Many moving parts have to be tied together for an ML model to execute and produce results successfully. This process of tying together different pieces of the ML process is known as pipelines. A pipeline is a generalized concept but a very important concept for a Data Scientist. In software engineering, people build pipelines to develop software that is exercised from source code to deployment. Similarly, in ML, a pipeline is created to allow data flow from its raw format to some useful information. It provides a mechanism to construct a multi-ML parallel pipeline system in order to compare the results of several ML methods.
In this tutorial, we see how to create our own AutoML pipelines. You will understand how to build pipelines in order to handle the model building process.
Each stage of a pipeline is fed processed data from its preceding stage; that is, the output of a processing unit is supplied as an input to its next step. The data flows through the pipeline just as water flows in a pipe. Mastering the pipeline concept is a powerful way to create error-free ML models, and pipelines form a crucial element for building an AutoML system.
The code files for this article are available on Github.
This article is an excerpt from a book written by Sibanjan Das, Umit Mert Cakmak titled Hands-On Automated Machine Learning.
Getting to know machine learning pipelines
Usually, an ML algorithm needs clean data to detect some patterns in the data and make predictions over a new dataset. However, in real-world applications, the data is often not ready to be directly fed into an ML algorithm. Similarly, the output from an ML model is just numbers or characters that need to be processed for performing some actions in the real world. To accomplish that, the ML model has to be deployed in a production environment. This entire framework of converting raw data to usable information is performed using a ML pipeline.
The following is a high-level illustration of an ML pipeline:
We will break down the blocks illustrated in the preceding figure as follows:
Data Ingestion: It is the process of obtaining data and importing data for use. Data can be sourced from multiple systems, such as Enterprise Resource Planning (ERP) software, Customer Relationship Management (CRM) software, and web applications. The data extraction can be in the real time or batches. Sometimes, acquiring the data is a tricky part and is one of the most challenging steps as we need to have a good business and data understanding abilities.
Data Preparation: There are several methods to preprocess the data to a suitable form for building models. Real-world data is often skewed—there is missing data, which is sometimes noisy. It is, therefore, necessary to preprocess the data to make it clean and transformed, so it's ready to be run through the ML algorithms.
ML model training: It involves the use of various ML techniques to understand essential features in the data, make predictions, or derive insights out of it. Often, the ML algorithms are already coded and available as API or programming interfaces. The most important responsibility we need to take is to tune the hyperparameters. The use of hyperparameters and optimizing them to create a best-fitting model are the most critical and complicated parts of the model training phase.
Model Evaluation: There are various criteria using which a model can be evaluated. It is a combination of statistical methods and business rules. In an AutoML pipeline, the evaluation is mostly based on various statistical and mathematical measures. If an AutoML system is developed for some specific business domain or use cases, then the business rules can also be embedded into the system to evaluate the correctness of a model.
Retraining: The first model that we create for a use case is not often the best model. It is considered as a baseline model, and we try to improve the model's accuracy by training it repetitively.
Deployment: The final step is to deploy the model that involves applying and migrating the model to business operations for their use. The deployment stage is highly dependent on the IT infrastructure and software capabilities an organization has.
As we see, there are several stages that we will need to perform to get results out of an ML model. The scikit-learn provides us a pipeline functionality that can be used to create several complex pipelines. While building an AutoML system, pipelines are going to be very complex, as many different scenarios have to be captured. However, if we know how to preprocess the data, utilizing an ML algorithm and applying various evaluation metrics, a pipeline is a matter of giving a shape to those pieces.
Let's design a very simple pipeline using scikit-learn.
Simple ML pipeline
We will first import a dataset known as Iris, which is already available in scikit-learn's sample dataset library (http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html). The dataset consists of four features and has 150 rows. We will be developing the following steps in a pipeline to train our model using the Iris dataset. The problem statement is to predict the species of an Iris data using four different features:
In this pipeline, we will use a MinMaxScaler method to scale the input data and logistic regression to predict the species of the Iris. The model will then be evaluated based on the accuracy measure:
The first step is to import various libraries from scikit-learn that will provide methods to accomplish our task. The only addition is the Pipeline method from sklearn.pipeline. This will provide us with necessary methods needed to create an ML pipeline:
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
The next step is to load the iris data and split it into training and test dataset. In this example, we will use 80% of the dataset to train the model and the remaining 20% to test the accuracy of the model. We can use the shape function to view the dimension of the dataset:
# Load and split the data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
X_train.shape
The following result shows the training dataset having 4 columns and 120 rows, which equates to 80% of the Iris dataset and is as expected:
Next, we print the dataset to take a glance at the data:
print(X_train)
The preceding code provides the following output:
The next step is to create a pipeline. The pipeline object is in the form of (key, value) pairs. Key is a string that has the name for a particular step, and value is the name of the function or actual method. In the following code snippet, we have named the MinMaxScaler() method as minmax and LogisticRegression(random_state=42) as lr:
pipe_lr = Pipeline([('minmax', MinMaxScaler()),
('lr', LogisticRegression(random_state=42))])
Then, we fit the pipeline object—pipe_lr—to the training dataset:
pipe_lr.fit(X_train, y_train)
When we execute the preceding code, we get the following output, which shows the final structure of the fitted model that was built:
The last step is to score the model on the test dataset using the score method:
score = pipe_lr.score(X_test, y_test)
print('Logistic Regression pipeline test accuracy: %.3f' % score)
As we can note from the following results, the accuracy of the model was 0.900, which is 90%:
In the preceding example, we created a pipeline, which constituted of two steps, that is, minmax scaling and LogisticRegression. When we executed the fit method on the pipe_lr pipeline, the MinMaxScaler performed a fit and transform method on the input data, and it was passed on to the estimator, which is a logistic regression model. These intermediate steps in a pipeline are known as transformers, and the last step is an estimator.
Transformers are used for data preprocessing and has two methods, fit and transform. The fit method is used to find parameters from the training data, and the transform method is used to apply the data preprocessing techniques to the dataset.
Estimators are used for creating machine learning model and has two methods, fit and predict. The fit method is used to train a ML model, and the predict method is used to apply the trained model on a test or new dataset.
This concept is summarized in the following figure:
We have to call only the pipeline's fit method to train a model and call the predict method to create predictions. Rest all functions that is, Fit and Transform are encapsulated in the pipeline's functionality and executed as shown in the preceding figure.
Sometimes, we will need to write some custom functions to perform custom transformations. The following section is about function transformer that can assist us in implementing this custom functionality.
FunctionTransformer
A FunctionTransformer is used to define a user-defined function that consumes the data from the pipeline and returns the result of this function to the next stage of the pipeline. This is used for stateless transformations, such as taking the square or log of numbers, defining custom scaling functions, and so on.
In the following example, we will build a pipeline using the CustomLog function and the predefined preprocessing method StandardScaler:
We import all the required libraries as we did in our previous examples. The only addition here is the FunctionTransformer method from the sklearn.preprocessing library. This method is used to execute a custom transformer function and stitch it together to other stages in a pipeline:
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
In the following code snippet, we will define a custom function, which returns the log of a number X:
def CustomLog(X):
return np.log(X)
Next, we will define a data preprocessing function named PreprocData, which accepts the input data (X) and target (Y) of a dataset. For this example, the Y is not necessary, as we are not going to build a supervised model and just demonstrate a data preprocessing pipeline. However, in the real world, we can directly use this function to create a supervised ML model.
Here, we use a make_pipeline function to create a pipeline. We used the pipeline function in our earlier example, where we have to define names for the data preprocessing or ML functions. The advantage of using a make_pipeline function is that it generates the names or keys of a function automatically:
def PreprocData(X, Y):
pipe = make_pipeline(
FunctionTransformer(CustomLog),StandardScaler()
)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y)
pipe.fit(X_train, Y_train)
return pipe.transform(X_test), Y_test
As we are ready with the pipeline, we can load the Iris dataset. We print the input data X to take a look at the data:
iris = load_iris()
X, Y = iris.data, iris.target
print(X)
The preceding code prints the following output:
Next, we will call the PreprocData function by passing the iris data. The result returned is a transformed dataset, which has been processed first using our CustomLog function and then using the StandardScaler data preprocessing method:
X_transformed, Y_transformed = PreprocData(X, Y)
print(X_transformed)
The preceding data transformation task yields the following transformed data results:
We will now need to build various complex pipelines for an AutoML system. In the following section, we will create a sophisticated pipeline using several data preprocessing steps and ML algorithms.
Complex ML pipeline
In this section, we will determine the best classifier to predict the species of an Iris flower using its four different features. We will use a combination of four different data preprocessing techniques along with four different ML algorithms for the task. The following is the pipeline design for the job:
We will proceed as follows:
We start with importing the various libraries and functions that are required for the task:
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn import tree
from sklearn.pipeline import Pipeline
Next, we load the Iris dataset and split it into train and test datasets. The X_train and Y_train dataset will be used for training the different models, and X_test and Y_test will be used for testing the trained model:
# Load and split the data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
Next, we will create four different pipelines, one for each model. In the pipeline for the SVM model, pipe_svm, we will first scale the numeric inputs using StandardScaler and then create the principal components using Principal Component Analysis (PCA). Finally, a Support Vector Machine (SVM) model is built using this preprocessed dataset.
Similarly, we will construct a pipeline to create the KNN model named pipe_knn. Only StandardScaler is used to preprocess the data before executing the KNeighborsClassifier to create the KNN model.
Then, we create a pipeline for building a decision tree model. We use the StandardScaler and MinMaxScaler methods to preprocess the data to be used by the DecisionTreeClassifier method.
The last model created using a pipeline is the random forest model, where only the StandardScaler is used to preprocess the data to be used by the RandomForestClassifier method.
The following is the code snippet for creating these four different pipelines used to create four different models:
# Construct svm pipeline
pipe_svm = Pipeline([('ss1', StandardScaler()),
('pca', PCA(n_components=2)),
('svm', svm.SVC(random_state=42))])
# Construct knn pipeline
pipe_knn = Pipeline([('ss2', StandardScaler()),
('knn', KNeighborsClassifier(n_neighbors=6, metric='euclidean'))])
# Construct DT pipeline
pipe_dt = Pipeline([('ss3', StandardScaler()),
('minmax', MinMaxScaler()),
('dt', tree.DecisionTreeClassifier(random_state=42))])
# Construct Random Forest pipeline
num_trees = 100
max_features = 1
pipe_rf = Pipeline([('ss4', StandardScaler()),
('pca', PCA(n_components=2)),
('rf', RandomForestClassifier(n_estimators=num_trees, max_features=max_features))])
Next, we will need to store the name of pipelines in a dictionary, which would be used to display results:
pipe_dic = {0: 'K Nearest Neighbours', 1: 'Decision Tree', 2:'Random Forest', 3:'Support Vector Machines'}
Then, we will list the four pipelines to execute those pipelines iteratively:
pipelines = [pipe_knn, pipe_dt,pipe_rf,pipe_svm]
Now, we are ready with the complex structure of the whole pipeline. The only things that remain are to fit the data to the pipeline, evaluate the results, and select the best model.
In the following code snippet, we fit each of the four pipelines iteratively to the training dataset:
# Fit the pipelines
for pipe in pipelines:
pipe.fit(X_train, y_train)
Once the model fitting is executed successfully, we will examine the accuracy of the four models using the following code snippet:
# Compare accuracies
for idx, val in enumerate(pipelines):
print('%s pipeline test accuracy: %.3f' % (pipe_dic[idx], val.score(X_test, y_test)))
We can note from the following results that the k-nearest neighbors and decision tree models lead the pack with a perfect accuracy of 100%. This is too good to believe and might be a result of using a small data set and/or overfitting:
We can use any one of the two winning models, k-nearest neighbors (KNN) or decision tree model, for deployment. We can accomplish this using the following code snippet:
best_accuracy = 0
best_classifier = 0
best_pipeline = ''
for idx, val in enumerate(pipelines):
if val.score(X_test, y_test) > best_accuracy:
best_accuracy = val.score(X_test, y_test)
best_pipeline = val
best_classifier = idx
print('%s Classifier has the best accuracy of %.2f' % (pipe_dic[best_classifier],best_accuracy))
As the accuracies were similar for k-nearest neighbor and decision tree, KNN was chosen to be the best model, as it was the first model in the pipeline. However, at this stage, we can also use some business rules or access the execution cost to decide the best model:
To summarize, we learned about building pipelines for ML systems. The concepts that we described in this article gave you a foundation for creating pipelines.
To have a clearer understanding of the different aspects of Automated Machine Learning, and how to incorporate automation tasks using practical datasets, do checkout the book Hands-On Automated Machine Learning.
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