Predict with Python

1. Install.

pip install tangram

2. Predict.

First, import the tangram library and load the model file. Then, make an object with info for a new patient that matches the CSV, excluding the diagnosis column. Finally, call predict and print out the result.

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import os import tangram # Get the path to the .tangram file. model_path = os.path.join(os.path.dirname(__file__), 'heart_disease.tangram') # Load the model from the path. model = tangram.Model.from_path(model_path) # Create an example input matching the schema of the CSV file the model was trained on. # Here the data is just hard-coded, but in your application you will probably get this # from a database or user input. input = { 'age': 63, 'gender': 'male', 'chest_pain': 'typical angina', 'resting_blood_pressure': 145, 'cholesterol': 233, 'fasting_blood_sugar_greater_than_120': 'true', 'resting_ecg_result': 'probable or definite left ventricular hypertrophy', 'exercise_max_heart_rate': 150, 'exercise_induced_angina': 'no', 'exercise_st_depression': 2.3, 'exercise_st_slope': 'downsloping', 'fluoroscopy_vessels_colored': '0', 'thallium_stress_test': 'fixed defect', } # Make the prediction! output = model.predict(input) # Print the output. print('Output:', output)