tldr

KggleのNYS Environmental Remediation SitesをClassifying Environmental Program Types - Data Every Day #041に沿ってやっていきます。

実行環境はGoogle Colaboratorです。

インポート

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split
import sklearn.linear_model as slm

import tensorflow as tf

データのダウンロード

Google Driveをマウントします。

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。

import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path

from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()

Kaggle APIを使ってデータをダウンロードします。

dataset_id = 'new-york-state/nys-environmental-remediation-sites'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path

'/content/environmental-remediation-sites.csv'

api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)

100%|██████████| 4.82M/4.82M [00:00<00:00, 50.2MB/s]

Downloading environmental-remediation-sites.csv.zip to /content









True

データの読み込み

Pedumagalhaes/quality-prediction-in-a-mining-processadasを使ってダウンロードしてきたCSVファイルを読み込みます。

data = pd.read_csv(file_path + '.zip')

data

	Program Number	Program Type	Program Facility Name	Site Class	Address1	Address2	Locality	County	ZIPCode	SWIS Code	DEC Region	Latitude	Longitude	Control Code	Control Type	OU	Project Name	Project Completion Date	Waste Name	Contaminants	Owner Name	Owner Address1	Owner Address2	Owner City	Owner State	Owner ZIP	Disposal Name	Disposal Address1	Disposal Address2	Disposal City	Disposal ZIP	Disposal State	Operator Name	Operator Address1	Operator Address2	Operator City	Operator State	Operator Zip	Georeference	NYS Municipal Boundaries 2	New York Zip Codes 2	Counties 2
0	C241159	BCP	11-28 31st Drive	C	11-28 31st Drive	NaN	Queens	Queens	11106	4101	2	40.768000	-73.935028	Environmental Easement	Environmental Easement	01	Remedial Design	2016-09-12T00:00:00.000	NaN	tetrachloroethene (PCE)	George Man	1083 Maple Lane	NaN	New Hyde Park	NY	11040	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-73.93502777...	196	878.0	2137.0
1	E633060	ERP	1333 East Dominick Street	A	1313-1333 East Dominick Street	NaN	Rome	Oneida	13440	3313	6	43.204978	-75.434194	Environmental Easement	Environmental Easement	01	NaN	2018-03-01T00:00:00.000	NaN	benzo(a)anthracene	JAMES F. BROWN	198 N. WASHINGTON ST.	NaN	ROME	NY	13440	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-75.43419382...	989	1366.0	625.0
2	C915320	BCP	166 Chandler Street	C	166 Chandler Street	NaN	Buffalo	Erie	14207	1502	9	42.943614	-78.885092	Environmental Easement	Environmental Easement	01	Remedial Investigation	2018-12-12T00:00:00.000	NaN	mercury	Rocco Termini	391 Washington Street	NaN	Buffalo	NY	14203	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-78.88509166...	300	63.0	2041.0
3	C828192	BCP	1777 East Henrietta Road	A	1777 East Henrietta Road	NaN	Henrietta	Monroe	14623	2832	8	43.084683	-77.610928	Environmental Easement	Environmental Easement	01	NaN	2020-12-31T00:00:00.000	NaN	benzo(a)pyrene	Kerry Ventures Tech Park, LLC	195 Middle road, suite 400	NaN	henrietta	NY	14467	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-77.61092777...	546	1682.0	2093.0
4	V00669	VCP	192 Ralph Avenue	C	192 Ralph Ave	NaN	Brooklyn	Kings	11233	2401	2	40.683078	-73.922924	Deed Restriction	Deed Restriction	01A	Remedial Action	2008-06-11T00:00:00.000	NaN	tetrachloroethene (PCE)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-73.92292392...	894	816.0	2090.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
82605	704005	HW	Tri-City Barrel Company	04	Old Route 7	NaN	Fenton (Port Crane)	Broome	13833	432	7	42.158504	-75.811645	Other Controls	Other Controls	01	Remedial Design	2003-01-16T00:00:00.000	DICHLOROETHYLENE, XYLENE, STYRENE,	styrene	Gary Warner	RD 1 / PO Box 88	NaN	Port Crane	NY	13833	TRI-CITY BARREL COMPANY, INC.	NaN	NaN	NaN	NaN	ZZ	WARNER GARY	R.D. 1, PO BOX 88	NaN	PORT CRANE	NY	NaN	{'type': 'Point', 'coordinates': [-75.811645, ...	945	453.0	2033.0
82606	C224043	BCP	U. S. Dredging Shipyard Site	C	One Beard Street	NaN	Brooklyn	Kings	11231	2401	2	40.671538	-74.011473	Environmental Easement	Environmental Easement	01	Remedial Investigation	2006-03-31T00:00:00.000	NaN	methylene chloride	cLAUDE bOISVERT	420 Alan Wood Road	NaN	Conshohocken	PA	19428	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	{'type': 'Point', 'coordinates': [-74.011473, ...	894	814.0	2090.0
82607	130043H	HW	Utility Manufacturing/Wonder King	04	700-712 Main Street	NaN	Westbury	Nassau	11590	3022	1	40.756999	-73.556776	Decision Document	Decision Document	01	Remedial Investigation	2003-03-19T00:00:00.000	TETRACHLOROETHYLENE	tetrachloroethene (PCE)	Audie Kranz and Wilbur Kranz	700-712 Main Street	NaN	Westbury	NY	11590	NEST EQUITIES, INC.	NaN	NaN	NaN	NaN	ZZ	Audie Kranz and Wilbur Kranz	700-712 Main Street	NaN	Westbury	NY	11590	{'type': 'Point', 'coordinates': [-73.55677553...	60	997.0	2094.0
82608	704005	HW	Tri-City Barrel Company	04	Old Route 7	NaN	Fenton (Port Crane)	Broome	13833	432	7	42.158504	-75.811645	Deed Restriction	Deed Restriction	01	Remedial Investigation	2000-03-31T00:00:00.000	DICHLOROETHYLENE, XYLENE, STYRENE,	styrene	Tri-Cities Barrel Co Inc	319 NYS Rte 7 PO Box 88	NaN	Port Crane	NY	13833	TRI-CITY BARREL COMPANY, INC.	NaN	NaN	NaN	NaN	ZZ	WARNER GARY	R.D. 1, PO BOX 88	NaN	PORT CRANE	NY	NaN	{'type': 'Point', 'coordinates': [-75.811645, ...	945	453.0	2033.0
82609	413010	HW	West Well and Parking Area (Amphenol)	02	40-60 Delaware Street	NaN	Sidney	Delaware	13838	1350	4	42.307482	-75.399926	Deed Restriction	Deed Restriction	01	Remedial Design	1987-11-01T00:00:00.000	NaN	trans-1,2-dichloroethene	joseph m bianchi	40-60 Delaware Avenue	NaN	Sidney	NY	13838	bendix corporation	NaN	NaN	NaN	NaN	NY	joseph m bianchi	40-60 Delaware Avenue	NaN	Sidney	NY	13838	{'type': 'Point', 'coordinates': [-75.39992579...	558	456.0	2039.0

82610 rows × 42 columns

下準備

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 82610 entries, 0 to 82609
Data columns (total 42 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   Program Number              82610 non-null  object 
 1   Program Type                82610 non-null  object 
 2   Program Facility Name       82610 non-null  object 
 3   Site Class                  82610 non-null  object 
 4   Address1                    82610 non-null  object 
 5   Address2                    1190 non-null   object 
 6   Locality                    82448 non-null  object 
 7   County                      82610 non-null  object 
 8   ZIPCode                     82610 non-null  object 
 9   SWIS Code                   82610 non-null  int64  
 10  DEC Region                  82610 non-null  int64  
 11  Latitude                    82610 non-null  float64
 12  Longitude                   82610 non-null  float64
 13  Control Code                82610 non-null  object 
 14  Control Type                82610 non-null  object 
 15  OU                          82610 non-null  object 
 16  Project Name                68153 non-null  object 
 17  Project Completion Date     82549 non-null  object 
 18  Waste Name                  36047 non-null  object 
 19  Contaminants                53667 non-null  object 
 20  Owner Name                  79344 non-null  object 
 21  Owner Address1              78689 non-null  object 
 22  Owner Address2              11719 non-null  object 
 23  Owner City                  78843 non-null  object 
 24  Owner State                 79344 non-null  object 
 25  Owner ZIP                   78547 non-null  object 
 26  Disposal Name               31861 non-null  object 
 27  Disposal Address1           8037 non-null   object 
 28  Disposal Address2           1354 non-null   object 
 29  Disposal City               6534 non-null   object 
 30  Disposal ZIP                5516 non-null   object 
 31  Disposal State              31927 non-null  object 
 32  Operator Name               55801 non-null  object 
 33  Operator Address1           53337 non-null  object 
 34  Operator Address2           2915 non-null   object 
 35  Operator City               53489 non-null  object 
 36  Operator State              55859 non-null  object 
 37  Operator Zip                51438 non-null  object 
 38  Georeference                82610 non-null  object 
 39  NYS Municipal Boundaries 2  82610 non-null  int64  
 40  New York Zip Codes 2        82056 non-null  float64
 41  Counties 2                  82517 non-null  float64
dtypes: float64(4), int64(3), object(35)
memory usage: 26.5+ MB

null_columns = data.loc[:, data.isna().sum() > 0.25 * data.shape[0]]

null_columns

	Address2	Waste Name	Contaminants	Owner Address2	Disposal Name	Disposal Address1	Disposal Address2	Disposal City	Disposal ZIP	Disposal State	Operator Name	Operator Address1	Operator Address2	Operator City	Operator State	Operator Zip
0	NaN	NaN	tetrachloroethene (PCE)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	NaN	NaN	benzo(a)anthracene	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	mercury	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	NaN	NaN	benzo(a)pyrene	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	NaN	NaN	tetrachloroethene (PCE)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
82605	NaN	DICHLOROETHYLENE, XYLENE, STYRENE,	styrene	NaN	TRI-CITY BARREL COMPANY, INC.	NaN	NaN	NaN	NaN	ZZ	WARNER GARY	R.D. 1, PO BOX 88	NaN	PORT CRANE	NY	NaN
82606	NaN	NaN	methylene chloride	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
82607	NaN	TETRACHLOROETHYLENE	tetrachloroethene (PCE)	NaN	NEST EQUITIES, INC.	NaN	NaN	NaN	NaN	ZZ	Audie Kranz and Wilbur Kranz	700-712 Main Street	NaN	Westbury	NY	11590
82608	NaN	DICHLOROETHYLENE, XYLENE, STYRENE,	styrene	NaN	TRI-CITY BARREL COMPANY, INC.	NaN	NaN	NaN	NaN	ZZ	WARNER GARY	R.D. 1, PO BOX 88	NaN	PORT CRANE	NY	NaN
82609	NaN	NaN	trans-1,2-dichloroethene	NaN	bendix corporation	NaN	NaN	NaN	NaN	NY	joseph m bianchi	40-60 Delaware Avenue	NaN	Sidney	NY	13838

82610 rows × 16 columns

data = data.drop(null_columns, axis=1)

unneeded_columns = [
    'Program Number', 
    'Project Name', 
    'Program Facility Name', 
    'Address1', 
    'Locality', 
    'ZIPCode', 
    'SWIS Code', 
    'Owner Name',
    'Owner Address1', 
    'Owner City', 
    'Owner State', 
    'Owner ZIP',
    'Georeference',
]

data = data.drop(unneeded_columns, axis=1)

data

	Program Type	Site Class	County	DEC Region	Latitude	Longitude	Control Code	Control Type	OU	Project Completion Date	NYS Municipal Boundaries 2	New York Zip Codes 2	Counties 2
0	BCP	C	Queens	2	40.768000	-73.935028	Environmental Easement	Environmental Easement	01	2016-09-12T00:00:00.000	196	878.0	2137.0
1	ERP	A	Oneida	6	43.204978	-75.434194	Environmental Easement	Environmental Easement	01	2018-03-01T00:00:00.000	989	1366.0	625.0
2	BCP	C	Erie	9	42.943614	-78.885092	Environmental Easement	Environmental Easement	01	2018-12-12T00:00:00.000	300	63.0	2041.0
3	BCP	A	Monroe	8	43.084683	-77.610928	Environmental Easement	Environmental Easement	01	2020-12-31T00:00:00.000	546	1682.0	2093.0
4	VCP	C	Kings	2	40.683078	-73.922924	Deed Restriction	Deed Restriction	01A	2008-06-11T00:00:00.000	894	816.0	2090.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
82605	HW	04	Broome	7	42.158504	-75.811645	Other Controls	Other Controls	01	2003-01-16T00:00:00.000	945	453.0	2033.0
82606	BCP	C	Kings	2	40.671538	-74.011473	Environmental Easement	Environmental Easement	01	2006-03-31T00:00:00.000	894	814.0	2090.0
82607	HW	04	Nassau	1	40.756999	-73.556776	Decision Document	Decision Document	01	2003-03-19T00:00:00.000	60	997.0	2094.0
82608	HW	04	Broome	7	42.158504	-75.811645	Deed Restriction	Deed Restriction	01	2000-03-31T00:00:00.000	945	453.0	2033.0
82609	HW	02	Delaware	4	42.307482	-75.399926	Deed Restriction	Deed Restriction	01	1987-11-01T00:00:00.000	558	456.0	2039.0

82610 rows × 13 columns

def get_uniques(df, columns):
    return {column: list(df[column].unique()) for column in columns}

def get_categorical_columns(df):
    return [column for column in df.columns if df.dtypes[column] == 'object']

get_uniques(data, get_categorical_columns(data))

{'Control Code': ['Environmental Easement',
  'Deed Restriction',
  'Environmental Notice',
  'Other Controls',
  'Decision Document'],
 'Control Type': ['Environmental Easement',
  'Deed Restriction',
  'Environmental Notice',
  'Other Controls',
  'Decision Document'],
 'County': ['Queens',
  'Oneida',
  'Erie',
  'Monroe',
  'Kings',
  'Bronx',
  'Cattaraugus',
  'Schenectady',
  'Broome',
  'Franklin',
  'Onondaga',
  'Delaware',
  'Richmond',
  'Nassau',
  'Dutchess',
  'Westchester',
  'Chenango',
  'Niagara',
  'New York',
  'Ulster',
  'Orleans',
  'Chemung',
  'Lewis',
  'Ontario',
  'Montgomery',
  'Herkimer',
  'Wayne',
  'Saratoga',
  'Rockland',
  'Jefferson',
  'Suffolk',
  'Washington',
  'Orange',
  'Rensselaer',
  'Oswego',
  'Fulton',
  'Albany',
  'Clinton',
  'St Lawrence',
  'Seneca',
  'Cayuga',
  'Chautauqua',
  'Tompkins',
  'Genesee',
  'Tioga',
  'Putnam',
  'Madison',
  'Greene',
  'Yates',
  'Sullivan',
  'Allegany',
  'Warren',
  'Schoharie',
  'Livingston',
  'Steuben',
  'Cortland',
  'Wyoming',
  'Columbia',
  'Schuyler',
  'Otsego',
  'Essex'],
 'OU': ['01',
  '01A',
  '02',
  '03',
  '04B',
  '00',
  '05',
  '09',
  '12',
  '21',
  '11',
  '16',
  '17',
  '04',
  '06',
  '01B',
  '02A',
  '01C',
  '01E',
  '05A',
  '03A',
  '06A',
  '04A',
  '03B',
  '02B',
  '04C',
  '07',
  '04D',
  '03C',
  '01H',
  '01J',
  '01F',
  '01D',
  '01K',
  '08',
  '01a',
  '02C',
  '18',
  '20',
  '14',
  '25',
  '15',
  '13',
  '19',
  '42'],
 'Program Type': ['BCP', 'ERP', 'VCP', 'HW', 'RCRA'],
 'Project Completion Date': ['2016-09-12T00:00:00.000',
  '2018-03-01T00:00:00.000',
  '2018-12-12T00:00:00.000',
  '2020-12-31T00:00:00.000',
  '2008-06-11T00:00:00.000',
  '2011-08-16T00:00:00.000',
  '2016-11-30T00:00:00.000',
  '2014-03-19T00:00:00.000',
  '2012-04-10T00:00:00.000',
  '2021-07-31T00:00:00.000',
  '2010-03-17T00:00:00.000',
  '2000-03-27T00:00:00.000',
  '1991-08-01T00:00:00.000',
  nan,
  '1999-03-01T00:00:00.000',
  '2000-12-22T00:00:00.000',
  '2007-07-30T00:00:00.000',
  '2001-05-04T00:00:00.000',
  '2014-10-30T00:00:00.000',
  '2012-11-16T00:00:00.000',
  '2008-01-10T00:00:00.000',
  '1995-09-01T00:00:00.000',
  '2014-05-14T00:00:00.000',
  '2020-05-29T00:00:00.000',
  '1990-10-01T00:00:00.000',
  '2005-10-27T00:00:00.000',
  '2022-06-30T00:00:00.000',
  '2010-09-30T00:00:00.000',
  '2011-03-15T00:00:00.000',
  '2003-09-25T00:00:00.000',
  '2003-09-30T00:00:00.000',
  '2016-12-20T00:00:00.000',
  '1993-03-01T00:00:00.000',
  '2003-09-04T00:00:00.000',
  '2019-12-31T00:00:00.000',
  '2016-12-21T00:00:00.000',
  '2002-03-26T00:00:00.000',
  '2021-08-31T00:00:00.000',
  '2015-03-20T00:00:00.000',
  '2018-02-21T00:00:00.000',
  '2002-03-19T00:00:00.000',
  '2014-10-15T00:00:00.000',
  '2001-03-26T00:00:00.000',
  '2012-07-03T00:00:00.000',
  '2011-03-29T00:00:00.000',
  '1994-09-01T00:00:00.000',
  '2006-12-26T00:00:00.000',
  '1987-01-01T00:00:00.000',
  '2021-08-30T00:00:00.000',
  '2021-02-05T00:00:00.000',
  '2014-03-31T00:00:00.000',
  '2018-02-12T00:00:00.000',
  '2020-10-31T00:00:00.000',
  '2011-05-13T00:00:00.000',
  '2019-05-08T00:00:00.000',
  '2018-07-12T00:00:00.000',
  '2021-10-31T00:00:00.000',
  '1989-10-01T00:00:00.000',
  '2021-06-30T00:00:00.000',
  '2001-09-14T00:00:00.000',
  '2021-04-30T00:00:00.000',
  '2007-07-09T00:00:00.000',
  '2009-12-04T00:00:00.000',
  '1991-12-01T00:00:00.000',
  '2017-07-12T00:00:00.000',
  '1995-02-01T00:00:00.000',
  '1998-06-17T00:00:00.000',
  '1991-11-01T00:00:00.000',
  '2007-03-30T00:00:00.000',
  '2009-03-25T00:00:00.000',
  '2010-03-30T00:00:00.000',
  '2014-09-02T00:00:00.000',
  '2002-11-12T00:00:00.000',
  '2021-12-31T00:00:00.000',
  '1999-01-01T00:00:00.000',
  '2018-07-23T00:00:00.000',
  '2001-04-01T00:00:00.000',
  '1992-09-01T00:00:00.000',
  '1988-01-01T00:00:00.000',
  '2021-03-01T00:00:00.000',
  '2014-04-11T00:00:00.000',
  '2007-04-30T00:00:00.000',
  '2002-08-19T00:00:00.000',
  '2021-05-31T00:00:00.000',
  '2011-03-31T00:00:00.000',
  '2021-11-30T00:00:00.000',
  '2016-03-25T00:00:00.000',
  '2011-10-12T00:00:00.000',
  '1995-07-01T00:00:00.000',
  '2004-05-18T00:00:00.000',
  '2004-03-30T00:00:00.000',
  '2003-11-14T00:00:00.000',
  '1998-03-01T00:00:00.000',
  '1995-06-01T00:00:00.000',
  '2014-12-01T00:00:00.000',
  '2008-09-02T00:00:00.000',
  '2005-11-25T00:00:00.000',
  '2002-12-31T00:00:00.000',
  '2008-12-29T00:00:00.000',
  '2021-10-29T00:00:00.000',
  '2008-03-31T00:00:00.000',
  '2008-04-29T00:00:00.000',
  '2006-10-17T00:00:00.000',
  '2007-01-31T00:00:00.000',
  '2021-01-29T00:00:00.000',
  '2016-10-28T00:00:00.000',
  '2001-10-20T00:00:00.000',
  '2008-03-28T00:00:00.000',
  '2012-02-07T00:00:00.000',
  '2018-03-31T00:00:00.000',
  '2021-05-30T00:00:00.000',
  '2010-09-17T00:00:00.000',
  '2007-12-11T00:00:00.000',
  '1997-07-01T00:00:00.000',
  '2003-01-21T00:00:00.000',
  '2013-04-30T00:00:00.000',
  '2009-03-31T00:00:00.000',
  '2014-12-22T00:00:00.000',
  '2016-08-16T00:00:00.000',
  '2003-11-20T00:00:00.000',
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  '2016-07-13T00:00:00.000',
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  '2014-06-12T00:00:00.000',
  '2004-03-31T00:00:00.000',
  '2000-11-27T00:00:00.000',
  '2014-12-12T00:00:00.000',
  '2010-10-19T00:00:00.000',
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  '2021-09-15T00:00:00.000',
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  '2015-04-15T00:00:00.000',
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  '2019-12-18T00:00:00.000',
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  '2013-04-05T00:00:00.000',
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  '2015-09-28T00:00:00.000',
  '2015-07-13T00:00:00.000',
  '2019-01-07T00:00:00.000',
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  '1987-05-01T00:00:00.000',
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  '2006-04-27T00:00:00.000',
  '2014-12-23T00:00:00.000',
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  '2015-10-19T00:00:00.000',
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  '2003-12-04T00:00:00.000',
  '2013-03-29T00:00:00.000',
  '2005-01-12T00:00:00.000',
  '2023-09-30T00:00:00.000',
  '2005-08-30T00:00:00.000',
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  '2008-04-01T00:00:00.000',
  '2009-01-26T00:00:00.000',
  '2012-12-31T00:00:00.000',
  '2017-08-09T00:00:00.000',
  '2006-05-25T00:00:00.000',
  '2015-09-01T00:00:00.000',
  '2004-03-16T00:00:00.000',
  '2002-03-28T00:00:00.000',
  '2019-01-02T00:00:00.000',
  '2013-08-23T00:00:00.000',
  '2005-09-20T00:00:00.000',
  '2015-03-12T00:00:00.000',
  '2016-03-30T00:00:00.000',
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  '2018-10-16T00:00:00.000',
  '2019-01-11T00:00:00.000',
  '2009-05-29T00:00:00.000',
  '2019-08-19T00:00:00.000',
  '2019-10-11T00:00:00.000',
  '2014-08-01T00:00:00.000',
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  '2015-12-08T00:00:00.000',
  '2006-12-28T00:00:00.000',
  '2019-12-16T00:00:00.000',
  '2018-03-28T00:00:00.000',
  '2009-04-06T00:00:00.000',
  '2018-08-09T00:00:00.000',
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  '2003-01-06T00:00:00.000',
  '2014-03-28T00:00:00.000',
  '2013-12-30T00:00:00.000',
  '2015-07-06T00:00:00.000',
  '2014-03-04T00:00:00.000',
  '2000-03-30T00:00:00.000',
  '2001-09-13T00:00:00.000',
  '2015-11-12T00:00:00.000',
  '2020-05-19T00:00:00.000',
  '2018-12-20T00:00:00.000',
  '2021-01-31T00:00:00.000',
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  '1996-11-08T00:00:00.000',
  '2011-08-24T00:00:00.000',
  '2018-12-03T00:00:00.000',
  '1996-09-01T00:00:00.000',
  '2017-02-15T00:00:00.000',
  '2011-09-20T00:00:00.000',
  '2019-05-20T00:00:00.000',
  '2018-11-02T00:00:00.000',
  '2008-12-30T00:00:00.000',
  '1995-01-01T00:00:00.000',
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  '2018-05-29T00:00:00.000',
  '2019-12-24T00:00:00.000',
  '2020-11-30T00:00:00.000',
  '2013-12-31T00:00:00.000',
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  '1996-10-01T00:00:00.000',
  '2016-11-21T00:00:00.000',
  '1988-05-01T00:00:00.000',
  '2015-12-22T00:00:00.000',
  '2013-11-25T00:00:00.000',
  '2015-05-15T00:00:00.000',
  '1991-07-01T00:00:00.000',
  '2010-12-17T00:00:00.000',
  '2011-10-13T00:00:00.000',
  '2014-07-21T00:00:00.000',
  '2015-10-07T00:00:00.000',
  '2018-06-29T00:00:00.000',
  '2009-07-31T00:00:00.000',
  '2012-02-28T00:00:00.000',
  '2017-08-24T00:00:00.000',
  '1999-08-30T00:00:00.000',
  '2016-12-28T00:00:00.000',
  '1999-10-01T00:00:00.000',
  '2015-01-06T00:00:00.000',
  '2015-06-01T00:00:00.000',
  '2017-10-31T00:00:00.000',
  '2015-02-03T00:00:00.000',
  '2009-02-17T00:00:00.000',
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  '1992-04-01T00:00:00.000',
  '1994-10-01T00:00:00.000',
  '2019-04-26T00:00:00.000',
  '2015-01-20T00:00:00.000',
  '2008-08-12T00:00:00.000',
  '2019-08-28T00:00:00.000',
  '2010-03-29T00:00:00.000',
  '2074-04-02T00:00:00.000',
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  '2003-01-03T00:00:00.000',
  '2020-05-20T00:00:00.000',
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  '2000-05-15T00:00:00.000',
  '1989-09-01T00:00:00.000',
  '2013-12-19T00:00:00.000',
  '2010-06-08T00:00:00.000',
  '2003-08-04T00:00:00.000',
  '1982-01-01T00:00:00.000',
  '2001-08-30T00:00:00.000',
  '2018-06-11T00:00:00.000',
  '2019-08-16T00:00:00.000',
  '2015-12-17T00:00:00.000',
  '2016-01-19T00:00:00.000',
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  '2015-12-21T00:00:00.000',
  '2005-05-10T00:00:00.000',
  '1992-10-01T00:00:00.000',
  '2012-12-21T00:00:00.000',
  '2011-09-28T00:00:00.000',
  '2005-03-18T00:00:00.000',
  '2015-03-16T00:00:00.000',
  '2018-03-19T00:00:00.000',
  '2016-12-05T00:00:00.000',
  '1999-09-01T00:00:00.000',
  '2012-12-07T00:00:00.000',
  '1996-07-01T00:00:00.000',
  '2017-12-27T00:00:00.000',
  '2012-05-03T00:00:00.000',
  '2000-08-30T00:00:00.000',
  '2024-12-02T00:00:00.000',
  '2020-09-30T00:00:00.000',
  '2019-03-21T00:00:00.000',
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  '2004-08-27T00:00:00.000',
  '2006-09-19T00:00:00.000',
  '2003-10-20T00:00:00.000',
  '2012-06-19T00:00:00.000',
  '2010-06-02T00:00:00.000',
  '1993-02-01T00:00:00.000',
  '1999-07-01T00:00:00.000',
  '2007-12-12T00:00:00.000',
  '2007-03-27T00:00:00.000',
  '2009-01-07T00:00:00.000',
  '2014-09-19T00:00:00.000',
  '1996-12-01T00:00:00.000',
  '2007-02-13T00:00:00.000',
  '2003-06-23T00:00:00.000',
  '2010-12-28T00:00:00.000',
  '2016-11-04T00:00:00.000',
  '2001-06-26T00:00:00.000',
  '2018-12-28T00:00:00.000',
  '2018-10-03T00:00:00.000',
  '2014-04-23T00:00:00.000',
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  '1990-09-01T00:00:00.000',
  '1995-12-01T00:00:00.000',
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  '2018-03-21T00:00:00.000',
  '2011-10-18T00:00:00.000',
  '2020-06-15T00:00:00.000',
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  '2019-03-22T00:00:00.000',
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  '2015-06-02T00:00:00.000',
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  '2016-07-06T00:00:00.000',
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  '2013-03-19T00:00:00.000',
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  '2016-03-10T00:00:00.000',
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  '2011-02-15T00:00:00.000',
  '2005-06-30T00:00:00.000',
  '2015-12-01T00:00:00.000',
  '2018-12-26T00:00:00.000',
  '2007-12-17T00:00:00.000',
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  '1991-10-01T00:00:00.000',
  '2018-02-15T00:00:00.000',
  '2018-03-30T00:00:00.000',
  '2015-05-29T00:00:00.000',
  '1995-10-01T00:00:00.000',
  '2016-01-26T00:00:00.000',
  '2021-05-07T00:00:00.000',
  '1995-11-01T00:00:00.000',
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  '2013-05-15T00:00:00.000',
  '1991-01-01T00:00:00.000',
  '2018-06-25T00:00:00.000',
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  '2014-12-18T00:00:00.000',
  '2010-07-22T00:00:00.000',
  '2014-05-08T00:00:00.000',
  '2008-03-10T00:00:00.000',
  '2012-12-04T00:00:00.000',
  '2006-05-29T00:00:00.000',
  '2003-09-15T00:00:00.000',
  '2018-03-08T00:00:00.000',
  '2012-04-06T00:00:00.000',
  '2017-09-06T00:00:00.000',
  '2017-11-14T00:00:00.000',
  '2008-04-10T00:00:00.000',
  '2014-06-25T00:00:00.000',
  '2011-01-21T00:00:00.000',
  '2015-12-10T00:00:00.000',
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  '1993-06-01T00:00:00.000',
  '2006-03-23T00:00:00.000',
  '2018-06-28T00:00:00.000',
  '2016-05-19T00:00:00.000',
  '2018-06-01T00:00:00.000',
  '2018-05-17T00:00:00.000',
  '2002-11-29T00:00:00.000',
  '2007-09-17T00:00:00.000',
  '2014-11-19T00:00:00.000',
  '2014-08-14T00:00:00.000',
  '2017-04-30T00:00:00.000',
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  '2019-01-03T00:00:00.000',
  '2020-10-23T00:00:00.000',
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  '2013-10-28T00:00:00.000',
  '2019-03-27T00:00:00.000',
  '2000-06-08T00:00:00.000',
  '2008-12-16T00:00:00.000',
  '2013-10-11T00:00:00.000',
  '2016-11-18T00:00:00.000',
  '2006-12-14T00:00:00.000',
  '2006-10-06T00:00:00.000',
  '2014-12-17T00:00:00.000',
  '2015-03-31T00:00:00.000',
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  '1995-08-01T00:00:00.000',
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  '2006-12-22T00:00:00.000',
  '2015-03-19T00:00:00.000',
  '2015-08-15T00:00:00.000',
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  '2004-04-27T00:00:00.000',
  '2008-08-14T00:00:00.000',
  '2019-11-15T00:00:00.000',
  '2007-03-22T00:00:00.000',
  '1999-06-01T00:00:00.000',
  '2001-11-20T00:00:00.000',
  '2004-11-19T00:00:00.000',
  '2013-05-16T00:00:00.000',
  '2014-09-26T00:00:00.000',
  '2016-11-09T00:00:00.000',
  '1987-09-01T00:00:00.000',
  '2014-12-30T00:00:00.000',
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  '2000-02-29T00:00:00.000',
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  '2009-01-30T00:00:00.000',
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  '2019-12-20T00:00:00.000',
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  '2012-03-19T00:00:00.000',
  '2015-06-11T00:00:00.000',
  '2000-10-04T00:00:00.000',
  '2023-03-15T00:00:00.000',
  '2011-12-22T00:00:00.000',
  '2010-11-29T00:00:00.000',
  '2000-06-09T00:00:00.000',
  '2015-12-15T00:00:00.000',
  '2017-02-21T00:00:00.000',
  '2014-03-24T00:00:00.000',
  '2015-03-06T00:00:00.000',
  '2015-04-16T00:00:00.000',
  '2013-09-25T00:00:00.000',
  '2008-05-16T00:00:00.000',
  '2020-08-13T00:00:00.000',
  '1997-11-01T00:00:00.000',
  '2018-04-16T00:00:00.000',
  '2015-12-23T00:00:00.000',
  '2016-12-27T00:00:00.000',
  '2017-05-30T00:00:00.000',
  '2018-04-02T00:00:00.000',
  '2010-03-18T00:00:00.000',
  '2012-11-28T00:00:00.000',
  '2002-12-19T00:00:00.000',
  '1988-03-01T00:00:00.000',
  '2012-01-20T00:00:00.000',
  '2002-11-18T00:00:00.000',
  '2015-03-26T00:00:00.000',
  '2012-03-14T00:00:00.000',
  '2006-10-19T00:00:00.000',
  '2007-07-31T00:00:00.000',
  '2009-12-01T00:00:00.000',
  '2009-07-17T00:00:00.000',
  '2008-05-28T00:00:00.000',
  '2007-12-14T00:00:00.000',
  '2015-06-04T00:00:00.000',
  '2010-01-08T00:00:00.000',
  '2017-08-04T00:00:00.000',
  '2007-06-27T00:00:00.000',
  '2019-04-19T00:00:00.000',
  '2020-10-15T00:00:00.000',
  '2019-10-29T00:00:00.000',
  '2000-07-10T00:00:00.000',
  '2001-06-27T00:00:00.000',
  '1988-08-01T00:00:00.000',
  '2018-11-16T00:00:00.000',
  '1992-07-01T00:00:00.000',
  '2004-09-27T00:00:00.000',
  '2002-01-31T00:00:00.000',
  '1997-09-01T00:00:00.000',
  '2022-12-01T00:00:00.000',
  '2013-02-05T00:00:00.000',
  '2015-05-31T00:00:00.000',
  '2004-03-01T00:00:00.000',
  '1993-04-01T00:00:00.000',
  '2011-09-30T00:00:00.000',
  '2018-01-24T00:00:00.000',
  '2018-08-10T00:00:00.000',
  '2011-09-09T00:00:00.000',
  '2015-05-27T00:00:00.000',
  '2013-02-25T00:00:00.000',
  '2000-11-29T00:00:00.000',
  '2011-02-14T00:00:00.000',
  '1994-05-01T00:00:00.000',
  '2012-03-30T00:00:00.000',
  '2004-04-14T00:00:00.000',
  '2022-09-16T00:00:00.000',
  '2016-12-13T00:00:00.000',
  '2006-09-05T00:00:00.000',
  '1988-07-01T00:00:00.000',
  '2018-11-15T00:00:00.000',
  '2010-12-22T00:00:00.000',
  '2018-09-25T00:00:00.000',
  '2004-04-04T00:00:00.000',
  '2014-09-18T00:00:00.000',
  '1998-05-01T00:00:00.000',
  '2013-10-18T00:00:00.000',
  '2000-02-25T00:00:00.000',
  '2017-03-01T00:00:00.000',
  '2016-01-07T00:00:00.000',
  '2015-09-15T00:00:00.000',
  '2003-09-12T00:00:00.000',
  '2006-07-27T00:00:00.000',
  '2019-07-26T00:00:00.000',
  '2015-08-03T00:00:00.000',
  '2000-11-09T00:00:00.000',
  '2011-03-30T00:00:00.000',
  '2002-12-30T00:00:00.000',
  '1981-01-01T00:00:00.000',
  '2004-12-31T00:00:00.000',
  '2008-02-13T00:00:00.000',
  '2007-05-01T00:00:00.000',
  '2017-11-16T00:00:00.000',
  '2016-05-24T00:00:00.000',
  '2000-10-02T00:00:00.000',
  '2014-07-24T00:00:00.000',
  '2003-04-09T00:00:00.000',
  '2009-06-02T00:00:00.000',
  '2009-10-27T00:00:00.000',
  '2014-06-03T00:00:00.000',
  '2019-06-12T00:00:00.000',
  '2007-04-27T00:00:00.000',
  '2004-06-28T00:00:00.000',
  '2002-02-05T00:00:00.000',
  '2008-03-05T00:00:00.000',
  '1997-10-01T00:00:00.000',
  '2004-12-20T00:00:00.000',
  '2019-09-19T00:00:00.000',
  '2008-12-11T00:00:00.000',
  '2002-03-25T00:00:00.000',
  '2004-07-30T00:00:00.000',
  '2008-12-31T00:00:00.000',
  '2008-09-25T00:00:00.000',
  '2011-07-06T00:00:00.000',
  '1999-03-15T00:00:00.000',
  '2010-12-13T00:00:00.000',
  '2010-07-14T00:00:00.000',
  '2021-09-02T00:00:00.000',
  '2011-11-18T00:00:00.000',
  '2020-07-27T00:00:00.000',
  '2002-05-22T00:00:00.000',
  '2009-01-16T00:00:00.000',
  '2018-12-14T00:00:00.000',
  '2003-07-03T00:00:00.000',
  '2010-05-24T00:00:00.000',
  '1999-07-21T00:00:00.000',
  '2017-05-25T00:00:00.000',
  '1998-06-30T00:00:00.000',
  '2004-06-01T00:00:00.000',
  '2005-12-22T00:00:00.000',
  '2002-09-24T00:00:00.000',
  '2003-06-30T00:00:00.000',
  '2012-06-14T00:00:00.000',
  '2007-08-27T00:00:00.000',
  '2000-10-06T00:00:00.000',
  '2020-08-06T00:00:00.000',
  '1992-11-01T00:00:00.000',
  '2006-01-17T00:00:00.000',
  '2011-06-23T00:00:00.000',
  '2016-03-23T00:00:00.000',
  '2008-05-29T00:00:00.000',
  '1993-01-01T00:00:00.000',
  '2008-07-17T00:00:00.000',
  '2001-10-22T00:00:00.000',
  '2014-05-20T00:00:00.000',
  '2001-08-15T00:00:00.000',
  '2010-05-03T00:00:00.000',
  '2014-03-06T00:00:00.000',
  '2012-04-04T00:00:00.000',
  '2013-03-12T00:00:00.000',
  '2006-04-19T00:00:00.000',
  '2016-09-23T00:00:00.000',
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  '2012-01-19T00:00:00.000',
  '1992-12-01T00:00:00.000',
  '2017-03-06T00:00:00.000',
  '2013-03-22T00:00:00.000',
  '1988-12-31T00:00:00.000',
  '2014-01-27T00:00:00.000',
  '2007-11-27T00:00:00.000',
  '2004-01-30T00:00:00.000',
  '2012-11-21T00:00:00.000',
  '2010-03-08T00:00:00.000',
  '1986-09-01T00:00:00.000',
  '2003-12-23T00:00:00.000',
  '2016-10-31T00:00:00.000',
  '2012-07-13T00:00:00.000',
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  '2000-02-02T00:00:00.000',
  '2011-11-22T00:00:00.000',
  '2018-07-18T00:00:00.000',
  '1998-05-20T00:00:00.000',
  '1998-04-01T00:00:00.000',
  '2006-10-25T00:00:00.000',
  '2012-03-02T00:00:00.000',
  '2008-01-23T00:00:00.000',
  '2002-03-04T00:00:00.000',
  '2011-06-27T00:00:00.000',
  '2007-03-28T00:00:00.000',
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  '1999-11-01T00:00:00.000',
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  '1991-05-01T00:00:00.000',
  '2005-10-25T00:00:00.000',
  '2013-10-01T00:00:00.000',
  '1999-08-24T00:00:00.000',
  '2003-08-05T00:00:00.000',
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  '2015-07-08T00:00:00.000',
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  '2018-10-17T00:00:00.000',
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  '2018-08-31T00:00:00.000',
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  '2019-10-22T00:00:00.000',
  '2018-02-09T00:00:00.000',
  '2020-07-01T00:00:00.000',
  '1990-11-01T00:00:00.000',
  '2002-05-01T00:00:00.000',
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  '2015-09-16T00:00:00.000',
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  '2004-03-23T00:00:00.000',
  '2001-03-23T00:00:00.000',
  '2018-07-13T00:00:00.000',
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  '2004-09-30T00:00:00.000',
  '2017-03-09T00:00:00.000',
  '2019-04-02T00:00:00.000',
  '1991-02-01T00:00:00.000',
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  '2011-06-03T00:00:00.000',
  '2005-11-03T00:00:00.000',
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  '2006-01-09T00:00:00.000',
  '2011-03-04T00:00:00.000',
  '2006-10-04T00:00:00.000',
  '2014-09-04T00:00:00.000',
  '2015-01-07T00:00:00.000',
  '2016-07-21T00:00:00.000',
  '2009-11-05T00:00:00.000',
  '2012-12-12T00:00:00.000',
  '2013-05-02T00:00:00.000',
  '2007-02-12T00:00:00.000',
  '2007-03-21T00:00:00.000',
  '2012-09-24T00:00:00.000',
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  '2015-08-27T00:00:00.000',
  '2008-02-22T00:00:00.000',
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  '2009-07-07T00:00:00.000',
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  '2016-06-24T00:00:00.000',
  '2000-01-12T00:00:00.000',
  '1986-12-31T00:00:00.000',
  '1987-12-01T00:00:00.000',
  '2009-09-25T00:00:00.000',
  '2016-12-29T00:00:00.000',
  '2018-02-20T00:00:00.000',
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  '2002-09-30T00:00:00.000',
  '2018-02-23T00:00:00.000',
  '2006-06-28T00:00:00.000',
  '2010-05-25T00:00:00.000',
  '2016-10-24T00:00:00.000',
  '2009-11-10T00:00:00.000',
  '2015-06-30T00:00:00.000',
  '2012-06-08T00:00:00.000',
  '2013-01-04T00:00:00.000',
  '2003-04-15T00:00:00.000',
  '2014-06-20T00:00:00.000',
  '2018-11-21T00:00:00.000',
  '2010-10-15T00:00:00.000',
  '2014-04-18T00:00:00.000',
  '2018-07-02T00:00:00.000',
  '1999-03-30T00:00:00.000',
  '2012-10-10T00:00:00.000',
  '2024-09-15T00:00:00.000',
  '2006-12-07T00:00:00.000',
  '2017-10-06T00:00:00.000',
  '1990-01-01T00:00:00.000',
  '1998-07-01T00:00:00.000',
  '1996-06-01T00:00:00.000',
  '2008-02-27T00:00:00.000',
  '2007-10-03T00:00:00.000',
  '2019-09-06T00:00:00.000',
  '2017-09-29T00:00:00.000',
  '2012-09-28T00:00:00.000',
  '2015-07-31T00:00:00.000',
  '2021-01-01T00:00:00.000',
  '2010-02-24T00:00:00.000',
  '2015-10-31T00:00:00.000',
  '2010-08-30T00:00:00.000',
  '2006-11-28T00:00:00.000',
  '2001-09-17T00:00:00.000',
  '2014-04-08T00:00:00.000',
  '2025-03-31T00:00:00.000',
  ...],
 'Site Class': ['C', 'A', '04', '02', 'N', 'P', '03']}

data['Project Completion Date'] = data['Project Completion Date'].apply(lambda x: x[0:7] if str(x) != 'nan' else x)

data['Year'] = data['Project Completion Date'].apply(lambda x: np.float(x[0:4]) if str(x) != 'nan' else x)
data['Month'] = data['Project Completion Date'].apply(lambda x: np.float(x[5:7]) if str(x) != 'nan' else x)

data = data.drop('Project Completion Date', axis=1)

for column in ['New York Zip Codes 2', 'Year', 'Month', 'Counties 2']:
    data[column] = data[column].fillna(data[column].mean())

data.isna().sum()

Program Type                  0
Site Class                    0
County                        0
DEC Region                    0
Latitude                      0
Longitude                     0
Control Code                  0
Control Type                  0
OU                            0
NYS Municipal Boundaries 2    0
New York Zip Codes 2          0
Counties 2                    0
Year                          0
Month                         0
dtype: int64

エンコード

def onehot_encode(df, column):
    df = df.copy()
    dummies = pd.get_dummies(df[column])
    df = pd.concat([df, dummies], axis=1)
    df = df.drop(column, axis=1)
    return df

get_categorical_columns(data)

['Program Type', 'Site Class', 'County', 'Control Code', 'Control Type', 'OU']

nominal_features = get_categorical_columns(data)
nominal_features.remove('Program Type')

for feature in nominal_features:
    data = onehot_encode(data, feature)

label_encoder = sp.LabelEncoder()
data['Program Type'] = label_encoder.fit_transform(data['Program Type'])
target_mapppings = {index: column for index, column in enumerate(label_encoder.classes_)}
target_mapppings

{0: 'BCP', 1: 'ERP', 2: 'HW', 3: 'RCRA', 4: 'VCP'}

データの分割とスケーリング

	DEC Region	Latitude	Longitude	NYS Municipal Boundaries 2	New York Zip Codes 2	Counties 2	Year	Month	02	03	04	A	C	N	P	Albany	Allegany	Bronx	Broome	Cattaraugus	Cayuga	Chautauqua	Chemung	Chenango	Clinton	Columbia	Cortland	Delaware	Dutchess	Erie	Essex	Franklin	Fulton	Genesee	Greene	Herkimer	Jefferson	Kings	Lewis	Livingston	...	01D	01E	01F	01H	01J	01K	01a	02	02A	02B	02C	03	03A	03B	03C	04	04A	04B	04C	04D	05	05A	06	06A	07	08	09	11	12	13	14	15	16	17	18	19	20	21	25	42
0	-0.748714	-0.963589	0.637439	-0.689064	0.093366	0.314497	1.002552	0.535128	-0.625374	-0.020588	-0.492479	-0.233580	1.819035	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
1	0.533197	1.277122	-0.140194	1.777746	1.188601	-4.472172	1.219252	-1.042891	-0.625374	-0.020588	-0.492479	4.281179	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
2	1.494630	1.036807	-1.930211	-0.365548	-1.735766	0.010582	1.219252	1.324138	-0.625374	-0.020588	-0.492479	-0.233580	1.819035	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	3.978476	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
3	1.174152	1.166515	-1.269289	0.399692	1.897811	0.175202	1.435952	1.324138	-0.625374	-0.020588	-0.492479	4.281179	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
4	-0.748714	-1.041672	0.643717	1.482227	-0.045783	0.165705	0.135751	-0.253882	-0.625374	-0.020588	-0.492479	-0.233580	1.819035	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	5.700842	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
82605	0.853675	0.314928	-0.335982	1.640874	-0.860476	-0.014745	-0.406000	-1.568898	-0.625374	-0.020588	2.030542	-0.233580	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	7.145222	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
82606	-0.748714	-1.052282	0.597786	1.482227	-0.050271	0.165705	-0.080949	-1.042891	-0.625374	-0.020588	-0.492479	-0.233580	1.819035	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	5.700842	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
82607	-1.069191	-0.973704	0.833642	-1.112123	0.360442	0.178368	-0.406000	-1.042891	-0.625374	-0.020588	2.030542	-0.233580	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
82608	0.853675	0.314928	-0.335982	1.640874	-0.860476	-0.014745	-0.731050	-1.042891	-0.625374	-0.020588	2.030542	-0.233580	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	7.145222	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	-0.062163	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561
82609	-0.107758	0.451908	-0.122419	0.437021	-0.853743	0.004250	-2.139601	1.061135	1.599044	-0.020588	-0.492479	-0.233580	-0.549742	-0.094942	-0.547296	-0.097821	-0.067979	-0.156868	-0.139954	-0.081267	-0.049016	-0.07772	-0.079897	-0.042793	-0.047504	-0.075805	-0.072589	16.086637	-0.166678	-0.251353	-0.017047	-0.015167	-0.060473	-0.025337	-0.038141	-0.082093	-0.061967	-0.175413	-0.013918	-0.059045	...	-0.015167	-0.012546	-0.006959	-0.009841	-0.009841	-0.006959	-0.00492	-0.186021	-0.024609	-0.012053	-0.00492	-0.138008	-0.020291	-0.008523	-0.00492	-0.087171	-0.006026	-0.00492	-0.00492	-0.00492	-0.055972	-0.006026	-0.072673	-0.006026	-0.047504	-0.058318	-0.065041	-0.093767	-0.038141	-0.04673	-0.058318	-0.044052	-0.064286	-0.144079	-0.04673	-0.02696	-0.044052	-0.115094	-0.031134	-0.015561

82610 rows × 131 columns

y = data['Program Type']
X = data.drop(['Program Type'], axis=1)

scaler = sp.StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X), index=X.index, columns=X.columns)

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)

トレーニング

X.shape

(82610, 131)

y.value_counts()

2    61673
0    14403
1     3455
4     3022
3       57
Name: Program Type, dtype: int64

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(131,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(5, activation='softmax'),
])

model.summary()

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'],
)

batch_size=64
epochs=100

history = model.fit(
    X_train,
    y_train,
    validation_split=0.2,
    batch_size=batch_size,
    epochs=epochs,
    callbacks=[tf.keras.callbacks.ReduceLROnPlateau()],
    verbose=0,
)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 64)                8448      
_________________________________________________________________
dense_7 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_8 (Dense)              (None, 5)                 325       
=================================================================
Total params: 12,933
Trainable params: 12,933
Non-trainable params: 0
_________________________________________________________________

plt.figure(figsize=(14, 10))

epochs_range = range(1, epochs+1)
train_loss = history.history['loss']
val_loss = history.history['val_loss']

plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')

plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.show()

model.evaluate(X_test, y_test)

775/775 [==============================] - 1s 884us/step - loss: 0.0262 - accuracy: 0.9958





[0.026238199323415756, 0.9958440661430359]

for label in range(5):
    label_indices = y_test[y_test == label].index
    label_acc = model.evaluate(X_test.loc[label_indices, :], y_test.loc[label_indices], verbose=0)
    print(f'Class {label} Accuracy: {label_acc[1]}')

Class 0 Accuracy: 0.9960345029830933
Class 1 Accuracy: 0.9609895348548889
Class 2 Accuracy: 0.9981631636619568
Class 3 Accuracy: 0.9230769276618958
Class 4 Accuracy: 0.9891657829284668

すべてのタイプで良い精度が出ていることを確認できました。