Natural Language Processing (NLP)
How to log your model schema for text classification use cases
NLP Cases | Expected Fields | Performance Metrics |
|---|---|---|
*prediction label, actual label, prediction score, actual score | Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity | |
*prediction label, actual label, prediction score, actual score | Accuracy, Recall, Precision, FPR, FNR, F1, Sensitivity, Specificity |
*all classification variant specifications apply to the NLP model type, with the addition of embeddings
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.Python Pandas
Python Single Row
UI Import JSON Input
Import for API
text_vector | text | prediction_label | actual_label | prediction_score | actual_score | Timestamp |
|---|---|---|---|---|---|---|
[4.0, 5.0, 6.0, 7.0] | "This is a test sentence" | positive | neutral | 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="text_vector", # column name of the vectors, required
data_column_name="text", # column name of the raw data vectors are representing, 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 = {
"nlp_embedding": Embedding(
vector=pd.Series([4.0, 5.0, 6.0, 7.0]),
data="This is a test sentence",
),
}
# 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
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="text_vector",
data_column_name="text"
)
}
The embedding
vector is the dense vector representation of the unstructured input. See here for more information on embeddings and options for generating them. The embedding data is the raw data associated with the vector. It is the field typically chosen for NLP use-cases since you can introduce both strings (full sentences) or list of strings (token arrays). Last modified 7d ago