Teradata Package for Python Function Reference on VantageCloud Lake - MLPR - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.

Teradata® Package for Python Function Reference on VantageCloud Lake

Deployment

VantageCloud

Edition

Lake

Product

Teradata Package for Python

Release Number

20.00.00.03

Published

December 2024

ft:locale

en-US

ft:lastEdition

2024-12-19

dita:id

TeradataPython_FxRef_Lake_2000

Product Category

Teradata Vantage

PMMLPredict using Multi-layer Perceptron model.¶

Setup¶

In [1]:

# Import required libraries
import tempfile
import getpass
from teradataml import PMMLPredict, DataFrame, load_example_data, create_context, \
db_drop_table, remove_context, save_byom, retrieve_byom, delete_byom, list_byom
from teradataml.options.configure import configure

In [2]:

# Create the connection.
host = getpass.getpass("Host: ")
username = getpass.getpass("Username: ")
password = getpass.getpass("Password: ")

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

Host: ········
Username: ········
Password: ········

Load example data¶

In [3]:

# Load the example data.
load_example_data("dataframe", "insurance")

WARNING: Skipped loading table insurance since it already exists in the database.

In [4]:

# Create teradataml DataFrames.
insurance_input = DataFrame("insurance")

In [5]:

insurance_input

Out[5]:

age	sex	bmi	children	smoker	region	charges
34	female	27.5	1	no	southwest	5003.853
34	male	25.3	2	yes	southeast	18972.495
34	female	26.73	1	no	southeast	5002.7827
34	female	33.7	1	no	southwest	5012.471
34	male	30.8	0	yes	southwest	35491.64
34	female	29.26	3	no	southeast	6184.2994
34	male	25.27	1	no	northwest	4894.7533
34	male	22.42	2	no	northeast	27375.90478
34	female	37.335	2	no	northwest	5989.52365
34	female	31.92	1	yes	northeast	37701.8768

Encode the data using One Hot Encoder and transform the data.¶

In [6]:

# import required library.
from teradataml import  OneHotEncoder, valib, Retain
configure.val_install_location = "MLDB"

In [7]:

# Perform One Hot Encoding on the categorical features.   
dc1 = OneHotEncoder(values=["southwest", "southeast", "northwest", "northeast" ], columns="region")
dc2 = OneHotEncoder(style="contrast", values="yes", reference_value=0, columns="smoker")
dc3 = OneHotEncoder(style="contrast", values="male", reference_value=0, columns="sex")

In [8]:

# Retaining Some columns unchanged.
retain = Retain(columns=["charges","bmi","children"])

In [9]:

# Execute Transform() function.
obj = valib.Transform(data=insurance_input, one_hot_encode=[dc1,dc2,dc3], key_columns="ID", retain=retain)
insurance_input = obj.result
insurance_input

Out[9]:

age	charges	bmi	children	southwest_region	southeast_region	northwest_region	northeast_region	yes_smoker	male_sex
61	13616.3586	22.04	0	0	0	0	1	0	0
61	48517.56315	36.385	1	0	0	0	1	1	0
61	13429.0354	31.16	0	0	0	1	0	0	0
61	13415.0381	21.09	0	0	0	1	0	0	0
61	28868.6639	28.31	1	0	0	1	0	1	1
61	24513.09126	25.08	0	0	1	0	0	0	0
61	46599.1084	35.86	0	0	1	0	0	1	1
61	12557.6053	31.57	0	0	1	0	0	0	1
61	30942.1918	29.92	3	0	1	0	0	1	0
61	14235.072	39.1	2	1	0	0	0	0	0

use DataFrame.sample() for splitting input data into testing and training dataset.¶

In [10]:

# Create 2 samples of input data - sample 1 will have 80% of total rows and sample 2 will have 20% of total rows. 
insurance_sample = insurance_input.sample(frac=[0.8, 0.2])
insurance_sample

Out[10]:

age	charges	bmi	children	southwest_region	southeast_region	northwest_region	northeast_region	yes_smoker	male_sex	sampleid
34	5003.853	27.5	1	1	0	0	0	0	0	1
61	30942.1918	29.92	3	0	1	0	0	1	0	1
61	13616.3586	22.04	0	0	0	0	1	0	0	1
19	16884.924	27.9	0	1	0	0	0	1	0	2
19	4687.797	28.6	5	1	0	0	0	0	0	1
40	8059.6791	28.69	3	0	0	1	0	0	0	1
40	6389.37785	26.315	1	0	0	1	0	0	1	1
40	5920.1041	36.19	0	0	1	0	0	0	0	1
19	1837.237	24.6	1	1	0	0	0	0	1	2
61	14235.072	39.1	2	1	0	0	0	0	0	2

In [11]:

# Create train dataset from sample 1 by filtering on "sampleid" and drop "sampleid" column as it is not required for training model.
insurance_train = insurance_sample[insurance_sample.sampleid == "1"].drop("sampleid", axis = 1)
insurance_train

Out[11]:

age	charges	bmi	children	southwest_region	southeast_region	northwest_region	northeast_region	yes_smoker	male_sex
34	5003.853	27.5	1	1	0	0	0	0	0
61	30942.1918	29.92	3	0	1	0	0	1	0
61	13616.3586	22.04	0	0	0	0	1	0	0
19	16884.924	27.9	0	1	0	0	0	1	0
19	1743.214	28.9	0	1	0	0	0	0	0
40	8059.6791	28.69	3	0	0	1	0	0	0
40	6389.37785	26.315	1	0	0	1	0	0	1
40	5920.1041	36.19	0	0	1	0	0	0	0
19	4687.797	28.6	5	1	0	0	0	0	0
61	14235.072	39.1	2	1	0	0	0	0	0

In [12]:

# Create test dataset from sample 2 by filtering on "sampleid" and drop "sampleid" column as it is not required for scoring.
insurance_test = insurance_sample[insurance_sample.sampleid == "2"].drop("sampleid", axis = 1)
insurance_test

Out[12]:

age	charges	bmi	children	southwest_region	southeast_region	northwest_region	yes_smoker	male_sex
34	5012.471	33.7	1	1	0	0	0	0
61	13415.0381	21.09	0	0	0	1	0	0
61	46599.1084	35.86	0	0	1	0	1	1
19	16884.924	27.9	0	1	0	0	1	0
19	1842.519	28.4	1	1	0	0	0	1
40	7682.67	33.0	3	0	1	0	0	0
40	6500.2359	29.81	1	0	1	0	0	0
40	15828.82173	29.3	4	1	0	0	0	0
19	4687.797	28.6	5	1	0	0	0	0
61	30942.1918	29.92	3	0	1	0	1	0

Prepare dataset for creating a Multi-layer Perceptron model.¶

In [13]:

# Import required libraries.
import numpy as np
from nyoka import skl_to_pmml
from sklearn.pipeline import Pipeline
from sklearn_pandas import DataFrameMapper
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler

In [14]:

# Convert teradataml dataframe to pandas dataframe.
# features : Training data.
# target : Training targets.
traid_pd = insurance_train.to_pandas()
features = traid_pd.columns.drop('charges')
target = 'charges'

Train Multi-layer Perceptron model.¶

In [15]:

# Import required libraries.
from sklearn.neural_network import MLPRegressor

In [16]:

# Generate the Multi-layer Perceptron Regressor model.
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
MLP_pipe_obj = Pipeline([
    ("mapping", DataFrameMapper([
    (['bmi'], StandardScaler()) ,
    (['male_sex', 'children'], imputer)
    ])),
    ("mlp", MLPRegressor(random_state=7, max_iter=200))
])

In [17]:

MLP_pipe_obj.fit(traid_pd[features], traid_pd[target])

C:\Users\pg255042\Anaconda3\envs\teraml\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:617: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  % self.max_iter, ConvergenceWarning)

Out[17]:

Pipeline(steps=[('mapping',
                 DataFrameMapper(drop_cols=[],
                                 features=[(['bmi'], StandardScaler()),
                                           (['male_sex', 'children'],
                                            SimpleImputer())])),
                ('mlp', MLPRegressor(random_state=7))])

Save the model in PMML format.¶

In [18]:

temp_dir = tempfile.TemporaryDirectory()
model_file_path = f"{temp_dir.name}/insurance_db_MLP_model.pmml"

In [19]:

skl_to_pmml(MLP_pipe_obj, features, target, model_file_path)

Save the model in Vantage.¶

In [20]:

# Save the PMML Model in Vantage.
save_byom("pmml_MLP_insurance", model_file_path, "byom_models")

Created the model table 'byom_models' as it does not exist.
Model is saved.

List the models from Vantage¶

In [21]:

# List the PMML Models in Vantage.
list_byom("byom_models")

                                       model
model_id                                    
pmml_MLP_insurance  b'3C3F786D6C20766572...'

Retrieve the model from Vantage.¶

In [22]:

# Retrieve the model from table "byom_models", using the model id 'pmml_MLP_insurance'.
modeldata = retrieve_byom("pmml_MLP_insurance", "byom_models")

In [23]:

configure.byom_install_location = getpass.getpass("byom_install_location: ")

byom_install_location: ········

Score the model.¶

In [24]:

# Perform prediction using PMMLPredict() and the PMML model stored in Vantage.
result = PMMLPredict(
                    modeldata = modeldata,
                    newdata = insurance_test,
                    accumulate = ['male_sex', 'bmi', 'children'],
                    overwrite_cached_models = '*',
                    )

In [25]:

# Print the query.
print(result.show_query())

SELECT * FROM "mldb".PMMLPredict(
	ON "MLDB"."ml__select__164656615949361" AS InputTable
	PARTITION BY ANY 
	ON (select model_id,model from "MLDB"."ml__filter__164661377060078") AS ModelTable
	DIMENSION
	USING
	Accumulate('male_sex','bmi','children')
	OverwriteCachedModel('*')
) as sqlmr

In [26]:

# Print the result.
result.result

Out[26]:

male_sex	bmi	children	prediction	json_report
1	25.3	2	866.2542248842337	{"predicted_charges":866.2542248842337}
1	33.915	0	643.1867081097179	{"predicted_charges":643.1867081097179}
1	33.535	0	629.8514774182736	{"predicted_charges":629.8514774182736}
1	20.425	0	169.78601856344287	{"predicted_charges":169.78601856344287}
0	32.11	0	319.8599458312356	{"predicted_charges":319.8599458312356}
0	28.69	3	987.9297200599833	{"predicted_charges":987.9297200599833}
1	26.315	1	639.1777110980797	{"predicted_charges":639.1777110980797}
1	29.355	1	745.8595566296347	{"predicted_charges":745.8595566296347}
0	28.4	1	452.3615998451724	{"predicted_charges":452.3615998451724}
1	43.4	0	976.0410847106386	{"predicted_charges":976.0410847106386}

Cleanup.¶

In [27]:

# Delete the model from table "byom_models", using the model id 'pmml_MLP_insurance'.
delete_byom("pmml_MLP_insurance", "byom_models")

Model is deleted.

In [28]:

# Drop models table.
db_drop_table("byom_models")

Out[28]:

True

In [29]:

# Drop input data tables.
db_drop_table("insurance")

Out[29]:

True

In [30]:

# One must run remove_context() to close the connection and garbage collect internally generated objects.
remove_context()

Out[30]:

True

In [ ]: