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Logging to Aporia directly

This section will teach you how to integrate Aporia using Python SDK, but you can also use our REST API or the integrate to your own DB.
Get Started
To get started, install the Aporia SDK:
pip3 install aporia --upgrade
And then initialize it in your code:
import aporia
aporia.init(token="<TOKEN>",
            environment="<ENVIRONMENT>", # e.g "production"
            verbose=True,
            raise_errors=True)
Create Model
To create a new model to be monitored in Aporia, you can call the aporia.create_model(...) API:
aporia.create_model("<MODEL_ID>", "<MODEL_NAME>")
This API would not recreate the model if the model ID already exists. You can also specify color, icon, tags, and model owner:
aporia.create_model(
    model_id="fraud-detection",
    model_name="Fraud Detection",
    color=ModelColor.ARCTIC_BLUE,
    icon=ModelIcon.FRAUD_DETECTION,
    tags={
        "framework": "xgboost",
        "coolness_level": 10
    },
    owner="[email protected]", # Integrates with your enterprise auth system ;)
)
Create Model Version
Each model in Aporia contains different Model Versions. When you (re)train your model, you should create a new model version in Aporia.
Manual
apr_model = aporia.create_model_version(
  model_id="<MODEL_ID>",
  model_version="v1",
  model_type="binary",
  features={
    "amount": "numeric",
    "owner": "string",
    "is_new": "boolean",
    "created_at": "datetime",
  },
  predictions={
    "will_buy_insurance": "boolean",
    "proba": "numeric",
  },
  # Optional
  feature_importance={
    "amount": 80,
    "owner": 10,
    "is_new": 70,
    "created_at": 20,
  }
)
Inferring from Pandas DataFrame
# Example DataFrames, each one with one row
features_df = pd.DataFrame([[12.3, "John", True, pd.Timestamp.now()]], 
  columns=["amount", "owner", "is_new", "created_at"])
​
predictions_df = pd.DataFrame([[True, 0.7]], 
  columns=["will_buy_insurance", "proba"])
​
​
# Create a model version by inferring schemas from pandas DataFrames
apr_model = aporia.create_model_version(
    model_id="<MODEL_ID>",
    model_version="v1",
    model_type="binary",
​
    features=aporia.pandas.infer_schema_from_dataframe(features_df),
    predictions=aporia.pandas.infer_schema_from_dataframe(predictions_df),
      
    # Optional
    feature_importance={
      "amount": 80,
      "owner": 10,
      "is_new": 70,
      "created_at": 20,
    }
)
Model version parameter can be any string - you can use the model file's hash, git commit hash, experiment/run ID from MLFlow or anything else.
Model type can be regression, binary, multiclass, multi-label, or ranking. Please refer to the relevant documentation on each model type for more info.
Field Types
numeric - valid examples: 1, 2.87, 0.53, 300.13
boolean - valid examples: True, False
categorical - a categorical field with integer values
string - a categorical field with string values
datetime - contains either python datetime objects, or an ISO-8601 timestamp string
text - freeform text
dict - dictionaries - at the moment keys are strings and values are numeric
tensor - useful for unstructured data, must specify shape, e.g. {"type": "tensor", "dimensions": [768]}
vector - useful for arrays that can be different in sizes
Get a reference to an existing version
If you already created a version, for example during your training, and you want to use it again, you can receive a reference to the version.
apr_model = aporia.Model("<MODEL_ID>", "v1")
Logging Training / Test Sets
To log the training or test sets of your model, you can use the apr_model.log_training_set or apr_model.log_test_set functions, respectively.
For example, if we have the following training set:
import pandas as pd
​
training_features = pd.DataFrame({
  "Age": [31, 20, 53],
  "Annual_Premium": [11234, 534534, 859403],
  "Previously_Insured": [False, True, True],
  "Vehicle_Age_LT_1_Year": [False, True, False],
  "Vehicle_Age_GT_2_Years": [True, False, True],
  "Vehicle_Damage_Yes": [True, False, False],
})
​
training_predictions = pd.DataFrame({
  "will_buy_insurance": [True, True, False],
})
​
training_labels = pd.DataFrame({
  "will_buy_insurance": [True, False, True],
})
Then you can run:
apr_model.log_training_set(
  features=training_features,
  predictions=training_predictions,
  labels=training_labels,
)
And similarly, you can use the apr_model.log_test_set to log your test set.
In both functions, you can pass raw_inputs to log the raw inputs of your training / test sets.
Logging Serving Data
Log Predictions
Use the apr_model.log_prediction API to log a new prediction.
apr_model.log_prediction(
  id=<PREDICTION_ID>,
  features={
    "amount": 15.3,
    "owner": "Joe",
    "is_new": True,
    "created_at": datetime.now(),
  },
  predictions={
    "will_buy_insurance": True,
    "proba": 0.55,
  },
)
Note that for each prediction you must specify an ID. This ID can later be used to log the actual value of the prediction. If you don't care about actuals, you can simply pass str(uuid.uuid4()) as prediction ID.
After logging your first prediction you'll be able to get into your model page on the dashboard.
To log multiple predictions in one call, check out Batching.
Raw Inputs
Raw inputs are the inputs of the model before preprocessing, and they're used to construct the features. Logging them is optional but can help you detect issues in your data pipeline.
Example: Log raw inputs separately
apr_model.log_raw_inputs(
  id="a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
  raw_inputs={
    "Age": 27,
    "Vehicle_Damage": "Yes",
    "Annual_Premium": 12345,
    "Vehicle_Age": ">2 years"
  },
)
Example: Log raw inputs in log_prediction
apr_model.log_prediction(
  id="a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
  features={
    "Age": 27,
    "Vehicle_Damage_Yes": True,
    "Annual_Premium": 12345,
    "Vehicle_Age_LT_1_Year": False,
    "Vehicle_Age_GT_2_Years": True,
  },
  predictions={
    "will_buy_insurance": True,
  },
  raw_inputs={
    "Age": 27,
    "Vehicle_Damage": "Yes",
    "Annual_Premium": 12345,
    "Vehicle_Age": ">2 years"
  },
)
Actuals
In some cases, you will have access to the actual value of the prediction, based on real-world data.
For example, if your model predicted that a client will buy insurance, and a day later the client actually makes a purchase, then the actual value of that prediction is True
Example: Log actuals separately
apr_model.log_actuals(
  id="a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
  actuals={
    "will_buy_insurance": True,
  },
)
Example: Log actuals in log_prediction
apr_model.log_prediction(
  id="a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
  features={
    "Age": 27,
    "Vehicle_Damage_Yes": True,
    "Annual_Premium": 12345,
    "Vehicle_Age_LT_1_Year": False,
    "Vehicle_Age_GT_2_Years": True,
  },
  predictions={
    "will_buy_insurance": True,
  },
  actuals={
    "will_buy_insurance": True,
  },
)
Batching
All of the function above log a single prediction. If you wish to log multiple predictions in one large batch, you can use the log_batch_* functions.
Each of these functions receive a list of dictionaries, such that each dict contains the parameters of the singular version of the function.
Example: Logging batch predictions
apr_model.log_batch_prediction(
  [
    {
      "id":"a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
      "features": {
        "Age": 27,
        "Vehicle_Damage_Yes": True,
        "Annual_Premium": 12345,
        "Vehicle_Age_LT_1_Year": False,
        "Vehicle_Age_GT_2_Years": True,
      },
      "predictions": {
        "will_buy_insurance": True,
      },
    },
    {
      "id":"f2d1dccb-1aef-4955-a274-69e1acb8772f",
      "features": {
        "Age": 54,
        "Vehicle_Damage_Yes": False,
        "Annual_Premium": 54324,
        "Vehicle_Age_LT_1_Year": True,
        "Vehicle_Age_GT_2_Years": False,
      },
      "predictions": {
        "will_buy_insurance": False,
      },
    },
  ]
)
Example: Logging batch actuals
apr_model.log_batch_actuals(
  [
    {
      "id":"a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
      "actuals": {
        "will_buy_insurance": True,
      },
    },
    {
      "id":"f2d1dccb-1aef-4955-a274-69e1acb8772f",
      "actuals": {
        "will_buy_insurance": False,
      },
    },
  ]
)
Example: Logging batch raw inputs
apr_model.log_batch_raw_inputs(
  [
    {
      "id":"a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
      "raw_inputs": {
        "Age": 27,
        "Vehicle_Damage": "Yes",
        "Annual_Premium": 12345,
        "Vehicle_Age": ">2 years"
      },
    },
    {
      "id":"f2d1dccb-1aef-4955-a274-69e1acb8772f",
      "raw_inputs": {
        "Age": 54,
        "Vehicle_Damage": "No",
        "Annual_Premium": 54324,
        "Vehicle_Age": "<1 Year"
      },
    },
  ]
)
Logging Pandas DataFrame / Series
If the data you wish to log is stored in a Pandas Series or DataFrame (with a single row), you can use the aporia.pandas utility API:
from aporia.pandas.pandas_utils import pandas_to_dict
​
apr_model.log_prediction(
  id="a4dfcd4c-356c-4eed-8b93-b129b64fd55c",
  features=pandas_to_dict(features_dataframe),
  predictions={
    "will_buy_insurance": True,
  },
)
Asynchronous logging
All of the logging functions described above log the data asynchronously to avoid blocking your program. If you wish to wait for the data to be sent, you can use the flush method:
apr_model.flush()
Troubleshooting
By default, the Aporia SDK is very silent: it doesn't raise exceptions and doesn't write debug logs. This was done because we never want to interrupt your application!
However, when first playing with the Aporia SDK, we highly recommend using the verbose argument, e.g:
aporia.init(..., verbose=True)
This will print errors in a convenient way to make integration easier to debug. You can also pass throw_errors=True, which will make sure you aren't missing any errors.
If you have any further issues, please contact us.
Important: Make sure to remove throw_errors=True before uploading to staging / production!
Prediction isn't sent?
If your application exits immediately after logging a prediction, the prediction might get discarded.
The reason for this is that predictions are added to a queue and are sent asynchronously.
In order to fix this, use the following API:
apr_model.flush()
Pyspark
To log a Pyspark DataFrames directly, you can use the:
apr_model.log_batch_pyspark_prediction for serving data
apr_model.log_pyspark_training_set for training set
apr_model.log_pyspark_test_set for test set
The API of these functions is similar to the connect_serving API (see Data Sources - Overview).
Example:
import aporia
aporia.init(host="<HOST>", 
            token="<TOKEN>", 
            environment="<ENVIRONMENT>", 
            verbose=True,
            raise_errors=True)
​
​
# Create a new model + model version in Aporia
model_id = aporia.create_model("my-model", "My Model")
apr_model = aporia.create_model_version(
    model_id=model_id,
    model_version="v1",
    model_type="binary",
    features={
      "f1": "numeric",
      "f2": "numeric",
      "f3": "numeric",
    },
    predictions={
      "score": "boolean",
    },
)
​
# Log training set
# We'll assume that there is a column in the dataframe for each feature / prediction
df_train = spark.sql("SELECT * FROM ...")
apr_model.log_pyspark_training_set(df)
​
​
# Load & log production data to Aporia
# We'll assume that there is a column in the dataframe for each feature / prediction
df = spark.sql("SELECT * FROM <>")
apr_model.log_batch_pyspark_prediction(
    data=df,
​
    # Names of the "ID" and "occurred_at" columns
    id_column="id",
    timestamp_column="occurred_at",
​
    # Map an prediction (from the schema) to a label
    labels={
        "<PREDICTION_NAME>": "<COLUMN_NAME>",
    },
)
Storing your Predictions - Previous
Kubeflow / KServe
Next - Model Types
Regression
Last modified 10mo ago