-not-inc, 176756, HS-grad, 9, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 35, United-States, <=50K
32, Private, 186824, HS-grad, 9, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 28887, 11th, 7, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
43, Self-emp-not-inc, 292175, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 45, United-States, >50K
40, Private, 193524, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, >50K
54, Private, 302146, HS-grad, 9, Separated, Other-service, Unmarried, Black, Female, 0, 0, 20, United-States, <=50K
35, Federal-gov, 76845, 9th, 5, Married-civ-spouse, Farming-fishing, Husband, Black, Male, 0, 0, 40, United-States, <=50K
43, Private, 117037, 11th, 7, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 2042, 40, United-States, <=50K
59, Private, 109015, HS-grad, 9, Divorced, Tech-support, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
56, Local-gov, 216851, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
19, Private, 168294, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, <=50K
54, ?, 180211, Some-college, 10, Married-civ-spouse, ?, Husband, Asian-Pac-Islander, Male, 0, 0, 60, South, >50K
39, Private, 367260, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 80, United-States, <=50K
49, Private, 193366, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
23, Local-gov, 190709, Assoc-acdm, 12, Never-married, Protective-serv, Not-in-family, White, Male, 0, 0, 52, United-States, <=50K
20, Private, 266015, Some-college, 10, Never-married, Sales, Own-child, Black, Male, 0, 0, 44, United-States, <=50K
45, Private, 386940, Bachelors, 13, Divorced, Exec-managerial, Own-child, White, Male, 0, 1408, 40, United-States, <=50K
30, Federal-gov, 59951, Some-college, 10, Married-civ-spouse, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, <=50K
22, State-gov, 311512, Some-college, 10, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 15, United-States, <=50K
48, Private, 242406, 11th, 7, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, Puerto-Rico, <=50K
21, Private, 197200, Some-college, 10, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 40, United-States, <=50K
19, Private, 544091, HS-grad, 9, Married-AF-spouse, Adm-clerical, Wife, White, Female, 0, 0, 25, United-States, <=50K
31, Private, 84154, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 38, ?, >50K
48, Self-emp-not-inc, 265477, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 507875, 9th, 5, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 43, United-States, <=50K
53, Self-emp-not-inc, 88506, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 172987, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, <=50K
49, Private, 94638, HS-grad, 9, Separated, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
25, Private, 289980, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 35, United-States, <=50K
57, Federal-gov, 337895, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Black, Male, 0, 0, 40, United-States, >50K
53, Private, 144361, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 38, United-States, <=50K
44, Private, 128354, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
41, State-gov, 101603, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
29, Private, 271466, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 43, United-States, <=50K
25, Private, 32275, Some-college, 10, Married-civ-spouse, Exec-managerial, Wife, Other, Female, 0, 0, 40, United-States, <=50K
18, Private, 226956, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 30, ?, <=50K
47, Private, 51835, Prof-school, 15, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1902, 60, Honduras, >50K
50, Federal-gov, 251585, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 55, United-States, >50K
47, Self-emp-inc, 109832, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
43, Private, 237993, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
46, Private, 216666, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, <=50K
35, Private, 56352, Assoc-voc, 11, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Puerto-Rico, <=50K
41, Private, 147372, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 48, United-States, <=50K
30, Private, 188146, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 5013, 0, 40, United-States, <=50K
30, Private, 59496, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 2407, 0, 40, United-States, <=50K
32, ?, 293936, 7th-8th, 4, Married-spouse-absent, ?, Not-in-family, White, Male, 0, 0, 40, ?, <=50K
48, Private, 149640, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
42, Private, 116632, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, >50K
29, Private, 105598, Some-college, 10, Divorced, Tech-support, Not-in-family, White, Male, 0, 0, 58, United-States, <=50K
36, Private, 155537, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
28, Private, 183175, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
53, Private, 169846, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, >50K
49, Self-emp-inc, 191681, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, >50K
25, ?, 200681, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, <=50K
19, Private, 101509, Some-college, 10, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 32, United-States, <=50K
31, Private, 309974, Bachelors, 13, Separated, Sales, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
29, Self-emp-not-inc, 162298, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 70, United-States, >50K
23, Private, 211678, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
79, Private, 124744, Some-college, 10, Married-civ-spouse, Prof-specialty, Other-relative, White, Male, 0, 0, 20, United-States, <=50K
27, Private, 213921, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, Mexico, <=50K
40, Private, 32214, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
67, ?, 212759, 10th, 6, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 2, United-States, <=50K
18, Private, 309634, 11th, 7, Never-married, Other-service, Own-child, White, Female, 0, 0, 22, United-States, <=50K
31, Local-gov, 125927, 7th-8th, 4, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, United-States, <=50K
18, Private, 446839, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K
52, Private, 276515, Bachelors, 13, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Cuba, <=50K
46, Private, 51618, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 40, United-States, <=50K
59, Private, 159937, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 48, United-States, <=50K
44, Private, 343591, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Female, 14344, 0, 40, United-States, >50K
53, Private, 346253, HS-grad, 9, Divorced, Sales, Own-child, White, Female, 0, 0, 35, United-States, <=50K
49, Local-gov, 268234, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K
33, Private, 202051, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, <=50K
30, Private, 54334, 9th, 5, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
43, Federal-gov, 410867, Doctorate, 16, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, >50K
57, Private, 249977, Assoc-voc, 11, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
37, Private, 286730, Some-college, 10, Divorced, Craft-repair, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
28, Private, 212563, Some-college, 10, Divorced, Machine-op-inspct, Unmarried, Black, Female, 0, 0, 25, United-States, <=50K
30, Private, 117747, HS-grad, 9, Married-civ-spouse, Sales, Wife, Asian-Pac-Islander, Female, 0, 1573, 35, ?, <=50K
34, Local-gov, 226296, Bachelors, 13, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K
29, Local-gov, 115585, Some-college, 10, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
48, Self-emp-not-inc, 191277, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 60, United-States, >50K
37, Private, 202683, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 48, United-States, >50K
48, Private, 171095, Assoc-acdm, 12, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, England, <=50K
32, Federal-gov, 249409, HS-grad, 9, Never-married, Other-service, Own-child, Black, Male, 0, 0, 40, United-States, <=50K
76, Private, 124191, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
44, Private, 198282, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, >50K
47, Self-emp-not-inc, 149116, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, <=50K
20, Private, 188300, Some-college, 10, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, <=50K
29, Private, 103432, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
32, Self-emp-inc, 317660, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 7688, 0, 40, United-States, >50K
17, ?, 304873, 10th, 6, Never-married, ?, Own-child, White, Female, 34095, 0, 32, United-States, <=50K
30, Private, 194901, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
31, Local-gov, 189265, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
42, Private, 124692, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 432376, Bachelors, 13, Never-married, Sales, Other-relative, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 65324, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
56, Self-emp-not-inc, 335605, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 1887, 50, Canada, >50K
28, Private, 377869, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 4064, 0, 25, United-States, <=50K
36, Private, 102864, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, White, Female, 0, 0, 40, United-States, <=50K
53, Private, 95647, 9th, 5, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 50, United-States, <=50K
56, Self-emp-inc, 303090, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
49, Local-gov, 197371, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, >50K
55, Private, 247552, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 56, United-States, <=50K
22, Private, 102632, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 41, United-States, <=50K
21, Private, 199915, Some-college, 10, Never-married, Other-service, Own-child, White, Female, 0, 0, 40, United-States, <=50K
40, Private, 118853, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, <=50K
30, Private, 77143, Bachelors, 13, Never-married, Exec-managerial, Own-child, Black, Male, 0, 0, 40, Germany, <=50K
29, State-gov, 267989, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, >50K
19, Private, 301606, Some-college, 10, Never-married, Other-service, Own-child, Black, Male, 0, 0, 35, United-States, <=50K
47, Private, 287828, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, >50K
20, Private, 111697, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 1719, 28, United-States, <=50K
31, Private, 114937, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, >50K
35, ?, 129305, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, <=50K
39, Private, 365739, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
28, Private, 69621, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
24, Private, 43323, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 1762, 40, United-States, <=50K
38, Self-emp-not-inc, 120985, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 4386, 0, 35, United-States, <=50K
37, Private, 254202, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
46, Private, 146195, Assoc-acdm, 12, Divorced, Tech-support, Not-in-family, Black, Female, 0, 0, 36, United-States, <=50K
38, Federal-gov, 125933, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, Iran, >50K
43, Self-emp-not-inc, 56920, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, United-States, <=50K
27, Private, 163127, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 35, United-States, <=50K
20, Private, 34310, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 20, United-States, <=50K
49, Private, 81973, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, >50K
61, Self-emp-inc, 66614, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
27, Private, 232782, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, <=50K
19, Private, 316868, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 30, Mexico, <=50K
45, Private, 196584, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1564, 40, United-States, >50K
70, Private, 105376, Some-college, 10, Never-married, Tech-support, Other-relative, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 185814, HS-grad, 9, Never-married, Transport-moving, Unmarried, Black, Female, 0, 0, 30, United-States, <=50K
22, Private, 175374, Some-college, 10, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 24, United-States, <=50K
36, Private, 108293, HS-grad, 9, Widowed, Other-service, Unmarried, White, Female, 0, 0, 24, United-States, <=50K
64, Private, 181232, 11th, 7, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 2179, 40, United-States, <=50K
43, ?, 174662, Some-college, 10, Divorced, ?, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
47, Local-gov, 186009, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 38, Mexico, <=50K
34, Private, 198183, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
33, Private, 163003, Bachelors, 13, Never-married, Exec-managerial, Other-relative, Asian-Pac-Islander, Female, 0, 0, 40, Philippines, <=50K
21, Private, 296158, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 35, United-States, <=50K
52, ?, 252903, HS-grad, 9, Divorced, ?, Not-in-family, White, Male, 0, 0, 45, United-States, >50K
48, Private, 187715, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 46, United-States, <=50K
23, Private, 214542, Bachelors, 13, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
71, Self-emp-not-inc, 494223, Some-college, 10, Separated, Sales, Unmarried, Black, Male, 0, 1816, 2, United-States, <=50K
29, Private, 191535, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
42, Private, 228456, Bachelors, 13, Separated, Other-service, Other-relative, Black, Male, 0, 0, 50, United-States, <=50K
68, ?, 38317, 1st-4th, 2, Divorced, ?, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
25, Private, 252752, HS-grad, 9, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
44, Self-emp-inc, 78374, Masters, 14, Divorced, Exec-managerial, Unmarried, Asian-Pac-Islander, Female, 0, 0, 40, United-States, <=50K
28, Private, 88419, HS-grad, 9, Never-married, Exec-managerial, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 40, England, <=50K
45, Self-emp-not-inc, 201080, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, >50K
36, Private, 207157, Some-college, 10, Divorced, Other-service, Unmarried, White, Female, 0, 0, 40, Mexico, <=50K
39, Federal-gov, 235485, Assoc-acdm, 12, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 42, United-States, <=50K
46, State-gov, 102628, Masters, 14, Widowed, Protective-serv, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
18, Private, 25828, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 16, United-States, <=50K
66, Local-gov, 54826, Assoc-voc, 11, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
27, Private, 124953, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Male, 0, 1980, 40, United-States, <=50K
28, State-gov, 175325, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, <=50K
51, Private, 96062, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 1977, 40, United-States, >50K
27, Private, 428030, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
28, State-gov, 149624, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
27, Private, 253814, HS-grad, 9, Married-spouse-absent, Sales, Unmarried, White, Female, 0, 0, 25, United-States, <=50K
21, Private, 312956, HS-grad, 9, Never-married, Craft-repair, Own-child, Black, Male, 0, 0, 40, United-States, <=50K
34, Private, 483777, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
18, Private, 183930, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 12, United-States, <=50K
33, Private, 37274, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 65, United-States, <=50K
44, Local-gov, 181344, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 38, United-States, >50K
43, Private, 114580, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
30, Private, 633742, Some-college, 10, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 45, United-States, <=50K
40, Private, 286370, 7th-8th, 4, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, >50K
37, Federal-gov, 29054, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 42, United-States, >50K
34, Private, 304030, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, Black, Male, 0, 0, 40, United-States, <=50K
41, Self-emp-not-inc, 143129, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
53, ?, 135105, Bachelors, 13, Divorced, ?, Not-in-family, White, Female, 0, 0, 50, United-States, <=50K
31, Private, 99928, Masters, 14, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 50, United-States, <=50K
58, State-gov, 109567, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 1, United-States, >50K
38, Private, 155222, Some-college, 10, Divorced, Machine-op-inspct, Not-in-family, Black, Female, 0, 0, 28, United-States, <=50K
24, Private, 159567, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
41, Local-gov, 523910, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, <=50K
47, Private, 120939, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 45, United-States, <=50K
41, Federal-gov, 130760, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 24, United-States, <=50K
23, Private, 197387, 5th-6th, 3, Married-civ-spouse, Transport-moving, Other-relative, White, Male, 0, 0, 40, Mexico, <=50K
36, Private, 99374, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
40, Federal-gov, 56795, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Female, 14084, 0, 55, United-States, >50K
35, Private, 138992, Masters, 14, Married-civ-spouse, Prof-specialty, Other-relative, White, Male, 7298, 0, 40, United-States, >50K
24, Self-emp-not-inc, 32921, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
26, Private, 397317, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1876, 40, United-States, <=50K
19, ?, 170653, HS-grad, 9, Never-married, ?, Own-child, White, Male, 0, 0, 40, Italy, <=50K
51, Private, 259323, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, >50K
42, Local-gov, 254817, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1340, 40, United-States, <=50K
37, State-gov, 48211, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 35, United-States, <=50K
18, Private, 140164, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, <=50K
36, Private, 128757, Bachelors, 13, Married-civ-spouse, Other-service, Husband, Black, Male, 7298, 0, 36, United-States, >50K
35, Private, 36270, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
58, Self-emp-inc, 210563, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 15024, 0, 35, United-States, >50K
17, Private, 65368, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 12, United-States, <=50K
44, Local-gov, 160943, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 40, United-States, <=50K
37, Private, 208358, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, >50K
35, Private, 153790, Some-college, 10, Never-married, Sales, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 40, United-States, <=50K
60, Private, 85815, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, <=50K
54, Self-emp-inc, 125417, 7th-8th, 4, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, >50K
37, Private, 635913, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, Black, Male, 0, 0, 60, United-States, >50K
50, Private, 313321, Assoc-acdm, 12, Divorced, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
38, Private, 182609, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, Poland, <=50K
45, Private, 109434, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 55, United-States, <=50K
25, Private, 255004, 10th, 6, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 197860, Some-college, 10, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, <=50K
64, ?, 187656, 1st-4th, 2, Divorced, ?, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
90, Private, 51744, HS-grad, 9, Never-married, Other-service, Not-in-family, Black, Male, 0, 2206, 40, United-States, <=50K
54, Private, 176681, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, Black, Male, 0, 0, 20, United-States, <=50K
53, Local-gov, 140359, Preschool, 1, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
18, Private, 243313, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, <=50K
60, ?, 24215, 10th, 6, Divorced, ?, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 10, United-States, <=50K
66, Self-emp-not-inc, 167687, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 1409, 0, 50, United-States, <=50K
75, Private, 314209, Assoc-voc, 11, Widowed, Adm-clerical, Not-in-family, White, Female, 0, 0, 20, Columbia, <=50K
65, Private, 176796, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
35, Private, 538583, 11th, 7, Separated, Transport-moving, Not-in-family, Black, Male, 3674, 0, 40, United-States, <=50K
41, Private, 130408, HS-grad, 9, Divorced, Sales, Unmarried, Black, Female, 0, 0, 38, United-States, <=50K
25, Private, 159732, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 42, United-States, <=50K
33, Private, 110978, Some-college, 10, Divorced, Craft-repair, Other-relative, Other, Female, 0, 0, 40, United-States, <=50K
28, Private, 76714, Prof-school, 15, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 55, United-States, >50K
59, State-gov, 268700, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
40, State-gov, 170525, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 38, United-States, <=50K
41, Private, 180138, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, Iran, >50K
38, Local-gov, 115076, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, >50K
23, Private, 115458, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 40, United-States, <=50K
40, Private, 347890, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
41, Self-emp-not-inc, 196001, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 20, United-States, <=50K
24, State-gov, 273905, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 50, United-States, <=50K
20, ?, 119156, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 20, United-States, <=50K
38, Private, 179488, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 1741, 40, United-States, <=50K
56, Private, 203580, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 35, ?, <=50K
58, Private, 236596, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 45, United-States, >50K
32, Private, 183916, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 34, United-States, <=50K
40, Private, 207578, Assoc-acdm, 12, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 1977, 60, United-States, >50K
45, Private, 153141, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, ?, <=50K
41, Private, 112763, Prof-school, 15, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
42, Private, 390781, Bachelors, 13, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, <=50K
59, Local-gov, 171328, 10th, 6, Widowed, Other-service, Unmarried, Black, Female, 0, 0, 30, United-States, <=50K
19, Local-gov, 27382, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, <=50K
58, Private, 259014, Some-college, 10, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 20, United-States, <=50K
42, Self-emp-not-inc, 303044, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, Asian-Pac-Islander, Male, 0, 0, 40, Cambodia, >50K
20, Private, 117789, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 40, United-States, <=50K
32, Private, 172579, HS-grad, 9, Separated, Other-service, Not-in-family, White, Female, 0, 0, 30, United-States, <=50K
45, Private, 187666, Assoc-voc, 11, Widowed, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
50, Private, 204518, 7th-8th, 4, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
36, Private, 150042, Bachelors, 13, Divorced, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, <=50K
45, Private, 98092, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, <=50K
17, Private, 245918, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 12, United-States, <=50K
59, Private, 146013, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 4064, 0, 40, United-States, <=50K
26, Private, 378322, 11th, 7, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
37, Self-emp-inc, 257295, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 75, Thailand, >50K
19, ?, 218956, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 24, Canada, <=50K
64, Private, 21174, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
33, Private, 185480, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
33, Private, 222205, HS-grad, 9, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 40, United-States, >50K
61, Private, 69867, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
17, Private, 191260, 9th, 5, Never-married, Other-service, Own-child, White, Male, 1055, 0, 24, United-States, <=50K
50, Self-emp-not-inc, 30653, Masters, 14, Married-civ-spouse, Farming-fishing, Husband, White, Male, 2407, 0, 98, United-States, <=50K
27, Local-gov, 209109, Masters, 14, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 35, United-States, <=50K
30, Private, 70377, HS-grad, 9, Divorced, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, <=50K
43, Private, 477983, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K
44, Private, 170924, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 7298, 0, 40, United-States, >50K
35, Private, 190174, Some-college, 10, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
25, Private, 193787, Some-college, 10, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, <=50K
24, Private, 279472, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Wife, White, Female, 7298, 0, 48, United-States, >50K
22, Private, 34918, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 15, Germany, <=50K
42, Local-gov, 97688, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 5178, 0, 40, United-States, >50K
34, Private, 175413, Assoc-acdm, 12, Divorced, Sales, Unmarried, Black, Female, 0, 0, 45, United-States, <=50K
60, Private, 173960, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 42, United-States, <=50K
21, Private, 205759, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
57, Federal-gov, 425161, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 15024, 0, 40, United-States, >50K
41, Private, 220531, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, >50K
50, Private, 176609, Some-college, 10, Divorced, Other-service, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
25, Private, 371987, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
50, Private, 193884, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Ecuador, <=50K
36, Private, 200352, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, <=50K
31, Private, 127595, HS-grad, 9, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
29, Local-gov, 220419, Bachelors, 13, Never-married, Protective-serv, Not-in-family, White, Male, 0, 0, 56, United-States, <=50K
21, Private, 231931, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 45, United-States, <=50K
27, Private, 248402, Bachelors, 13, Never-married, Tech-support, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
65, Private, 111095, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 16, United-States, <=50K
37, Self-emp-inc, 57424, Bachelors, 13, Divorced, Sales, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
39, ?, 157443, Masters, 14, Married-civ-spouse, ?, Wife, Asian-Pac-Islander, Female, 3464, 0, 40, ?, <=50K
24, Private, 278130, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 169469, HS-grad, 9, Divorced, Sales, Not-in-family, White, Male, 0, 0, 80, United-States, <=50K
48, Private, 146268, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 7688, 0, 40, United-States, >50K
21, Private, 153718, Some-college, 10, Never-married, Other-service, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 25, United-States, <=50K
31, Private, 217460, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, >50K
55, Private, 238638, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 4386, 0, 40, United-States, >50K
24, Private, 303296, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, Asian-Pac-Islander, Female, 0, 0, 40, Laos, <=50K
43, Private, 173321, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
26, Private, 193945, Assoc-acdm, 12, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
46, Private, 83082, Assoc-acdm, 12, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 33, United-States, <=50K
35, Private, 193815, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, >50K
41, Self-emp-inc, 34987, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 54, United-States, >50K
26, Private, 59306, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
34, Private, 142897, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 7298, 0, 35, Taiwan, >50K
19, ?, 860348, Some-college, 10, Never-married, ?, Own-child, Black, Female, 0, 0, 25, United-States, <=50K
36, Self-emp-not-inc, 205607, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, Black, Female, 0, 0, 40, United-States, >50K
22, Private, 199698, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 15, United-States, <=50K
24, Private, 191954, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
77, Self-emp-not-inc, 138714, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, <=50K
22, Private, 399087, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Other-relative, White, Female, 0, 0, 40, Mexico, <=50K
29, Private, 423158, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
62, Private, 159841, HS-grad, 9, Widowed, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, <=50K
39, Self-emp-not-inc, 174308, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, <=50K
43, Private, 50356, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1485, 50, United-States, <=50K
35, Private, 186110, HS-grad, 9, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
29, Private, 200381, 11th, 7, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
76, Self-emp-not-inc, 174309, Masters, 14, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, <=50K
63, Self-emp-not-inc, 78383, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 45, United-States, <=50K
23, ?, 211601, Assoc-voc, 11, Never-married, ?, Own-child, Black, Female, 0, 0, 15, United-States, <=50K
43, Private, 187728, Some-college, 10, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1887, 50, United-States, >50K
58, Self-emp-not-inc, 321171, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, <=50K
66, Private, 127921, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 2050, 0, 55, United-States, <=50K
41, Private, 206565, Some-college, 10, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 45, United-States, <=50K
26, Private, 224563, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
47, Private, 178686, Assoc-voc, 11, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
55, Local-gov, 98545, 10th, 6, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
53, Private, 242606, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
17, Private, 270942, 5th-6th, 3, Never-married, Other-service, Other-relative, White, Male, 0, 0, 48, Mexico, <=50K
30, Private, 94235, HS-grad, 9, Never-married, Craft-repair, Other-relative, White, Male, 0, 0, 40, United-States, <=50K
49, Private, 71195, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
19, Private, 104112, HS-grad, 9, Never-married, Sales, Unmarried, Black, Male, 0, 0, 30, Haiti, <=50K
45, Private, 261192, HS-grad, 9, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, United-States, <=50K
26, Private, 94936, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 296478, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 7298, 0, 40, United-States, >50K
36, State-gov, 119272, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 7298, 0, 40, United-States, >50K
33, Private, 85043, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 20, United-States, <=50K
22, State-gov, 293364, Some-college, 10, Never-married, Protective-serv, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
43, Self-emp-not-inc, 241895, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 42, United-States, <=50K
67, ?, 36135, 11th, 7, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 8, United-States, <=50K
30, ?, 151989, Assoc-voc, 11, Divorced, ?, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
56, Private, 101128, Assoc-acdm, 12, Married-spouse-absent, Other-service, Not-in-family, White, Male, 0, 0, 25, Iran, <=50K
31, Private, 156464, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 25, United-States, <=50K
33, Private, 117963, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
26, Private, 192262, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 111363, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
46, Local-gov, 329752, 11th, 7, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 30, United-States, <=50K
59, ?, 372020, Bachelors, 13, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, >50K
38, Federal-gov, 95432, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, >50K
65, Private, 161400, 11th, 7, Widowed, Other-service, Unmarried, Other, Male, 0, 0, 40, United-States, <=50K
40, Private, 96129, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
42, Private, 111949, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 35, United-States, <=50K
26, Self-emp-not-inc, 117125, 9th, 5, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Portugal, <=50K
36, Private, 348022, 10th, 6, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 24, United-States, <=50K
62, Private, 270092, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
43, Private, 180609, Bachelors, 13, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, <=50K
43, Private, 174575, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 1564, 45, United-States, >50K
22, Private, 410439, HS-grad, 9, Married-spouse-absent, Sales, Not-in-family, White, Male, 0, 0, 55, United-States, <=50K
28, Private, 92262, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
56, Self-emp-not-inc, 183081, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 45, United-States, <=50K
22, Private, 362589, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Female, 0, 0, 15, United-States, <=50K
57, Private, 212448, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, >50K
39, Private, 481060, HS-grad, 9, Divorced, Sales, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
26, Federal-gov, 185885, Some-college, 10, Never-married, Adm-clerical, Unmarried, White, Female, 0, 0, 15, United-States, <=50K
17, Private, 89821, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 10, United-States, <=50K
40, State-gov, 184018, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 38, United-States, >50K
45, Private, 256649, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 0, 0, 40, United-States, <=50K
44, Private, 160323, HS-grad, 9, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
20, Local-gov, 350845, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 10, United-States, <=50K
33, Private, 267404, HS-grad, 9, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 40, United-States, <=50K
23, Private, 35633, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
46, Self-emp-not-inc, 80914, Masters, 14, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K
38, Private, 172927, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
54, Private, 174319, HS-grad, 9, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
46, Private, 214955, 5th-6th, 3, Divorced, Craft-repair, Not-in-family, White, Female, 0, 2339, 45, United-States, <=50K
25, Private, 344991, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
46, Private, 108699, Some-college, 10, Divorced, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
36, Local-gov, 117312, Some-college, 10, Married-civ-spouse, Transport-moving, Wife, White, Female, 0, 0, 40, United-States, <=50K
23, Private, 396099, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 25, United-States, <=50K
29, Private, 134152, HS-grad, 9, Separated, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 162028, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 2415, 6, United-States, >50K
19, Private, 25429, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 16, United-States, <=50K
19, Private, 232392, HS-grad, 9, Never-married, Other-service, Other-relative, White, Female, 0, 0, 40, United-States, <=50K
35, Private, 220098, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 40, United-States, >50K
27, Private, 301302, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
46, Self-emp-not-inc, 277946, Assoc-acdm, 12, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
34, State-gov, 98101, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 45, ?, >50K
34, Private, 196164, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
44, Private, 115562, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
45, Private, 96975, Some-college, 10, Divorced, Handlers-cleaners, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
20, ?, 137300, HS-grad, 9, Never-married, ?, Other-relative, White, Female, 0, 0, 35, United-States, <=50K
25, Private, 86872, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 55, United-States, >50K
52, Self-emp-inc, 132178, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, >50K
20, Private, 416103, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
28, Private, 108574, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
50, State-gov, 288353, Bachelors, 13, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K
34, Private, 227689, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Female, 0, 0, 64, United-States, <=50K
28, Private, 166481, 7th-8th, 4, Married-civ-spouse, Handlers-cleaners, Husband, Other, Male, 0, 2179, 40, Puerto-Rico, <=50K
41, Private, 445382, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1977, 65, United-States, >50K
28, Private, 110145, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
46, Self-emp-not-inc, 317253, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 25, United-States, <=50K
28, ?, 123147, Some-college, 10, Married-civ-spouse, ?, Wife, White, Female, 0, 1887, 40, United-States, >50K
32, Private, 364657, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, <=50K
41, Local-gov, 42346, Some-college, 10, Divorced, Other-service, Not-in-family, Black, Female, 0, 0, 24, United-States, <=50K
24, Private, 241951, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
33, Private, 118500, Some-college, 10, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
46, Private, 188386, Doctorate, 16, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, >50K
31, State-gov, 1033222, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
35, Private, 92440, 12th, 8, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, >50K
52, Private, 190762, 1st-4th, 2, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, <=50K
30, Private, 426017, 11th, 7, Never-married, Other-service, Own-child, White, Female, 0, 0, 19, United-States, <=50K
34, Local-gov, 243867, 11th, 7, Separated, Machine-op-inspct, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
34, State-gov, 240283, HS-grad, 9, Divorced, Transport-moving, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
20, Private, 61777, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 30, United-States, <=50K
17, Private, 175024, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 2176, 0, 18, United-States, <=50K
32, State-gov, 92003, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, >50K
29, Private, 188401, HS-grad, 9, Divorced, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 228528, 10th, 6, Never-married, Craft-repair, Unmarried, White, Female, 0, 0, 35, United-States, <=50K
25, Private, 133373, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, <=50K
36, Federal-gov, 255191, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 1408, 40, United-States, <=50K
23, Private, 204653, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 72, Dominican-Republic, <=50K
63, Self-emp-inc, 222289, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
47, Local-gov, 287480, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
80, ?, 107762, HS-grad, 9, Widowed, ?, Not-in-family, White, Male, 0, 0, 24, United-States, <=50K
17, ?, 202521, 11th, 7, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, <=50K
40, Self-emp-not-inc, 204116, Bachelors, 13, Married-spouse-absent, Prof-specialty, Not-in-family, White, Female, 2174, 0, 40, United-States, <=50K
30, Private, 29662, Assoc-acdm, 12, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 25, United-States, >50K
27, Private, 116358, Some-college, 10, Never-married, Craft-repair, Own-child, Asian-Pac-Islander, Male, 0, 1980, 40, Philippines, <=50K
33, Private, 208405, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, >50K
34, Local-gov, 284843, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, Black, Male, 594, 0, 60, United-States, <=50K
34, Local-gov, 117018, Some-college, 10, Never-married, Protective-serv, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
23, Private, 81281, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
42, Local-gov, 340148, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, <=50K
29, Private, 363425, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
45, Private, 45857, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 28, United-States, <=50K
24, Federal-gov, 191073, HS-grad, 9, Never-married, Armed-Forces, Own-child, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 116632, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
27, Private, 405855, 9th, 5, Never-married, Craft-repair, Other-relative, White, Male, 0, 0, 40, Mexico, <=50K
20, Private, 298227, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 35, United-States, <=50K
44, Private, 290521, HS-grad, 9, Widowed, Exec-managerial, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
51, Private, 56915, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, <=50K
20, Private, 146538, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
17, ?, 258872, 11th, 7, Never-married, ?, Own-child, White, Female, 0, 0, 5, United-States, <=50K
19, Private, 206399, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
45, Self-emp-inc, 197332, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 55, United-States, >50K
60, Private, 245062, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, >50K
42, Private, 197583, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 40, ?, >50K
44, Self-emp-not-inc, 234885, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, >50K
40, Private, 72887, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, >50K
30, Private, 180374, HS-grad, 9, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K
38, Private, 351299, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 50, United-States, <=50K
23, Private, 54012, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, <=50K
32, ?, 115745, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 116632, Assoc-acdm, 12, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 40, United-States, <=50K
54, Local-gov, 288825, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 40, United-States, <=50K
32, Private, 132601, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, <=50K
50, Private, 193374, 1st-4th, 2, Married-spouse-absent, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 170070, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
37, Private, 126708, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 60, United-States, <=50K
52, Private, 35598, HS-grad, 9, Divorced, Transport-moving, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
38, Private, 33983, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
49, Private, 192776, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1977, 45, United-States, >50K
30, Private, 118551, Bachelors, 13, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 16, United-States, >50K
60, Private, 201965, Some-college, 10, Never-married, Prof-specialty, Unmarried, White, Male, 0, 0, 40, United-States, >50K
22, ?, 139883, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, <=50K
35, Private, 285020, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
30, Private, 303990, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
67, Private, 49401, Assoc-voc, 11, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, <=50K
46, Private, 279196, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
17, Private, 211870, 9th, 5, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 6, United-States, <=50K
22, Private, 281432, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 30, United-States, <=50K
27, Private, 161155, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 197904, HS-grad, 9, Never-married, Other-service, Unmarried, White, Female, 0, 0, 35, United-States, <=50K
33, Private, 111746, Assoc-acdm, 12, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, Portugal, <=50K
43, Self-emp-not-inc, 170721, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, <=50K
28, State-gov, 70100, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 20, United-States, <=50K
41, Private, 193626, HS-grad, 9, Married-spouse-absent, Craft-repair, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
52, ?, 271749, 12th, 8, Never-married, ?, Other-relative, Black, Male, 594, 0, 40, United-States, <=50K
25, Private, 189775, Some-college, 10, Married-spouse-absent, Adm-clerical, Own-child, Black, Female, 0, 0, 20, United-States, <=50K
63, ?, 401531, 1st-4th, 2, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 35, United-States, <=50K
59, Local-gov, 286967, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, <=50K
45, Local-gov, 164427, Bachelors, 13, Divorced, Prof-specialty, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
38, Private, 91039, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 15024, 0, 60, United-States, >50K
40, Private, 347934, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
46, Federal-gov, 371373, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
35, Private, 32220, Assoc-acdm, 12, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
34, Private, 187251, HS-grad, 9, Divorced, Prof-specialty, Unmarried, White, Female, 0, 0, 25, United-States, <=50K
33, Private, 178107, Bachelors, 13, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 20, United-States, <=50K
41, Private, 343121, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 36, United-States, <=50K
20, Private, 262749, Some-college, 10, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 403107, 5th-6th, 3, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, El-Salvador, <=50K
26, Private, 64293, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
72, ?, 303588, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 20, United-States, <=50K
23, Local-gov, 324960, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, Poland, <=50K
62, Local-gov, 114060, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
52, Private, 48925, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
58, Private, 180980, Some-college, 10, Divorced, Other-service, Unmarried, White, Female, 0, 0, 42, France, <=50K
25, Private, 181054, Bachelors, 13, Never-married, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
24, Private, 388093, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
19, Private, 249609, Some-college, 10, Never-married, Protective-serv, Own-child, White, Male, 0, 0, 8, United-States, <=50K
43, Private, 112131, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, <=50K
47, Local-gov, 543162, HS-grad, 9, Separated, Adm-clerical, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
39, Private, 91996, HS-grad, 9, Divorced, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
49, Private, 141944, Assoc-voc, 11, Married-spouse-absent, Handlers-cleaners, Unmarried, White, Male, 0, 1380, 42, United-States, <=50K
53, ?, 251804, 5th-6th, 3, Widowed, ?, Unmarried, Black, Female, 0, 0, 30, United-States, <=50K
32, Private, 37070, Assoc-voc, 11, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
34, Private, 337587, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, >50K
28, Private, 189346, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
57, ?, 222216, Assoc-voc, 11, Widowed, ?, Unmarried, White, Female, 0, 0, 38, United-States, <=50K
25, Private, 267044, Some-college, 10, Never-married, Adm-clerical, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 20, United-States, <=50K
20, ?, 214635, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 24, United-States, <=50K
21, ?, 204226, Some-college, 10, Never-married, ?, Unmarried, White, Female, 0, 0, 35, United-States, <=50K
34, Private, 108116, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, >50K
38, Self-emp-inc, 99146, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 80, United-States, >50K
50, Private, 196232, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 50, United-States, >50K
24, Local-gov, 248344, Some-college, 10, Divorced, Handlers-cleaners, Not-in-family, Black, Male, 0, 0, 50, United-States, <=50K
37, Local-gov, 186035, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 45, United-States, >50K
44, Private, 177905, Some-college, 10, Divorced, Machine-op-inspct, Unmarried, White, Male, 0, 0, 58, United-States, >50K
28, Private, 85812, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, <=50K
42, Private, 221172, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
74, Private, 99183, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 9, United-States, <=50K
38, Self-emp-not-inc, 190387, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, <=50K
44, Self-emp-not-inc, 202692, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 109339, 11th, 7, Divorced, Machine-op-inspct, Unmarried, Other, Female, 0, 0, 46, Puerto-Rico, <=50K
26, Private, 108658, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
36, Private, 197202, HS-grad, 9, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, United-States, <=50K
41, Private, 101739, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 50, United-States, >50K
67, Private, 231559, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 20051, 0, 48, United-States, >50K
39, Local-gov, 207853, 12th, 8, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, <=50K
57, Private, 190942, 1st-4th, 2, Widowed, Priv-house-serv, Not-in-family, Black, Female, 0, 0, 30, United-States, <=50K
29, Private, 102345, Assoc-voc, 11, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
31, Self-emp-inc, 41493, Bachelors, 13, Never-married, Farming-fishing, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
34, ?, 190027, HS-grad, 9, Never-married, ?, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
44, Private, 210525, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
29, Private, 133937, Doctorate, 16, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 40, United-States, <=50K
30, Private, 237903, Some-college, 10, Never-married, Handlers-cleaners, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
27, Private, 163862, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
27, Private, 201872, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
32, Private, 84179, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
58, Private, 51662, 10th, 6, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 8, United-States, <=50K
35, Local-gov, 233327, Some-college, 10, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, <=50K
21, Private, 259510, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 36, United-States, <=50K
28, Private, 184831, Some-college, 10, Never-married, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
46, Self-emp-not-inc, 245724, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
36, Self-emp-not-inc, 27053, HS-grad, 9, Separated, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
72, Private, 205343, 11th, 7, Widowed, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
35, Private, 229328, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Wife, Black, Female, 0, 0, 40, United-States, <=50K
33, Federal-gov, 319560, Assoc-voc, 11, Divorced, Craft-repair, Unmarried, Black, Female, 0, 0, 40, United-States, >50K
69, Private, 136218, 11th, 7, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
35, Private, 54576, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 323069, HS-grad, 9, Separated, Adm-clerical, Unmarried, White, Female, 0, 0, 20, ?, <=50K
34, Private, 148291, HS-grad, 9, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 32, United-States, <=50K
30, Private, 152453, 11th, 7, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Mexico, <=50K
28, Private, 114053, Bachelors, 13, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 55, United-States, <=50K
54, Private, 212960, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 35, United-States, >50K
47, Private, 264052, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, >50K
24, Private, 82804, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
52, Self-emp-not-inc, 334273, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, >50K
20, Private, 27337, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, Amer-Indian-Eskimo, Male, 0, 0, 48, United-States, <=50K
43, Self-emp-inc, 188436, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5013, 0, 45, United-States, <=50K
45, Private, 433665, 7th-8th, 4, Separated, Other-service, Unmarried, White, Female, 0, 0, 40, Mexico, <=50K
29, Self-emp-not-inc, 110663, HS-grad, 9, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 35, United-States, <=50K
47, Private, 87490, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Male, 0, 0, 42, United-States, <=50K
24, Private, 354351, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, <=50K
51, Private, 95469, HS-grad, 9, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
17, Private, 242718, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 12, United-States, <=50K
37, Private, 22463, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1977, 40, United-States, >50K
27, Private, 158156, Doctorate, 16, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 70, United-States, <=50K
29, Private, 350162, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Male, 0, 0, 40, United-States, >50K
18, ?, 165532, 12th, 8, Never-married, ?, Own-child, White, Male, 0, 0, 25, United-States, <=50K
36, Self-emp-not-inc, 28738, Assoc-acdm, 12, Divorced, Sales, Unmarried, White, Female, 0, 0, 35, United-States, <=50K
58, Local-gov, 283635, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
26, Self-emp-not-inc, 86646, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, <=50K
65, ?, 195733, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 30, United-States, >50K
57, Private, 69884, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, >50K
59, Private, 199713, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
27, Private, 181659, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
31, Self-emp-not-inc, 340939, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
21, Private, 197747, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 24, United-States, <=50K
29, Private, 34292, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
18, Private, 156764, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, United-States, <=50K
52, Private, 25826, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1887, 47, United-States, >50K
57, Self-emp-inc, 103948, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 80, United-States, <=50K
42, ?, 137390, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, <=50K
55, ?, 105138, HS-grad, 9, Married-civ-spouse, ?, Wife, Asian-Pac-Islander, Female, 0, 0, 40, United-States, <=50K
60, Private, 39352, 7th-8th, 4, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 48, United-States, >50K
31, Private, 168387, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 7688, 0, 40, Canada, >50K
23, Private, 117789, Bachelors, 13, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 40, United-States, <=50K
27, Private, 267147, HS-grad, 9, Never-married, Sales, Own-child, White, Male, 0, 0, 40, United-States, <=50K
23, ?, 99399, Some-college, 10, Never-married, ?, Unmarried, Amer-Indian-Eskimo, Female, 0, 0, 25, United-States, <=50K
42, Self-emp-not-inc, 214242, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 50, United-States, >50K
25, Private, 200408, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K
49, Private, 136455, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
32, Private, 239824, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
19, Private, 217039, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 28, United-States, <=50K
60, Private, 51290, 7th-8th, 4, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
42, Local-gov, 175674, 9th, 5, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
35, Self-emp-not-inc, 194404, Assoc-acdm, 12, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
48, Private, 45612, HS-grad, 9, Never-married, Adm-clerical, Unmarried, Black, Female, 0, 0, 37, United-States, <=50K
51, Private, 410114, Masters, 14, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, >50K
29, Private, 182521, HS-grad, 9, Never-married, Craft-repair, Not-in-family, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, <=50K
36, Local-gov, 339772, HS-grad, 9, Separated, Exec-managerial, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
17, Private, 169658, 10th, 6, Never-married, Other-service, Own-child, White, Female, 0, 0, 21, United-States, <=50K
52, Private, 200853, Masters, 14, Divorced, Prof-specialty, Not-in-family, White, Female, 6849, 0, 60, United-States, <=50K
24, Private, 247564, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 249909, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 50, United-States, <=50K
26, Local-gov, 208122, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Male, 1055, 0, 40, United-States, <=50K
27, Private, 109881, Bachelors, 13, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, <=50K
39, Private, 207824, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 60, United-States, <=50K
30, Private, 369027, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 45, United-States, <=50K
50, Self-emp-not-inc, 114117, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 32, United-States, <=50K
52, Self-emp-inc, 51048, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 55, United-States, <=50K
46, Private, 102388, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 15024, 0, 45, United-States, >50K
23, Private, 190483, Bachelors, 13, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, <=50K
45, Private, 462440, 11th, 7, Widowed, Other-service, Not-in-family, Black, Female, 0, 0, 20, United-States, <=50K
65, Private, 109351, 9th, 5, Widowed, Priv-house-serv, Unmarried, Black, Female, 0, 0, 24, United-States, <=50K
29, Private, 34383, Assoc-voc, 11, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 55, United-States, <=50K
47, Private, 241832, 9th, 5, Married-spouse-absent, Handlers-cleaners, Unmarried, White, Male, 0, 0, 40, El-Salvador, <=50K
30, Private, 124187, HS-grad, 9, Never-married, Farming-fishing, Own-child, Black, Male, 0, 0, 60, United-States, <=50K
34, Private, 153614, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 45, United-States, >50K
38, Self-emp-not-inc, 267556, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 64, United-States, <=50K
33, Private, 205469, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
49, Private, 268090, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 26, United-States, >50K
47, Self-emp-not-inc, 165039, Some-college, 10, Never-married, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
49, Local-gov, 120451, 10th, 6, Separated, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
43, Private, 154374, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 15024, 0, 60, United-States, >50K
30, Private, 103649, Bachelors, 13, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, >50K
58, Self-emp-not-inc, 35723, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, United-States, <=50K
19, Private, 262601, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 14, United-States, <=50K
21, Private, 226181, Bachelors, 13, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 175697, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, >50K
47, Self-emp-inc, 248145, 5th-6th, 3, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, Cuba, <=50K
52, Self-emp-not-inc, 289436, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, >50K
26, Private, 75654, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 55, United-States, <=50K
60, Private, 199378, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
21, Private, 160968, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
36, Private, 188563, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 5178, 0, 50, United-States, >50K
31, Private, 55849, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, <=50K
50, Self-emp-inc, 195322, Doctorate, 16, Separated, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, >50K
31, Local-gov, 402089, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
71, Private, 78277, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 15, United-States, <=50K
58, ?, 158611, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 50, United-States, <=50K
30, State-gov, 169496, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
20, Private, 130959, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 20, United-States, <=50K
24, Private, 556660, HS-grad, 9, Never-married, Exec-managerial, Other-relative, White, Male, 4101, 0, 50, United-States, <=50K
35, Private, 292472, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, Taiwan, >50K
38, State-gov, 143774, Some-college, 10, Separated, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
27, Private, 288341, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, White, Female, 0, 0, 32, United-States, <=50K
29, State-gov, 71592, Some-college, 10, Never-married, Adm-clerical, Unmarried, Asian-Pac-Islander, Female, 0, 0, 40, Philippines, <=50K
70, ?, 167358, 9th, 5, Widowed, ?, Unmarried, White, Female, 1111, 0, 15, United-States, <=50K
34, Private, 106742, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, <=50K
44, Private, 219288, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, <=50K
43, Private, 174524, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, >50K
44, Self-emp-not-inc, 335183, 12th, 8, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, >50K
35, Private, 261293, Masters, 14, Never-married, Sales, Not-in-family, White, Male, 0, 0, 60, United-States, <=50K
27, Private, 111900, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
43, Local-gov, 194360, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 38, United-States, <=50K
20, Private, 81145, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 25, United-States, <=50K
42, Private, 341204, Assoc-acdm, 12, Divorced, Prof-specialty, Unmarried, White, Female, 8614, 0, 40, United-States, >50K
27, State-gov, 249362, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 3411, 0, 40, United-States, <=50K
42, Private, 247019, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, >50K
20, ?, 114746, 11th, 7, Married-spouse-absent, ?, Own-child, Asian-Pac-Islander, Female, 0, 1762, 40, South, <=50K
24, Private, 172146, 9th, 5, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 1721, 40, United-States, <=50K
48, Federal-gov, 110457, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
17, ?, 80077, 11th, 7, Never-married, ?, Own-child, White, Female, 0, 0, 20, United-States, <=50K
17, Self-emp-not-inc, 368700, 11th, 7, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 10, United-States, <=50K
33, Private, 182556, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
50, Self-emp-inc, 219420, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, >50K
22, Private, 240817, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 2597, 0, 40, United-States, <=50K
17, Private, 102726, 12th, 8, Never-married, Other-service, Own-child, White, Male, 0, 0, 16, United-States, <=50K
32, Private, 226267, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, Mexico, <=50K
31, Private, 125457, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
58, Self-emp-not-inc, 204021, HS-grad, 9, Widowed, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
29, Local-gov, 92262, HS-grad, 9, Never-married, Protective-serv, Own-child, White, Male, 0, 0, 48, United-States, <=50K
37, Private, 161141, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Portugal, >50K
34, Self-emp-not-inc, 190290, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, >50K
23, Local-gov, 430828, Some-college, 10, Separated, Exec-managerial, Unmarried, Black, Male, 0, 0, 40, United-States, <=50K
18, State-gov, 59342, 11th, 7, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 5, United-States, <=50K
34, Private, 136721, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
66, ?, 149422, 7th-8th, 4, Never-married, ?, Not-in-family, White, Male, 0, 0, 4, United-States, <=50K
45, Local-gov, 86644, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 55, United-States, <=50K
41, Private, 195124, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 35, Dominican-Republic, <=50K
26, Private, 167350, HS-grad, 9, Never-married, Other-service, Other-relative, White, Male, 0, 0, 30, United-States, <=50K
54, Local-gov, 113000, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 140027, Some-college, 10, Never-married, Machine-op-inspct, Own-child, Black, Female, 0, 0, 45, United-States, <=50K
42, Private, 262425, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, <=50K
20, Private, 316702, Some-college, 10, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 20, United-States, <=50K
23, State-gov, 335453, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
25, ?, 202480, Assoc-acdm, 12, Never-married, ?, Other-relative, White, Male, 0, 0, 45, United-States, <=50K
35, Private, 203628, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 60, United-States, >50K
31, Private, 118710, Masters, 14, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 1902, 40, United-States, >50K
30, Private, 189620, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 40, Poland, <=50K
19, Private, 475028, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, <=50K
36, Local-gov, 110866, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
31, Private, 243605, Bachelors, 13, Widowed, Sales, Unmarried, White, Female, 0, 1380, 40, Cuba, <=50K
21, Private, 163870, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 30, United-States, <=50K
31, Self-emp-not-inc, 80145, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
46, Private, 295566, Doctorate, 16, Divorced, Prof-specialty, Unmarried, White, Female, 25236, 0, 65, United-States, >50K
44, Private, 63042, Bachelors, 13, Divorced, Exec-managerial, Own-child, White, Female, 0, 0, 50, United-States, >50K
40, Private, 229148, 12th, 8, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, Jamaica, <=50K
45, Private, 242552, Some-college, 10, Never-married, Sales, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
60, Private, 177665, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, <=50K
18, Private, 208103, 11th, 7, Never-married, Other-service, Other-relative, White, Male, 0, 0, 25, United-States, <=50K
28, Private, 296450, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
36, Private, 70282, Some-college, 10, Divorced, Adm-clerical, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
36, Private, 271767, Bachelors, 13, Separated, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, ?, <=50K
40, Private, 144995, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 4386, 0, 40, United-States, <=50K
36, Local-gov, 382635, Bachelors, 13, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 35, Honduras, <=50K
31, Private, 295697, HS-grad, 9, Separated, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
33, Private, 194141, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
19, State-gov, 378418, HS-grad, 9, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, <=50K
22, Private, 214399, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 15, United-States, <=50K
34, Private, 217460, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
33, Private, 182556, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
41, Private, 125831, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 2051, 60, United-States, <=50K
29, Private, 271328, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 4650, 0, 40, United-States, <=50K
50, Local-gov, 50459, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 42, United-States, >50K
42, Private, 162140, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7298, 0, 45, United-States, >50K
43, Private, 177937, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, ?, >50K
44, Private, 111502, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, <=50K
20, Private, 299047, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
31, Private, 223212, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Mexico, <=50K
65, Self-emp-not-inc, 118474, 11th, 7, Married-civ-spouse, Exec-managerial, Husband, White, Male, 9386, 0, 59, ?, >50K
23, Private, 352139, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, <=50K
55, Private, 173093, Some-college, 10, Divorced, Adm-clerical, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 40, United-States, <=50K
26, Private, 181655, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 2377, 45, United-States, <=50K
25, Private, 332702, Assoc-voc, 11, Never-married, Other-service, Own-child, White, Female, 0, 0, 15, United-States, <=50K
45, ?, 51164, Some-college, 10, Married-civ-spouse, ?, Wife, Black, Female, 0, 0, 40, United-States, <=50K
35, Private, 234901, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 2407, 0, 40, United-States, <=50K
36, Private, 131414, Some-college, 10, Never-married, Sales, Not-in-family, Black, Female, 0, 0, 36, United-States, <=50K
43, State-gov, 260960, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, <=50K
56, Private, 156052, HS-grad, 9, Widowed, Other-service, Unmarried, Black, Female, 594, 0, 20, United-States, <=50K
42, Private, 279914, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
19, Private, 192453, Some-college, 10, Never-married, Other-service, Other-relative, White, Female, 0, 0, 25, United-States, <=50K
55, Self-emp-not-inc, 200939, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 72, United-States, <=50K
42, Private, 151408, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Female, 14084, 0, 50, United-States, >50K
26, Private, 112847, Assoc-voc, 11, Never-married, Tech-support, Own-child, White, Male, 0, 0, 40, United-States, <=50K
17, Private, 316929, 12th, 8, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 20, United-States, <=50K
42, Local-gov, 126319, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
55, Private, 197422, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 7688, 0, 40, United-States, >50K
32, Private, 267736, Some-college, 10, Never-married, Adm-clerical, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
29, Private, 267034, 11th, 7, Never-married, Craft-repair, Own-child, Black, Male, 0, 0, 40, Haiti, <=50K
46, State-gov, 193047, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 37, United-States, <=50K
29, State-gov, 356089, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 40, United-States, >50K
22, Private, 223515, Bachelors, 13, Never-married, Prof-specialty, Unmarried, White, Male, 0, 0, 20, United-States, <=50K
58, Self-emp-not-inc, 87510, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 145111, HS-grad, 9, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 50, United-States, <=50K
39, Private, 48093, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
27, Private, 31757, Assoc-voc, 11, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 38, United-States, <=50K
54, Private, 285854, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, >50K
33, Local-gov, 120064, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
46, Federal-gov, 167381, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, >50K
37, Private, 103408, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
36, Private, 101460, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 18, United-States, <=50K
59, Local-gov, 420537, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 38, United-States, >50K
34, Local-gov, 119411, HS-grad, 9, Divorced, Protective-serv, Unmarried, White, Male, 0, 0, 40, Portugal, <=50K
53, Self-emp-inc, 128272, Doctorate, 16, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, >50K
51, Private, 386773, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 55, United-States, >50K
32, Private, 283268, 10th, 6, Separated, Other-service, Unmarried, White, Female, 0, 0, 42, United-States, <=50K
31, State-gov, 301526, Some-college, 10, Married-spouse-absent, Other-service, Other-relative, White, Male, 0, 0, 40, United-States, <=50K
22, Private, 151790, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 30, Germany, <=50K
47, Self-emp-not-inc, 106252, Bachelors, 13, Divorced, Sales, Not-in-family, White, Female, 0, 0, 50, United-States, <=50K
32, Private, 188557, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
26, Private, 171114, Some-college, 10, Never-married, Farming-fishing, Not-in-family, White, Female, 0, 0, 38, United-States, <=50K
37, Private, 327323, 5th-6th, 3, Separated, Farming-fishing, Not-in-family, White, Male, 0, 0, 32, Guatemala, <=50K
31, Private, 244147, HS-grad, 9, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 55, United-States, <=50K
37, Private, 280282, Assoc-voc, 11, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 24, United-States, >50K
55, Private, 116442, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 38, United-States, <=50K
23, Local-gov, 282579, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Male, 0, 0, 56, United-States, <=50K
36, Private, 51838, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
34, Private, 73585, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, ?, <=50K
43, Private, 226902, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, >50K
54, Private, 279129, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
43, State-gov, 146908, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, ?, <=50K
28, Private, 196690, Assoc-voc, 11, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 1669, 42, United-States, <=50K
40, Private, 130760, Assoc-voc, 11, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, >50K
41, Self-emp-not-inc, 49572, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, <=50K
40, Private, 237601, Bachelors, 13, Never-married, Sales, Not-in-family, Other, Female, 0, 0, 55, United-States, >50K
42, Private, 169628, Some-college, 10, Divorced, Adm-clerical, Not-in-family, Black, Female, 0, 0, 38, United-States, <=50K
61, Self-emp-not-inc, 36671, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 2352, 50, United-States, <=50K
18, Private, 231193, 12th, 8, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 30, United-States, <=50K
59, ?, 192130, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 16, United-States, <=50K
21, ?, 149704, HS-grad, 9, Never-married, ?, Not-in-family, White, Female, 1055, 0, 40, United-States, <=50K
48, Private, 102102, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, >50K
41, Self-emp-inc, 32185, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
18, ?, 196061, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 33, United-States, <=50K
23, Private, 211046, HS-grad, 9, Never-married, Sales, Not-in-family, White, Female, 2463, 0, 40, United-States, <=50K
60, Private, 31577, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, >50K
22, Private, 162343, Some-college, 10, Never-married, Other-service, Other-relative, Black, Male, 0, 0, 20, United-States, <=50K
61, Private, 128831, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
25, Private, 316688, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
46, Private, 90758, Masters, 14, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 35, United-States, >50K
43, Private, 274363, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1902, 40, England, >50K
43, Private, 154538, Assoc-acdm, 12, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 106085, HS-grad, 9, Never-married, Other-service, Own-child, Black, Male, 0, 1721, 30, United-States, <=50K
68, Self-emp-not-inc, 315859, 11th, 7, Never-married, Farming-fishing, Unmarried, White, Male, 0, 0, 20, United-States, <=50K
31, Private, 51471, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 38, United-States, <=50K
17, Private, 193830, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, <=50K
32, Private, 231043, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 5178, 0, 48, United-States, >50K
50, ?, 23780, Masters, 14, Married-spouse-absent, ?, Other-relative, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 169879, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 47, United-States, >50K
64, Private, 270333, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
20, Private, 138768, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 30, United-States, <=50K
30, Private, 191571, HS-grad, 9, Separated, Other-service, Own-child, White, Female, 0, 0, 36, United-States, <=50K
22, ?, 219941, Some-college, 10, Never-married, ?, Own-child, Black, Male, 0, 0, 40, United-States, <=50K
43, Private, 94113, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
22, Private, 137510, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, <=50K
17, Private, 32607, 10th, 6, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 20, United-States, <=50K
47, Self-emp-not-inc, 93208, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 75, Italy, <=50K
41, Private, 254440, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
56, Private, 186556, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, >50K
64, Private, 169871, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, <=50K
47, Private, 191277, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, United-States, >50K
48, Private, 167159, Assoc-voc, 11, Never-married, Adm-clerical, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 171871, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 46, United-States, <=50K
29, Private, 154411, Assoc-voc, 11, Never-married, Tech-support, Own-child, White, Male, 0, 0, 40, United-States, <=50K
30, Private, 129227, HS-grad, 9, Widowed, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
32, Private, 110331, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1672, 60, United-States, <=50K
57, Private, 34269, HS-grad, 9, Widowed, Transport-moving, Unmarried, White, Male, 0, 653, 42, United-States, >50K
62, Private, 174355, HS-grad, 9, Widowed, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
39, Private, 680390, HS-grad, 9, Separated, Machine-op-inspct, Unmarried, White, Female, 0, 0, 24, United-States, <=50K
43, Private, 233130, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 25, United-States, <=50K
24, Self-emp-inc, 165474, Bachelors, 13, Never-married, Sales, Own-child, White, Male, 0, 0, 40, United-States, <=50K
42, ?, 257780, 11th, 7, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 15, United-States, <=50K
53, Private, 194259, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 4386, 0, 40, United-States, >50K
26, Private, 280093, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
73, Self-emp-not-inc, 177387, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, <=50K
72, ?, 28929, 11th, 7, Widowed, ?, Not-in-family, White, Female, 0, 0, 24, United-States, <=50K
55, Private, 105304, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
25, Private, 499233, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
41, Private, 180572, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, >50K
24, Private, 321435, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, <=50K
63, Private, 86108, HS-grad, 9, Widowed, Farming-fishing, Not-in-family, White, Male, 0, 0, 6, United-States, <=50K
17, Private, 198124, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 20, United-States, <=50K
35, Private, 135162, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, <=50K
51, Private, 146813, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
62, Local-gov, 291175, Bachelors, 13, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 48, United-States, <=50K
55, Private, 387569, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 4386, 0, 40, United-States, >50K
43, Private, 102895, Some-college, 10, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
40, Local-gov, 33274, HS-grad, 9, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 50, United-States, <=50K
37, Private, 86551, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
39, Private, 138192, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 118966, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 18, United-States, <=50K
61, Private, 99784, Masters, 14, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, >50K
26, Private, 90980, Assoc-voc, 11, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 55, United-States, <=50K
46, Self-emp-not-inc, 177407, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, <=50K
26, Private, 96467, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
48, State-gov, 327886, Doctorate, 16, Divorced, Prof-specialty, Own-child, White, Male, 0, 0, 50, United-States, >50K
34, Private, 111567, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 40, United-States, <=50K
34, Local-gov, 166545, HS-grad, 9, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 40, United-States, <=50K
59, Private, 142182, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, >50K
34, Private, 188798, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, <=50K
49, Private, 38563, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 56, United-States, >50K
18, Private, 216284, 11th, 7, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 20, United-States, <=50K
43, Private, 191547, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, Mexico, <=50K
48, Private, 285335, 11th, 7, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, <=50K
28, Self-emp-inc, 142712, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, <=50K
33, Private, 80945, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
24, Private, 309055, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 15, United-States, <=50K
21, Private, 62339, 10th, 6, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
17, Private, 368700, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 28, United-States, <=50K
39, Private, 176186, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, >50K
29, Self-emp-not-inc, 266855, Bachelors, 13, Separated, Prof-specialty, Own-child, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 48087, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
24, Private, 121313, Some-college, 10, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 50, United-States, <=50K
71, Self-emp-not-inc, 143437, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 10605, 0, 40, United-States, >50K
51, Self-emp-not-inc, 160724, Bachelors, 13, Married-civ-spouse, Sales, Husband, Asian-Pac-Islander, Male, 0, 2415, 40, China, >50K
55, Private, 282753, 5th-6th, 3, Divorced, Other-service, Unmarried, Black, Male, 0, 0, 25, United-States, <=50K
41, Private, 194636, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, >50K
23, Private, 153044, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 7, United-States, <=50K
38, Private, 411797, Assoc-voc, 11, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
39, Private, 117683, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, >50K
19, Private, 376540, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 30, United-States, <=50K
49, Private, 72393, 9th, 5, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
32, Private, 270335, Bachelors, 13, Married-civ-spouse, Adm-clerical, Other-relative, White, Male, 0, 0, 40, Philippines, >50K
27, Private, 96226, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 70, United-States, <=50K
38, Private, 95336, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, >50K
33, Private, 258498, Some-college, 10, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 60, United-States, <=50K
63, ?, 149698, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 15, United-States, <=50K
23, Private, 205865, Bachelors, 13, Never-married, Exec-managerial, Own-child, White, Male, 0, 0, 28, United-States, <=50K
33, Self-emp-inc, 155781, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, ?, <=50K
54, Self-emp-not-inc, 406468, HS-grad, 9, Married-civ-spouse, Sales, Husband, Black, Male, 0, 0, 40, United-States, <=50K
29, Private, 177119, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Female, 2174, 0, 45, United-States, <=50K
48, ?, 144397, Some-college, 10, Divorced, ?, Unmarried, Black, Female, 0, 0, 30, United-States, <=50K
35, Self-emp-not-inc, 372525, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
28, Private, 164170, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Wife, Asian-Pac-Islander, Female, 0, 0, 40, India, <=50K
37, Private, 183800, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 7688, 0, 50, United-States, >50K
42, Self-emp-not-inc, 177307, Prof-school, 15, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 65, United-States, >50K
40, Private, 170108, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
47, Private, 341995, Some-college, 10, Divorced, Sales, Own-child, White, Male, 0, 0, 55, United-States, <=50K
22, Private, 226508, Bachelors, 13, Never-married, Exec-managerial, Own-child, White, Female, 0, 0, 50, United-States, <=50K
30, Private, 87418, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
28, Private, 109165, HS-grad, 9, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
63, Local-gov, 28856, 7th-8th, 4, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 55, United-States, <=50K
51, Self-emp-not-inc, 175897, 9th, 5, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, <=50K
22, Private, 99697, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Female, 0, 0, 40, United-States, <=50K
27, ?, 90270, Assoc-acdm, 12, Married-civ-spouse, ?, Own-child, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, <=50K
35, Private, 152375, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
46, Private, 171550, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 38, United-States, <=50K
37, Private, 211154, Some-college, 10, Divorced, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 52, United-States, <=50K
24, Private, 202570, Bachelors, 13, Never-married, Prof-specialty, Own-child, Black, Male, 0, 0, 15, United-States, <=50K
37, Self-emp-not-inc, 168496, HS-grad, 9, Divorced, Handlers-cleaners, Own-child, White, Male, 0, 0, 10, United-States, <=50K
53, Private, 68898, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
27, Private, 93235, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K
38, Private, 278924, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 44, United-States, <=50K
53, Self-emp-not-inc, 311020, 10th, 6, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 60, United-States, <=50K
34, Private, 175878, Some-college, 10, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 543028, HS-grad, 9, Never-married, Sales, Own-child, Black, Male, 0, 0, 40, United-States, <=50K
39, Private, 202027, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 45, United-States, >50K
43, Private, 158926, Masters, 14, Married-civ-spouse, Prof-specialty, Wife, Asian-Pac-Islander, Female, 0, 0, 50, South, <=50K
67, Self-emp-inc, 76860, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, >50K
81, Self-emp-not-inc, 136063, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 30, United-States, <=50K
21, Private, 186648, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 20, United-States, <=50K
23, Private, 257509, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 25, United-States, <=50K
25, Private, 98155, Some-college, 10, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
42, Private, 274198, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Wife, White, Female, 0, 0, 38, Mexico, <=50K
38, Private, 97083, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, <=50K
64, ?, 29825, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 5, United-States, <=50K
32, Private, 262153, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
37, Private, 214738, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
51, Private, 138022, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, >50K
22, Private, 91842, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 42, United-States, <=50K
33, Private, 373662, 1st-4th, 2, Married-spouse-absent, Priv-house-serv, Not-in-family, White, Female, 0, 0, 40, Guatemala, <=50K
42, Private, 162003, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 55, United-States, <=50K
19, ?, 52114, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 10, United-States, <=50K
51, Local-gov, 241843, Preschool, 1, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 375871, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, Mexico, <=50K
37, Private, 186934, 11th, 7, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 3103, 0, 44, United-States, >50K
37, Private, 176900, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 99, United-States, >50K
47, Private, 21906, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 25, United-States, <=50K
41, Private, 132222, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 2415, 40, United-States, >50K
33, Private, 143653, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, United-States, <=50K
31, Private, 111567, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, >50K
31, Private, 78602, Assoc-acdm, 12, Divorced, Other-service, Unmarried, Amer-Indian-Eskimo, Female, 0, 0, 40, United-States, <=50K
35, Private, 465507, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 0, 0, 40, United-States, <=50K
38, Self-emp-inc, 196373, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
18, Private, 293227, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 45, United-States, <=50K
20, Private, 241752, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
54, Local-gov, 166398, Some-college, 10, Divorced, Exec-managerial, Unmarried, Black, Female, 0, 0, 35, United-States, <=50K
40, Private, 184682, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
36, Self-emp-inc, 108293, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 1977, 45, United-States, >50K
43, Private, 250802, Some-college, 10, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 35, United-States, <=50K
44, Self-emp-not-inc, 325159, Some-college, 10, Divorced, Farming-fishing, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
44, State-gov, 174675, HS-grad, 9, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
43, Private, 227065, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 43, United-States, >50K
51, Private, 269080, 7th-8th, 4, Widowed, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
18, Private, 177722, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, <=50K
51, Private, 133461, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, <=50K
41, Private, 239683, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, ?, <=50K
44, Self-emp-inc, 398473, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 70, United-States, >50K
33, Local-gov, 298785, 10th, 6, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
33, Self-emp-not-inc, 123424, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
42, Private, 176286, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
25, Private, 150062, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, <=50K
32, Private, 169240, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 38, United-States, <=50K
32, Private, 288273, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 70, Mexico, <=50K
36, Private, 526968, 10th, 6, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
28, Private, 57066, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
20, Private, 323573, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, <=50K
35, Self-emp-inc, 368825, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, >50K
55, Self-emp-not-inc, 189721, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, <=50K
48, Private, 164966, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, >50K
36, ?, 94954, Assoc-voc, 11, Widowed, ?, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
34, Private, 202046, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, >50K
28, Private, 161538, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
67, Private, 105252, Bachelors, 13, Widowed, Exec-managerial, Not-in-family, White, Male, 0, 2392, 40, United-States, >50K
37, Private, 200153, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 32185, HS-grad, 9, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 70, United-States, <=50K
25, Private, 178326, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
21, Private, 255957, Some-college, 10, Never-married, Exec-managerial, Not-in-family, White, Female, 4101, 0, 40, United-States, <=50K
40, State-gov, 188693, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 35, United-States, >50K
78, Private, 182977, HS-grad, 9, Widowed, Other-service, Not-in-family, Black, Female, 2964, 0, 40, United-States, <=50K
34, Private, 159929, HS-grad, 9, Divorced, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, <=50K
49, Private, 123207, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 44, United-States, <=50K
22, Private, 284317, Assoc-acdm, 12, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
23, ?, 184699, HS-grad, 9, Never-married, ?, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
60, Self-emp-not-inc, 154474, HS-grad, 9, Never-married, Farming-fishing, Unmarried, White, Male, 0, 0, 42, United-States, <=50K
45, Local-gov, 318280, HS-grad, 9, Widowed, Protective-serv, Not-in-family, White, Male, 0, 0, 40, United-States, >50K
63, Private, 254907, Assoc-voc, 11, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
41, Private, 349221, HS-grad, 9, Never-married, Craft-repair, Own-child, Black, Female, 0, 0, 35, United-States, <=50K
47, Private, 335973, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
44, Private, 126701, HS-grad, 9, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
51, Private, 122159, Some-college, 10, Widowed, Prof-specialty, Not-in-family, White, Female, 3325, 0, 40, United-States, <=50K
46, Private, 187370, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 1504, 40, United-States, <=50K
41, Private, 194636, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
50, Self-emp-not-inc, 124793, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, United-States, <=50K
47, Private, 192835, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 50, United-States, >50K
35, Private, 290226, HS-grad, 9, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
56, Private, 112840, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 55, United-States, >50K
45, Private, 89325, Masters, 14, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
48, Federal-gov, 33109, Bachelors, 13, Divorced, Exec-managerial, Unmarried, White, Male, 0, 0, 58, United-States, >50K
40, Private, 82465, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 2580, 0, 40, United-States, <=50K
39, Self-emp-inc, 329980, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 50, United-States, >50K
20, Private, 148294, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, United-States, <=50K
50, Private, 168212, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 65, United-States, >50K
38, State-gov, 343642, HS-grad, 9, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
23, Local-gov, 115244, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 60, United-States, <=50K
31, Private, 162572, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 16, United-States, <=50K
58, Private, 356067, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
66, Private, 271567, HS-grad, 9, Separated, Machine-op-inspct, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
39, Self-emp-inc, 180804, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, >50K
54, Self-emp-not-inc, 123011, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 15024, 0, 52, United-States, >50K
26, Private, 109186, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, Germany, <=50K
51, Private, 220537, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
34, Private, 124827, Assoc-voc, 11, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 40, United-States, <=50K
50, Private, 767403, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 3103, 0, 40, United-States, >50K
42, Private, 118494, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 44, United-States, >50K
38, Private, 173208, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 25, United-States, <=50K
48, Private, 107373, 7th-8th, 4, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 26973, Assoc-voc, 11, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 40, United-States, >50K
51, Private, 191965, HS-grad, 9, Widowed, Other-service, Unmarried, White, Female, 0, 0, 32, United-States, <=50K
22, Private, 122346, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
19, ?, 117201, HS-grad, 9, Never-married, ?, Own-child, White, Male, 0, 0, 30, United-States, <=50K
41, Private, 198316, Some-college, 10, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 50, Japan, <=50K
48, Local-gov, 123075, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 35, United-States, >50K
42, Private, 209370, HS-grad, 9, Separated, Sales, Not-in-family, White, Female, 0, 0, 30, United-States, <=50K
34, Private, 33117, Some-college, 10, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
23, Private, 129042, HS-grad, 9, Never-married, Machine-op-inspct, Unmarried, Black, Female, 0, 0, 40, United-States, <=50K
56, Private, 169133, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 50, Yugoslavia, <=50K
30, Private, 201624, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, Black, Male, 0, 0, 45, ?, <=50K
45, Private, 368561, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 55, United-States, >50K
48, Private, 207848, 10th, 6, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, <=50K
48, Self-emp-inc, 138370, Masters, 14, Married-spouse-absent, Sales, Not-in-family, Asian-Pac-Islander, Male, 0, 0, 50, India, <=50K
31, Private, 93106, Assoc-voc, 11, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
20, State-gov, 223515, Assoc-acdm, 12, Never-married, Other-service, Own-child, White, Male, 0, 1719, 20, United-States, <=50K
27, Private, 389713, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
32, Private, 206365, HS-grad, 9, Never-married, Other-service, Not-in-family, Black, Female, 0, 0, 40, United-States, <=50K
76, ?, 431192, 7th-8th, 4, Widowed, ?, Not-in-family, White, Male, 0, 0, 2, United-States, <=50K
19, ?, 241616, HS-grad, 9, Never-married, ?, Unmarried, White, Male, 0, 2001, 40, United-States, <=50K
66, Self-emp-inc, 150726, 9th, 5, Married-civ-spouse, Exec-managerial, Husband, White, Male, 1409, 0, 1, ?, <=50K
37, Private, 123785, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 75, United-States, <=50K
34, Private, 289984, HS-grad, 9, Divorced, Priv-house-serv, Unmarried, Black, Female, 0, 0, 30, United-States, <=50K
34, ?, 164309, 11th, 7, Married-civ-spouse, ?, Wife, White, Female, 0, 0, 8, United-States, <=50K
90, Private, 137018, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
23, Private, 137994, Some-college, 10, Never-married, Machine-op-inspct, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
43, Private, 341204, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
44, Private, 167005, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 60, United-States, >50K
24, Private, 34446, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 37, United-States, <=50K
28, Private, 187160, Prof-school, 15, Divorced, Prof-specialty, Unmarried, White, Male, 0, 0, 55, United-States, <=50K
64, ?, 196288, Assoc-acdm, 12, Never-married, ?, Not-in-family, White, Female, 0, 0, 20, United-States, <=50K
23, Private, 217961, Some-college, 10, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
20, Private, 74631, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 50, United-States, <=50K
36, Private, 156667, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 50, United-States, >50K
61, Private, 125155, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
53, Self-emp-not-inc, 263925, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, Canada, >50K
30, Private, 296453, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 7298, 0, 40, United-States, >50K
52, Self-emp-not-inc, 44728, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 55, United-States, >50K
38, Private, 193026, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, Iran, <=50K
32, Private, 87643, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, <=50K
30, Self-emp-not-inc, 106742, 12th, 8, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 75, United-States, <=50K
41, Private, 302122, Assoc-voc, 11, Divorced, Craft-repair, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
49, Local-gov, 193960, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 40, United-States, >50K
45, Private, 185385, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 47, United-States, >50K
43, Self-emp-not-inc, 277647, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 35, United-States, <=50K
61, Private, 128848, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 3471, 0, 40, United-States, <=50K
54, Private, 377701, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 32, Mexico, <=50K
34, Private, 157886, Assoc-acdm, 12, Separated, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
49, Private, 175958, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 80, United-States, >50K
38, Private, 223004, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, United-States, <=50K
35, Private, 199352, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1977, 80, United-States, >50K
36, Private, 29984, 12th, 8, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
30, Private, 181651, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, <=50K
36, Private, 117312, Assoc-acdm, 12, Divorced, Tech-support, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
22, Local-gov, 34029, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 20, United-States, <=50K
38, Private, 132879, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1902, 40, United-States, >50K
37, Private, 215310, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, >50K
48, State-gov, 55863, Doctorate, 16, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1902, 46, United-States, >50K
17, Private, 220384, 11th, 7, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 15, United-States, <=50K
19, Self-emp-not-inc, 36012, Some-college, 10, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 20, United-States, <=50K
27, Private, 137645, Bachelors, 13, Never-married, Sales, Not-in-family, Black, Female, 0, 1590, 40, United-States, <=50K
22, Private, 191342, Bachelors, 13, Never-married, Sales, Own-child, Asian-Pac-Islander, Male, 0, 0, 50, Taiwan, <=50K
49, Private, 31339, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
43, State-gov, 227910, Assoc-voc, 11, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
43, Private, 173728, Bachelors, 13, Separated, Prof-specialty, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
19, Local-gov, 167816, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 35, United-States, <=50K
58, Self-emp-not-inc, 81642, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 60, United-States, <=50K
41, Local-gov, 195258, Some-college, 10, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, >50K
31, Private, 232475, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
30, Private, 241259, Assoc-voc, 11, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, <=50K
32, Private, 118161, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, <=50K
29, Private, 201954, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 35, United-States, <=50K
42, Private, 150533, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7298, 0, 52, United-States, >50K
38, Private, 412296, HS-grad, 9, Divorced, Craft-repair, Own-child, White, Male, 0, 0, 28, United-States, <=50K
41, Federal-gov, 133060, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, >50K
44, Self-emp-not-inc, 120539, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 50, United-States, >50K
31, Private, 196025, Doctorate, 16, Married-spouse-absent, Prof-specialty, Not-in-family, Asian-Pac-Islander, Male, 0, 0, 60, China, <=50K
34, Private, 107793, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
21, Private, 163870, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, <=50K
22, Self-emp-not-inc, 361280, Bachelors, 13, Never-married, Prof-specialty, Own-child, Asian-Pac-Islander, Male, 0, 0, 20, India, <=50K
62, Private, 92178, 10th, 6, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, <=50K
19, ?, 80710, HS-grad, 9, Never-married, ?, Own-child, White, Female, 0, 0, 40, United-States, <=50K
29, Self-emp-inc, 260729, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 1977, 25, United-States, >50K
43, Private, 182254, Some-college, 10, Divorced, Prof-specialty, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
68, ?, 140282, 7th-8th, 4, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 8, United-States, <=50K
45, Self-emp-inc, 149865, Bachelors, 13, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 60, United-States, >50K
39, Self-emp-inc, 218184, 9th, 5, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1651, 40, Mexico, <=50K
41, Private, 118619, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 50, United-States, <=50K
34, Self-emp-not-inc, 196791, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 25, United-States, >50K
34, Local-gov, 167999, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 33, United-States, <=50K
31, Private, 51259, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 47, United-States, <=50K
29, Private, 131088, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 25, United-States, <=50K
41, Private, 118212, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, White, Male, 3103, 0, 40, United-States, >50K
41, Private, 293791, Assoc-voc, 11, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 55, United-States, <=50K
35, Self-emp-inc, 289430, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 45, Mexico, >50K
33, Private, 35378, Bachelors, 13, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 45, United-States, >50K
37, State-gov, 60227, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 38, United-States, <=50K
69, Private, 168139, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, <=50K
34, Private, 290763, HS-grad, 9, Divorced, Handlers-cleaners, Own-child, White, Female, 0, 0, 40, United-States, <=50K
60, Self-emp-inc, 226355, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 2415, 70, ?, >50K
36, Private, 51100, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
41, Private, 227644, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, United-States, <=50K
58, Local-gov, 205267, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, >50K
53, Private, 288020, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, Japan, <=50K
29, Private, 140863, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
45, Federal-gov, 170915, HS-grad, 9, Divorced, Tech-support, Not-in-family, White, Female, 4865, 0, 40, United-States, <=50K
34, State-gov, 50178, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 38, United-States, <=50K
36, Private, 112497, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, <=50K
48, Private, 95244, Some-college, 10, Divorced, Other-service, Unmarried, Black, Female, 0, 0, 35, United-States, <=50K
20, Private, 117606, Assoc-voc, 11, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 40, United-States, <=50K
35, Private, 89508, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, United-States, >50K
63, Federal-gov, 124244, HS-grad, 9, Widowed, Handlers-cleaners, Not-in-family, Black, Male, 0, 0, 40, United-States, <=50K
41, Self-emp-not-inc, 154374, Some-college, 10, Divorced, Other-service, Unmarried, White, Male, 0, 0, 45, United-States, <=50K
28, Private, 294936, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, <=50K
30, Private, 347132, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, Black, Female, 0, 0, 40, United-States, <=50K
34, ?, 181934, HS-grad, 9, Divorced, ?, Not-in-family, White, Female, 0, 0, 40, United-States, <=50K
31, Private, 316672, HS-grad, 9, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, Mexico, <=50K
37, Private, 189382, Assoc-voc, 11, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 38, United-States, <=50K
42, ?, 184018, Some-college, 10, Divorced, ?, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
31, Private, 184307, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, Jamaica, >50K
46, Self-emp-not-inc, 246212, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
35, Federal-gov, 250504, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, Black, Male, 0, 0, 60, United-States, >50K
27, Private, 138705, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 53, United-States, <=50K
41, Private, 328447, 1st-4th, 2, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, Mexico, <=50K
19, Private, 194608, Some-college, 10, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, <=50K
20, Private, 230891, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 45, United-States, <=50K
59, Federal-gov, 212448, HS-grad, 9, Widowed, Sales, Unmarried, White, Female, 0, 0, 40, Germany, <=50K
40, Private, 214010, Bachelors, 13, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 37, United-States, <=50K
56, Self-emp-not-inc, 200235, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, <=50K
33, Private, 354573, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 15024, 0, 44, United-States, >50K
30, Self-emp-inc, 205733, Some-college, 10, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 60, United-States, <=50K
46, Private, 185041, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 50, United-States, <=50K
61, Self-emp-inc, 84409, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 35, United-States, >50K
50, Self-emp-inc, 293196, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7298, 0, 40, United-States, >50K
25, Private, 241626, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 30, United-States, <=50K
40, Private, 520586, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 39, United-States, <=50K
24, ?, 35633, Some-college, 10, Never-married, ?, Not-in-family, White, Male, 0, 0, 40, ?, <=50K
51, Private, 302847, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 54, United-States, <=50K
43, State-gov, 165309, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, <=50K
34, Private, 117529, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 54, Mexico, <=50K
46, Private, 106092, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K
28, State-gov, 445824, Masters, 14, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 50, United-States, >50K
26, Private, 227332, Bachelors, 13, Never-married, Transport-moving, Unmarried, Asian-Pac-Islander, Male, 0, 0, 40, ?, <=50K
20, Private, 275691, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 28, United-States, <=50K
44, Private, 193459, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 3411, 0, 40, United-States, <=50K
51, Private, 284329, HS-grad, 9, Widowed, Transport-moving, Unmarried, White, Male, 0, 0, 40, United-States, <=50K
33, Private, 114691, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, >50K
54, Private, 96062, Assoc-acdm, 12, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, >50K
50, Private, 133963, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1977, 40, United-States, >50K
33, Private, 178506, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, Black, Female, 0, 0, 40, United-States, <=50K
65, Private, 350498, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 10605, 0, 20, United-States, >50K
22, ?, 131573, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 8, United-States, <=50K
88, Self-emp-not-inc, 206291, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
40, Private, 182302, HS-grad, 9, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K
51, Private, 241346, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 43, United-States, <=50K
50, Private, 157043, 11th, 7, Divorced, Other-service, Not-in-family, Black, Female, 0, 0, 40, United-States, <=50K
25, Private, 404616, Masters, 14, Married-civ-spouse, Farming-fishing, Not-in-family, White, Male, 0, 0, 99, United-States, >50K
20, Private, 411862, Assoc-voc, 11, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 30, United-States, <=50K
47, Private, 183013, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, <=50K
58, ?, 169982, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, >50K
22, Private, 188544, Assoc-voc, 11, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 30, United-States, <=50K
50, State-gov, 356619, HS-grad, 9, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 48, United-States, >50K
47, Private, 45857, HS-grad, 9, Never-married, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, <=50K
24, Local-gov, 289886, 11th, 7, Never-married, Other-service, Not-in-family, Asian-Pac-Islander, Male, 0, 0, 45, United-States, <=50K
50, ?, 146015, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, >50K
40, Private, 216237, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, >50K
36, Private, 416745, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, <=50K
32, Private, 202952, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, <=50K
44, Private, 167725, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, <=50K
51, ?, 165637, Masters, 14, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, <=50K
59, Federal-gov, 43280, Some-college, 10, Never-married, Exec-managerial, Own-child, Black, Female, 0, 0, 40, United-States, <=50K
65, Private, 118779, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, United-States, <=50K
24, State-gov, 191269, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 65, United-States, <=