Client — Initialize to begin logging model data to Arize
log — Log inferences record-by-record
Install the Package
%pip install -q "arize<8.0.0"
Initialize Arize Client
Initialize ArizeClientto begin logging model inferences.
from arize.api import Client
# create Arize client
SPACE_ID = "YOUR_SPACE_ID"
API_KEY = "YOUR_API_KEY"
arize_client = Client(space_id=SPACE_ID, api_key=API_KEY)
Single Record Examples
For examples and interactive notebooks, see CookbooksExample 1: Logging Features, Tags, and Predictions Only
# Example features; features & tags can be optionally defined with typing
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': TypedValue(value=False, type=ArizeTypes.INT),
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': TypedValue(value=20.11, type=ArizeTypes.FLOAT),
}
# example tags
tags = {
'age': 30,
'zip_code': '94610',
'device_os': 'iOS',
'server_node_id': 12,
}
# example embeddings
embedding_features = {
'image_embedding': Embedding(
vector=np.array([1.0, 2, 3]),
link_to_data='https://my-bucket.s3.us-west-2.amazonaws.com/puppy.png',
),
'nlp_embedding_sentence': Embedding(
vector=pd.Series([4.0, 5.0, 6.0, 7.0]),
data='This is a test sentence',
),
'nlp_embedding_tokens': Embedding(
vector=pd.Series([4.0, 5.0, 6.0, 7.0]),
data=['This', 'is', 'a', 'sample', 'token', 'array'],
),
}
# log the prediction
response = arize_client.log(
prediction_id='plED4eERDCasd9797ca34',
model_id='sample-model-1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
model_version='v1',
prediction_timestamp=1618590882,
prediction_label=('Fraud',.4)
features=features,
embedding_features=embedding_features
tags=tags
)
# Listen to response code to ensure successful delivery
res = response.result()
if res.status_code == 200:
print('Success sending Prediction!')
else:
print(f'Log failed with response code {res.status_code}, {res.text}')
Example 2: Logging Features & Predictions First, Then Delayed Actuals
# Example features; features & tags can be optionally defined with typing
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': TypedValue(value=False, type=ArizeTypes.INT),
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': TypedValue(value=20.11, type=ArizeTypes.FLOAT),
}
# log the features & prediction
response = arize_client.log(
prediction_id='plED4eERDCasd9797ca34',
model_id='sample-model-1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
model_version='v1',
prediction_timestamp=1618590882,
features=features,
prediction_label=('Fraud',.4),
tags=tags
)
res = response.result()
if res.status_code == 200:
print('Success sending Prediction!')
else:
print(f'Log failed with response code {res.status_code}, {res.text}')
# log the actual
actual_response = arize_client.log(
prediction_id='plED4eERDCasd9797ca34',
model_id='sample-model-1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
actual_label=('Fraud',1),
tags=tags)
# Listen to response code to ensure successful delivery
res = actual_response.result()
if res.status_code == 200:
print('Success sending Actual!')
else:
print(f'Log failed with response code {res.status_code}, {res.text}')
Example 3: Logging Features, Predictions and Actuals Together
# Example features; features & tags can be optionally defined with typing
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': TypedValue(value=False, type=ArizeTypes.INT),
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': TypedValue(value=20.11, type=ArizeTypes.FLOAT),
}
# log the prediction, actual, and features
response = arize_client.log(
prediction_id='plED4eERDCasd9797ca34',
model_id='sample-model-1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
model_version='v1',
prediction_timestamp=1618590882,
features=features,
prediction_label=('False', .4),
actual_label=('True', 1),
tags=tags
)
# Listen to response code to ensure successful delivery
res = response.result()
if res.status_code == 200:
print('Success sending Prediction and Actual!')
else:
print(f'Log failed with response code {res.status_code}, {res.text}')
Example 4: Logging Predictions, Actuals, and SHAP Together
# Example features; features & tags can be optionally defined with typing
features = {
'state': 'ca',
'city': 'berkeley',
'merchant_name': 'Peets Coffee',
'pos_approved': TypedValue(value=False, type=ArizeTypes.INT),
'item_count': 10,
'merchant_type': 'coffee shop',
'charge_amount': TypedValue(value=20.11, type=ArizeTypes.FLOAT),
}
# example SHAP values
shaps = {
'state': 0.23,
'city': 0.31,
'merchant_name': 0.10,
'pos_approved': 0.02,
'item_count': 0.06,
'merchant_type': 0.11,
'charge_amount': 0.29,
}
# log the prediction, actual, features, and shap
response = arize_client.log(
prediction_id='plED4eERDCasd9797ca34',
model_id='sample-model-1',
model_type=ModelTypes.SCORE_CATEGORICAL,
environment=Environments.PRODUCTION,
model_version='v1',
prediction_timestamp=1618590882,
features=features,
prediction_label=('False', .4),
actual_label=('True',1),
tags=tags,
shap_values=shaps)
# Listen to response code to ensure successful delivery
res = response.result()
if res.status_code == 200:
print('Success sending Prediction, Actual, and SHAPs!')
else:
print(f'Log failed with response code {res.status_code}, {res.text}')
Questions? Email us at support@arize.com or Slack us in the #arize-support channel