In this example, we build a machine learning application to predict turbofan engine degradation. This application is structured into three important steps:

• Prediction Engineering

• Feature Engineering

• Machine Learning

In the first step, we generate new labels from the data by using Compose. In the second step, we generate features for the labels by using Featuretools. In the third step, we search for the best machine learning pipeline by using EvalML. After working through these steps, you will learn how to build machine learning applications for real-world problems like predictive maintenance. Let’s get started.

[1]:

from demo.turbofan_degredation import load_sample
from matplotlib.pyplot import subplots
import composeml as cp
import featuretools as ft
import evalml


We will use a dataset provided by NASA simulating turbofan engine degradation. In this dataset, we have engines which are monitored over time. Each engine had operational settings and sensor measurements recorded for each cycle. The remaining useful life (RUL) is the amount of cycles an engine has left before it needs maintenance. What makes this dataset special is that the engines run all the way until failure, giving us precise RUL information for every engine at every point in time.

[2]:

records = load_sample()


[2]:

engine_no time_in_cycles operational_setting_1 operational_setting_2 operational_setting_3 sensor_measurement_1 sensor_measurement_2 sensor_measurement_3 sensor_measurement_4 sensor_measurement_5 ... sensor_measurement_13 sensor_measurement_14 sensor_measurement_15 sensor_measurement_16 sensor_measurement_17 sensor_measurement_18 sensor_measurement_19 sensor_measurement_20 sensor_measurement_21 time
id
0 1 1 42.0049 0.8400 100.0 445.00 549.68 1343.43 1112.93 3.91 ... 2387.99 8074.83 9.3335 0.02 330 2212 100.00 10.62 6.3670 2000-01-01 00:00:00
1 1 2 20.0020 0.7002 100.0 491.19 606.07 1477.61 1237.50 9.35 ... 2387.73 8046.13 9.1913 0.02 361 2324 100.00 24.37 14.6552 2000-01-01 00:10:00
2 1 3 42.0038 0.8409 100.0 445.00 548.95 1343.12 1117.05 3.91 ... 2387.97 8066.62 9.4007 0.02 329 2212 100.00 10.48 6.4213 2000-01-01 00:20:00
3 1 4 42.0000 0.8400 100.0 445.00 548.70 1341.24 1118.03 3.91 ... 2388.02 8076.05 9.3369 0.02 328 2212 100.00 10.54 6.4176 2000-01-01 00:30:00
4 1 5 25.0063 0.6207 60.0 462.54 536.10 1255.23 1033.59 7.05 ... 2028.08 7865.80 10.8366 0.02 305 1915 84.93 14.03 8.6754 2000-01-01 00:40:00

5 rows × 27 columns

## Prediction Engineering¶

Which range is the RUL of a turbofan engine in?

In this prediction problem, we want to group the RUL into ranges. Then, predict which range the RUL is in. We can make variations of the ranges to create different prediction problems. For example, the ranges can be manually defined (0 - 150, 150 - 300, etc.) or based on the quartiles from historical observations. These variations can be done by simply binning the RUL. This helps us explore different scenarios which is crucial for making better decisions.

### Defining the Labeling Process¶

Let’s start by defining the labeling function of an engine that calculates the RUL. Given that engines run all the way until failure, the RUL is just the remaining number of observations.

[3]:

def rul(ds):
return len(ds) - 1


### Representing the Prediction Problem¶

Then, let’s represent the prediction problem by creating a label maker with the following parameters:

• The target_entity as the column for the engine ID, since we want to process records for each engine.

• The labeling_function as the function we defined previously.

• The time_index as the column for the event time.

[4]:

lm = cp.LabelMaker(
target_entity='engine_no',
labeling_function=rul,
time_index='time',
)


### Finding the Training Examples¶

Now, let’s run a search to get the training examples by using the following parameters:

• The data sorted by the event time.

• num_examples_per_instance as the number of training examples to find for each engine.

• minimum_data as the amount of data that will be used to make features for the first training example.

• gap as the number of rows to skip between examples. This is done to cover different points in time of an engine.

We can easily tweak these parameters and run more searches for training examples as the requirements of our model changes.

[5]:

lt = lm.search(
records.sort_values('time'),
num_examples_per_instance=20,
minimum_data=5,
gap=20,
verbose=False,
)


[5]:

engine_no time rul
0 1 2000-01-01 00:50:00 315
1 1 2000-01-01 04:10:00 295
2 1 2000-01-01 07:30:00 275
3 1 2000-01-01 10:50:00 255
4 1 2000-01-01 14:10:00 235

The output from the search is a label times table with three columns:

• The engine ID associated to the records.

• The event time of the engine. This is also known as a cutoff time for building features. Only data that existed beforehand is valid to use for predictions.

• The value of the RUL. This is calculated by our labeling function.

At this point, we only have continuous values of the RUL. As a helpul reference, we can print out the search settings that were used to generate these labels.

[6]:

lt.describe()

Settings
--------
gap                                  20
minimum_data                          5
num_examples_per_instance            20
target_column                       rul
target_entity                 engine_no
target_type                  continuous
window_size                        None

Transforms
----------
No transforms applied



We can also get a better look at the values by plotting the distribution and the cumulative count across time.

[7]:

%matplotlib inline
fig, ax = subplots(nrows=2, ncols=1, figsize=(6, 8))
lt.plot.distribution(ax=ax[0])
lt.plot.count_by_time(ax=ax[1])


With the continuous values, we can explore different ranges without running the search again. In this case, we will just use quartiles to bin the values into ranges.

[8]:

lt = lt.bin(4, quantiles=True, precision=0)


When we print out the settings again, we can now see that the description of the labels has been updated and reflects the latest changes.

[9]:

lt.describe()

Label Distribution
------------------
(0.0, 64.0]       13
(141.0, 227.0]    12
(227.0, 315.0]    13
(64.0, 141.0]     13
Total:            51

Settings
--------
gap                                 20
minimum_data                         5
num_examples_per_instance           20
target_column                      rul
target_entity                engine_no
target_type                   discrete
window_size                       None

Transforms
----------
1. bin
- bins:            4
- labels:       None
- precision:       0
- quantiles:    True
- right:        True



Let’s have a look at the new label distribution and cumulative count across time.

[10]:

fig, ax = subplots(nrows=2, ncols=1, figsize=(6, 8))
lt.plot.distribution(ax=ax[0])
lt.plot.count_by_time(ax=ax[1])


## Feature Engineering¶

In the previous step, we generated the labels. The next step is to generate the features.

### Representing the Data¶

We will represent the data using an entity set. We currently have a single table of records where one engine can many records. This one-to-many relationship can be represented in an entity set by normalizing an entity for the engines. The same can be done for engine cycles. Let’s start by structuring the entity set.

[11]:

es = ft.EntitySet('observations')

es.entity_from_dataframe(
dataframe=records.reset_index(),
entity_id='records',
index='id',
time_index='time',
)

es.normalize_entity(
base_entity_id='records',
new_entity_id='engines',
index='engine_no',
)

es.normalize_entity(
base_entity_id='records',
new_entity_id='cycles',
index='time_in_cycles',
)

es.plot()

[11]:


### Calculating the Features¶

Now, we can generate features by using a method called Deep Feature Synthesis (DFS). This will automatically build features by stacking and applying mathematical operations called primitives across relationships in an entity set. The more structured an entity set is, the better DFS can leverage the relationships to generate better features. Let’s run DFS using the following parameters:

• entity_set as the entity set we structured previously.

• target_entity as the engines, since we want to generate features for each engine.

• cutoff_time as the label times that we generated in the previous step.

[12]:

fm, fd = ft.dfs(
entityset=es,
target_entity='engines',
agg_primitives=['sum'],
trans_primitives=[],
cutoff_time=lt,
cutoff_time_in_index=True,
include_cutoff_time=False,
verbose=False,
)


[12]:

SUM(records.operational_setting_1) SUM(records.operational_setting_2) SUM(records.operational_setting_3) SUM(records.sensor_measurement_1) SUM(records.sensor_measurement_10) SUM(records.sensor_measurement_11) SUM(records.sensor_measurement_12) SUM(records.sensor_measurement_13) SUM(records.sensor_measurement_14) SUM(records.sensor_measurement_15) ... SUM(records.sensor_measurement_20) SUM(records.sensor_measurement_21) SUM(records.sensor_measurement_3) SUM(records.sensor_measurement_4) SUM(records.sensor_measurement_5) SUM(records.sensor_measurement_6) SUM(records.sensor_measurement_7) SUM(records.sensor_measurement_8) SUM(records.sensor_measurement_9) rul
engine_no time
1 2000-01-01 00:50:00 171.0170 3.8418 460.0 2288.73 5.04 205.45 865.90 11579.79 40129.43 48.0990 ... 70.04 42.5365 6760.63 5619.10 28.13 39.70 920.44 10874.54 41638.90 (227.0, 315.0]
2000-01-01 04:10:00 639.0537 15.8218 2300.0 11809.97 26.48 1049.86 6212.29 57897.83 200818.30 236.7149 ... 490.85 295.2742 34941.71 29358.68 193.41 276.33 6605.79 55252.70 211714.13 (227.0, 315.0]
2000-01-01 07:30:00 1128.1028 28.0260 4060.0 21211.42 47.58 1874.82 11083.94 103495.65 361091.30 428.9520 ... 880.67 528.9393 62509.49 52491.99 349.26 496.56 11781.82 98664.69 379760.09 (227.0, 315.0]
2000-01-01 10:50:00 1600.1473 39.2949 5980.0 30722.92 69.59 2734.53 16627.16 150532.67 522348.94 613.1760 ... 1312.52 788.4988 90981.87 76598.79 514.70 736.42 17673.33 143694.65 550823.50 (227.0, 315.0]
2000-01-01 14:10:00 2134.2039 51.8739 7940.0 40118.03 91.37 3591.92 21653.49 197930.92 683766.02 797.0065 ... 1706.80 1025.8136 119254.98 100389.55 663.76 953.22 23012.97 188788.43 721240.61 (227.0, 315.0]

5 rows × 25 columns

There are two outputs from DFS: a feature matrix and feature definitions. The feature matrix is a table that contains the feature values based on the cutoff times from our labels. Feature definitions are features in a list that can be stored and reused later to calculate the same set of features on future data.

## Machine Learning¶

In the previous steps, we generated the labels and features. The final step is to build the machine learning pipeline.

### Splitting the Data¶

We will start by extracting the labels from the feature matrix and splitting the data into a training set and holdout set.

[13]:

y = fm.pop('rul').cat.codes

splits = evalml.preprocessing.split_data(
X=fm,
y=y,
test_size=0.2,
random_state=0,
)

X_train, X_holdout, y_train, y_holdout = splits


### Finding the Best Model¶

Then, let’s run a search on the training set for the best machine learning pipeline.

[14]:

automl = evalml.AutoMLSearch(
problem_type='multiclass',
objective='f1_macro',
)

automl.search(
X_train,
y_train,
data_checks='disabled',
show_iteration_plot=False,
)

Using default limit of max_pipelines=5.

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

Optimizing for F1 Macro.
Greater score is better.

Searching up to 5 pipelines.
Allowed model families: extra_trees, linear_model, catboost, xgboost, random_forest

(1/5) Mode Baseline Multiclass Classificati... Elapsed:00:00
Starting cross validation
Finished cross validation - mean F1 Macro: 0.092
(2/5) Extra Trees Classifier w/ Imputer        Elapsed:00:00
Starting cross validation
Finished cross validation - mean F1 Macro: 0.735
(3/5) Elastic Net Classifier w/ Imputer + S... Elapsed:00:01
Starting cross validation
Finished cross validation - mean F1 Macro: 0.190
(4/5) CatBoost Classifier w/ Imputer           Elapsed:00:01
Starting cross validation
Finished cross validation - mean F1 Macro: 0.839
(5/5) XGBoost Classifier w/ Imputer            Elapsed:00:01
Starting cross validation
Finished cross validation - mean F1 Macro: 0.702

Search finished after 00:02
Best pipeline: CatBoost Classifier w/ Imputer
Best pipeline F1 Macro: 0.838823


Once the search is complete, we can print out information about the best pipeline found, such as the parameters in each component.

[15]:

automl.best_pipeline.describe()
automl.best_pipeline.graph()

**********************************
* CatBoost Classifier w/ Imputer *
**********************************

Problem Type: Multiclass Classification
Model Family: CatBoost

Pipeline Steps
==============
1. Imputer
* categorical_impute_strategy : most_frequent
* numeric_impute_strategy : mean
* categorical_fill_value : None
* numeric_fill_value : None
2. CatBoost Classifier
* n_estimators : 10
* eta : 0.03
* max_depth : 6
* bootstrap_type : None

[15]:


Now, let’s score the model performance by evaluating predictions on the holdout set.

[16]:

best_pipeline = automl.best_pipeline.fit(X_train, y_train)

score = best_pipeline.score(
X=X_holdout,
y=y_holdout,
objectives=['f1_macro'],
)

dict(score)

[16]:

{'F1 Macro': 0.8142857142857144}


From the pipeline, we can see which features are most important for predictions.

[17]:

feature_importance = best_pipeline.feature_importance
feature_importance = feature_importance.set_index('feature')['importance']
top_k = feature_importance.abs().sort_values().tail(20).index
feature_importance[top_k].plot.barh(figsize=(8, 8), fontsize=14, width=.7);


### Next Steps¶

At this point, we have completed the machine learning application. We can revisit each step to explore and fine-tune with different parameters until the model is ready for deployment. For more information on how to work with the features produced by Featuretools, take a look at the Featuretools documentation. For more information on how to work with the models produced by EvalML, take a look at the EvalML documentation.