Example 10-4 Using the oml.automl.ModelSelection Class

This example creates an oml.automl.ModelSelection object and then uses the object to select and tune the best model.

import oml
from oml import automl
import pandas as pd
from sklearn import datasets

# Load the breast cancer data set.
bc = datasets.load_breast_cancer()
bc_data = bc.data.astype(float)
X = pd.DataFrame(bc_data, columns = bc.feature_names)
y = pd.DataFrame(bc.target, columns = ['TARGET'])

# Create the database table BreastCancer.
oml_df = oml.create(pd.concat([X, y], axis=1), 
                    table = 'BreastCancer')

# Split the data set into training and test data.
train, test = oml_df.split(ratio=(0.8, 0.2), seed = 1234)
X, y = train.drop('TARGET'), train['TARGET']
X_test, y_test = test.drop('TARGET'), test['TARGET']

# Create an automated model selection object with f1_macro as the 
# score_metric argument.
ms = automl.ModelSelection(mining_function='classification', 
                           score_metric='f1_macro', parallel=4)

# Run model selection to get the top (k=1) predicted algorithm 
# (defaults to the tuned model).
select_model = ms.select(X, y, k=1)

# Show the selected and tuned model.
select_model

# Score on the selected and tuned model.
"{:.2}".format(select_model[0].score(X_test, y_test))

# Drop the database table.
oml.drop('BreastCancer')

Listing for This Example

>>> import oml
>>> from oml import automl
>>> import pandas as pd
>>> from sklearn import datasets
>>>
>>> # Load the breast cancer data set.
... bc = datasets.load_breast_cancer()
>>> bc_data = bc.data.astype(float)
>>> X = pd.DataFrame(bc_data, columns = bc.feature_names)
>>> y = pd.DataFrame(bc.target, columns = ['TARGET'])
>>>
>>> # Create the database table BreastCancer.
>>> oml_df = oml.create(pd.concat([X, y], axis=1),
...                     table = 'BreastCancer')
>>> 
>>> # Split the data set into training and test data.
... train, test = oml_df.split(ratio=(0.8, 0.2), seed = 1234)
>>> X, y = train.drop('TARGET'), train['TARGET']
>>> X_test, y_test = test.drop('TARGET'), test['TARGET']
>>>
>>> # Create an automated model selection object with f1_macro as the 
... # score_metric argument.
... ms = automl.ModelSelection(mining_function='classification', 
...                            score_metric='f1_macro', parallel=4)
>>>
>>> # Run the model selection to get the top (k=1) predicted algorithm 
... # (defaults to the tuned model).
... select_model = ms.select(X, y, k=1)
>>> 
>>> # Show the selected and tuned model.
... select_model

Algorithm Name: Support Vector Machine

Mining Function: CLASSIFICATION

Target: TARGET

Settings: 
                    setting name                 setting value
0                      ALGO_NAME  ALGO_SUPPORT_VECTOR_MACHINES
1          CLAS_WEIGHTS_BALANCED                           OFF
2                   ODMS_DETAILS                  ODMS_DISABLE
3   ODMS_MISSING_VALUE_TREATMENT       ODMS_MISSING_VALUE_AUTO
4                  ODMS_SAMPLING         ODMS_SAMPLING_DISABLE
5                      PREP_AUTO                            ON
6         SVMS_COMPLEXITY_FACTOR                            10
7            SVMS_CONV_TOLERANCE                         .0001
8           SVMS_KERNEL_FUNCTION                 SVMS_GAUSSIAN
9                SVMS_NUM_PIVOTS                           ...
10                  SVMS_STD_DEV            5.3999999999999995

Attributes:
area error
compactness error
concave points error
concavity error
fractal dimension error
mean area
mean compactness
mean concave points
mean concavity
mean fractal dimension
mean perimeter
mean radius
mean smoothness
mean symmetry
mean texture
perimeter error
radius error
smoothness error
symmetry error
texture error
worst area
worst compactness
worst concave points
worst concavity
worst fractal dimension
worst perimeter
worst radius
worst smoothness
worst symmetry
worst texture
Partition: NO

>>>
>>> # Score on the selected and tuned model.
... "{:.2}".format(select_model[0].score(X_test, y_test))
'0.99'
>>>
>>> # Drop the database table.
... oml.drop('BreastCancer')