PMMLPredict() with Vector Machine model using classification¶
Setup¶
In [1]:
# Import required libraries
import getpass
import tempfile
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 and use sample() for splitting input data into testing and training dataset.¶
In [3]:
# Load the example data.
load_example_data("byom", "iris_input")
iris_input = DataFrame("iris_input")
In [4]:
# Create 2 samples of input data - sample 1 will have 80% of total rows and sample 2 will have 20% of total rows.
iris_sample = iris_input.sample(frac=[0.8, 0.2])
In [5]:
# Create train dataset from sample 1 by filtering on "sampleid" and drop "sampleid" column as it is not required for training model.
iris_train = iris_sample[iris_sample.sampleid == "1"].drop("sampleid", axis = 1)
iris_train
Out[5]:
| id | sepal_length | sepal_width | petal_length | petal_width | species |
|---|---|---|---|---|---|
| 139 | 6.0 | 3.0 | 4.8 | 1.8 | 3 |
| 38 | 4.9 | 3.6 | 1.4 | 0.1 | 1 |
| 78 | 6.7 | 3.0 | 5.0 | 1.7 | 2 |
| 19 | 5.7 | 3.8 | 1.7 | 0.3 | 1 |
| 36 | 5.0 | 3.2 | 1.2 | 0.2 | 1 |
| 40 | 5.1 | 3.4 | 1.5 | 0.2 | 1 |
| 80 | 5.7 | 2.6 | 3.5 | 1.0 | 2 |
| 118 | 7.7 | 3.8 | 6.7 | 2.2 | 3 |
| 99 | 5.1 | 2.5 | 3.0 | 1.1 | 2 |
| 61 | 5.0 | 2.0 | 3.5 | 1.0 | 2 |
In [6]:
# Create test dataset from sample 2 by filtering on "sampleid" and drop "sampleid" column as it is not required for scoring.
iris_test = iris_sample[iris_sample.sampleid == "2"].drop("sampleid", axis = 1)
iris_test
Out[6]:
| id | sepal_length | sepal_width | petal_length | petal_width | species |
|---|---|---|---|---|---|
| 87 | 6.7 | 3.1 | 4.7 | 1.5 | 2 |
| 18 | 5.1 | 3.5 | 1.4 | 0.3 | 1 |
| 98 | 6.2 | 2.9 | 4.3 | 1.3 | 2 |
| 122 | 5.6 | 2.8 | 4.9 | 2.0 | 3 |
| 70 | 5.6 | 2.5 | 3.9 | 1.1 | 2 |
| 40 | 5.1 | 3.4 | 1.5 | 0.2 | 1 |
| 35 | 4.9 | 3.1 | 1.5 | 0.2 | 1 |
| 54 | 5.5 | 2.3 | 4.0 | 1.3 | 2 |
| 30 | 4.7 | 3.2 | 1.6 | 0.2 | 1 |
| 133 | 6.4 | 2.8 | 5.6 | 2.2 | 3 |
Train SVC model¶
In [7]:
# Import required libraries.
import numpy as np
from sklearn import tree
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
from sklearn.svm import SVC
In [8]:
# Convert teradataml dataframe to pandas dataframe.
# features : Training data.
# target : Training targets.
traid_pd = iris_train.to_pandas()
features = traid_pd.columns.drop('species')
target = 'species'
In [9]:
# Generate the SVC model
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
svc_pipe_obj = Pipeline([
("mapping", DataFrameMapper([
(['sepal_length', 'sepal_width'], StandardScaler()) ,
(['petal_length', 'petal_width'], imputer)
])),
("svc", SVC(gamma='auto',kernel='linear'))
])
In [10]:
svc_pipe_obj.fit(traid_pd[features], traid_pd[target])
Out[10]:
Pipeline(steps=[('mapping',
DataFrameMapper(drop_cols=[],
features=[(['sepal_length', 'sepal_width'],
StandardScaler()),
(['petal_length', 'petal_width'],
SimpleImputer())])),
('svc', SVC(gamma='auto', kernel='linear'))])
Save the model in PMML format.¶
In [11]:
temp_dir = tempfile.TemporaryDirectory()
model_file_path = f"{temp_dir.name}/iris_db_svc_model.pmml"
In [12]:
skl_to_pmml(svc_pipe_obj, features, target, model_file_path)
Save the model in Vantage.¶
In [13]:
# Save the PMML Model in Vantage.
save_byom("pmml_svc_iris", model_file_path, "byom_models")
Created the model table 'byom_models' as it does not exist. Model is saved.
List the model from Vantage.¶
In [14]:
# List the PMML Models in Vantage.
list_byom("byom_models")
model model_id pmml_svc_iris b'3C3F786D6C20766572...'
Retrieve the model from Vantage.¶
In [15]:
# Retrieve the model from table "byom_models", using the model id 'pmml_svc_iris'.
modeldata = retrieve_byom("pmml_svc_iris", "byom_models")
Set "configure.byom_install_location" to the database where BYOM functions are installed.¶
In [16]:
configure.byom_install_location = getpass.getpass("byom_install_location: ")
byom_install_location: ········
Score the model.¶
In [17]:
# Perform prediction using PMMLPredict() and the PMML model stored in Vantage.
result = PMMLPredict(
modeldata = modeldata,
newdata = iris_test,
accumulate = ['id', 'sepal_length', 'petal_length'],
overwrite_cached_models = '*',
)
In [18]:
# Print the query.
print(result.show_query())
SELECT * FROM "mldb".PMMLPredict(
ON "MLDB"."ml__select__1644981761551165" AS InputTable
PARTITION BY ANY
ON (select model_id,model from "MLDB"."ml__filter__1644981845854836") AS ModelTable
DIMENSION
USING
Accumulate('id','sepal_length','petal_length')
OverwriteCachedModel('*')
) as sqlmr
In [19]:
# Print the result.
result.result
Out[19]:
| id | sepal_length | petal_length | prediction | json_report |
|---|---|---|---|---|
| 131 | 7.4 | 6.1 | 3 | {"probability_1":0.0,"predicted_species":3,"probability_2":0.3333333333333333,"probability_3":0.6666666666666666} |
| 34 | 5.5 | 1.4 | 1 | {"probability_1":0.6666666666666666,"predicted_species":1,"probability_2":0.3333333333333333,"probability_3":0.0} |
| 28 | 5.2 | 1.5 | 1 | {"probability_1":0.6666666666666666,"predicted_species":1,"probability_2":0.3333333333333333,"probability_3":0.0} |
| 40 | 5.1 | 1.5 | 1 | {"probability_1":0.6666666666666666,"predicted_species":1,"probability_2":0.3333333333333333,"probability_3":0.0} |
| 55 | 6.5 | 4.6 | 2 | {"probability_1":0.0,"predicted_species":2,"probability_2":0.6666666666666666,"probability_3":0.3333333333333333} |
| 53 | 6.9 | 4.9 | 2 | {"probability_1":0.0,"predicted_species":2,"probability_2":0.6666666666666666,"probability_3":0.3333333333333333} |
| 30 | 4.7 | 1.6 | 1 | {"probability_1":0.6666666666666666,"predicted_species":1,"probability_2":0.3333333333333333,"probability_3":0.0} |
| 70 | 5.6 | 3.9 | 2 | {"probability_1":0.0,"predicted_species":2,"probability_2":0.6666666666666666,"probability_3":0.3333333333333333} |
| 120 | 6.0 | 5.0 | 3 | {"probability_1":0.0,"predicted_species":3,"probability_2":0.3333333333333333,"probability_3":0.6666666666666666} |
| 17 | 5.4 | 1.3 | 1 | {"probability_1":0.6666666666666666,"predicted_species":1,"probability_2":0.3333333333333333,"probability_3":0.0} |
Cleanup.¶
In [20]:
# Delete the saved Model.
delete_byom("pmml_svc_iris", table_name="byom_models")
Model is deleted.
In [21]:
# Drop model table.
db_drop_table("byom_models")
Out[21]:
True
In [22]:
# Drop input data table.
db_drop_table("iris_input")
Out[22]:
True
In [23]:
# One must run remove_context() to close the connection and garbage collect internally generated objects.
remove_context()
Out[23]:
True
In [ ]: