# This example is the API example for this Ludwig command line example
# (https://ludwig-ai.github.io/ludwig-docs/examples/#kaggles-titanic-predicting-survivors).
# Import required libraries
import logging
import os
import shutil
from ludwig.api import LudwigModel
from ludwig.datasets import titanic
# clean out prior results
shutil.rmtree("./results", ignore_errors=True)
# Download and prepare the dataset
training_set, _, _ = titanic.load(split=True)
# Define Ludwig model object that drive model training
model = LudwigModel(config="./model1_config.yaml", logging_level=logging.INFO)
# initiate model training
(
train_stats, # dictionary containing training statistics
preprocessed_data, # tuple Ludwig Dataset objects of pre-processed training data
output_directory, # location of training results stored on disk
) = model.train(dataset=training_set, experiment_name="simple_experiment", model_name="simple_model")
# list contents of output directory
print("contents of output directory:", output_directory)
for item in os.listdir(output_directory):
print("\t", item)
import sys
import requests
import pandas as pd
from ludwig.datasets import titanic
# Ludwig model server default values
LUDWIG_HOST = '0.0.0.0'
LUDWIG_PORT = '8000'
#
# retrieve data to make predictions
#
test_df = titanic.load()
print('retrieved {:d} records for predictions'.format(test_df.shape[0]))
#
# execute REST API /predict for a single record
#
# get a single record from dataframe and convert to list of dictionaries
prediction_request_dict_list = test_df.head(1).to_dict(orient='records')
# extract dictionary for the single record only
prediction_request_dict = prediction_request_dict_list[0]
print('single record for prediction:\n', prediction_request_dict)
# construct URL
predict_url = ''.join(['http://', LUDWIG_HOST, ':', LUDWIG_PORT, '/predict'])
print('\ninvoking REST API /predict for single record...')
