In this guide, we’ll show how you can use EvalML to automatically find the best pipeline for predicting whether a patient has breast cancer. Along the way, we’ll highlight EvalML’s built-in tools and features for understanding and interacting with the search process.

import evalml
from evalml import AutoMLSearch
from evalml.utils import infer_feature_types

First, we load in the features and outcomes we want to use to train our model.

X, y = evalml.demos.load_fraud(n_rows=1000, return_pandas=True)
             Number of Features
Boolean                       1
Categorical                   6
Numeric                       5

Number of training examples: 1000
False    85.90%
True     14.10%
Name: fraud, dtype: object

First, we will clean the data. Since EvalML accepts a pandas input, it can run type inference on this data directly. Since we’d like to change the types inferred by EvalML, we can use the infer_feature_types utility method. Here’s what we’re going to do with the following dataset:

  • Reformat the expiration_date column so it reflects a more familiar date format.

  • Cast the lat and lng columns from float to str.

  • Use infer_feature_types to specify what types certain columns should be. For example, to avoid having the provider column be inferred as natural language text, we have specified it as a categorical column instead.

The infer_feature_types utility method takes a pandas or numpy input and converts it to a Woodwork data structure, providing us with flexibility to cast the data as necessary.

X['expiration_date'] = X['expiration_date'].apply(lambda x: '20{}-01-{}'.format(x.split("/")[1], x.split("/")[0]))
X[['lat', 'lng']] = X[['lat', 'lng']].astype('str')
X = infer_feature_types(X, feature_types= {'store_id': 'categorical',
                                           'expiration_date': 'datetime',
                                           'lat': 'categorical',
                                           'lng': 'categorical',
                                           'provider': 'categorical'})
Physical Type Logical Type Semantic Tag(s)
Data Column
card_id Int64 Integer ['numeric']
store_id category Categorical ['category']
datetime datetime64[ns] Datetime []
amount Int64 Integer ['numeric']
currency category Categorical ['category']
customer_present boolean Boolean []
expiration_date category Datetime []
provider category Categorical ['category']
lat category Categorical ['category']
lng category Categorical ['category']
region category Categorical ['category']
country category Categorical ['category']

In order to validate the results of the pipeline creation and optimization process, we will save some of our data as a holdout set.

X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, problem_type='binary', test_size=.2)

Note: To provide data to EvalML, it is recommended that you create a DataTable object using the Woodwork project. Here, split_data() returns Woodwork data structures.

EvalML also accepts and works well with pandas DataFrames. But using the DataTable makes it easy to control how EvalML will treat each feature, as a numeric feature, a categorical feature, a text feature or other type of feature. Woodwork’s DataTable includes features like inferring when a categorical feature should be treated as a text feature.

EvalML has many options to configure the pipeline search. At the minimum, we need to define an objective function. For simplicity, we will use the F1 score in this example. However, the real power of EvalML is in using domain-specific objective functions or building your own.

Below EvalML utilizes Bayesian optimization (EvalML’s default optimizer) to search and find the best pipeline defined by the given objective.

EvalML provides a number of parameters to control the search process. max_batches is one of the parameters which controls the stopping criterion for the AutoML search. It indicates the maximum number of rounds of AutoML to evaluate, where each round may train and score a variable number of pipelines. In this example, max_batches is set to 1.

** Graphing methods, like AutoMLSearch, on Jupyter Notebook and Jupyter Lab require ipywidgets to be installed.

** If graphing on Jupyter Lab, jupyterlab-plotly required. To download this, make sure you have npm installed.

automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary', objective='f1', max_batches=1)

When we call search(), the search for the best pipeline will begin. There is no need to wrangle with missing data or categorical variables as EvalML includes various preprocessing steps (like imputation, one-hot encoding, feature selection) to ensure you’re getting the best results. As long as your data is in a single table, EvalML can handle it. If not, you can reduce your data to a single table by utilizing Featuretools and its Entity Sets.

You can find more information on pipeline components and how to integrate your own custom pipelines into EvalML here.

Generating pipelines to search over...
* Beginning pipeline search *

Optimizing for F1.
Greater score is better.

Searching up to 1 batches for a total of 9 pipelines.
Allowed model families: xgboost, random_forest, catboost, linear_model, decision_tree, extra_trees, lightgbm

Batch 1: (1/9) Mode Baseline Binary Classification P... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean F1: 0.000
Batch 1: (2/9) Logistic Regression Classifier w/ Imp... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean F1: 0.268
Batch 1: (3/9) Random Forest Classifier w/ Imputer +... Elapsed:00:04
        Starting cross validation
        Finished cross validation - mean F1: 0.744
Batch 1: (4/9) XGBoost Classifier w/ Imputer + DateT... Elapsed:00:06
        Starting cross validation
        Finished cross validation - mean F1: 0.762
Batch 1: (5/9) CatBoost Classifier w/ Imputer + Date... Elapsed:00:09
        Starting cross validation
        Finished cross validation - mean F1: 0.759
Batch 1: (6/9) Elastic Net Classifier w/ Imputer + D... Elapsed:00:10
        Starting cross validation
        Finished cross validation - mean F1: 0.000
Batch 1: (7/9) Extra Trees Classifier w/ Imputer + D... Elapsed:00:12
        Starting cross validation
        Finished cross validation - mean F1: 0.000
Batch 1: (8/9) LightGBM Classifier w/ Imputer + Date... Elapsed:00:15
        Starting cross validation
        Finished cross validation - mean F1: 0.756
Batch 1: (9/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:17
        Starting cross validation
        Finished cross validation - mean F1: 0.472
High coefficient of variation (cv >= 0.2) within cross validation scores. Decision Tree Classifier w/ Imputer + DateTime Featurization Component + One Hot Encoder may not perform as estimated on unseen data.

Search finished after 00:19
Best pipeline: XGBoost Classifier w/ Imputer + DateTime Featurization Component + One Hot Encoder
Best pipeline F1: 0.762019

After the search is finished we can view all of the pipelines searched, ranked by score. Internally, EvalML performs cross validation to score the pipelines. If it notices a high variance across cross validation folds, it will warn you. EvalML also provides additional data checks to analyze your data to assist you in producing the best performing pipeline.

id pipeline_name score validation_score percent_better_than_baseline high_variance_cv parameters
0 3 XGBoost Classifier w/ Imputer + DateTime Featu... 0.762019 0.800000 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
1 4 CatBoost Classifier w/ Imputer + DateTime Feat... 0.758720 0.812500 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
2 7 LightGBM Classifier w/ Imputer + DateTime Feat... 0.756061 0.818182 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
3 2 Random Forest Classifier w/ Imputer + DateTime... 0.744112 0.754098 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
4 8 Decision Tree Classifier w/ Imputer + DateTime... 0.471917 0.192308 NaN True {'Imputer': {'categorical_impute_strategy': 'm...
5 1 Logistic Regression Classifier w/ Imputer + Da... 0.267744 0.291667 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
6 0 Mode Baseline Binary Classification Pipeline 0.000000 0.000000 NaN False {'Baseline Classifier': {'strategy': 'mode'}}
7 5 Elastic Net Classifier w/ Imputer + DateTime F... 0.000000 0.000000 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
8 6 Extra Trees Classifier w/ Imputer + DateTime F... 0.000000 0.000000 NaN False {'Imputer': {'categorical_impute_strategy': 'm...

If we are interested in see more details about the pipeline, we can view a summary description using the id from the rankings table:

* XGBoost Classifier w/ Imputer + DateTime Featurization Component + One Hot Encoder *

Problem Type: binary
Model Family: XGBoost

Pipeline Steps
1. Imputer
         * categorical_impute_strategy : most_frequent
         * numeric_impute_strategy : mean
         * categorical_fill_value : None
         * numeric_fill_value : None
2. DateTime Featurization Component
         * features_to_extract : ['year', 'month', 'day_of_week', 'hour']
         * encode_as_categories : False
3. One Hot Encoder
         * top_n : 10
         * features_to_encode : None
         * categories : None
         * drop : None
         * handle_unknown : ignore
         * handle_missing : error
4. XGBoost Classifier
         * eta : 0.1
         * max_depth : 6
         * min_child_weight : 1
         * n_estimators : 100

Training for binary problems.
Total training time (including CV): 2.4 seconds

Cross Validation
               F1  MCC Binary  Log Loss Binary   AUC  Precision  Balanced Accuracy Binary  Accuracy Binary # Training # Validation
0           0.800       0.788            0.222 0.828      0.963                     0.840            0.951    533.000      267.000
1           0.774       0.771            0.246 0.821      1.000                     0.816            0.948    533.000      267.000
2           0.712       0.708            0.292 0.798      0.955                     0.782            0.936    534.000      266.000
mean        0.762       0.756            0.254 0.816      0.973                     0.812            0.945          -            -
std         0.045       0.042            0.036 0.016      0.024                     0.029            0.008          -            -
coef of var 0.059       0.056            0.140 0.019      0.025                     0.036            0.008          -            -

We can also view the pipeline parameters directly:

pipeline = automl.get_pipeline(3)
{'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'categorical_fill_value': None, 'numeric_fill_value': None}, 'DateTime Featurization Component': {'features_to_extract': ['year', 'month', 'day_of_week', 'hour'], 'encode_as_categories': False}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': None, 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'XGBoost Classifier': {'eta': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100}}

We can now select the best pipeline and score it on our holdout data:

pipeline = automl.best_pipeline
pipeline.score(X_holdout, y_holdout, ["f1"])
OrderedDict([('F1', 0.8333333333333333)])

We can also visualize the structure of the components contained by the pipeline: