# Start¶

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.

[1]:

import evalml
from evalml import AutoMLSearch


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

[2]:

X, y = evalml.demos.load_breast_cancer()


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

[3]:

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.

[4]:

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.

[5]:

automl.search()

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: lightgbm, extra_trees, catboost, decision_tree, xgboost, linear_model, random_forest


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: 0.976
Batch 1: (3/9) Random Forest Classifier w/ Imputer      Elapsed:00:02
Starting cross validation
Finished cross validation - mean F1: 0.949
Batch 1: (4/9) XGBoost Classifier w/ Imputer            Elapsed:00:03
Starting cross validation
Finished cross validation - mean F1: 0.932
Batch 1: (5/9) CatBoost Classifier w/ Imputer           Elapsed:00:03
Starting cross validation
Finished cross validation - mean F1: 0.901
Batch 1: (6/9) Elastic Net Classifier w/ Imputer + S... Elapsed:00:04
Starting cross validation
Finished cross validation - mean F1: 0.472
High coefficient of variation (cv >= 0.2) within cross validation scores. Elastic Net Classifier w/ Imputer + Standard Scaler may not perform as estimated on unseen data.
Batch 1: (7/9) Extra Trees Classifier w/ Imputer        Elapsed:00:05
Starting cross validation
Finished cross validation - mean F1: 0.933
Batch 1: (8/9) LightGBM Classifier w/ Imputer           Elapsed:00:06
Starting cross validation
Finished cross validation - mean F1: 0.943
Batch 1: (9/9) Decision Tree Classifier w/ Imputer      Elapsed:00:07
Starting cross validation
Finished cross validation - mean F1: 0.899

Search finished after 00:07
Best pipeline: Logistic Regression Classifier w/ Imputer + Standard Scaler
Best pipeline F1: 0.976081


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.

[6]:

automl.rankings

[6]:

id pipeline_name score validation_score percent_better_than_baseline high_variance_cv parameters
0 1 Logistic Regression Classifier w/ Imputer + St... 0.976081 0.973451 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
1 2 Random Forest Classifier w/ Imputer 0.948543 0.944444 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
2 7 LightGBM Classifier w/ Imputer 0.942803 0.925926 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
3 6 Extra Trees Classifier w/ Imputer 0.932522 0.915888 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
4 3 XGBoost Classifier w/ Imputer 0.931968 0.927273 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
5 4 CatBoost Classifier w/ Imputer 0.901433 0.890909 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
6 8 Decision Tree Classifier w/ Imputer 0.898505 0.880734 NaN False {'Imputer': {'categorical_impute_strategy': 'm...
7 5 Elastic Net Classifier w/ Imputer + Standard S... 0.471727 0.556962 NaN True {'Imputer': {'categorical_impute_strategy': 'm...
8 0 Mode Baseline Binary Classification Pipeline 0.000000 0.000000 NaN False {'Baseline Classifier': {'strategy': 'mode'}}

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

[7]:

automl.describe_pipeline(3)

*********************************
* XGBoost Classifier w/ Imputer *
*********************************

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. XGBoost Classifier
* eta : 0.1
* max_depth : 6
* min_child_weight : 1
* n_estimators : 100

Training
========
Training for binary problems.
Total training time (including CV): 0.8 seconds

Cross Validation
----------------
F1  MCC Binary  Log Loss Binary   AUC  Precision  Balanced Accuracy Binary  Accuracy Binary # Training # Validation
0           0.927       0.888            0.132 0.990      0.962                     0.937            0.947    303.000      152.000
1           0.948       0.917            0.096 0.994      0.932                     0.961            0.961    303.000      152.000
2           0.920       0.873            0.135 0.989      0.912                     0.938            0.940    304.000      151.000
mean        0.932       0.892            0.121 0.991      0.936                     0.945            0.949          -            -
std         0.015       0.022            0.022 0.003      0.025                     0.014            0.010          -            -
coef of var 0.016       0.025            0.181 0.003      0.027                     0.015            0.011          -            -


We can also view the pipeline parameters directly:

[8]:

pipeline = automl.get_pipeline(3)
print(pipeline.parameters)

{'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'categorical_fill_value': None, 'numeric_fill_value': None}, '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:

[9]:

pipeline = automl.best_pipeline
pipeline.fit(X_train, y_train)
pipeline.score(X_holdout, y_holdout, ["f1"])

[9]:

OrderedDict([('F1', 0.963855421686747)])


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

[10]:

pipeline.graph()

[10]: