Pipelines

EvalML pipelines represent a sequence of operations to be applied to data, where each operation is either a data transformation or an ML modeling algorithm.

A pipeline class holds a combination of one or more components, which will be applied to new input data in sequence.

Each component and pipeline class supports a set of parameters which configure its behavior. The AutoML search process seeks to find the combination of pipeline structure and pipeline parameters which perform the best on the data.

Class Definition

Pipeline definitions must inherit from the proper pipeline base class, RegressionPipeline, BinaryClassificationPipeline or MulticlassClassificationPipeline. They must also include a component_graph list as a class variable containing the sequence of components to be fit and evaluated. Each component in the graph can be provided as either a string name or as a reference to the component class.

[1]:
from evalml.pipelines import MulticlassClassificationPipeline

class CustomMulticlassClassificationPipeline(MulticlassClassificationPipeline):
    component_graph = ['Simple Imputer', 'Random Forest Classifier']
2020-08-06 20:00:05,244 featuretools - WARNING    Featuretools failed to load plugin nlp_primitives from library nlp_primitives. For a full stack trace, set logging to debug.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/stable/lib/python3.7/site-packages/evalml/pipelines/components/transformers/preprocessing/text_featurizer.py:31: RuntimeWarning: No text columns were given to TextFeaturizer, component will have no effect
  warnings.warn("No text columns were given to TextFeaturizer, component will have no effect", RuntimeWarning)

If you’re using your own custom components you can refer to them like so:

[2]:
from evalml.pipelines.components import Transformer

class NewTransformer(Transformer):
    name = 'New Transformer'
    hyperparameter_ranges = {
        "parameter_1":['a', 'b', 'c']
    }

    def __init__(self, parameter_1, random_state):
        transformer = ThirdPartyTransformer(parameter_1)
        parameters = {"parameter_1": parameter_1}
        super().__init__(parameters=parameters,
                         component_obj=transformer,
                         random_state=random_state)

class CustomComponentMulticlassClassificationPipeline(MulticlassClassificationPipeline):
    component_graph = [NewTransformer, 'Random Forest Classifier']

Pipeline Usage

All pipelines define the following methods:

  • fit fits each component on the provided training data, in order.

  • predict computes the predictions of the component graph on the provided data.

  • score computes the value of an objective on the provided data.

[3]:
from evalml.demos import load_wine
X, y = load_wine()

pipeline = CustomMulticlassClassificationPipeline({})
pipeline.fit(X, y)
print(pipeline.predict(X))
print(pipeline.score(X, y, objectives=['log_loss_multi']))
0      class_0
1      class_0
2      class_0
3      class_0
4      class_0
        ...
173    class_2
174    class_2
175    class_2
176    class_2
177    class_2
Length: 178, dtype: object
OrderedDict([('Log Loss Multiclass', 0.04132737017536148)])

Custom Name

By default, a pipeline class’s name property is the result of adding spaces between each Pascal case capitalization in the class name. E.g. LogisticRegressionPipeline.name will return ‘Logistic Regression Pipeline’. Therefore, we suggest custom pipelines use Pascal case for their class names.

If you’d like to override the pipeline classes name attribute so it isn’t derived from the class name, you can set the custom_name attribute, like so:

[4]:
from evalml.pipelines import MulticlassClassificationPipeline

class CustomPipeline(MulticlassClassificationPipeline):
    component_graph = ['Simple Imputer', 'One Hot Encoder', 'Logistic Regression Classifier']
    custom_name = 'A custom pipeline name'

print(CustomPipeline.name)
A custom pipeline name

Override Component Hyperparameter Ranges

To specify custom hyperparameter ranges, set the custom_hyperparameters property to be a dictionary where each key-value pair consists of a parameter name and range. AutoML will use this dictionary to override the hyperparameter ranges collected from each component in the component graph.

[5]:
class CustomPipeline(MulticlassClassificationPipeline):
    component_graph = ['Simple Imputer', 'One Hot Encoder', 'Standard Scaler', 'Logistic Regression Classifier']

print("Without custom hyperparameters:")
print(CustomPipeline.hyperparameters)

class CustomPipeline(MulticlassClassificationPipeline):
        component_graph = ['Simple Imputer', 'One Hot Encoder', 'Standard Scaler', 'Logistic Regression Classifier']
        custom_hyperparameters = {
        'Simple Imputer' : {
            'impute_strategy': ['most_frequent']
        }
    }

print()
print("With custom hyperparameters:")
print(CustomPipeline.hyperparameters)
Without custom hyperparameters:
{'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}, 'One Hot Encoder': {}, 'Standard Scaler': {}, 'Logistic Regression Classifier': {'penalty': ['l2'], 'C': Real(low=0.01, high=10, prior='uniform', transform='identity')}}

With custom hyperparameters:
{'Simple Imputer': {'impute_strategy': ['most_frequent']}, 'One Hot Encoder': {}, 'Standard Scaler': {}, 'Logistic Regression Classifier': {'penalty': ['l2'], 'C': Real(low=0.01, high=10, prior='uniform', transform='identity')}}

To initialize our new custom pipeline class, we must pass in a parameters argument. If we want to use the defaults for each component, we can simply pass in an empty dictionary.

[6]:
CustomPipeline(parameters={})
[6]:
<__main__.CustomPipeline at 0x7f691f664a20>

Pipeline Parameters

You can also pass in custom parameters. The parameters dictionary needs to be in the format of a two-layered dictionary where the first key-value pair is the component name and component parameters dictionary. The component parameters dictionary consists of a key value pair of parameter name and parameter values. An example will be shown below and component parameters can be found here.

[7]:
parameters = {
        'Simple Imputer': {
            'impute_strategy': 'mean'
        },
        'Logistic Regression Classifier': {
            'penalty': 'l2',
            'C': 1.0,
        }
    }

cp = CustomPipeline(parameters=parameters, random_state=5)

Pipeline Description

You can call .graph() to see each component and its parameters. Each component takes in data and feeds it to the next.

[8]:
cp.graph()
[8]:
../_images/user_guide_pipelines_16_0.svg

You can see a textual representation of the pipeline by calling .describe():

[9]:
cp.describe()
*******************
* Custom Pipeline *
*******************

Problem Type: Multiclass Classification
Model Family: Linear

Pipeline Steps
==============
1. Simple Imputer
         * impute_strategy : mean
         * fill_value : None
2. One Hot Encoder
         * top_n : 10
         * categories : None
         * drop : None
         * handle_unknown : ignore
         * handle_missing : error
3. Standard Scaler
4. Logistic Regression Classifier
         * penalty : l2
         * C : 1.0
         * n_jobs : -1