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CV Classification

How to log your model schema for image classification models

CV Model Overview

Image classification models take an image as input and return a predicted label for the image.
*all classification variant specifications apply to the CV model type, with the addition of embeddings

Performance Metrics

Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity

Code Example

The EmbeddingColumnNames class constructs your embedding objects. You can log them into the platform using a dictionary that maps the embedding feature names to the embedding objects. See our API reference for more details.
Navigate here for step-by-step instructions to view private AWS S3 image links.
Python Pandas
Python Single Row
UI Import JSON Input
Input for API

Example Row

image_vector
image_link
prediction_label
actual_label
prediction_score
actual_score
Timestamp
[1.0, 2, 3]
"https://link-to-my-image.png"
car
bus
0.3
1
1618590882
from arize.pandas.logger import Client, Schema
from arize.utils.types import ModelTypes, Environments, EmbeddingColumnNames
​
API_KEY = 'ARIZE_API_KEY'
SPACE_KEY = 'YOUR SPACE KEY'
arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
​
​
# Declare which columns are the feature columns
feature_column_names=[
"MERCHANT_TYPE",
"ENTRY_MODE",
"STATE",
"MEAN_AMOUNT",
"STD_AMOUNT",
"TX_AMOUNT",
]
​
# Declare embedding feature columns
embedding_feature_column_names = {
# Dictionary keys will be the name of the embedding feature in the app
"embedding_display_name": EmbeddingColumnNames(
vector_column_name="image_vector", # column name of the vectors, required
link_to_data_column_name="image_link", # column name of the link to the images, optional
)
}
​
# Defina the Schema, including embedding information
schema = Schema(
prediction_id_column_name="prediction_id",
timestamp_column_name="prediction_ts",
prediction_label_column_name="PREDICTION",
prediction_score_column_name="PREDICTION_SCORE",
actual_label_column_name="ACTUAL",
actual_score_column_name="ACTUAL_SCORE",
feature_column_names=feature_column_names,
embedding_feature_column_names=embedding_feature_column_names,
)
​
# Log the dataframe with the schema mapping
response = arize_client.log(
model_id="sample-model-1",
model_version= "v1",
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
dataframe=test_dataframe,
schema=schema,
)
from arize.api import Client
from arize.utils.types import ModelTypes, Environments, Embedding
​
API_KEY = 'ARIZE_API_KEY'
SPACE_KEY = 'YOUR SPACE KEY'
arize_client = Client(space_key=SPACE_KEY, api_key=API_KEY)
​
# Example features
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': True,
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': 20.11,
}
# Example embedding features
embedding_features = {
"image_embedding": Embedding(
vector=np.array([1.0, 2, 3]),
link_to_data="https://link-to-my-image.png",
),
}
​
# Log data into the Arize platform
response = arize.log(
model_id='sample-model-1',
model_version='v1",
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
features=features
prediction_label="not fraud",
prediction_score = 0.3
actual_label="fraud",
actual_score = 1
features=features,
embedding_features=embedding_features
)
When configuring an embedding in the UI using File Import
"embedding_features": [{
"my_feature": // #required, my_feature is the name of the feature
{
vector: "vector_col", // #required, vector_col is the column name of the vector
raw_data: "raw_data_col", // #optional
link_to_data: "link_to_data_col" // #optional
}
}]
Example file schema with embedding features
{
"prediction_id": "prediction_id",
"timestamp": "timestamp",
"tags": "tag/",
"prediction_score": "prediction_score",
"prediction_label": "prediction_label",
"actual_label": "actual_label",
"actual_score": "actual_score",
"shap_values": "shap/",
"version": "version", // lookup the column "version" in the file
"batch_id": "batch_id",
"exclude": [
"<column1 name>",
"<column2 name>"
],
"embedding_features": [
{
"embedding_1": {
"vector": "vector_column_1"
"raw_data": "raw_data_column_1",
"link_to_data": "link_to_data_column"
}
}
]
}
When configuring an embedding in the UI using the API
"embeddingFeatures": [{
"featureName": "my_feature",
"vectorCol": "vector_col",
"rawDataCol": "raw_data_col",
"linkToDataCol": "link_to_data_col"
}]
Example file schema with embedding features
prediction_id: prediction_id
timestamp: timestamp
features: feature/
tags: tag/
prediction_score: prediction_score
prediction_label: prediction_label
actual_label: actual_label
actual_score: actual_score
shap_values: shap/
version: version // lookup the column "version" in the file
batch_id: batch_id
exclude: // leave empty to omit column exclusions
embedding_features: // leave empty to omit embeddings

CV Embedding Features

Arize supports logging the embedding features associated with the text the model is acting on and the text itself using the EmbeddingColumnNames object.
{
"embedding_display_name": EmbeddingColumnNames(
vector_column_name="image_vector",
link_to_data_column_name="image_link"
)
}
The embedding vector is the dense vector representation of the unstructured input. Note the feature embeddings are not sparse vectors. See here for more information on embeddings and options for generating them. The embedding link_to_data field is used to pass URL links to the source image your model is classifying.