Introducing the ml_base Package

The ml_base package defines a common set of base classes that are useful for working with machine learning model prediction code. The base classes define a set of interfaces that help to write ML code that is reusable and testable. The core of the ml_base package is the MLModel class which defines a simple interface for doing machine learning model prediction. In this blog post, we’ll show how to use the MLModel class.

To get started we’ll install the ml_base package:

pip install ml_base

Creating a Simple Model

pip install scikit-learn

Now we can write some code to train a model:

from sklearn import datasets
from sklearn import svm
import pickle
# loading the Iris dataset
iris = datasets.load_iris()
# instantiating an SVM model from scikit-learn
svm_model = svm.SVC(gamma=1.0, C=1.0)
# fitting the model
svm_model.fit(iris.data[:-1], iris.target[:-1])
# serializing the model and saving it
file = open(“svc_model.pickle”, ‘wb’)
pickle.dump(svm_model, file)
file.close()

Creating a Wrapper Class for Your Model

import os
from numpy import array

class IrisModel(object):
def __init__(self):
dir_path = os.path.abspath(‘’)
file = open(os.path.join(dir_path, “svc_model.pickle”), ‘rb’)
self._svm_model = pickle.load(file)
file.close()
def predict(self, data: dict):
X = array([data[“sepal_length”],
data[“sepal_width”],
data[“petal_length”],
data[“petal_width”]]).reshape(1, -1)
y_hat = int(self._svm_model.predict(X)[0])
targets = [‘setosa’, ‘versicolor’, ‘virginica’]
species = targets[y_hat]
return {“species”: species}

The class above wraps the pickled model object and makes the model easier to use by converting the inputs and outputs.

To use the model, all we need to do is this:

model = IrisModel()prediction = model.predict(data={
“sepal_length”:1.0,
“sepal_width”:1.1,
“petal_length”: 1.2,
“petal_width”: 1.3})
prediction

The result is

{‘species’: ‘virginica’}

Creating an MLModel Class for Your Model

from ml_base import MLModel
class IrisModel(MLModel):
@property
def display_name(self):
return “Iris Model”
@property
def qualified_name(self):
return “iris_model”
@property
def description(self):
return “A model to predict the species of a flower based on its measurements.”
@property
def version(self):
return “1.0.0”

@property
def input_schema(self):
raise NotImplementedError()
@property
def output_schema(self):
raise NotImplementedError()
def __init__(self):
dir_path = os.path.abspath(‘’)
file = open(os.path.join(dir_path, “svc_model.pickle”), ‘rb’)
self._svm_model = pickle.load(file)
file.close()
def predict(self, data: dict):
X = array([data[“sepal_length”],
data[“sepal_width”],
data[“petal_length”],
data[“petal_width”]]).reshape(1, -1)
y_hat = int(self._svm_model.predict(X)[0])
targets = [‘setosa’, ‘versicolor’, ‘virginica’]
species = targets[y_hat]
return {“species”: species}

The MLModel base class defines a set of properties that must be provided by any class that inherits from it. Because the IrisModel class now provides this metadata about the model, we can access it directly from the model object like this:

model = IrisModel()
print(model.qualified_name)

The result is:

iris_model

The qualified name of the model uniquely identifies the instance of the model within the system. Right now the qualified name is hardcoded in the code of the model’s class, but this can be made more dynamic in the future. The qualified name should also be a string that is easy to embed in a URL, so it shouldn’t have spaces or special characters.

The model’s display name is also available from the model object:

print(model.display_name)

The result is:

Iris Model

The display name of a model should be a string that looks good in a user interface.

The model description is also available from the model object:

print(model.description)

The result is:

A model to predict the species of a flower based on its measurements.

The model version is also available as a string from the model object:

print(model.version)

The result is:

1.0.0

As you can see, we didn’t implement the input_schema and output_schema properties above, we’ll add those next.

Adding Schemas to Your Model

from pydantic import BaseModel, Field
from pydantic import ValidationError
from enum import Enum

class ModelInput(BaseModel):
sepal_length: float = Field(
gt=5.0, lt=8.0,
description=”The length of the sepal of the flower.”)
sepal_width: float = Field(
gt=2.0, lt=6.0,
description=”The width of the sepal of the flower.”)
petal_length: float = Field(
gt=1.0, lt=6.8,
description=”The length of the petal of the flower.”)
petal_width: float = Field(
gt=0.0, lt=3.0,
description=”The width of the petal of the flower.”)

class Species(str, Enum):
iris_setosa = “Iris setosa”
iris_versicolor = “Iris versicolor”
iris_virginica = “Iris virginica”

class ModelOutput(BaseModel):
species: Species = Field(
description=”The predicted species of the flower.”)

The ModelInput class inherits from the pydantic BaseModel class and it defines four required fields, all of them floating point numbers. The pydantic package allows for defining upper bounds and lower bounds for the values accepted by each field, and also a description for the field.

The ModelOutput is made up of a single field, which is an enumerated string that contains the predicted species of the flower.

Now that we have the ModelInput and ModelOutput schemas defined as pydantic BaseModel classes, we’ll add them to the IrisModel class by returning them from the input_schema and output_schema properties:

from ml_base.ml_model import MLModel,   
MLModelSchemaValidationException

class IrisModel(MLModel):
@property
def display_name(self):
return “Iris Model”
@property
def qualified_name(self):
return “iris_model”
@property
def description(self):
return “A model to predict the species of a flower based on its
measurements.”
@property
def version(self):
return “1.0.0”
@property
def input_schema(self):
return ModelInput
@property
def output_schema(self):
return ModelOutput
def __init__(self):
dir_path = os.path.abspath(‘’)
with open(os.path.join(dir_path, “svc_model.pickle”), ‘rb’)
as f:
self._svm_model = pickle.load(f)

def predict(self, data: dict):
model_input = ModelInput(**data)
# creating a numpy array using the fields in the input object
X = array([model_input.sepal_length,
model_input.sepal_width,
model_input.petal_length,
model_input.petal_width]).reshape(1, -1)
# making a prediction, at this point it’s a number
y_hat = int(self._svm_model.predict(X)[0])
# converting the prediction from a number to a string
targets = [“Iris setosa”, “Iris versicolor”, “Iris virginica”]
species = targets[y_hat]
# returning the prediction inside an object
return ModelOutput(species=species)

Notice that we are also using the pydantic models to validate the input before prediction and to create an object that will be returned from the model’s predict() method.

If we use the model class now, we’ll get this result:

model = IrisModel()prediction = model.predict(data={
“sepal_length”: 6.0, “sepal_width”: 2.1,
“petal_length”: 1.2, “petal_width”: 1.3})
print(prediction)

The result was:

ModelOutput(species=<Species.iris_virginica: ‘Iris virginica’>)

By adding input and output schemas to the model, we can automate many other operations later. Also, we can query the model object itself for the schema. The pydantic package is able to create JSON schema from the fields in the input and output schema objects of the model:

model = IrisModel()
model.input_schema.schema()

The result was:

{
‘title’: ‘ModelInput’,
‘type’: ‘object’,
‘properties’: {
‘sepal_length’: {
‘title’: ‘Sepal Length’,
‘description’: ‘The length of the sepal of the flower.’,
‘exclusiveMinimum’: 5.0,
‘exclusiveMaximum’: 8.0,
‘type’: ‘number’
},
‘sepal_width’: {
‘title’: ‘Sepal Width’,
‘description’: ‘The width of the sepal of the flower.’,
‘exclusiveMinimum’: 2.0,
‘exclusiveMaximum’: 6.0,
‘type’: ‘number’
},
‘petal_length’: {
‘title’: ‘Petal Length’,
‘description’: ‘The length of the petal of the flower.’,
‘exclusiveMinimum’: 1.0,
‘exclusiveMaximum’: 6.8,
‘type’: ‘number’
},
‘petal_width’: {
‘title’: ‘Petal Width’,
‘description’: ‘The width of the petal of the flower.’,
‘exclusiveMinimum’: 0.0,
‘exclusiveMaximum’: 3.0,
‘type’: ‘number’
}
},
‘required’: [
‘sepal_length’,
‘sepal_width’,
‘petal_length’,
‘petal_width’
]
}

The output schema can be accessed like this:

model.output_schema.schema()

The result was:

{
‘title’: ‘ModelOutput’,
‘type’: ‘object’,
‘properties’: {
‘species’: {‘$ref’: ‘#/definitions/Species’}
},
‘required’: [‘species’],
‘definitions’: {
‘Species’: {
‘title’: ‘Species’,
‘description’: ‘An enumeration.’,
‘enum’: [‘Iris setosa’, ‘Iris versicolor’, ‘Iris virginica’],
‘type’: ‘string’
}
}
}

Although it is not required to use the pydantic package to create model schemas, it is recommended. The pydantic package is installed as a dependency of the ml_base package.

Using the ModelManager Class

from ml_base.utilities import ModelManagermodel_manager = ModelManager()

Because it is a singleton object, a reference to the same object is returned no matter how many times we instantiate it:

print(id(model_manager))
another_model_manager = ModelManager()
print(id(another_model_manager))

The result is:

4851978496
4851978496

You can add model instances to the ModelManager singleton by asking it to instantiate the model class:

model_manager.load_model(“__main__.IrisModel”)

The load_model() method is able to find the MLModel class that we defined above and instantiate it, after that it stores a reference to the instance internally.

The ModelManager is also able to save references to model instances that were instantiated in some other way by using the add_model() method:

another_iris_model = IrisModel()
try:
model_manager.add_model(another_iris_model)
except ValueError as e:
print(e)

The result is:

A model with the same qualified name is already in the ModelManager singleton.

In this case, the ModelManager did not save the instance of the IrisModel because we already had an instance of the model. The models are uniquely identified by their qualified name properties.

The ModelManager instance can list the models that it contains with the get_models() method, the details of the instance of IrisModel that we just created are returned:

model_manager.get_models()

The result is:

[
{
‘display_name’: ‘Iris Model’,
‘qualified_name’: ‘iris_model’,
‘description’: ‘A model to predict the species of a flower based on its measurements.’,
‘version’: ‘1.0.0’
}
]

The ModelManager instance can return the metadata of any of the models. The metadata includes the input and output schemas as well:

model_manager.get_model_metadata(“iris_model”)

The result is:

{
‘display_name’: ‘Iris Model’,
‘qualified_name’: ‘iris_model’,
‘description’: ‘A model to predict the species of a flower based on its measurements.’,
‘version’: ‘1.0.0’,
‘input_schema’: {
‘title’: ‘ModelInput’,
‘type’: ‘object’,
‘properties’: {
‘sepal_length’: {
‘title’: ‘Sepal Length’,
‘description’: ‘The length of the sepal of the flower.’,
‘exclusiveMinimum’: 5.0,
‘exclusiveMaximum’: 8.0,
‘type’: ‘number’
},
‘sepal_width’: {
‘title’: ‘Sepal Width’,
‘description’: ‘The width of the sepal of the flower.’,
‘exclusiveMinimum’: 2.0,
‘exclusiveMaximum’: 6.0,
‘type’: ‘number’
},
‘petal_length’: {
‘title’: ‘Petal Length’,
‘description’: ‘The length of the petal of the flower.’,
‘exclusiveMinimum’: 1.0,
‘exclusiveMaximum’: 6.8,
‘type’: ‘number’
},
‘petal_width’: {
‘title’: ‘Petal Width’,
‘description’: ‘The width of the petal of the flower.’,
‘exclusiveMinimum’: 0.0,
‘exclusiveMaximum’: 3.0,
‘type’: ‘number’
}
},
‘required’: [
‘sepal_length’,
‘sepal_width’,
‘petal_length’,
‘petal_width’
]
},
‘output_schema’: {
‘title’: ‘ModelOutput’,
‘type’: ‘object’,
‘properties’: {
‘species’: {
‘$ref’: ‘#/definitions/Species’
}
},
‘required’: [‘species’],
‘definitions’: {
‘Species’: {
‘title’: ‘Species’,
‘description’: ‘An enumeration.’,
‘enum’: [
‘Iris setosa’,
‘Iris versicolor’,
‘Iris virginica’
],
‘type’: ‘string’
}
}
}
}

The ModelManager can return a reference to the instance of any model that it is holding:

iris_model = model_manager.get_model(“iris_model”)
print(iris_model.display_name)

The result is:

Iris Model

The instance is identified by the qualified name of the model.

Lastly, a model instance can be removed by calling the remove_model() method:

model_manager.remove_model(“iris_model”)
model_manager.get_models()

The result is:

[]

To clear the ModelManager instance, you can call the clear_instance() method:

model_manager.clear_instance()

To create a new singleton you have to instantiate the ModelManager again:

model_manager = ModelManager()

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