smart-fruit 1.0.2

A Python schema-based machine learning library

A Python schema-based machine learning library, wrapping scikit-learn, for creating quick and easy machine learning models.

Usage

Create and use a machine learning model in 3 steps:

1. Create a schema representing your input and output features.

2. Train a model from your data.

3. Make predictions from your model.

Example

To get a feel for the library, consider the classic Iris dataset, where we predict the class of iris plant from measurements of the sepal, and petal.

First, we create a schema describing our inputs and outputs:

from smart_fruit import Model
from smart_fruit.feature_types import Number, Label


class Iris(Model):
    class Input:
        sepal_length_cm = Number()
        sepal_width_cm = Number()
        petal_length_cm = Number()
        petal_width_cm = Number()

    class Output:
        iris_class = Label(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])

Then, we train a model:

model = Iris.train(Iris.features_from_csv('iris_data.csv'))

with data file iris_data.csv

sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,iris_class
5.1,3.5,1.4,0.2,Iris-setosa
...

Finally, we use our new model to make predictions:

for prediction in model.predict([Iris.Input(5.1, 3.5, 1.4, 0.2)]):
    print(prediction.iris_class)

Reference

Models

• Model.Input, Model.Output

  Model.Input and Model.Output are the schemas for defining your input and output features. Define them as classes with FeatureType attributes.

  eg. Consider the Iris class defined above.

  These classes can then be used to create objects representing the appropriate collections of features.

  eg.

  >>> iris_input = Iris.Input(5.1, 3.5, 1.4, 0.2)
  >>> iris_input
  Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2)
  >>> iris_input.sepal_length
  5.1
  
  >>> Iris.Input.from_json({'sepal_length_cm': 5.1, 'sepal_width_cm': 3.5, 'petal_length_cm': 1.4, 'petal_width_cm': 0.2})
  Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2)

• Model.features_from_list(lists), Model.input_features_from_list(lists)

  Deserialize an iterable of lists into an iterable of the appropriate feature objects.

  eg.

  >>> list(Iris.features_from_list([[5.1, 3.5, 1.4, 0.2, 'Iris-setosa']]))
  [(Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2), Output(iris_class='Iris-setosa'))]
  
  >>> list(Iris.input_features_from_list([[5.1, 3.5, 1.4, 0.2]]))
  [Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2)]

• Model.features_from_json(json), Model.input_features_from_json(json)

  Deserialize an iterable of dictionaries into an iterable of the appropriate feature objects.

  eg.

  >>> list(Iris.features_from_json([{'sepal_length_cm': 5.1, 'sepal_width_cm': 3.5, 'petal_length_cm': 1.4, 'petal_width_cm': 0.2, 'iris_class': 'Iris-setosa'}]))
  [(Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2), Output(iris_class='Iris-setosa'))]
  
  >>> list(Iris.input_features_from_json([{'sepal_length_cm': 5.1, 'sepal_width_cm': 3.5, 'petal_length_cm': 1.4, 'petal_width_cm': 0.2}]))
  [Input(sepal_length_cm=5.1, sepal_width_cm=3.5, petal_length_cm=1.4, petal_width_cm=0.2)]

• Model.features_from_csv(csv_path), Model.input_features_from_csv(csv_path)

  Take a path to a CSV file, and deserialize it into an iterable of the appropriate feature objects.

  eg.

  >>> list(Iris.features_from_csv('iris_data.csv'))
  [(Input(sepal_length_cm='5.1', sepal_width_cm='3.5', petal_length_cm='1.4', petal_width_cm='0.2'), Output(iris_class='Iris-setosa')), ...]
  
  >>> list(Iris.input_features_from_csv('iris_data.csv'))
  [Input(sepal_length_cm='5.1', sepal_width_cm='3.5', petal_length_cm='1.4', petal_width_cm='0.2'), ...]

• Model.train(features)

  Train a new model on the given iterable of input/output pairs.

  eg.

  >>> iris_model = Iris.train([(Iris.Input(5.1, 3.5, 1.4, 0.2), Iris.Output('Iris-setosa'))])

• model.predict(input_features)

  Predict the outputs for a given iterable of inputs.

  eg.

  >>> list(iris_model.predict([Iris.Input(5.1, 3.5, 1.4, 0.2)]))
  [Output(iris_class='Iris-setosa')]

Feature Types

Smart Fruit recognizes the following data types for input and output features. Custom types may be made by extending the FeatureType class.

• Number() - A real-valued feature.

  eg. 0, 1, 3.141592, -17, …

• Label(labels) - An enumerated feature, which may take one of a pre-defined list of available values.

  eg. For labels = ['red', 'green', 'blue'], our label may take the value 'red', but not 'purple'.

Requirements

Smart Fruit requires Python 3.6+, and uses scikit-learn, scipy, and pandas.