Predict with Python
1. Install.
pip install modelfox
2. Predict.
First, import the modelfox 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 modelfox
# Get the path to the .modelfox file.
model_path = os.path.join(os.path.dirname(__file__), 'heart_disease.modelfox')
# Load the model from the path.
model = modelfox.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)