Using SageMaker PCA with tdapiclient | API Integration - Using SageMaker PCA with tdapiclient

This use case shows the steps to use SageMaker PCA with tdapiclient.

You can download the aws-usecases.zip file in the attachment as a reference. The pca folder in the zip file includes a Jupyter notebook file (ipynb) and a CSV file containing the dataset required to run this use case.

Import necessary libraries.

import getpass
from tdapiclient import create_tdapi_context, TDApiClient
from teradataml import create_context, DataFrame, copy_to_sql,load_example_data, configure, LabelEncoder, valib,Retain
import pandas as pd
from teradatasqlalchemy.types import *

Create the connection.

host = input("Host: ")
username = input("Username: ")
password = getpass.getpass("Password: ")

td_context = create_context(host=host, username=username, password=password)

Create TDAPI context and TDApiClient object.

s3_bucket = input("S3 Bucket(Please provide just the bucket name, for example: test-bucket): ")
access_id = input("Access ID:")
access_key = getpass.getpass("Acess Key: ")
region = input("AWS Region: ")

os.environ["AWS_ACCESS_KEY_ID"] = access_id
os.environ["AWS_SECRET_ACCESS_KEY"] = access_key
os.environ["AWS_REGION"] = region

tdapi_context = create_tdapi_context("aws", bucket_name=s3_bucket)

td_apiclient = TDApiClient(tdapi_context)

Set up data.

Read the breast cancer dataset.
```
data = pd.read_csv ("cancer_data.csv")
```
Drop unnecessary columns.
```
data=data.drop(['Unnamed: 32'], axis=1)
```

Rename columns for creating teradataml DataFrame.

data.rename(columns={'concave points_mean':'concave_points_mean',
                           "concave points_se":"concave_points_se",
                           "concave points_worst":"concave_points_worst"},                   inplace=True)

Insert the dataframe in the tables.

data_table = "cancer_data"

column_types = {
    "id":INTEGER,
    "diagnosis": CHAR(1),
    "radius_mean": FLOAT,
    "texture_mean": FLOAT,
    "perimeter_mean": FLOAT,        
    "area_mean": FLOAT,                
    "smoothness_mean": FLOAT ,         
    "compactness_mean": FLOAT ,        
    "concavity_mean": FLOAT    ,       
    "concave_points_mean": FLOAT,   
    "symmetry_mean": FLOAT        ,    
    "fractal_dimension_mean": FLOAT,   
    "radius_se": FLOAT              ,  
    "texture_se": FLOAT              , 
    "perimeter_se": FLOAT             ,
    "area_se": FLOAT                 ,
    "smoothness_se": FLOAT           ,
    "compactness_se": FLOAT           ,
    "concavity_se": FLOAT             ,
    "concave_points_se": FLOAT       ,
    "symmetry_se": FLOAT              ,
    "fractal_dimension_se": FLOAT     ,
    "radius_worst": FLOAT             ,
    "texture_worst": FLOAT           ,
    "perimeter_worst": FLOAT          ,
    "area_worst": FLOAT               ,
    "smoothness_worst": FLOAT         ,
    "compactness_worst": FLOAT        ,
    "concavity_worst": FLOAT          ,
    "concave_points_worst": FLOAT   ,
    "symmetry_worst": FLOAT          , 
    "fractal_dimension_worst": FLOAT 
               }

copy_to_sql(df=data, table_name=data_table, if_exists="replace", types=column_types)

Create a teradataml DataFrame using the table.
```
df = DataFrame(table_name=data_table)
```

Prepare dataset.

Encode the target column using label encoder.

from teradataml import LabelEncoder

rc = LabelEncoder(values=("M", 1), columns=["diagnosis"], default=0)

feature_columns_names= Retain(columns=["radius_mean",
   "texture_mean",
   "perimeter_mean",        
   "area_mean",                
   "smoothness_mean" ,         
   "compactness_mean" ,        
   "concavity_mean"  ,       
   "concave_points_mean",   
   "symmetry_mean"       ,    
   "fractal_dimension_mean",   
   "radius_se"              ,  
   "texture_se"           , 
   "perimeter_se"           ,
   "area_se"             ,
   "smoothness_se"          ,
   "compactness_se"           ,
   "concavity_se"         ,
   "concave_points_se"      ,
   "symmetry_se"             ,
   "fractal_dimension_se"   ,
   "radius_worst"           ,
   "texture_worst"          ,
   "perimeter_worst"         ,
   "area_worst"               ,
   "smoothness_worst"        ,
   "compactness_worst"       ,
   "concavity_worst"        ,
   "concave_points_worst"  ,
   "symmetry_worst"        , 
   "fractal_dimension_worst" ])

configure.val_install_location = "alice"
data = valib.Transform(data=df, label_encode=rc,index_columns="id",unique_index=True,retain=feature_columns_names)

df=data.result

Drop unnecessary columns.
```
df=df.drop(["id","diagnosis"],axis=1)
```
Create two samples of input data: sample 1 has 80% of total rows and sample 2 has 20% of total rows.
```
cancer_sample = df.sample(frac=[0.8, 0.2])
```

Create train dataset from sample 1 by filtering on "sampleid" and drop "sampleid" column as it is not required for training model.

train = cancer_sample[cancer_sample.sampleid == "1"].drop("sampleid", axis = 1)

train

The output:

radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave_points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave_points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave_points_worst	symmetry_worst	fractal_dimension_worst
18.08	21.84	117.4	1024.0	0.07371	0.08642	0.1103	0.05778	0.177	0.0534	0.6362	1.305	4.312	76.36	0.00553	0.05296	0.0611	0.01444	0.0214	0.005036	19.76	24.7	129.1	1228.0	0.08822	0.1963	0.2535	0.09181	0.2369	0.06558
18.05	16.15	120.2	1006.0	0.1065	0.2146	0.1684	0.108	0.2152	0.06673	0.9806	0.5505	6.311	134.8	0.00794	0.05839	0.04658	0.0207	0.02591	0.007054	22.39	18.91	150.1	1610.0	0.1478	0.5634	0.3786	0.2102	0.3751	0.1108
19.07	24.81	128.3	1104.0	0.09081	0.219	0.2107	0.09961	0.231	0.06343	0.9811	1.666	8.83	104.9	0.006548	0.1006	0.09723	0.02638	0.05333	0.007646	24.09	33.17	177.4	1651.0	0.1247	0.7444	0.7242	0.2493	0.467	0.1038
16.17	16.07	106.3	788.5	0.0988	0.1438	0.06651	0.05397	0.199	0.06572	0.1745	0.489	1.349	14.91	0.00451	0.01812	0.01951	0.01196	0.01934	0.003696	16.97	19.14	113.1	861.5	0.1235	0.255	0.2114	0.1251	0.3153	0.0896
15.3	25.27	102.4	732.4	0.1082	0.1697	0.1683	0.08751	0.1926	0.0654	0.439	1.012	3.498	43.5	0.005233	0.03057	0.03576	0.01083	0.01768	0.002967	20.27	36.71	149.3	1269.0	0.1641	0.611	0.6335	0.2024	0.4027	0.09876
14.5	10.89	94.28	640.7	0.1101	0.1099	0.08842	0.05778	0.1856	0.06402	0.2929	0.857	1.928	24.19	0.003818	0.01276	0.02882	0.012	0.0191	0.002808	15.7	15.98	102.8	745.5	0.1313	0.1788	0.256	0.1221	0.2889	0.08006
15.04	16.74	98.73	689.4	0.09883	0.1364	0.07721	0.06142	0.1668	0.06869	0.372	0.8423	2.304	34.84	0.004123	0.01819	0.01996	0.01004	0.01055	0.003237	16.76	20.43	109.7	856.9	0.1135	0.2176	0.1856	0.1018	0.2177	0.08549
8.196	16.84	51.71	201.9	0.086	0.05943	0.01588	0.005917	0.1769	0.06503	0.1563	0.9567	1.094	8.205	0.008968	0.01646	0.01588	0.005917	0.02574	0.002582	8.964	21.96	57.26	242.2	0.1297	0.1357	0.0688	0.02564	0.3105	0.07409
16.26	21.88	107.5	826.8	0.1165	0.1283	0.1799	0.07981	0.1869	0.06532	0.5706	1.457	2.961	57.72	0.01056	0.03756	0.05839	0.01186	0.04022	0.006187	17.73	25.21	113.7	975.2	0.1426	0.2116	0.3344	0.1047	0.2736	0.07953
9.042	18.9	60.07	244.5	0.09968	0.1972	0.1975	0.04908	0.233	0.08743	0.4653	1.911	3.769	24.2	0.009845	0.0659	0.1027	0.02527	0.03491	0.007877	10.06	23.4	68.62	297.1	0.1221	0.3748	0.4609	0.1145	0.3135	0.1055

Create test dataset from sample 2 by filtering on "sampleid" and drop "sampleid" column as it is not required for scoring.

test = cancer_sample[cancer_sample.sampleid == "2"].drop("sampleid", axis = 1)

test

The output:

radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave_points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave_points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave_points_worst	symmetry_worst	fractal_dimension_worst
18.31	20.58	120.8	1052.0	0.1068	0.1248	0.1569	0.09451	0.186	0.05941	0.5449	0.9225	3.218	67.36	0.006176	0.01877	0.02913	0.01046	0.01559	0.002725	21.86	26.2	142.2	1493.0	0.1492	0.2536	0.3759	0.151	0.3074	0.07863
15.1	22.02	97.26	712.8	0.09056	0.07081	0.05253	0.03334	0.1616	0.05684	0.3105	0.8339	2.097	29.91	0.004675	0.0103	0.01603	0.009222	0.01095	0.001629	18.1	31.69	117.7	1030.0	0.1389	0.2057	0.2712	0.153	0.2675	0.07873
19.8	21.56	129.7	1230.0	0.09383	0.1306	0.1272	0.08691	0.2094	0.05581	0.9553	1.186	6.487	124.4	0.006804	0.03169	0.03446	0.01712	0.01897	0.004045	25.73	28.64	170.3	2009.0	0.1353	0.3235	0.3617	0.182	0.307	0.08255
15.3	25.27	102.4	732.4	0.1082	0.1697	0.1683	0.08751	0.1926	0.0654	0.439	1.012	3.498	43.5	0.005233	0.03057	0.03576	0.01083	0.01768	0.002967	20.27	36.71	149.3	1269.0	0.1641	0.611	0.6335	0.2024	0.4027	0.09876
10.29	27.61	65.67	321.4	0.0903	0.07658	0.05999	0.02738	0.1593	0.06127	0.2199	2.239	1.437	14.46	0.01205	0.02736	0.04804	0.01721	0.01843	0.004938	10.84	34.91	69.57	357.6	0.1384	0.171	0.2	0.09127	0.2226	0.08283
9.268	12.87	61.49	248.7	0.1634	0.2239	0.0973	0.05252	0.2378	0.09502	0.4076	1.093	3.014	20.04	0.009783	0.04542	0.03483	0.02188	0.02542	0.01045	10.28	16.38	69.05	300.2	0.1902	0.3441	0.2099	0.1025	0.3038	0.1252
11.04	16.83	70.92	373.2	0.1077	0.07804	0.03046	0.0248	0.1714	0.0634	0.1967	1.387	1.342	13.54	0.005158	0.009355	0.01056	0.007483	0.01718	0.002198	12.41	26.44	79.93	471.4	0.1369	0.1482	0.1067	0.07431	0.2998	0.07881
13.61	24.98	88.05	582.7	0.09488	0.08511	0.08625	0.04489	0.1609	0.05871	0.4565	1.29	2.861	43.14	0.005872	0.01488	0.02647	0.009921	0.01465	0.002355	16.99	35.27	108.6	906.5	0.1265	0.1943	0.3169	0.1184	0.2651	0.07397
14.03	21.25	89.79	603.4	0.0907	0.06945	0.01462	0.01896	0.1517	0.05835	0.2589	1.503	1.667	22.07	0.007389	0.01383	0.007302	0.01004	0.01263	0.002925	15.33	30.28	98.27	715.5	0.1287	0.1513	0.06231	0.07963	0.2226	0.07617
14.64	15.24	95.77	651.9	0.1132	0.1339	0.09966	0.07064	0.2116	0.06346	0.5115	0.7372	3.814	42.76	0.005508	0.04412	0.04436	0.01623	0.02427	0.004841	16.34	18.24	109.4	803.6	0.1277	0.3089	0.2604	0.1397	0.3151	0.08473

Create PCA SageMaker instance through tdapiclient.

exec_role_arn = "arn:aws:iam::076782961461:role/service-role/AmazonSageMaker-ExecutionRole-20210112T215668"

pca = td_apiclient.PCA(
    role=exec_role_arn,
    instance_count=1,
    instance_type="ml.m5.xlarge",
    feature_dim=30,
    num_components=20,
    subtract_mean=True,
    mini_batch_size=20
)

Covert teradataml DataFrame to NumPy ndarray and store it as PCA RecordSet object.
```
train_set=pca.record_set(train.get_values().astype('float32'))
```
Start training using RecordSet object.
```
pca.fit(train_set) 
```

Create Serializer and Deserializer, so predictor can handle CSV input and output.

from sagemaker.serializers import CSVSerializer
from sagemaker.deserializers import CSVDeserializer
csv_ser = CSVSerializer()
csv_dser = CSVDeserializer()

predictor = pca.deploy("aws-endpoint",
                       sagemaker_kw_args={"instance_type": "ml.m5.large", "initial_instance_count": 1, "serializer": csv_ser, "deserializer": csv_dser})

Try prediction integration using teradataml DataFrame and the predictor object created in previous step.

Confirm that predictor is correctly configured for accepting csv input.
```
print(predictor.cloudObj.accept)
```
The output:
```
('text/csv',)
```

Try prediction with UDF and Client options.

Prediction with Client option:

output = predictor.predict(test, mode="client",content_type='csv')

output

The output:

[['{"projections": [{"projection": [0.003895685076713562',
  ' 0.0061229318380355835',
  ' -0.0047997236251831055',
  ' 0.004468563944101334',
  ' 0.010653555393218994',
  ' -0.016420233994722366',
  ' -0.008977413177490234',
  ' 0.03230781853199005',
  ' -0.10400402545928955',
  ' 0.17058533430099487',
  ' -0.053495049476623535',
  ' -0.3316737413406372',
  ' -0.23530852794647217',
  ' 0.17611587047576904',
  ' 0.27698516845703125',
  ' -1.7408714294433594',
  ' 1.8240509033203125',
  ' 5.520957946777344',
  ' -42.47268295288086',
  ' -280.5753173828125]}',
  ' {"projection": [-0.0140230692923069',
  ' -0.015108399093151093',
  ' 0.006812885403633118',
  ' 0.10005844384431839',
  ' 0.01633429527282715',
  ' 0.16064375638961792',
  ' 0.025818675756454468',
  ' -0.07550624012947083',
  ' 0.35952305793762207',
  ' -0.3493649363517761',
  ' -0.7519358992576599',
  ' -0.9317904710769653',
  ' 1.6834189891815186',
  ' 3.1111135482788086',
  ' 4.511608123779297',
  ' 2.9455032348632812',
  ' 7.4655609130859375',
  ' 69.96951293945312',
  ' 132.8956298828125',
  ' -144.5096435546875]}',
  ' {"projection": [0.013529308140277863',
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  ' 0.19230115413665771',
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  ' 1.1135780811309814',
  ' 0.38793373107910156',
  ' -10.569334030151367',
  ' 14.767555236816406',
  ' 93.7164306640625',
  ' -329.31646728515625',
  ' 807.404296875]}',
  ' {"projection": [0.006501875817775726',
  ' -0.006571635603904724',
  ' 0.017702504992485046',
  ' 0.00018531735986471176',
  ' -0.05006831884384155',
  ' 0.0026388168334960938',
  ' -0.019286125898361206',
  ' 0.022142529487609863',
  ' 0.036237359046936035',
  ' 0.044234514236450195',
  ' 0.313662052154541',
  ' 0.38445520401000977',
  ' 0.060160279273986816',
  ' 1.3223055601119995',
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  ' 16.1127872467041',
  ' 2.8048934936523438',
  ' 10.53080940246582',
  ' -11.20751953125',
  ' -638.9435424804688]}',
  ' {"projection": [0.022381030023097992',
  ' -0.010654665529727936',
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  ' {"projection": [-0.0057791247963905334',
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  ' {"projection": [-0.003955855965614319',
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  ' 0.002250414341688156',
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  ' 0.12275081872940063',
  ' 0.13149809837341309',
  ' -0.030761003494262695',
  ' -0.4380854368209839',
  ' 0.14547276496887207',
  ' 2.1504993438720703',
  ' -0.1942920684814453',
  ' -2.5969772338867188',
  ' -16.122665405273438',
  ' 14.819549560546875',
  ' 714.8115234375]}',
  ' {"projection": [-0.0013190172612667084',
  ' -0.005996823310852051',
  ' 0.004772022366523743',
  ' -0.004289710894227028',
  ' -0.0012611150741577148',
  ' -0.0062460340559482574',
  ' 0.006042569875717163',
  ' 0.02545413374900818',
  ' -0.10425147414207458',
  ' -0.08818507194519043',
  ' -0.04215693473815918',
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  ' -0.006448090076446533',
  ' -2.233273506164551',
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  ' 2.628972053527832',
  ' -2.0869674682617188',
  ' 2.5925235748291016',
  ' -26.846969604492188',
  ' -514.7208251953125]}',
  ' {"projection": [4.703924059867859e-05',
  ' 0.007313564419746399',
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  ' -0.011149733327329159',
  ' -0.022066742181777954',
  ' 0.03328853100538254',
  ' 0.02997729182243347',
  ' -0.05656731128692627',
  ' 0.05033278465270996',
  ' -0.06869763135910034',
  ' -0.3203369975090027',
  ' -0.15362143516540527',
  ' 0.2306044101715088',
  ' 0.6654086112976074',
  ' 0.6523857116699219',
  ' -10.483545303344727',
  ' 1.816558837890625',
  ' 4.529842376708984',
  ' 36.65260314941406',
  ' -83.1636962890625]}',
  ' {"projection": [0.013735678046941757',
  ' -0.013584569096565247',
  ' 0.0011353492736816406',
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Prediction with UDF option:

output = predictor.predict(test, mode="UDF",content_type='csv')

output

The output:

radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave_points_mean	symmetry_mean	fractal_dimension_mean	radius_se	texture_se	perimeter_se	area_se	smoothness_se	compactness_se	concavity_se	concave_points_se	symmetry_se	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave_points_worst	symmetry_worst	fractal_dimension_worst	Output
11.26	19.96	73.72	394.1	0.0802	0.1181	0.09274	0.05588	0.2595	0.06233	0.4866	1.905	2.877	34.68	0.01574	0.08262	0.08099	0.03487	0.03418	0.006517	11.86	22.33	78.27	437.6	0.1028	0.1843	0.1546	0.09314	0.2955	0.07009	{"projections": [{"projection": [0.03280608355998993, 0.04537193104624748, 0.022922128438949585, -0.04075419902801514, -0.002420380711555481, 0.06853789836168289, 0.07840818166732788, -0.012627199292182922, -0.059899359941482544, 0.07341676950454712, 0.32374298572540283, 0.39884722232818604, 0.5078041553497314, 1.8295392990112305, 1.9261531829833984, 0.6402130126953125, -0.379180908203125, 23.91823387145996, 9.325920104980469, -531.2207641601562]}]}
14.54	27.54	96.73	658.8	0.1139	0.1595	0.1639	0.07364	0.2303	0.07077	0.37	1.033	2.879	32.55	0.005607	0.0424	0.04741	0.0109	0.01857	0.005466	17.46	37.13	124.1	943.2	0.1678	0.6577	0.7026	0.1712	0.4218	0.1341	{"projections": [{"projection": [-0.0009193867444992065, 0.0017432048916816711, -0.02580226957798004, 0.031872328370809555, 0.013675473630428314, 0.046332910656929016, 0.04852712154388428, -0.08409607410430908, 0.10175716876983643, 0.10230475664138794, 0.03288924694061279, 0.5122510194778442, -0.933474063873291, 1.1933152675628662, -1.9981422424316406, 3.7658538818359375, 18.38623046875, -12.721473693847656, -31.42217254638672, 39.1029052734375]}]}
17.19	22.07	111.6	928.3	0.09726	0.08995	0.09061	0.06527	0.1867	0.0558	0.4203	0.7383	2.819	45.42	0.004493	0.01206	0.02048	0.009875	0.01144	0.001575	21.58	29.33	140.5	1436.0	0.1558	0.2567	0.3889	0.1984	0.3216	0.0757	{"projections": [{"projection": [0.024966612458229065, 0.00318821519613266, 0.011346578598022461, 0.013895014300942421, -0.035234734416007996, -0.04609498381614685, 0.022311151027679443, 0.04100880026817322, 0.0029187798500061035, 0.07415980100631714, 0.023444533348083496, -0.466349720954895, -0.44150519371032715, 0.7395851612091064, -0.10807037353515625, 1.4590539932250977, -0.01204681396484375, -30.102813720703125, -61.1049690246582, 600.102783203125]}]}
16.26	21.88	107.5	826.8	0.1165	0.1283	0.1799	0.07981	0.1869	0.06532	0.5706	1.457	2.961	57.72	0.01056	0.03756	0.05839	0.01186	0.04022	0.006187	17.73	25.21	113.7	975.2	0.1426	0.2116	0.3344	0.1047	0.2736	0.07953	{"projections": [{"projection": [-0.011844988912343979, -0.02179451286792755, -0.010386645793914795, 0.008400904946029186, 0.021189682185649872, 0.05686948448419571, 0.03128334879875183, 0.07214194536209106, -0.10330820083618164, 0.22019651532173157, 0.26925790309906006, 0.1362295150756836, -0.3018451929092407, 0.7699693441390991, 4.445920944213867, 0.6706113815307617, 1.6569290161132812, 4.774696350097656, 95.55809020996094, 155.3846435546875]}]}
12.8	17.46	83.05	508.3	0.08044	0.08895	0.0739	0.04083	0.1574	0.0575	0.3639	1.265	2.668	30.57	0.005421	0.03477	0.04545	0.01384	0.01869	0.004067	13.74	21.06	90.72	591.0	0.09534	0.1812	0.1901	0.08296	0.1988	0.07053	{"projections": [{"projection": [-0.0067559704184532166, 0.014604754745960236, 0.01802411675453186, -0.010528565384447575, 0.006629347801208496, 0.016444597393274307, -0.05062919855117798, 0.03539012372493744, 0.04209035634994507, -0.0821576714515686, 0.025034427642822266, 0.1480346918106079, 0.20329409837722778, 0.7110903263092041, 1.1703987121582031, -3.158243179321289, -0.9195556640625, 8.130979537963867, 25.84123992919922, -340.67529296875]}]}
13.56	13.9	88.59	561.3	0.1051	0.1192	0.0786	0.04451	0.1962	0.06303	0.2569	0.4981	2.011	21.03	0.005851	0.02314	0.02544	0.00836	0.01842	0.002918	14.98	17.13	101.1	686.6	0.1376	0.2698	0.2577	0.0909	0.3065	0.08177	{"projections": [{"projection": [-0.0014752037823200226, -0.0001922324299812317, -0.022153660655021667, 0.004760150797665119, 0.005818486213684082, 0.005189649760723114, 0.018100589513778687, -0.007178470492362976, -0.011704623699188232, -0.061644792556762695, -0.01568889617919922, 0.24525690078735352, -0.3842885494232178, 0.44938480854034424, -0.03153419494628906, -10.148139953613281, -1.5808181762695312, -8.009380340576172, 20.58032989501953, -231.72698974609375]}]}
15.78	22.91	105.7	782.6	0.1155	0.1752	0.2133	0.09479	0.2096	0.07331	0.552	1.072	3.598	58.63	0.008699	0.03976	0.0595	0.0139	0.01495	0.005984	20.19	30.5	130.3	1272.0	0.1855	0.4925	0.7356	0.2034	0.3274	0.1252	{"projections": [{"projection": [0.01153247058391571, 0.0015450343489646912, -0.015420198440551758, 0.03970148414373398, 0.00606585294008255, -0.06252686679363251, -0.04455491900444031, 0.0688396692276001, -0.032323360443115234, 0.432451069355011, -0.34291768074035645, -0.024285316467285156, -0.05630701780319214, -0.05635809898376465, 3.764974594116211, 0.5162458419799805, 5.687568664550781, -4.091133117675781, -98.36248779296875, 384.857666015625]}]}
19.45	19.33	126.5	1169.0	0.1035	0.1188	0.1379	0.08591	0.1776	0.05647	0.5959	0.6342	3.797	71.0	0.004649	0.018	0.02749	0.01267	0.01365	0.00255	25.7	24.57	163.1	1972.0	0.1497	0.3161	0.4317	0.1999	0.3379	0.0895	{"projections": [{"projection": [-0.003980252891778946, -0.010626271367073059, -0.006792634725570679, -0.008252562023699284, -0.017011135816574097, -0.08394775539636612, -0.004725664854049683, -0.02420821785926819, 0.012678205966949463, 0.2570701241493225, -0.06448578834533691, -0.7454780340194702, 0.19521880149841309, 1.0544383525848389, 1.7985343933105469, -1.3808135986328125, -10.75503158569336, -35.819427490234375, -137.74705505371094, 1183.744873046875]}]}
14.5	10.89	94.28	640.7	0.1101	0.1099	0.08842	0.05778	0.1856	0.06402	0.2929	0.857	1.928	24.19	0.003818	0.01276	0.02882	0.012	0.0191	0.002808	15.7	15.98	102.8	745.5	0.1313	0.1788	0.256	0.1221	0.2889	0.08006	{"projections": [{"projection": [-0.0006802044808864594, 0.0011173263192176819, -0.0009288638830184937, 0.010933362878859043, -0.012361034750938416, 0.012531246989965439, 0.027433812618255615, 0.04611416161060333, 0.01518857479095459, -0.024760305881500244, 0.03486478328704834, 0.038599371910095215, 0.06981098651885986, -1.8400537967681885, 0.7896766662597656, -11.008148193359375, -5.187385559082031, -10.673084259033203, 57.32084655761719, -139.78204345703125]}]}
15.5	21.08	102.9	803.1	0.112	0.1571	0.1522	0.08481	0.2085	0.06864	1.37	1.213	9.424	176.5	0.008198	0.03889	0.04493	0.02139	0.02018	0.005815	23.17	27.65	157.1	1748.0	0.1517	0.4002	0.4211	0.2134	0.3003	0.1048	{"projections": [{"projection": [0.013529270887374878, 0.011912591755390167, 0.015189513564109802, -0.003150681033730507, -0.01537679135799408, 0.014327986165881157, -0.038580626249313354, 0.02162991464138031, -0.010568737983703613, -0.08091932535171509, 0.19230222702026367, -0.6716643571853638, -0.362035870552063, 1.1135786771774292, 0.38793373107910156, -10.569330215454102, 14.767555236816406, 93.7164306640625, -329.31646728515625, 807.404296875]}]}

Clean up.

predictor.cloudObj.delete_model()
predictor.cloudObj.delete_endpoint()
remove_tdapi_context(tdapi_context)

Using SageMaker PCA with tdapiclient | API Integration - Using SageMaker PCA with tdapiclient - Teradata Vantage

Teradata Vantage™ - API Integration Guide for Cloud Machine Learning