API Methods 3.x

Alerts

Alert Rule

Alert rule object contains the below fields.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the alert rule.
name	str	-	Unique name of the alert rule.
model	Model	-	Details of the model.
project	Project	-	Details of the project to which the dataset belongs.
baseline	Optional[Baseline]	None	Details of the baseline for the alert.
segment	Optional[Segment]	None	Details of segment for the alert.
priority	Union[str, Priority]	-	To set the priority for the alert rule. Select from: 1. Priority.LOW 2. Priority.MEDIUM 3. Priority.HIGH.
compare_to	Union[str, CompareTo]	-	Select from the two: 1. CompareTo.RAW_VALUE 2. CompareTo.TIME_PERIOD
metric_id	Union[str, UUID]	-	Type of alert metric UUID or string denoting metric name.
critical_threshold	float	-	Threshold value to crossing which a critical level severity alert will be triggered.
condition	Union[str, AlertCondition]	-	Select from: 1. AlertCondition.LESSER 2. AlertCondition.GREATER
bin_size	Union[str, BinSize]	-	Size of the bin for alert rule.
columns	Optional[List[str]]	None	List of column names on which alert rule is to be created. It can take ['ANY'] to check for all columns.
baseline_id	Optional[UUID]	None	UUID of the baseline for the alert.
segment_id	Optional[UUID]	None	UUID of segment for the alert
compare_bin_delta	Optional[int]	None	Number of bin_size to compare the metric to a previous time period.
warning_threshold	Optional[float]	None	Threshold value to crossing which a warning level severity alert will be triggered.
created_at	datetime	-	Time at which alert rule was created.
updated_at	datetime	-	Latest time at which alert rule was updated.
evaluation_delay	int	0	To introduce a delay in the evaluation of the alert, specifying the duration in hours. The delay period must not exceed one year(8760 hours).

constructor()

Initialise a new alert rule on Fiddler Platform.

Parameters

Parameter	Type	Default	Description
name	str	-	Unique name of the model
model_id	UUID	-	Details of the model.
metric_id	Union[str, UUID]	-	Type of alert metric UUID or enum.
columns	Optional[List[str]]	None	List of column names on which alert rule is to be created. It can take ['ANY'] to check for all columns.
baseline_id	Optional[UUID]	None	UUID of the baseline for the alert.
segment_id	Optional[UUID]	None	UUID of the segment for the alert.
priority	Union[str, Priority]	-	To set the priority for the alert rule. Select from: 1. Priority.LOW 2. Priority.MEDIUM 3. Priority.HIGH.
compare_to	Union[str, CompareTo]	-	Select from the two: 1. CompareTo.RAW_VALUE (absolute alert) 2. CompareTo.TIME_PERIOD (relative alert)
compare_bin_delta	Optional[int]	None	Compare the metric to a previous time period in units of bin_size.
warning_threshold	Optional[float]	None	Threshold value to crossing which a warning level severity alert will be triggered.
critical_threshold	float	-	Threshold value to crossing which a critical level severity alert will be triggered.
condition	Union[str, AlertCondition]	-	Select from: 1. AlertCondition.LESSER 2. AlertCondition.GREATER
bin_size	Union[str, BinSize]	-	Size of the bin for alert rule.
evaluation_delay	int	0	To introduce a delay in the evaluation of the alert, specifying the duration in hours. The delay period must not exceed one year(8760 hours).

Usage

MODEL_NAME = 'test_model'
PROJECT_NAME = 'test_project'
SEGMENT_NAME = 'test_segment'
project = fdl.Project.from_name(name=PROJECT_NAME)
model = fdl.Model.from_name(name=MODEL_NAME, project_id=project.id)
segment = fdl.Segment.from_name(name=SEGMENT_NAME, model_id=model.id)

alert_rule = fdl.AlertRule(
        name='alert_name',
        model_id=model.id,
        metric_id='drift',
        priority=fdl.Priority.HIGH,
        compare_to=fdl.CompareTo.TIME_PERIOD,
        compare_bin_delta=1,
        condition=fdl.AlertCondition.GREATER,
        bin_size=fdl.BinSize.HOUR,
        critical_threshold=0.5,
        warning_threshold=0.1,
        columns=['gender', 'creditscore'],
        segment_id=segment.id,
        evaluation_delay=10
    )

---

create()

Set a new alert rule.

Parameters

No

Usage

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'
SEGMENT_NAME = 'test_segment'
project = fdl.Project.from_name(name=PROJECT_NAME)
model = fdl.Model.from_name(name=MODEL_NAME, project_id=project.id)
segment = fdl.Segment.from_name(name=SEGMENT_NAME, model_id=model.id)

alert_rule = fdl.AlertRule(
        name='alert_name',
        model_id=model.id,
        metric_id='drift',
        priority=fdl.Priority.HIGH,
        compare_to=fdl.CompareTo.TIME_PERIOD,
        compare_bin_delta=1,
        condition=fdl.AlertCondition.GREATER,
        bin_size=fdl.BinSize.HOUR,
        critical_threshold=0.5,
        warning_threshold=0.1,
        columns=['gender', 'creditscore'],
        segment_id=segment.id,
    ).create()

Returns

Return Type	Description
AlertRule	Alert rule instance.

get()

Get a single alert rule.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique identifier for the alert rule.

Usage

ALERT_RULE_ID='ed8f18e6-c319-4374-8884-71126a6bab85'

alert = fdl.AlertRule.get(id_=ALERT_RULE_ID)

Returns

Return Type	Description
AlertRule	Alert rule instance.

Raises

Error code	Issue
NotFound	Alert rule with given identifier not found.
Forbidden	Current user may not have permission to view details of alert rule.

---

list()

Get a list of all alert rules in the organization.

Parameters

Parameter	Type	Default	Description
model_id	Optional[UUID]	None	Unique identifier for the model to which alert rule belongs.
project_id	Optional[UUID]	None	Unique identifier for the project to which alert rule belongs
metric_id	Union[str, UUID]	-	Type of alert metric UUID or enum.
columns	Optional[List[str]]	None	List of column names on which alert rule is to be created. It can take ['ANY'] to check for all columns.
baseline_id	Optional[UUID]	None	UUID of the baseline for the alert.
ordering	Optional[List[str]]	None	List of Alert Rule fields to order by. Eg. [‘alert_time_bucket’] or [‘- alert_time_bucket’] for descending order.

Usage

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)

alerts = fdl.AlertRule.list(model_id=model.id)

Returns

Return Type	Description
Iterator[AlertRule]	Iterable of alert rule instances.

---

delete()

Delete an existing alert rule.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the alert rule .

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

alert_rule.delete()

Returns

No

Raises

Error code	Issue
NotFound	Alert rule with given identifier not found.
Forbidden	Current user may not have permission to view details of alert rule.

---

enable_notifications()

Enable notification for an alert rule.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the alert rule .

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

alert_rule.enable_notifications()

Returns

None

Raises

Error code	Issue
NotFound	Alert rule with given identifier not found.
Forbidden	Current user may not have permission to view details of alert rule.

---

disable_notification()

Disable notification for an alert rule.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the alert rule .

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

alert_rule.disable_notification()

Returns

None

Raises

Error code	Issue
NotFound	Alert rule with given identifier not found.
Forbidden	Current user may not have permission to view details of alert rule.

---

AlertNotifications

Alert notifications for an alert rule.

Parameter	Type	Default	Description
emails	Optional[List[str]]	None	List of emails to send notification to.
pagerduty_services	Optional[List[str]]	None	List of pagerduty services to trigger the alert to.
pagerduty_severity	Optional[str]	None	Severity of pagerduty.
webhooks	Optional[List[UUID]]	None	List of webhook UUIDs.

set_notification_config()

Set notifications for an alert rule.

Parameters

Parameter	Type	Default	Description
emails	Optional[List[str]]	None	List of emails to send notification to.
pagerduty_services	Optional[List[str]]	None	List of pagerduty services to trigger the alert to.
pagerduty_severity	Optional[str]	None	Severity of pagerduty.
webhooks	Optional[List[UUID]]	None	List of webhook UUIDs.

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

notifications = alert_rule.set_notification_config(
        emails=['[e-mail]', '[e-mail]'],
        webhooks=[
            'e20bf4cc-d2cf-4540-baef-d96913b14f1b',
            '6e796fda-0111-4a72-82cd-f0f219e903e1',
        ],
    )

Returns

Return Type	Description
AlertNotifications	Alert notifications for an alert rule.

If we pagerduty_severity is passed without pagerduty_services then the pagerduty_severity is ignored.

Raises

Error code	Issue
BadRequest	All 4 input parameters are empty.
ValueError	Webhook ID is incorrect.

get_notification_config()

Get notification configuration for an alert rule.

Parameters

None

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

notification_config = alert_rule.get_notification_config()

Returns

Return Type	Description
Notification Config	Alert notifications for an alert rule.

Raises

Error code	Issue
BadRequest	All 4 input parameters are empty.
ValueError	Webhook ID is incorrect.

---

TriggeredAlert

Alert records triggered for an alert rule.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the triggered alert rule.
alert_rule_id	UUID	-	Unique identifier for the alert rule which needs to be triggered.
alert_run_start_time	int	-	Timestamp of alert rule evaluation in epoch.
alert_time_bucket	int	-	Timestamp pointing to the start of the time bucket in epoch.
alert_value	float	-	Value of the metric for alert_time_bucket.
baseline_time_bucket	Optional[int]	None	Timestamp pointing to the start of the baseline time bucket in epoch, only if alert rule is of 'time period' based comparison.
baseline_value	Optional[float]	None	Value of the metric for baseline_time_bucket.
is_alert	bool	-	Boolean to indicate if alert was supposed to be triggered.
severity	str	-	Severity of alert represented by SeverityEnum, calculated based on value of metric and alert rule thresholds.
failure_reason	str	-	String message if there was a failure sending notification.
message	str	-	String message sent as a part of email notification.
feature_name	Optional[str]	None	Name of feature for which alert was triggered.
alert_record_main_version	int	-	Main version of triggered alert record in int, incremented when the value of severity changes.
alert_record_sub_version	int	-	Sub version of triggered alert record in int, incremented when another alert with same severity as before is triggered.
created_at	datetime	-	Time at which trigger alert rule was created.
updated_at	datetime	-	Latest time at which trigger alert rule was updated.

list()

List alert records triggered for an alert rule.

Parameters

Parameter	Type	Default	Description
alert_rule_id	UUID	-	Unique identifier for the alert rule which needs to be triggered.
start_time	Optional[datetime]	None	Start time to filter trigger alerts in yyyy-MM-dd format, inclusive.
end_time	Optional[datetime]	None	End time to filter trigger alerts in yyyy-MM-dd format, inclusive.
ordering	Optional[List[str]]	None	List of Alert Rule fields to order by. Eg. [‘alert_time_bucket’] or [‘- alert_time_bucket’] for descending order.

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
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)

alerts_list = fdl.AlertRule.list(model_id=model.id)
alert_rule = None

for alert in alerts_list:
    if ALERT_NAME == alert.name:
    alert_rule = alert

alert_records = fdl.AlertRecord.list(
        alert_rule_id=alert_rule.id,
        start_time=datetime(2023, 12, 18),
        end_time=datetime(2023, 12, 25),
    )

Returns

Return Type	Description
Iterator[AlertRecord]	Iterable of triggered alert rule instances for an alert rule.

---

Baselines

Baseline datasets are used for making comparisons with production data.

A baseline dataset should be sampled from your model's training set, so it can serve as a representation of what the model expects to see in production.

Baseline

Baseline object contains the below fields.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the baseline.
name	str	-	Baseline name.
type_	str	-	Type of baseline. Type can be static(Pre-production or production) or rolling(production).
start_time	Optional[int]	None	Epoch to be used as start time for STATIC baseline.
end_time	Optional[int]	None	Epoch to be used as end time for STATIC baseline.
offset	Optional[int]	None	Offset in seconds relative to current time to be used for ROLLING baseline.
window_size	Optional[int]	None	Span of window in seconds to be used for ROLLING baseline.
row_count	Optional[int]	None	Number of rows in baseline.
model	Model	-	Details of the model.
project	Project	-	Details of the project to which the baseline belongs.
dataset	Dataset	-	Details of the dataset from which baseline is derived.
created_at	datetime	-	Time at which baseline was created.
updated_at	datetime	-	Latest time at which baseline was updated.

constructor()

Initialize a new baseline instance.

Parameters

Parameter	Type	Default	Description
name	str	-	Unique name of the baseline.
model_id	UUID	-	Unique identifier for the model to add baseline to.
environment	EnvType	-	Type of environment. Can either be PRE_PRODUCTION or PRODUCTION.
type_	str	-	Type of Baseline. Type can be static(Pre-production or production) or rolling(production).
dataset_id	Optional[UUID]	None	Unique identifier for the dataset on which the baseline is created.
start_time	Optional[int]	None	Epoch to be used as start time for STATIC baseline.
end_time	Optional[int]	None	Epoch to be used as end time for STATIC baseline.
offset_delta	Optional[int]	None	Number of times of WindowBinSize to be used for ROLLING baseline. offset = offset_delta * window_bin_size
window_bin_size	Optional[str]	None	Span of window in seconds to be used for ROLLING baseline using WindowBinSize

Usage

BASELINE_NAME = 'YOUR_BASELINE_NAME'
PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'
DATASET_NAME = 'YOUR_DATASET_NAME'

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

baseline = fdl.Baseline(
        name=BASELINE_NAME,
        model_id=model.id,
        environment=fdl.EnvType.PRE_PRODUCTION,
        dataset_id=dataset.id,
        type_=fdl.BaselineType.STATIC,
    )

create()

Adds a baseline to Fiddler.

Parameters

No

Usage

BASELINE_NAME = 'YOUR_BASELINE_NAME'
PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'
DATASET_NAME = 'YOUR_DATASET_NAME'

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

baseline = fdl.Baseline(
        name=BASELINE_NAME,
        model_id=model.id,
        environment=fdl.EnvType.PRE_PRODUCTION,
        dataset_id=dataset.id,
        type_=fdl.BaselineType.STATIC,
    ).create()

Returns

Return Type	Description
Baseline	Baseline instance.

Raises

Error code	Issue
Conflict	Baseline with same name may exist in project .
NotFound	Given dataset may not exist in for the input model.
ValueError	Validation failures like wrong window size, start_time, end_time etc

get()

Get baseline from Fiddler Platform based on UUID.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique identifier for the baseline.

Usage

BASELINE_ID = 'af05646f-0cef-4638-84c9-0d195df2575d'
baseline = fdl.Baseline.get(id_=BASELINE_ID)

Returns

Return Type	Description
Baseline	Baseline instance.

Raises

Error code	Issue
NotFound	Baseline with given identifier not found.
Forbidden	Current user may not have permission to view details of baseline.

---

from_name()

Get baseline from Fiddler Platform based on name.

Parameters

Parameter	Type	Default	Description
name	str	-	Name of the baseline.
model_id	UUID | str	-	Unique identifier for the model.

Usage

BASELINE_NAME = 'YOUR_BASELINE_NAME'
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)


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

Returns

Return Type	Description
Baseline	Baseline instance.

Raises

Error code	Issue
NotFound	Baseline with given identifier not found.
Forbidden	Current user may not have permission to view details of baseline.

---

list()

List all baselines accessible to user.

Parameters

Parameter	Type	Default	Description
model_id	UUID	-	UUID of the model associated with baseline.

Usage

MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'
baselines = fdl.Baseline.list(model_id=MODEL_ID)

Returns

Return Type	Description
Iterable[Baseline]	Iterable of all baseline objects.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of baseline.

---

delete()

Deletes a baseline.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the baseline .

Usage

BASELINE_NAME = 'YOUR_BASELINE_NAME'
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)
baseline = fdl.Baseline.from_name(name=BASELINE_NAME, model_id=model.id)

baseline.delete()

Returns

None

Raises

Error code	Issue
NotFound	Baseline with given identifier not found.
Forbidden	Current user may not have permission to delete baseline.

---

CustomMetrics

Customized metrics for your specific use case.

CustomMetric

CustomMetric object contains the below parameters.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the custom metric.
name	str	-	Custom metric name.
model_id	UUID	-	UUID of the model in which the custom metric is being added.
definition	str	-	Definition of the custom metric.
description	Optional[str]	None	Description of the custom metric.
created_at	datetime	-	Time of creation of custom metric.

constructor()

Initialise a new custom metric.

Parameters

Parameter	Type	Default	Description
name	str	-	Custom metric name.
model_id	UUID	-	UUID of the model in which the custom metric is being added.
definition	str	-	Definition of the custom metric.
description	Optional[str]	None	Description of the custom metric.

Usage

METRIC_NAME = 'YOUR_CUSTOM_METRIC_NAME'
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)

metric = fdl.CustomMetric(
        name=METRIC_NAME,
        model_id=model.id,
        definition="average(if(\"spend_amount\">1000, \"spend_amount\", 0))", #Use Fiddler Query Languge (FQL) to define your custom metrics
        description='Get average spend for users spending over $1000',
    )

get()

Get CustomMetric from Fiddler Platform based on model UUID.

Parameters

Parameter	Type	Default	Description
model_id	UUID	-	UUID of the model associated with the custom metrics.

Usage

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)

metrics = fdl.CustomMetric.list(model_id=model.id)

Returns

Return Type	Description
Iterable[CustomMetric]	Iterable of all custom metric objects.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of custom metric.

from_name()

Get CustomMetric from Fiddler Platform based on name and model UUID.

Parameters

Parameter	Type	Default	Description
name	str	-	Name of the custom metric.
model_id	UUID | str	-	Unique identifier for the model.

Usage

METRIC_NAME = 'YOUR_CUSTOM_METRIC_NAME'
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)

metric = fdl.CustomMetric.from_name(
        name=METRIC_NAME,
        model_id=model.id
    )

Returns

Return Type	Description
CustomMetric	Custom Metric instance.

Raises

Error code	Issue
NotFound	Custom metric with given identifier not found.
Forbidden	Current user may not have permission to view details of custom metric.

create()

Creates a custom metric for a model on Fiddler Platform.

Parameters

None

Usage

METRIC_NAME = 'YOUR_CUSTOM_METRIC_NAME'
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)

metric = fdl.CustomMetric(
        name=METRIC_NAME,
        model_id=model.id,
        definition="average(if(\"spend_amount\">1000, \"spend_amount\", 0))", #Use Fiddler Query Languge (FQL) to define your custom metrics
        description='Get average spend for users spending over $1000',
    ).create()

Returns

Return Type	Description
CustomMetric	Custom Metric instance.

Raises

Error code	Issue
Conflict	Custom metric with same name may exist in project .
BadRequest	Invalid definition.
NotFound	Given model may not exist.

delete()

Delete a custom metric.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the custom metric.

Usage

METRIC_NAME = 'YOUR_CUSTOM_METRIC_NAME'
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)

metric = fdl.CustomMetric.from_name(name=METRIC_NAME, model_id=model.id)
metric.delete()

Returns

No

Raises

Error code	Issue
NotFound	Custom metric with given identifier not found.
Forbidden	Current user may not have permission to delete custom metric.

---

Datasets

Datasets (or baseline datasets) are used for making comparisons with production data.

A baseline dataset should be sampled from your model's training set, so it can serve as a representation of what the model expects to see in production.

For more information, see Uploading a Baseline Dataset.

For guidance on how to design a baseline dataset, see Designing a Baseline Dataset.

---

Dataset

Dataset object contains the below parameters.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the dataset.
name	str	-	Dataset name.
row_count	Optional[int]	None	Number of rows in dataset.
model	Model	-	Details of the model.
project	Project	-	Details of the project to which the dataset belongs.
organization	Organization	-	Details of the organization to which the dataset belongs.

---

get()

Get dataset from Fiddler Platform based on UUID.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique identifier for the dataset.

Usage

DATASET_ID = 'ba6ec4e4-7188-44c5-ba84-c2cb22b4bb00'
dataset = fdl.Dataset.get(id_=DATASET_ID)

Returns

Return Type	Description
Dataset	Dataset instance.

Raises

Error code	Issue
NotFound	Dataset with given identifier not found.
Forbidden	Current user may not have permission to view details of dataset.

---

from_name()

Get dataset from Fiddler Platform based on name and model UUID.

Usage params

Parameter	Type	Default	Description
name	str	-	Name of the dataset.
model_id	UUID | str	-	Unique identifier for the model.

Usage

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'
DATASET_NAME = 'YOUR_DATASET_NAME'

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

DATASET = fdl.Dataset.from_name(name=DATASET_NAME,
                                model_id=model.id
                               )

Returns

Return Type	Description
Dataset	Dataset instance.

Raises

Error code	Issue
NotFound	Dataset not found in the given project name.
Forbidden	Current user may not have permission to view details of dataset.

---

list()

Get a list of all datasets associated to a model.

Parameters

Parameter	Type	Default	Description
model_id	UUID	-	UUID of the model associated with baseline.

Usage

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'
DATASET_NAME = 'YOUR_DATASET_NAME'

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

datasets = fdl.Dataset.list(model_id=model.id)

Returns

Return Type	Description
Iterable[Dataset]	Iterable of all dataset objects.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of dataset.

---

Jobs

Get job details.

Job

Job object contains the below fields.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the job.
name	str	-	Name of the job.
status	str	-	Current status of job.
progress	float	-	Progress of job completion.
info	dict	-	Dictionary containing resource_type, resource_name, project_name.
error_message	Optional[str]	None	Message for job failure, if any.
error_reason	Optional[str]	None	Reason for job failure, if any.
extras	Optional[dict]	None	Metadata regarding the job.

get()

Get the job instance using job UUID.

Parameters

Parameter	Type	Default	Description
id_	UUID	-	Unique UUID of the project to which model is associated.
verbose	bool	False	Flag to get extras metadata about the tasks executed.

Usage

JOB_ID = '1531bfd9-2ca2-4a7b-bb5a-136c8da09ca1'
job = fdl.Job.get(id_=JOB_ID)

Returns

Return Type	Description
Job	Single job object for the input params.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of job.

---

wait()

Wait for job to complete either with success or failure status.

Parameters

Parameter	Type	Default	Description
interval	Optional[int]	3	Interval in seconds between polling for job status.
timeout	Optional[int]	1800	Timeout in seconds for iterator to stop.

Usage

JOB_ID = '1531bfd9-2ca2-4a7b-bb5a-136c8da09ca1'
job = fdl.Job.get(id_=JOB_ID)
job.wait()

Returns

Return Type	Description
Job	Single job object for the input params.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of job.
TimeoutError	When the default time out of 1800 secs.

watch()

Watch job status at given interval and yield job object.

Parameters

Parameter	Type	Default	Description
interval	Optional[int]	3	Interval in seconds between polling for job status.
timeout	Optional[int]	1800	Timeout in seconds for iterator to stop.

Usage

JOB_ID = '1531bfd9-2ca2-4a7b-bb5a-136c8da09ca1'
job = fdl.Job.get(id_=JOB_ID)
job.watch()

Returns

Return Type	Description
Iterator[Job]	Iterator of job objects.

Raises

Error code	Issue
Forbidden	Current user may not have permission to view details of job.
TimeoutError	When the default time out of 1800 secs.

---

Models

A model is a representation of your machine learning model. Each model can be used for monitoring, explainability, and fairness capabilities.

You do not need to upload your model artifact in order to onboard your model, but doing so will significantly improve the quality of explanations generated by Fiddler.

Model

Model object contains the below parameters.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the model.
name	str	-	Unique name of the model (only alphanumeric and underscores are allowed).
input_type	ModelInputType	ModelInputType.TABULAR	Input data type used by the model.
task	ModelTask	ModelTask.NOT_SET	Task the model is designed to address.
task_params	ModelTaskParams	-	Task parameters given to a particular model.
schema	ModelSchema	-	Model schema defines the details of each column.
version	Optional[str]	-	Unique version name within a model
spec	ModelSpec	-	Model spec defines how model columns are used along with model task.
description	str	-	Description of the model.
event_id_col	str	-	Column containing event id.
event_ts_col	str	-	Column containing event timestamp.
xai_params	XaiParams	-	Explainability parameters of the model.
artifact_status	str	-	Artifact Status of the model.
artifact_files	list[dict]	-	Dictionary containing file details of model artifact.
is_binary_ranking_model	bool	-	True if model is ModelTask.RANKING and has only 2 target classes.
created_at	datetime	-	Time at which model was created.
updated_at	datetime	-	Latest time at which model was updated.
created_by	User	-	Details of the who created the model.
updated_by	User	-	Details of the who last updated the model.
project	Project	-	Details of the project to which the model belongs.
organization	Organization	-	Details of the organization to which the model belongs.

constructor()

Initialize a new model instance.

Parameters

Parameter	Type	Default	Description
name	str	-	Unique name of the model
project_id	UUID	-	Unique identifier for the project to which model belongs.
input_type	ModelInputType	ModelInputType.TABULAR	Input data type used by the model.
task	ModelTask	ModelTask.NOT_SET	Task the model is designed to address.
schema	ModelSchema	-	Model schema defines the details of each column.
spec	ModelSpec	-	Model spec defines how model columns are used along with model task.
version	Optional[str]	-	Unique version name within a model
task_params	ModelTaskParams	-	Task parameters given to a particular model.
description	str	-	Description of the model.
event_id_col	str	-	Column containing event id.
event_ts_col	str	-	Column containing event timestamp.
xai_params	XaiParams	-	Explainability parameters of the model.

from_data()

Build model instance from the given dataframe or file(csv/parquet).

Parameters

Parameter	Type	Default	Description
source	pd.DataFrame | Path | str	-	Pandas dataframe or path to csv/parquet file
name	str	-	Unique name of the model
project_id	UUID | str	-	Unique identifier for the project to which model belongs.
input_type	ModelInputType	ModelInputType.TABULAR	Input data type used by the model.
task	ModelTask	ModelTask.NOT_SET	Task the model is designed to address.
spec	ModelSpec	-	Model spec defines how model columns are used along with model task.
version	Optional[str]	-	Unique version name within a model
task_params	ModelTaskParams	-	Task parameters given to a particular model.
description	Optional[str]	-	Description of the model.
event_id_col	Optional[str]	-	Column containing event id.
event_ts_col	Optional[str]	-	Column containing event timestamp.
xai_params	XaiParams	-	Explainability parameters of the model.
max_cardinality	Optional[int]	None	Max cardinality to detect categorical columns.
sample_size	Optional[int]	-	No. of samples to use for generating schema.

Usage

MODEL_NAME = 'example_model'
PROJECT_ID = '1531bfd9-2ca2-4a7b-bb5a-136c8da09ca1'
MODEL_SPEC = {
  'custom_features': [],
  'decisions': ['Decisions'],
  'inputs': [
    'CreditScore',
    'Geography',
  ],
  'metadata': [],
  'outputs': ['probability_churned'],
  'schema_version': 1,
  'targets': ['Churned'],
}

# Without version
model = fdl.Model.from_data(
  source=<file_path>,
  name=MODEL_NAME,
  project_id=PROJECT_ID,
  spec=fdl.ModelSpec(**MODEL_SPEC),
)

# With version
model = fdl.Model.from_data(
  source=<file_path>,
  name=MODEL_NAME,
  version='v2',
  project_id=PROJECT_ID,
  spec=fdl.ModelSpec(**MODEL_SPEC),
)

Returns

Return Type	Description
Model	Model instance.

Notes

• from_data will not create a model entry on Fiddler Platform. Instead this method only returns a model instance which can be edited, call .create() to onboard the model to Fiddler Platform.

• spec is optional to from_data method. However, a spec with at least inputs is required for model onboarding.

• Make sure spec is passed to from_data method if model requires custom features. This method generates centroids which are needed for custom feature drift computation

• If version is not explicitly passed, Fiddler Platform will treat it as v1 version of the model.

create()

Onboard a new model to Fiddler Platform

Parameters

No

Usage

model = fdl.Model.from_data(...)
model.create()

Returns

Return Type	Description
Model	Model instance.

Raises

Error code	Issue
Conflict	Model with same name may exist in project .

get()

Get model from Fiddler Platform based on UUID.

Parameters

Parameter	Type	Default	Description
id_	UUID | str	-	Unique identifier for the model.

Returns

Return Type	Description
Model	Model instance.

Raises

Error code	Issue
NotFound	Model with given identifier not found.
Forbidden	Current user may not have permission to view details of model.

Usage

MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'
model = fdl.Model.get(id_=MODEL_ID)

from_name()

Get model from Fiddler Platform based on name and project UUID.

Parameters

Parameter	Type	Default	Description
name	str	-	Name of the model.
project_id	UUID | str	-	Unique identifier for the project.
version	Optiona[str]	-	Unique version name within a model

version parameter is available from fiddler-client==3.1 onwards

\

Usage

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'

PROJECT = fdl.Project.from_name(name=PROJECT_NAME)

# Without version
MODEL = fdl.Model.from_name(name=MODEL_NAME, project_id=PROJECT.id)

# With version
MODEL = fdl.Model.from_name(name=MODEL_NAME, project_id=PROJECT.id, version='v2')

Returns

Return Type	Description
Model	Model instance.

Notes

• When the version is not passed, then the model created without any version will be fetched. Fiddler internally assigns version=v1 when not passed.

• When the version is passed, method will fetch the model corresponding to that specific version.

\

Raises

Error code	Issue
NotFound	Model not found in the given project name.
Forbidden	Current user may not have permission to view details of model.

list()

Gets all models of a project.

Parameters

Parameter	Type	Default	Description
project_id	Optional[UUID]	-	Unique UUID of the project to which model is associated.
name	<