Upgrade from 2.x To 3.x

With platform version 24.4 bringing changes in the model onboarding flow, the Python client has been upgraded to reflect these changes using an object-oriented design. This article is a guide on how your existing 2.x API method calls change when upgrading to Fiddler Python client 3.x. Please note that the below 3.x methods are more fully documented in API documentation. You can also take a look at this notebook which walks you through usage changes with the new client

Notable Changes

Dataset management: In client 3.x and platform 24.4+, the dataset is a model asset rather than a project asset. Multiple datasets can be associated with a model for different purposes like feature impact computation or surrogate model generation.
Explicit triggering: In client 3.x, actions such as feature importance and impact computation must be triggered explicitly unlike in client 2.x where they were part of the add artifact/surrogate model methods.
Onboarding prerequisites: A dataset is no longer a perquisite for onboarding a model into Fiddler and the DatasetInfo object has been deprecated.

Naming conventions: In client 2.x API method parameters for unique identifiers used semantic names such as project_id='my_project' and model_id='my_model'. With client 3.x we will expose automatically generated unique identifiers for objects like projects and models. This unique identifier is what is to be used for any "id" or "_id" parameter. Your semantic names will be associated with an object's "name" parameter and will allow retrieval by your semantic name in addition to get by "id".

Flow Changes

Import

2.x: import fiddler as fdl
3.x: import fiddler as fdl

Initialization

2.x:

client = fdl.FiddlerApi(
    url=URL,
    org_id=ORG_ID,
    auth_token=AUTH_TOKEN
)

3.x:
```
fdl.init(
    url=URL,
    token=AUTH_TOKEN
)
```
org_id is no longer required and will be inferred from the token.

Projects

Get Projects

2.x: projects = client.get_projects()
3.x: projects = fdl.Project.list()

Add Project

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'

project = client.create_project(project_id=PROJECT_ID)

3.x:

PROJECT_NAME = 'YOUR_PROJECT_NAME'

project = fdl.Project(name=PROJECT_NAME)
project.create()

Delete Project

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'

client.delete_project(project_id=PROJECT_ID)

3.x:

PROJECT_NAME = 'YOUR_PROJECT_NAME'

project = fdl.Project.from_name(name=PROJECT_NAME)
project.delete()

Models

Get Model

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

model = client.get_model(
    project_id=PROJECT_ID,
    model_id=MODEL_ID
)

3.x:

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'

project = fdl.Project.from_name(name=PROJECT_NAME)
model = fdl.Model.from_name(name=MODEL_NAME, project_id=project.id)

List Models

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'

models = client.list_models(project_id=PROJECT_ID))

3.x:

PROJECT_ID = '6dbf8656-1b6b-4f80-ba2b-b75739526dc2'

models = fdl.Model.list(project_id=PROJECT_ID)

Add Model

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
DATASET_ID = 'YOUR_DATASET_NAME'

client.add_model(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    model_id=MODEL_ID,
    model_info=model_info
)

3.x:

DATASET_FILE_PATH = <path_to_file>
MODEL_NAME = 'YOUR_MODEL_NAME'
PROJECT_ID = '6dbf8656-1b6b-4f80-ba2b-b75739526dc2'

MODEL_SPEC = fdl.ModelSpec(
    inputs=['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance'],
    outputs=['probability_churned'],
    targets=['Churned'],
    decisions=[],
    metadata=[],
    custom_features=[],
)

model = fdl.Model.from_data(
    source=DATASET_FILE_PATH,
    name=MODEL_NAME,
    project_id=PROJECT_ID,
    spec=MODEL_SPEC,
)
model.create()

Add Model Artifact

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
MODEL_DIR = <path_to_dir_containing_artifact>
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

job_id = client.add_model_artifact(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    model_dir=MODEL_DIR,
    deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}
MODEL_DIR = <path_to_dir_containing_artifact>

model = fdl.Model.get(id_=MODEL_ID)
job = model.add_artifact(
        model_dir=MODEL_DIR,
        deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS)
      )
job.wait()

📘 Computation of Feature Importance
Pre-compute of feature importance and feature impact needs to be done manually in 3.x. Below blocks showcase how it can be done with client 3.x

3.x: New steps

DATASET_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'

importance_job = model.precompute_feature_importance(
          dataset_id=DATASET_ID
      )
importance_job.wait()

impact_job = model.precompute_feature_impact(
          dataset_id=DATASET_ID
      )
impact_job.wait()

Update Model

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

models = client.update_model(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

model = fdl.Model.get(id_=MODEL_ID)
model.xai_params.default_explain_method = 'SHAP'
model.update()

Update Model Artifact

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
MODEL_DIR = <path_to_dir_containing_artifact>
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

job_id = client.update_model_artifact(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    model_dir=MODEL_DIR,
    deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}
MODEL_DIR = <path_to_dir_containing_artifact>

model = fdl.Model.get(id_=MODEL_ID)
job = model.update_artifact(
        model_dir=MODEL_DIR,
        deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS)
      )
job.wait()

📘 Computation of Feature Importance
Pre-compute of feature importance and feature impact needs to be done manually in 3.x. Below blocks showcase how it can be done with client 3.x

3.x: New steps

DATASET_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'

importance_job = model.precompute_feature_importance(
          dataset_id=DATASET_ID
      )
importance_job.wait()

impact_job = model.precompute_feature_impact(
          dataset_id=DATASET_ID
      )
impact_job.wait()

Download Artifacts

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
OUTPUT_DIR = <path_to_dir_to_download_artifact>

client.download_artifacts(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    output_dir=OUTPUT_DIR
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
OUTPUT_DIR = <path_to_dir_to_download_artifact>

model = fdl.Model.get(id_=MODEL_ID)
model.download_artifact(
    output_dir=OUTPUT_DIR
)

Get Model Deployment

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

model_deployment = client.get_model_deployment(
                      project_id=PROJECT_ID,
                      model_id=MODEL_ID
                  )

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# Using Model
model = fdl.Model.get(id_=MODEL_ID)
model_deployment = model.deployment

# Using ModelDeployment
model_deployment = fdl.ModelDeployment.of(model_id=MODEL_ID)

Add Model Surrogate

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

job_id = client.add_model_surrogate(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DATASET_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

model = fdl.Model.get(id_=MODEL_ID)
job = model.add_surrogate(
        dataset_id=DATASET_ID,
        deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
job.wait()

Update Model Surrogate

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

job_id = client.update_model_surrogate(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DATASET_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'
DEPLOYMENT_PARAMS = {'deployment_type': 'MANUAL', 'cpu': 1000}

model = fdl.Model.get(id_=MODEL_ID)
job = model.update_surrogate(
        dataset_id=DATASET_ID,
        deployment_params=fdl.DeploymentParams(**DEPLOYMENT_PARAMS))
job.wait()

Update Model Deployment

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

model_deployment = client.update_model_deployment(
                      project_id=PROJECT_ID,
                      model_id=MODEL_ID,
                      cpu=1000
                  )

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

model_deployment = fdl.ModelDeployment.of(
                    model_id=MODEL_ID
                   )
model_deployment.cpu = 1000
model_deployment.update()

Delete Model

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

client.delete_model(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

model = fdl.Model.get(id_=MODEL_ID)
job = model.delete()
job.wait()

Datasets

Get Dataset

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
DATASET_ID = 'YOUR_MODEL_NAME'

dataset = client.get_dataset(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

3.x:

# From id
DATASET_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'
dataset = fdl.Dataset.get(id_=DATASET_ID)

# From name
DATASET_NAME = 'test_dataset'
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
dataset = fdl.Dataset.from_name(
      name=DATASET_NAME,
      model_id=MODEL_ID
)

List Datasets

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
datasets = client.list_datasets(project_id=PROJECT_ID)

3.x:

# In 3.x, datasets are stored at a model level rather than project level.
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
datasets = fdl.Dataset.list(model_id=MODEL_ID)

Upload Dataset

2.x:

baseline_df = pd.read_csv(PATH_TO_BASELINE_CSV)
dataset_info = fdl.DatasetInfo.from_dataframe(baseline_df)

PROJECT_ID = 'YOUR_PROJECT_NAME'
DATASET_ID = 'YOUR_MODEL_NAME'

client.upload_dataset(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    dataset={
        'baseline': baseline_df
    },
    info=dataset_info
)

3.x:

# Horizon does not require a dataset to be added before model creation.
# However, you can optionally add a dataset to model using publish method as below.
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DATASET_NAME = 'YOUR_DATASET_NAME'
DATASET_FILE_PATH = <path_to_dataset>

job = model.publish(
    source=DATASET_FILE_PATH,
    environment=fdl.EnvType.PRE_PRODUCTION,
    dataset_name=DATASET_NAME,
)
job.wait()

Baselines

Get Baseline

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
DATASET_ID = 'YOUR_MODEL_NAME'
BASELINE_ID = 'YOUR_BASELINE_NAME'

baseline = client.get_baseline(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    baseline_id=BASELINE_ID
)

3.x:

# From UUID
BASELINE_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'
baseline = fdl.Baseline.get(id_=BASELINE_ID)

# From name
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
BASELINE_NAME = 'YOUR_BASELINE_NAME'

baseline = fdl.Baseline.from_name(
      name=BASELINE_NAME,
      model_id=MODEL_ID
)

Add Baseline

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
BASELINE_ID = 'YOUR_BASELINE_NAME'
DATASET_ID = 'YOUR_DATASET_NAME'

# Static baseline
baseline = client.add_baseline(
      project_id=PROJECT_ID,
      model_id=MODEL_ID,
      baseline_id=BASELINE_ID,
      type=fdl.BaselineType.STATIC,
      dataset_id=DATASET_ID
)

# Rolling baseline
baseline = client.add_baseline(
      project_id=PROJECT_ID,
      model_id=MODEL_ID,
      baseline_id=BASELINE_NAME,
      type=fdl.BaselineType.ROLLING_PRODUCTION,
      offset=fdl.WindowSize.ONE_MONTH, # How far back to set our window
      window_size=fdl.WindowSize.ONE_WEEK, # Size of the sliding window
)

3.x:

BASELINE_NAME = 'YOUR_BASELINE_NAME'
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# Static baseline
baseline = fdl.Baseline(
    name=BASELINE_NAME,
    model_id=MODEL_ID,
    environment=fdl.EnvType.PRE_PRODUCTION,
    type_=fdl.BaselineType.STATIC,
    dataset_id=DATASET_ID,
)
baseline.create()

# Rolling baseline
baseline = fdl.Baseline(
    name=BASELINE_NAME,
    model_id=MODEL_ID,
    environment=fdl.EnvType.PRODUCTION,
    type_=fdl.BaselineType.ROLLING,
    window_bin_size=fdl.WindowBinSize.HOUR,
    offset_delta=fdl.WindowBinSize.HOUR,
)
baseline.create()

List Baselines

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'

baselines = client.list_baselines(project_id=PROJECT_ID)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

baselines = fdl.Baseline.list(model_id=MODEL_ID)

Delete Baselines

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
BASELINE_ID = 'YOUR_BASELINE_NAME'

client.delete_baseline(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    baseline_id=BASELINE_ID
)

3.x:

BASELINE_ID = '5e1e67d2-5170-45ce-a851-68bdde1ac1ad'

baseline = fdl.Baseline.get(id_=BASELINE_ID)
baseline.delete()

Event Publishing

📘 Source
Source can be either through Batch upload(CSV filepath, Parquet filepath, Pandas dataframe) or Streaming upload(python list) in 3.x

Publish batch production events

Pre-requisite:

  PROJECT_ID = 'YOUR_PROJECT_NAME'
  MODEL_ID = 'YOUR_MODEL_NAME'
  production_df = pd.read_csv(PATH_TO_EVENTS_CSV)

2.x:

job = client.publish_events_batch(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    id_field='event_id',
    batch_source=production_df,
    timestamp_field='timestamp',
    update_event=False,
)

3.x:

# setup project and model object in 3.x
project = fdl.Project.from_name(name=PROJECT_ID)
model = fdl.Model.from_name(project_id=project.id, name=MODEL_ID)

# Before publish events, make sure you update the necessary fields
# of the model(if any). One-time only after you switch to 3.x client.
model.event_ts_col = 'timestamp'
model.event_id_col = 'event_id'
model.update()

job = model.publish(source=production_df, update=False)
job.wait()

For more examples of update=True in 3.x, refer to Publish.

Publish production events streaming

Pre-requisite:

  PROJECT_ID = 'YOUR_PROJECT_NAME'
  MODEL_ID = 'YOUR_MODEL_NAME'
  event_dict = {...}

2.x:

client.publish_event(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    event=event_dict,
    event_timestamp=event_time,
    event_id=event_id_tmp,
    update_event= False
)

3.x:

# setup project and model object in 3.x
project = fdl.Project.from_name(name=PROJECT_ID)
model = fdl.Model.from_name(project_id=project.id, name=MODEL_ID)

# Before publishing events, make sure you update the necessary fields of the
# model(if any). You only need to run one-time after switching to 3.x client.
model.event_ts_col = 'timestamp'
model.event_id_col = 'event_id'
model.update()

# Add the corresponding fields to every source you want to update
event_dict['event_id'] = event_id_tmp
event_dict['timestamp'] = event_time

# convert the dictionary to a list
model.publish(source=[event_dict])

Custom Metrics

Get Custom Metric

2.x:

CUSTOM_METRIC_ID = 'YOUR_METRIC_NAME'

client.get_custom_metric(metric_id=METRIC_ID)

3.x:

# From UUID
CUSTOM_METRIC_ID = '7057867c-6dd8-4915-89f2-a5f253dd4a3a'
custom_metric = fdl.CustomMetric.get(id_=CUSTOM_METRIC_ID)

# From name
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
CUSTOM_METRIC_NAME = 'YOUR_METRIC_NAME'

custom_metric = fdl.CustomMetric.from_name(
        name=CUSTOM_METRIC_NAME,
        model_id=MODEL_ID,
    )

List Custom Metrics

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

client.get_custom_metrics(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

custom_metrics = fdl.CustomMetric.list(model_id=MODEL_ID)

Add Custom Metric

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
CUSTOM_METRIC_NAME = 'YOUR_METRIC_NAME'
DEFINITION = 'average("Age")'
DESCRIPTION = 'Testing custom metrics'

client.get_custom_metrics(
    name=CUSTOM_METRIC_NAME,
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    definition=DEFINITION,
    description=DESCRIPTION,
)

3.x:

CUSTOM_METRIC_NAME = 'YOUR_METRIC_NAME'
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DEFINITION = 'average("Age")'
DESCRIPTION = 'Testing custom metrics'

custom_metric = fdl.CustomMetric(
    name=CUSTOM_METRIC_NAME,
    model_id=MODEL_ID,
    definition=DEFINITION,
    description=DESCRIPTION,
)
custom_metric.create()

Delete Custom Metric

2.x:

CUSTOM_METRIC_ID = 'YOUR_METRIC_NAME'

client.delete_custom_metric(metric_id=METRIC_ID)

3.x:

CUSTOM_METRIC_ID = '7057867c-6dd8-4915-89f2-a5f253dd4a3a'

custom_metric = fdl.CustomMetric.get(id_=CUSTOM_METRIC_ID)
custom_metric.delete()

Segments

Get Segment

2.x:

SEGMENT_ID = 'YOUR_SEGMENT_NAME'

client.get_segment(segment_id=SEGMENT_ID)

3.x:

# From UUID
SEGMENT_ID = '2c22a28b-08b8-4dd6-9238-7d7f1b5b4cb7'
segment = fdl.Segment.get(id_=SEGMENT_ID)

# From name
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
SEGMENT_NAME = 'YOUR_SEGMENT_NAME'

segment = fdl.Segment.from_name(
    name=SEGMENT_NAME,
    model_id=MODEL_ID,
)

List Segments

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

client.get_segments(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
)

3.x:

MODEL_ID = '2c22a28b-08b8-4dd6-9238-7d7f1b5b4cb7'

segment = fdl.Segment.list(model_id=MODEL_ID)

Add Segment

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'
SEGMENT_NAME = 'YOUR_SEGMENT_NAME'
DEFINITION = 'Age < 60'
DESCRIPTION = 'Testing segment'

client.add_segment(
    name=SEGMENT_NAME,
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    definition=DEFINITION,
    description=DESCRIPTION,
)

3.x:

SEGMENT_NAME = 'YOUR_SEGMENT_NAME'
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'
DEFINITION = 'Age < 60'
DESCRIPTION = 'Testing segment'

segment = fdl.Segment(
    name=SEGMENT_NAME,
    model_id=MODEL_ID,
    definition=DEFINITION,
    description=DESCRIPTION
)
segment.create()

Delete Segment

2.x:

SEGMENT_ID = 'YOUR_SEGMENT_NAME'
client.delete_segment(
  segment_id=SEGMENT_ID
)

3.x:

SEGMENT_ID = '2c22a28b-08b8-4dd6-9238-7d7f1b5b4cb7'

segment = fdl.Segment.get(id_=SEGMENT_ID)
segment.delete()

Alerts

List Alert Rules

2.x:

MODEL_ID = 'YOUR_MODEL_NAME'

rules = client.get_alert_rules(model_id=MODEL_ID)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

rules = fdl.AlertRule.list(model_id=MODEL_ID)

Add Alert Rule

2.x:

notifications_config = client.build_notifications_config(
    emails = "name@google.com",
)
PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

rule = client.add_alert_rule(
    name = "Bank Churn Range Violation Alert1",
    project_id = PROJECT_ID,
    model_id = MODEL_ID,
    alert_type = fdl.AlertType.DATA_INTEGRITY,
    metric = fdl.Metric.RANGE_VIOLATION,
    bin_size = fdl.BinSize.ONE_DAY,
    compare_to = fdl.CompareTo.RAW_VALUE,
    compare_period = None,
    priority = fdl.Priority.HIGH,
    warning_threshold = 2,
    critical_threshold = 3,
    condition = fdl.AlertCondition.GREATER,
    column = "numofproducts",
    notifications_config = notifications_config
)

3.x:

ALERT_NAME = 'YOUR_ALERT_NAME'
METRIC_NAME = 'null_violation_count'
MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

rule = fdl.AlertRule(
  name=ALERT_NAME,
  model_id=MODEL_ID,
  metric_id=METRIC_NAME,
  priority=fdl.Priority.MEDIUM,
  compare_to=fdl.CompareTo.RAW_VALUE,
  condition=fdl.AlertCondition.GREATER,
  bin_size=fdl.BinSize.HOUR,
  critical_threshold=1,
  warning_threshold=0.32,
)
rule.create()

Delete Alert Rule

2.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

client.delete_alert_rule(alert_rule_uuid=ALERT_RULE_ID)

3.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

rule = fdl.AlertRule.get(id_=ALERT_RULE_ID)
rule.delete()

Get Triggered Alerts

2.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

rules = client.get_triggered_alerts(alert_rule_uuid=ALERT_RULE_ID)

3.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

rules = fdl.AlertRecord.list(
    alert_rule_id=ALERT_RULE_ID,
    start_time=datetime(),
    end_time=datetime()
)

Enable Notifications

2.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

notifications = client.update_alert_rule(
    alert_config_uuid=ALERT_RULE_ID,
    enable_notification=True
)

3.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

rule = fdl.AlertRule.get(id_=ALERT_RULE_ID)
rule.enable_notifications()

Disable Notifications

2.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

notifications = client.update_alert_rule(
    alert_config_uuid=ALERT_RULE_ID,
    enable_notification=False
)

3.x:

ALERT_RULE_ID = '31109d19-b8aa-4db0-a4d5-aa0706987b1b'

rule = fdl.AlertRule.get(id_=ALERT_RULE_ID)
rule.disable_notifications()

Webhooks

Get Webhook

2.x:

WEBHOOK_UUID = '00cb3169-7983-497c-8f3c-d25df26543b0'

webhook = client.get_webhook(uuid=WEBHOOK_UUID)

3.x:

WEBHOOK_ID = '00cb3169-7983-497c-8f3c-d25df26543b0'

webhook = fdl.Webhook.get(id_=WEBHOOK_ID)

List Webhooks

2.x:

webhooks = client.get_webhooks()

3.x:

webhooks = fdl.Webhook.list()

Add Webhook

2.x:

WEBHOOK_NAME = 'YOUR_WEBHOOK_NAME'
WEBHOOK_URL = 'https://hooks.slack.com/services/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'
WEBHOOK_PROVIDER = 'SLACK'

webhook = client.add_webhook(
    name=WEBHOOK_NAME,
    url=WEBHOOK_URL,
    provider=WEBHOOK_PROVIDER
)

3.x:

WEBHOOK_NAME = 'YOUR_WEBHOOK_NAME'
WEBHOOK_URL = 'https://hooks.slack.com/services/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'
WEBHOOK_PROVIDER = 'SLACK'

webhook = fdl.Webhook(
    name=WEBHOOK_NAME,
    url=WEBHOOK_URL,
    provider=WEBHOOK_PROVIDER
)
webhook.create()

Update Webhook

2.x:

WEBHOOK_UUID = '00cb3169-7983-497c-8f3c-d25df26543b0'
WEBHOOK_NAME = 'YOUR_WEBHOOK_NAME'
WEBHOOK_URL = 'https://hooks.slack.com/services/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'
WEBHOOK_PROVIDER = 'SLACK'

webhook = client.update_webhook(
  uuid=WEBHOOK_UUID,
  name=WEBHOOK_NAME,
  url=WEBHOOK_URL,
  provider=WEBHOOK_PROVIDER
)

3.x:

WEBHOOK_ID = '00cb3169-7983-497c-8f3c-d25df26543b0'

webhook = fdl.Webhook.get(id_=WEBHOOK_ID)
webhook.name = 'YOUR_WEBHOOK_NAME_CHANGE'
webhook.update()

Delete Webhook

2.x:

WEBHOOK_UUID = '00cb3169-7983-497c-8f3c-d25df26543b0'

webhook = client.delete_webhook(uuid=WEBHOOK_UUID)

3.x:

WEBHOOK_ID = '00cb3169-7983-497c-8f3c-d25df26543b0'

webhook = fdl.Webhook.get(id_=WEBHOOK_ID)
webhook.delete()

XAI

Get Explanation

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

# RowDataSource
explain = client.get_explanation(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  input_data_source=fdl.RowDataSource(row={})
)
# EventIdDataSource
explain = client.get_explanation(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  input_data_source=fdl.EventIdDataSource()
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# RowDataSource
model = fdl.Model.get(id_=MODEL_ID)
explain = model.explain(
    input_data_source=fdl.RowDataSource(row={})
)

# EventIdDataSource
model = fdl.Model.get(id_=MODEL_ID)
explain = model.explain(
    input_data_source=fdl.EventIdDataSource()
)

Get Fairness

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

# DatasetDataSource
fairness = client.get_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(),
    protected_features=[],
    positive_outcome='',
)
# SqlSliceQueryDataSource
fairness = client.get_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.SqlSliceQueryDataSource(),
    protected_features=[],
    positive_outcome='',
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# DatasetDataSource
model = fdl.Model.get(id_=MODEL_ID)
fairness = model.get_fairness(
    data_source=fdl.DatasetDataSource(),
    protected_features=[],
    positive_outcome='',
)

# SqlSliceQueryDataSource
model = fdl.Model.get(id_=MODEL_ID)
fairness = model.get_fairness(
    data_source=fdl.SqlSliceQueryDataSource(),
    protected_features=[],
    positive_outcome='',
)

Get Feature Impact

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

# DatasetDataSource
impact =client.get_feature_impact(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  data_source=fdl.DatasetDataSource()
)

# SqlSliceQueryDataSource
impact = client.get_feature_impact(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  data_source=fdl.SqlSliceQueryDataSource()
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# DatasetDataSource
model = fdl.Model.get(id_=MODEL_ID)
impact = model.get_feature_impact(
  data_source=fdl.DatasetDataSource()
)

# SqlSliceQueryDataSource
model = fdl.Model.get(id_=MODEL_ID)
impact = model.get_feature_impact(
    data_source=fdl.SqlSliceQueryDataSource()
)

Get Feature Importance

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
MODEL_ID = 'YOUR_MODEL_NAME'

# DatasetDataSource
importance = client.get_feature_importance(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  data_source=fdl.DatasetDataSource()
)

# SqlSliceQueryDataSource
importance = client.get_feature_importance(
  project_id=PROJECT_ID,
  model_id=MODEL_ID,
  data_source=fdl.SqlSliceQueryDataSource()
)

3.x:

MODEL_ID = 'e38ab1ad-1a50-40b8-8bee-ab33cd8b9b93'

# DatasetDataSource
model = fdl.Model.get(id_=MODEL_ID)
importance = model.get_feature_importance(
        data_source=fdl.DatasetDataSource()
)

# SqlSliceQueryDataSource
model = fdl.Model.get(id_=MODEL_ID)
importance = model.get_feature_importance(
        data_source=fdl.SqlSliceQueryDataSource()
)

Get Mutual Information

2.x:

PROJECT_ID = 'YOUR_PROJECT_NAME'
DATASET_ID = 'YOUR_DATASET_NAME'
QUERY=f'select * from production.{MODEL_NAME} limit 10'

mutual_info = client.get_mutual_information(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    query=QUERY,
    COLUMN_NAME=''
)

3.x: