/examples/mnist/assess_model_performance.py

#!/usr/bin/env python

#
# Load a previously saved model and make predictions on the test data set
#

import os.path

# ## Import required libraries
import pandas as pd
from sklearn.metrics import accuracy_score

from ludwig.api import LudwigModel
from ludwig.datasets import mnist

# create data set for predictions
test_data = {"image_path": [], "label": []}
dataset = mnist.Mnist()
test_dir = os.path.join(dataset.processed_dataset_path, "testing")
for label in os.listdir(test_dir):
    files = os.listdir(os.path.join(test_dir, label))
    test_data["image_path"] += [os.path.join(test_dir, label, f) for f in files]
    test_data["label"] += len(files) * [label]

# collect data into a data frame
test_df = pd.DataFrame(test_data)
print(test_df.head())

# retrieve a trained model
model = LudwigModel.load("./results/multiple_experiment_Option3/model")

# make predictions
pred_df, _ = model.predict(dataset=test_df)
print(pred_df.head())

# print accuracy on test data set
print("predicted accuracy", accuracy_score(test_df["label"], pred_df["label_predictions"]))