import fiddler as fdl

URL = 'https://app.fiddler.ai'
ORG_ID = 'my_org'
AUTH_TOKEN = 'p9uqlkKz1zAA3KAU8kiB6zJkXiQoqFgkUgEa1sv4u58'

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

import fiddler as fdl

URL = 'https://app.fiddler.ai'
ORG_ID = 'my_org'
AUTH_TOKEN = 'p9uqlkKz1zAA3KAU8kiB6zJkXiQoqFgkUgEa1sv4u58'

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

proxies = {
    'http' : 'http://proxy.example.com:1234',
    'https': 'https://proxy.example.com:5678'
}

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

%%writefile fiddler.ini

[FIDDLER]
url = https://app.fiddler.ai
org_id = my_org
auth_token = p9uqlkKz1zAA3KAU8kiB6zJkXiQoqFgkUgEa1sv4u58

client = fdl.FiddlerApi()

response = client.list_projects()

[
  'project_a',
  'project_b',
  'project_c'
]

PROJECT_ID = 'example_project'

client.create_project(
    project_id=PROJECT_ID
)

{
    'project_name': 'example_project'
}

PROJECT_ID = 'example_project'

client.delete_project(
    project_id=PROJECT_ID
)

True

PROJECT_ID = "example_project"

client.list_datasets(
    project_id=PROJECT_ID
)

[
    'dataset_a',
    'dataset_b',
    'dataset_c'
]

import pandas as pd

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(
    df=df
)

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

{'uuid': '7046dda1-2779-4987-97b4-120e6185cc0b',
 'name': 'Ingestion dataset Upload',
 'info': {'project_name': 'example_model',
  'resource_name': 'acme_data',
  'resource_type': 'DATASET'},
 'status': 'SUCCESS',
 'progress': 100.0,
 'error_message': None,
 'error_reason': None}

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'

client.delete_dataset(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

'Dataset deleted example_dataset'

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'

dataset_info = client.get_dataset_info(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

#NA

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'
MODEL_ID = 'example_model'

dataset_info = client.get_dataset_info(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

model_task = fdl.ModelTask.BINARY_CLASSIFICATION
model_target = 'target_column'
model_output = 'output_column'
model_features = [
    'feature_1',
    'feature_2',
    'feature_3'
]

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    target=model_target,
    outputs=[model_output],
    model_task=model_task
)

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

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

client.add_model_artifact(  
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    model_dir='model_dir/',
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

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

# with deployment_params
client.add_model_surrogate(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    deployment_params=fdl.DeploymentParams(cpu=250, memory=500)
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

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

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

model_info = client.get_model_info(
    project_id=PROJECT_ID,
    model_id=MODEL_ID
)

PROJECT_ID = 'example_project'

client.list_models(
    project_id=PROJECT_ID
)

[
    'model_a',
    'model_b',
    'model_c'
]

import pathlib

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

model_dir = pathlib.Path('model_dir')

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

True

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

client.update_model_artifact(  
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    model_dir='model_dir/',
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

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

# with deployment_params
client.update_model_surrogate(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    deployment_params=fdl.DeploymentParams(cpu=250, memory=500)
)

PROJECT_NAME = 'example_project'
MODEL_NAME = 'example_model'

client.get_model_deployment(
    project_id=PROJECT_NAME,
    model_id=MODEL_NAME,
)

{
  id: 106548,
  uuid: UUID("123e4567-e89b-12d3-a456-426614174000"),
  model_id: "MODEL_NAME",
  project_id : "PROJECT_NAME",
  organization_id: "ORGANIZATION_NAME",
  artifact_type: "PYTHON_PACKAGE",
  deployment_type: "BASE_CONTAINER",
  active: True,
  image_uri: "md-base/python/machine-learning:1.0.0",
  replicas: 1,
  cpu: 250,
  memory: 512,
  created_by: {
    id: 4839,
    full_name: "first_name last_name",
    email: "[email protected]",
  },
  updated_by: {
    id: 4839,
    full_name: "first_name last_name",
    email: "[email protected]",
  },
  created_at: datetime(2023, 1, 27, 10, 9, 39, 793829),
  updated_at: datetime(2023, 1, 30, 17, 3, 17, 813865),
  job_uuid: UUID("539j9630-a69b-98d5-g496-326117174805")
}

{
  id: 106548,
  uuid: UUID("123e4567-e89b-12d3-a456-426614174000"),
  model_id: "MODEL_NAME",
  project_id : "PROJECT_NAME",
  organization_id: "ORGANIZATION_NAME",
  artifact_type: "PYTHON_PACKAGE",
  deployment_type: "BASE_CONTAINER",
  active: True,
  image_uri: "md-base/python/machine-learning:1.0.0",
  replicas: 1,
  cpu: 250,
  memory: 512,
  created_by: {
    id: 4839,
    full_name: "first_name last_name",
    email: "[email protected]",
  },
  updated_by: {
    id: 4839,
    full_name: "first_name last_name",
    email: "[email protected]",
  },
  created_at: datetime(2023, 1, 27, 10, 9, 39, 793829),
  updated_at: datetime(2023, 1, 30, 17, 3, 17, 813865),
  job_uuid: UUID("539j9630-a69b-98d5-g496-326117174805")
}

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

example_event = {
    'feature_1': 20.7,
    'feature_2': 45000,
    'feature_3': True,
    'output_column': 0.79,
    'target_column': 1
}

client.publish_event(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    event=example_event,
    event_id='event_001',
    event_timestamp=1637344470000
)

'66cfbeb6-5651-4e8b-893f-90286f435b8d'

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

df_events = pd.read_csv('events.csv')

client.publish_events_batch(
        project_id=PROJECT_ID,
        model_id=MODEL_ID,
        batch_source=df_events,
        timestamp_field='inference_date')

{'status': 202,
 'job_uuid': '4ae7bd3a-2b3f-4444-b288-d51e07b6736d',
 'files': ['ssoqj_tmpzmczjuob.csv'],
 'message': 'Successfully received the event data. Please allow time for the event ingestion to complete in the Fiddler platform.'}

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

path_to_batch = 'events_batch.avro'

schema = {
    '__static': {
        '__project': PROJECT_ID,
        '__model': MODEL_ID
    },
    '__dynamic': {
        'feature_1': 'features/feature_1',
        'feature_2': 'features/feature_2',
        'feature_3': 'features/feature_3',
        'output_column': 'outputs/output_column',
        'target_column': 'targets/target_column'
      ORG = '__org'
13      MODEL = '__model'
14      PROJECT = '__project'
15      TIMESTAMP = '__timestamp'
16      DEFAULT_TIMESTAMP = '__default_timestamp'
17      TIMESTAMP_FORMAT = '__timestamp_format'
18      EVENT_ID = '__event_id'
19      IS_UPDATE_EVENT = '__is_update_event'
20      STATUS = '__status'
21      LATENCY = '__latency'
22      ITERATOR_KEY = '__iterator_key'
    }
}

client.publish_events_batch_schema(
    batch_source=path_to_batch,
    publish_schema=schema
)

{'status': 202,
 'job_uuid': '5ae7bd3a-2b3f-4444-b288-d51e098a01d',
 'files': ['rroqj_tmpzmczjttb.csv'],
 'message': 'Successfully received the event data. Please allow time for the event ingestion to complete in the Fiddler platform.'}

PROJECT_NAME = 'example_project'
BASELINE_NAME = 'example_pre'
DATASET_NAME = 'example_validation'
MODEL_NAME = 'example_model'


client.add_baseline(
  project_id=PROJECT_NAME,
  model_id=MODEL_NAME,
  baseline_id=BASELINE_NAME,
  type=BaselineType.PRE_PRODUCTION, 
  dataset_id=DATASET_NAME, 
)

from datetime import datetime
from fiddler import BaselineType, WindowSize

start = datetime(2023, 1, 1, 0, 0) # 12 am, 1st Jan 2023
end = datetime(2023, 1, 2, 0, 0) # 12 am, 2nd Jan 2023

PROJECT_NAME = 'example_project'
BASELINE_NAME = 'example_static'
DATASET_NAME = 'example_dataset'
MODEL_NAME = 'example_model'
START_TIME = start.timestamp()
END_TIME = end.timestamp()


client.add_baseline(
  project_id=PROJECT_NAME,
  model_id=MODEL_NAME,
  baseline_id=BASELINE_NAME,
  type=BaselineType.STATIC_PRODUCTION,
  start_time=START_TIME,
  end_time=END_TIME,
)

from fiddler import BaselineType, WindowSize

PROJECT_NAME = 'example_project'
BASELINE_NAME = 'example_rolling'
DATASET_NAME = 'example_validation'
MODEL_NAME = 'example_model'

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

PROJECT_NAME = 'example_project'
MODEL_NAME = 'example_model'
BASELINE_NAME = 'example_preconfigured'


baseline = client.get_baseline(
  project_id=PROJECT_NAME,
  model_id=MODEL_NAME,
  baseline_id=BASELINE_NAME,
)

PROJECT_NAME = 'example_project'
MODEL_NAME = 'example_model'

# list baselines across all models within a project
client.list_baselines(
  project_id=ROJECT_NAME
)

# list baselines within a model
client.list_baselines(
  project_id=PROJECT_NAME,
  model_id=MODEL_NAME,
)

PROJECT_NAME = 'example_project'
MODEL_NAME = 'example_model'
BASELINE_NAME = 'example_preconfigured'


client.delete_baseline(
  project_id=PROJECT_NAME,
  model_id=MODEL_NAME,
  baseline_id=BASELINE_NAME,
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

monitoring_config = {
    'min_bin_value': 3600,
    'time_ranges': ['Day', 'Week', 'Month', 'Quarter', 'Year'],
    'default_time_range': 7200
}

client.add_monitoring_config(
    config_info=monitoring_config,
    project_id=PROJECT_ID,
    model_id=MODEL_ID
)

# To add a Performance type alert rule which triggers an email notification 
# when precision metric is 5% higher than that from 1 hr bin one day ago.

import fiddler as fdl

notifications_config = client.build_notifications_config(
    emails = "[email protected], [email protected]",
)
client.add_alert_rule(
    name = "perf-gt-5prec-1hr-1d-ago",
    project_id = 'project-a',
    model_id = 'model-a',
    alert_type = fdl.AlertType.PERFORMANCE,
    metric = fdl.Metric.PRECISION,
    bin_size = fdl.BinSize.ONE_HOUR, 
    compare_to = fdl.CompareTo.TIME_PERIOD,
    compare_period = fdl.ComparePeriod.ONE_DAY,
    warning_threshold = 0.05,
    critical_threshold = 0.1,
    condition = fdl.AlertCondition.GREATER,
    priority = fdl.Priority.HIGH,
    notifications_config = notifications_config
)

# To add Data Integrity alert rule which triggers an email notification when 
# published events have more than 5 null values in any 1 hour bin for the _age_ column. 
# Notice compare_to = fdl.CompareTo.RAW_VALUE.

import fiddler as fdl

client.add_alert_rule(
    name = "age-null-1hr",
    project_id = 'project-a',
    model_id = 'model-a',
    alert_type = fdl.AlertType.DATA_INTEGRITY,
    metric = fdl.Metric.MISSING_VALUE,
    bin_size = fdl.BinSize.ONE_HOUR, 
    compare_to = fdl.CompareTo.RAW_VALUE,
    priority = fdl.Priority.HIGH,
    warning_threshold = 5,
    critical_threshold = 10,
    condition = fdl.AlertCondition.GREATER,
    column = "age",
    notifications_config = notifications_config
)

# To add a Data Drift type alert rule which triggers an email notification 
# when PSI metric for 'age' column from an hr is 5% higher than that from 'baseline_name' dataset.

import fiddler as fdl

client.add_baseline(project_id='project-a', 
                    model_id='model-a', 
                    baseline_name='baseline_name', 
                    type=fdl.BaselineType.PRE_PRODUCTION, 
                    dataset_id='dataset-a')

notifications_config = client.build_notifications_config(
    emails = "[email protected], [email protected]",
)

client.add_alert_rule(
    name = "psi-gt-5prec-age-baseline_name",
    project_id = 'project-a',
    model_id = 'model-a',
    alert_type = fdl.AlertType.DATA_DRIFT,
    metric = fdl.Metric.PSI,
    bin_size = fdl.BinSize.ONE_HOUR, 
    compare_to = fdl.CompareTo.RAW_VALUE,
    warning_threshold = 0.05,
    critical_threshold = 0.1,
    condition = fdl.AlertCondition.GREATER,
    priority = fdl.Priority.HIGH,
    notifications_config = notifications_config,
    columns = ["age"],
    baseline_id = 'baseline_name'
)

# To add Drift type alert rule which triggers an email notification when 
# value of JSD metric is more than 0.5 for one hour bin for  _age_ or _gender_ columns. 
# Notice compare_to = fdl.CompareTo.RAW_VALUE.

import fiddler as fdl
notifications_config = client.build_notifications_config(
    emails = "[email protected], [email protected]",
)

client.add_alert_rule(
    name = "jsd_multi_col_1hr",
    project_id = 'project-a',
    model_id = 'model-a',
    alert_type = fdl.AlertType.DATA_DRIFT,
    metric = fdl.Metric.JSD,
    bin_size = fdl.BinSize.ONE_HOUR, 
    compare_to = fdl.CompareTo.RAW_VALUE,
    warning_threshold = 0.4,
    critical_threshold = 0.5,
    condition = fdl.AlertCondition.GREATER,
    priority = fdl.Priority.HIGH,
    notifications_config = notifications_config,
    columns = ["age", "gender"],
)

# To add Data Integrity alert rule which triggers an email notification when 
# published events have more than 5 percent null values in any 1 hour bin for the _age_ column. 

import fiddler as fdl

client.add_alert_rule(
    name = "age_null_percentage_greater_than_10",
    project_id = 'project-a',
    model_id = 'model-a',
    alert_type = fdl.AlertType.DATA_INTEGRITY,
    metric = 'null_violation_percentage',
    bin_size = fdl.BinSize.ONE_HOUR, 
    compare_to = fdl.CompareTo.RAW_VALUE,
    priority = fdl.Priority.HIGH,
    warning_threshold = 5,
    critical_threshold = 10,
    condition = fdl.AlertCondition.GREATER,
    column = "age",
    notifications_config = notifications_config
)

[AlertRule(alert_rule_uuid='9b8711fa-735e-4a72-977c-c4c8b16543ae',
           organization_name='some_org_name',
           project_id='project-a',
           model_id='model-a',
           name='perf-gt-5prec-1hr-1d-ago',
           alert_type=AlertType.PERFORMANCE,
           metric=Metric.PRECISION,
           priority=Priority.HIGH,
           compare_to='CompareTo.TIME_PERIOD,
           compare_period=ComparePeriod.ONE_DAY,
           compare_threshold=None,
           raw_threshold=None,
           warning_threshold=0.05,
           critical_threshold=0.1,
           condition=AlertCondition.GREATER,
           bin_size=BinSize.ONE_HOUR)]

AlertRule(alert_rule_uuid='e1aefdd5-ef22-4e81-b869-3964eff8b5cd', 
organization_name='some_org_name', 
project_id='project-a', 
model_id='model-a', 
name='age-null-1hr', 
alert_type=AlertType.DATA_INTEGRITY, 
metric=Metric.MISSING_VALUE, 
column='age', 
priority=Priority.HIGH, 
compare_to=CompareTo.RAW_VALUE, 
compare_period=None, 
warning_threshold=5, 
critical_threshold=10, 
condition=AlertCondition.GREATER,
bin_size=BinSize.ONE_HOUR)

AlertRule(alert_rule_uuid='e1aefdd5-ef22-4e81-b869-3964eff8b5cd', 
organization_name='some_org_name', 
project_id='project-a', 
model_id='model-a', 
name='psi-gt-5prec-age-baseline_name', 
alert_type=AlertType.DATA_DRIFT, 
metric=Metric.PSI, 
priority=Priority.HIGH, 
compare_to=CompareTo.RAW_VALUE, 
compare_period=None, 
warning_threshold=5, 
critical_threshold=10, 
condition=AlertCondition.GREATER,
bin_size=BinSize.ONE_HOUR,
columns=['age'],
baseline_id='baseline_name')

[AlertRule(alert_rule_uuid='9b8711fa-735e-4a72-977c-c4c8b16543ae',
           organization_name='some_org_name',
           project_id='project-a',
           model_id='model-a',
           name='perf-gt-5prec-1hr-1d-ago',
           alert_type=AlertType.DRIFT,
           metric=Metric.JSD,
           priority=Priority.HIGH,
           compare_to='CompareTo.RAW_VALUE,
           compare_period=ComparePeriod.ONE_HOUR,
           compare_threshold=None,
           raw_threshold=None,
           warning_threshold=0.4,
           critical_threshold=0.5,
           condition=AlertCondition.GREATER,
           bin_size=BinSize.ONE_HOUR,
           columns=['age', 'gender'])]

import fiddler as fdl

alert_rules = client.get_alert_rules(
    project_id = 'project-a',
    model_id = 'model-a', 
    alert_type = fdl.AlertType.DATA_INTEGRITY, 
    metric = fdl.Metric.MISSING_VALUE,
    columns = ["age", "gender"], 
    ordering = ['critical_threshold'], #['-critical_threshold'] for descending
    limit= 4, ## to set number of rules to show in one go
    offset = 0, # page offset (multiple of limit)
)

trigerred_alerts = client.get_triggered_alerts(
    alert_rule_uuid = "588744b2-5757-4ae9-9849-1f4e076a58de",
    start_time = "2022-05-01",
    end_time = "2022-09-30",
  	ordering = ['alert_time_bucket'], #['-alert_time_bucket'] for descending
    limit= 4, ## to set number of rules to show in one go
    offset = 0, # page offset
)

client.delete_alert_rule(
    alert_rule_uuid = "588744b2-5757-4ae9-9849-1f4e076a58de",
)

notifications_config = client.build_notifications_config(
    emails = "[email protected]",
)

notifications_config = client.build_notifications_config(
  emails = "[email protected],[email protected]",
  pagetduty_services = 'pd_service_1',
  pagerduty_severity = 'critical'
)

notifications_config = client.build_notifications_config(
    webhooks = ["894d76e8-2268-4c2e-b1c7-5561da6f84ae", "3814b0ac-b8fe-4509-afc9-ae86c176ef13"]
)

{'emails': {'email': '[email protected]'}, 'pagerduty': {'service': '', 'severity': ''}, 'webhooks': []}

client.add_webhook(
        name='range_violation_channel',
        url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d',
        provider='SLACK')
)

Webhook(uuid='df2397d3-23a8-4eb3-987a-2fe43b758b08',
        name='range_violation_channel', organization_name='some_org_name',
        url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d',
        provider='SLACK')

client.delete_webhook(
    uuid = "ffcc2ddf-f896-41f0-bc50-4e7b76bb9ace",
)

client.get_webhook(
    alert_rule_uuid = "a5f085bc-6772-4eff-813a-bfc20ff71002",
)

Webhook(uuid='a5f085bc-6772-4eff-813a-bfc20ff71002',
        name='binary_classification_alerts_channel',
        organization_name='some_org',
        url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d,
        provider='SLACK')

response = client.get_webhooks()

[
  Webhook(uuid='e20bf4cc-d2cf-4540-baef-d96913b14f1b', name='model_1_alerts', organization_name='some_org', url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d', provider='SLACK'),
 	Webhook(uuid='bd4d02d7-d1da-44d7-b194-272b4351cff7', name='drift_alerts_channel', organization_name='some_org', url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d', provider='SLACK'),
 	Webhook(uuid='761da93b-bde2-4c1f-bb17-bae501abd511', name='project_1_alerts', organization_name='some_org', url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d', provider='SLACK')
]

client.update_webhook(uuid='e20bf4cc-d2cf-4540-baef-d96913b14f1b',
                      name='drift_violation',
                      url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d',
                      provider='SLACK')

Webhook(uuid='e20bf4cc-d2cf-4540-baef-d96913b14f1b',
        name='drift_violation', organization_name='some_org_name',
        url='https://hooks.slack.com/services/T9EAVLUQ5/P982J/G8ISUczk37hxQ15C28d',
        provider='SLACK')

updated_alert_configs = client.update_alert_notification_status(
    notification_status = True,
    model_id = "9f8180d3-3fa0-40c4-8656-b9b1d2de1b69",
)

updated_alert_configs = client.update_alert_notification_status(
    notification_status = True,
    alert_config_ids = ["9b8711fa-735e-4a72-977c-c4c8b16543ae"],
)

[AlertRule(alert_rule_uuid='9b8711fa-735e-4a72-977c-c4c8b16543ae',
           organization_name='some_org_name',
           project_id='project-a',
           model_id='model-a',
           name='perf-gt-5prec-1hr-1d-ago',
           alert_type=AlertType.PERFORMANCE,
           metric=Metric.PRECISION,
           priority=Priority.HIGH,
           compare_to='CompareTo.TIME_PERIOD,
           compare_period=ComparePeriod.ONE_DAY,
           compare_threshold=None,
           raw_threshold=None,
           warning_threshold=0.05,
           critical_threshold=0.1,
           condition=AlertCondition.GREATER,
           bin_size=BinSize.ONE_HOUR)]

METRIC_ID = '7d06f905-80b1-4a41-9711-a153cbdda16c'

custom_metric = client.get_custom_metric(
  metric_id=METRIC_ID
)

PROJECT_ID = 'my_project'
MODEL_ID = 'my_model'

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

PROJECT_ID = 'my_project'
MODEL_ID = 'my_model'

definition = """
    average(if(Prediction < 0.5 and Target == 1, -40, if(Prediction >= 0.5 and Target == 0, -400, 250)))
"""

client.add_custom_metric(
    name='Loan Value',
    description='A custom value score assigned to a loan',
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    definition=definition
)

METRIC_ID = '7d06f905-80b1-4a41-9711-a153cbdda16c'

client.delete_custom_metric(
  metric_id=METRIC_ID
)

SEGMENT_ID = '7d06f905-80b1-4a41-9711-a153cbdda16c'

segment = client.get_segment(
  segment_id=SEGMENT_ID
)

PROJECT_ID = 'my_project'
MODEL_ID = 'my_model'

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

PROJECT_ID = 'my_project'
MODEL_ID = 'my_model'

definition = """
    age > 50
"""

client.add_segment(
    name='Over 50',
    description='All people over the age of 50',
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    definition=definition
)

Segment(
  id='50a1c32d-c2b4-4faf-9006-f4aeadd7a859',
  name='Over 50',
  project_name='my_project',
  organization_name='mainbuild',
  definition='age > 50',
  description='All people over the age of 50',
  created_at=None,
  created_by=None
)

SEGMENT_ID = '7d06f905-80b1-4a41-9711-a153cbdda16c'

client.delete_segment(
  segment_id=SEGMENT_ID
)

import pandas as pd

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

input_df = pd.read_csv('example_data.csv')

# Example without chunk size specified:
predictions = client.get_predictions(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_df=input_df,
)


# Example with chunk size specified:
predictions = client.get_predictions(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_df=input_df,
    chunk_size=1000,
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset

# FIDDLER SHAP - Dataset reference data source
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=300),
    explanation_type='FIDDLER_SHAP',
    num_permutations=200,
    ci_level=0.95,
)

# FIDDLER SHAP - Slice ref data source
row = df.to_dict(orient='records')[0]
query = f'SELECT * from {DATASET_ID}.{MODEL_ID} WHERE sulphates >= 0.8'
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=100),
    explanation_type='FIDDLER_SHAP',
    num_permutations=200,
    ci_level=0.95,
)

# FIDDLER SHAP - Multi-class classification (top classes)
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    ref_data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID),
    explanation_type='FIDDLER_SHAP',
    top_n_class=2
)

# IG (Not available by default, need to be enabled via package.py)
row = df.to_dict(orient='records')[0]
client.get_explanation(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    input_data_source=fdl.RowDataSource(row=row),
    explanation_type='IG',
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset'

# Feature Impact for TABULAR data - Dataset Data Source
feature_impact = client.get_feature_impact(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=200),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

# Feature Impact for TABULAR data - Slice Query data source
query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditScore > 700'
feature_impact = client.get_feature_impact(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=80),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

# Feature Impact for TEXT data
feature_impact = client.get_feature_impact(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=50),
    output_columns= ['probability_A', 'probability_B'],
  	min_support=30
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset'


# Feature Importance - Dataset data source
feature_importance = client.get_feature_importance(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=200),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

# Feature Importance - Slice Query data source
query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditScore > 700'
feature_importance = client.get_feature_importance(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=80),
    num_iterations=300,
    num_refs=200,
    ci_level=0.90,
)

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'
MODEL_ID = 'example_model'

query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditScore > 700'
mutual_info = client.get_mutual_information(
  project_id=PROJECT_ID,
  dataset_id=DATASET_ID,
  query=query,
  column_name='Geography',
  normalized=True,
  num_samples=20000,
)

import pandas as pd

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'
MODEL_ID = 'example_model'

query = f""" SELECT * FROM "{DATASET_ID}.{MODEL_ID}" """

slice_df = client.get_slice(
    sql_query=query,
    project_id=PROJECT_ID
)

import pandas as pd

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

query = f""" SELECT * FROM "production.{MODEL_ID}" """

slice_df = client.get_slice(
    sql_query=query,
    project_id=PROJECT_ID
)

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
DATASET_ID = 'example_dataset'

# Fairness - Dataset data source
fairness_metrics = client.get_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.DatasetDataSource(dataset_id=DATASET_ID, num_samples=200),
    protected_features=['feature_1', 'feature_2'],
    positive_outcome='Approved',
    score_threshold=0.6
)

# Fairness - Slice Query data source
query = f'SELECT * FROM {DATASET_ID}.{MODEL_ID} WHERE CreditSCore > 700'
fairness_metrics = client.get_fairness(
    project_id=PROJECT_ID,
    model_id=MODEL_ID,
    data_source=fdl.SqlSliceQueryDataSource(query=query, num_samples=200),
    protected_features=['feature_1', 'feature_2'],
    positive_outcome='Approved',
    score_threshold=0.6
)

client.list_org_roles()

{
    'members': [
        {
            'id': 1,
            'user': '[email protected]',
            'email': '[email protected]',
            'isLoggedIn': True,
            'firstName': 'Example',
            'lastName': 'Administrator',
            'imageUrl': None,
            'settings': {'notifyNews': True,
                'notifyAccount': True,
                'sliceTutorialCompleted': True},
            'role': 'ADMINISTRATOR'
        },
        {
            'id': 2,
            'user': '[email protected]',
            'email': '[email protected]',
            'isLoggedIn': True,
            'firstName': 'Example',
            'lastName': 'User',
            'imageUrl': None,
            'settings': {'notifyNews': True,
                'notifyAccount': True,
                'sliceTutorialCompleted': True},
            'role': 'MEMBER'
        }
    ],
    'invitations': [
        {
            'id': 3,
            'user': '[email protected]',
            'role': 'MEMBER',
            'invited': True,
            'link': 'http://app.fiddler.ai/signup/vSQWZkt3FP--pgzmuYe_-3-NNVuR58OLZalZOlvR0GY'
        }
    ]
}

PROJECT_ID = 'example_project'

client.list_project_roles(
    project_id=PROJECT_ID
)

{
    'roles': [
        {
            'user': {
                'email': '[email protected]'
            },
            'team': None,
            'role': {
                'name': 'OWNER'
            }
        },
        {
            'user': {
                'email': '[email protected]'
            },
            'team': None,
            'role': {
                'name': 'READ'
            }
        }
    ]
}

client.list_teams()

{
    'example_team': {
        'members': [
            {
                'user': '[email protected]',
                'role': 'MEMBER'
            },
            {
                'user': '[email protected]',
                'role': 'MEMBER'
            }
        ]
    }
}

PROJECT_ID = 'example_project'

client.share_project(
    project_name=PROJECT_ID,
    role='READ',
    user_name='[email protected]'
)

PROJECT_ID = 'example_project'

client.unshare_project(
    project_name=PROJECT_ID,
    role='READ',
    user_name='[email protected]'
)

columns = [
    fdl.Column(
        name='feature_1',
        data_type=fdl.DataType.FLOAT
    ),
    fdl.Column(
        name='feature_2',
        data_type=fdl.DataType.INTEGER
    ),
    fdl.Column(
        name='feature_3',
        data_type=fdl.DataType.BOOLEAN
    ),
    fdl.Column(
        name='output_column',
        data_type=fdl.DataType.FLOAT
    ),
    fdl.Column(
        name='target_column',
        data_type=fdl.DataType.INTEGER
    )
]

dataset_info = fdl.DatasetInfo(
    display_name='Example Dataset',
    columns=columns
)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(df=df, max_inferred_cardinality=100)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(df=df, max_inferred_cardinality=100)

dataset_info_dict = dataset_info.to_dict()

new_dataset_info = fdl.DatasetInfo.from_dict(
    deserialized_json={
        'dataset': dataset_info_dict
    }
)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(df=df, max_inferred_cardinality=100)

dataset_info_dict = dataset_info.to_dict()

{
    'name': 'Example Dataset',
    'columns': [
        {
            'column-name': 'feature_1',
            'data-type': 'float'
        },
        {
            'column-name': 'feature_2',
            'data-type': 'int'
        },
        {
            'column-name': 'feature_3',
            'data-type': 'bool'
        },
        {
            'column-name': 'output_column',
            'data-type': 'float'
        },
        {
            'column-name': 'target_column',
            'data-type': 'int'
        }
    ],
    'files': []
}

inputs = [
    fdl.Column(
        name='feature_1',
        data_type=fdl.DataType.FLOAT
    ),
    fdl.Column(
        name='feature_2',
        data_type=fdl.DataType.INTEGER
    ),
    fdl.Column(
        name='feature_3',
        data_type=fdl.DataType.BOOLEAN
    )
]

outputs = [
    fdl.Column(
        name='output_column',
        data_type=fdl.DataType.FLOAT
    )
]

targets = [
    fdl.Column(
        name='target_column',
        data_type=fdl.DataType.INTEGER
    )
]

model_info = fdl.ModelInfo(
    display_name='Example Model',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION,
    inputs=inputs,
    outputs=outputs,
    targets=targets
)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(
    df=df
)

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    features=[
        'feature_1',
        'feature_2',
        'feature_3'
    ],
    outputs=[
        'output_column'
    ],
    target='target_column',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION
)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(
    df=df
)

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    features=[
        'feature_1',
        'feature_2',
        'feature_3'
    ],
    outputs=[
        'output_column'
    ],
    target='target_column',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION
)

model_info_dict = model_info.to_dict()

new_model_info = fdl.ModelInfo.from_dict(
    deserialized_json={
        'model': model_info_dict
    }
)

import pandas as pd

df = pd.read_csv('example_dataset.csv')

dataset_info = fdl.DatasetInfo.from_dataframe(
    df=df
)

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    features=[
        'feature_1',
        'feature_2',
        'feature_3'
    ],
    outputs=[
        'output_column'
    ],
    target='target_column',
    input_type=fdl.ModelInputType.TABULAR,
    model_task=fdl.ModelTask.BINARY_CLASSIFICATION
)

model_info_dict = model_info.to_dict()

{
    'name': 'Example Model',
    'input-type': 'structured',
    'model-task': 'binary_classification',
    'inputs': [
        {
            'column-name': 'feature_1',
            'data-type': 'float'
        },
        {
            'column-name': 'feature_2',
            'data-type': 'int'
        },
        {
            'column-name': 'feature_3',
            'data-type': 'bool'
        },
        {
            'column-name': 'target_column',
            'data-type': 'int'
        }
    ],
    'outputs': [
        {
            'column-name': 'output_column',
            'data-type': 'float'
        }
    ],
    'datasets': [],
    'targets': [
        {
            'column-name': 'target_column',
            'data-type': 'int'
        }
    ],
    'custom-explanation-names': []
}