This notebook demonstrates the use of the responsibleai API to assess an image classification pytorch model trained on the fridge dataset. It walks through the API calls necessary to create a widget with model analysis insights, then guides a visual analysis of the model.
The following section examines the code necessary to create datasets and a model. It then generates insights using the responsibleai API that can be visually analyzed.
import os
import sys
from zipfile import ZipFile
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from responsibleai_vision.common.constants import ImageColumns
import json
from fastai.learner import load_learner
from raiutils.common.retries import retry_function
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
EPOCHS = 10
LEARNING_RATE = 1e-4
IM_SIZE = 300
BATCH_SIZE = 16
FRIDGE_MODEL_NAME = 'fridge_model'
FRIDGE_MODEL_WINDOWS_NAME = 'fridge_model_windows'
WIN = 'win'
def load_fridge_dataset():
# create data folder if it doesnt exist.
os.makedirs("data", exist_ok=True)
# download data
download_url = ("https://cvbp-secondary.z19.web.core.windows.net/" +
"datasets/image_classification/fridgeObjects.zip")
data_file = "./data/fridgeObjects.zip"
urlretrieve(download_url, filename=data_file)
# extract files
with ZipFile(data_file, "r") as zip:
print("extracting files...")
zip.extractall(path="./data")
print("done")
# delete zip file
os.remove(data_file)
# get all file names into a pandas dataframe with the labels
data = pd.DataFrame(columns=[ImageColumns.IMAGE.value,
ImageColumns.LABEL.value])
feats = []
for folder in os.listdir("./data/fridgeObjects"):
for file in os.listdir("./data/fridgeObjects/" + folder):
image_path = "./data/fridgeObjects/" + folder + "/" + file
feats.append({ImageColumns.IMAGE.value: image_path,
ImageColumns.LABEL.value: folder})
# get all file names into a pandas dataframe with the labels
data = pd.DataFrame(feats, columns=[ImageColumns.IMAGE.value,
ImageColumns.LABEL.value])
return data
class FetchModel(object):
def __init__(self):
pass
def fetch(self):
if sys.platform.startswith(WIN):
model_name = FRIDGE_MODEL_WINDOWS_NAME
else:
model_name = FRIDGE_MODEL_NAME
url = ('https://publictestdatasets.blob.core.windows.net/models/' +
model_name)
urlretrieve(url, FRIDGE_MODEL_NAME)
def retrieve_or_train_fridge_model(df, force_train=False):
if force_train:
model = train_fastai_image_classifier(df)
# Save model to disk
model.export(FRIDGE_MODEL_NAME)
else:
fetcher = FetchModel()
action_name = "Dataset download"
err_msg = "Failed to download dataset"
max_retries = 4
retry_delay = 60
retry_function(fetcher.fetch, action_name, err_msg,
max_retries=max_retries,
retry_delay=retry_delay)
model = load_learner(FRIDGE_MODEL_NAME)
return model
data = load_fridge_dataset()
model = retrieve_or_train_fridge_model(data)
train_data = data
test_data = data
class_names = data[ImageColumns.LABEL.value].unique()
from raiwidgets import ResponsibleAIDashboard
from responsibleai_vision import ModelTask, RAIVisionInsights
To use Responsible AI Toolbox, initialize a RAIInsights object upon which different components can be loaded.
RAIInsights accepts the model, the full dataset, the test dataset, the target feature string and the task type string as its arguments.
rai_insights = RAIVisionInsights(model, test_data.sample(10, random_state=42),
"label",
task_type=ModelTask.IMAGE_CLASSIFICATION,
classes=class_names)
rai_insights
Add the components of the toolbox that are focused on model assessment.
# Interpretability
rai_insights.explainer.add()
# Error Analysis
rai_insights.error_analysis.add()
Once all the desired components have been loaded, compute insights on the test set.
rai_insights.compute()
Finally, visualize and explore the model insights. Use the resulting widget or follow the link to view this in a new tab.
ResponsibleAIDashboard(rai_insights)