SmartPredict
  • Documentation
  • OVERVIEW
    • Presentation
      • Key features
      • Who may benefit from its use
      • The SmartPredict Graphical User Interface (GUI)
        • The SmartPredict Modules
        • Focus on the Notebooks
        • Practical shortcuts
    • Prerequisites
  • Getting started
    • Getting started (Part1) : Iris classification project
      • Project description
      • Step 1. Create the project
      • Step 2. Upload the dataset
      • Step 3. Preprocess the dataset
      • Step 4. Build the flowchart
        • Set up the flowchart
        • Configure the modules
      • Step 5. Run the build
      • Step 6. Deploy the project
      • Step 7. Make inferences with our model
      • Conclusion
  • Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck
    • Project description
    • Step 1. Create the project
    • Step 2. Upload the dataset
    • Step 3. Preprocess the dataset
    • Step 4. Build the flowchart
    • Step 5. Run the build
    • Step 6. Deploy the pipeline
    • Step 7. Make inferences with our pipeline
  • MODULE REFERENCE
    • CORE MODULES
      • Introduction
      • Basic Operations
        • Item Saver
      • Web Services
        • Web Service IN and OUT
      • Data retrieval
        • Data fetcher
        • Data frame loader/converter
        • Image data loader
      • Data preprocessing
        • Introduction
        • Array Reshaper
        • Generic Data Preprocessor
        • Missing data handler
        • Normalizer
        • One Hot Encoder
        • Ordinal Encoder
      • Data selection
        • Features selector
        • Generic data splitter
        • Labeled data splitter
      • Training and Prediction
        • Predictor DL models
        • Predictor ML models
        • Predictor ML models (Probabilistic models)
        • Trainer ML models
        • Trainer/Evaluator DL models
      • Evaluation and fine-tuning
        • Cross Validator for ML
        • Evaluator for ML models
      • Machine Learning algorithms
        • ML modules in SmartPredict
        • Decision Tree Regressor
        • KNeighbors Classifier
        • KNeighbors Regressors
        • Linear Regressor
        • Logistic Regressor
        • MLP Regressor
        • Naive Bayes Classifier
        • Random Forest Classifier
        • Random Forest Regressor
        • Support Vector Classifier
        • Support Vector Regressor
        • XGBoost Classifier
        • XGBoost Regressor
      • Deep learning algorithms
        • Dense Neural Network
        • Recurrent Neural Networks
      • Computer Vision
        • Convolutional Recurrent Networks
        • Fully Convolutional Neural Networks
        • Face detector
        • Image IO
        • Image matcher
        • Yolo
      • Natural Language Processing
        • Introduction
        • Text cleaner
        • Text vectorizer
      • Times Series processing
        • TS features selector
      • TensorFlow API
        • LSTM Layer
        • Dense Layer
      • Helpers
        • Data/Object Logger
        • Object Selector (5 ports)
      • Conclusion
  • CUSTOM MODULES
    • Function
    • Class
    • Use cases
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  1. Getting started (Part 2): Predicting the passengers' survival in the Titanic shipwreck

Step 7. Make inferences with our pipeline

After having built and deployed our pipeline, we are now finally going to make inferences with it.

PreviousStep 6. Deploy the pipelineNextCORE MODULES

Last updated 5 years ago

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Perform testing with SmartPredict

Making inferences with our pipeline is somehow the ultimate objective of the previous stages . , SmartPredict is conceived to include needed for easily managing every stage of an AI project from end to end , even until the testing stage.

To test if our model performs as expected, let us test it right from SmartPredict' s testing tab.

SmartPredict Test tab makes it possible to directly upload a JSON file or insert the JSON codes ourselves.

For our Titanic project, to test if a given passenger survived or not , let us for instance choose a passenger in the test dataset quite randomly.

Paste the below code into the Test tab:

{
   "Pclass":3,
   "Sex":[
      "male"
   ],
   "Age":34.5,
   "SibSp":0,
   "Parch":0,
   "Fare":7.8292
}

The anticipated result is '' Not Survived' since this passenger is a typical third class person. Plus, he was a man who also traveled alone. Hence the obtained result should be < 0 > i.e this fated passenger did not survived .

Let us test if our model provides the expected output.

N.B Testing with an external software

To do so , we need to get the URL of our API which is how we can call it anytime we need to inject the model into the target use case.

Enter in the Monitor tab and Copy and Paste into clipboard the following information:

  • The active URL

  • The access token

Then, insert them into the corresponding field within the testing software.

If we test with an external software , do not forget to insert the access token into the code.

As an alternative, we may also opt for testing with the help of external tools such as , which is a handy unit testing software.

To see the details of the typical test operation, check this and place the previous code block in the field intended for it within the code body in Postman workspace.

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The output of our model is Not survived, just like we expected.