API Methods 3.x

Alerts

AlertRule

AlertRule object contains the below fields.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier of the AlertRule.
name	str	-	Unique name of the AlertRule.
model		-	The associated model details.
project		-	The associated project details
baseline		None	The associated baseline.
segment		None	Details of segment for the alert.
priority		-	To set the priority for the AlertRule. Select from: 1. Priority.LOW 2. Priority.MEDIUM 3. Priority.HIGH.
compare_to		-	Select from the two: 1. CompareTo.RAW_VALUE 2. CompareTo.TIME_PERIOD
metric_id	Union[str, UUID]	-
critical_threshold	float	-	Critical alert is triggered when this value satisfies the condition to the selected metric_id.
condition		-	Select from: 1. AlertCondition.LESSER 2. AlertCondition.GREATER
bin_size		-	Bin size for example fdl.BinSize.HOUR.
columns	Optional[List[str]]	None	List of 1 or more column names for the rule to evaluate. Use ['__ANY__'] to evaluate 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	Indicates previous period for comparison e.g. for fdl.BinSize.DAY, compare_bin_delta=1 will compare 1 day back, compare_bin_delta=7 will compare 7 days back.
warning_threshold	Optional[float]	None	Warning alert is triggered when this value satisfies the condition to the selected metric_id.
created_at	datetime	-	The creation timestamp.
updated_at	datetime	-	The timestamp of most recent update.
evaluation_delay	int	0	Specifies a delay in hours before AlertRule is evaluated. The delay period must not exceed one year(8760 hours).

constructor()

Initialize a new AlertRule 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 AlertRule 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		-	To set the priority for the AlertRule. Select from: 1. Priority.LOW 2. Priority.MEDIUM 3. Priority.HIGH.
compare_to		-	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		-	Select from: 1. AlertCondition.LESSER 2. AlertCondition.GREATER
bin_size		-	Size of the bin for AlertRule.
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'
BASELINE_NAME = 'test_baseline'
SEGMENT_NAME = 'test_segment'

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

alert_rule = fdl.AlertRule(
    name='Bank Churn Drift Hawaii Region',
    model_id=model.id,
    baseline_id=baseline.id,
    metric_id='jsd',
    priority=fdl.Priority.HIGH,
    compare_to=fdl.CompareTo.TIME_PERIOD,
    compare_bin_delta=1,
    condition=fdl.AlertCondition.GREATER,
    bin_size=fdl.BinSize.DAY,
    critical_threshold=0.5,
    warning_threshold=0.1,
    columns=['gender', 'creditscore'],
    segment_id=segment.id,
    evaluation_delay=1
)

---

create()

Create a new AlertRule.

Parameters

No

Usage

MODEL_NAME = 'test_model'
PROJECT_NAME = 'test_project'
BASELINE_NAME = 'test_baseline'
SEGMENT_NAME = 'test_segment'

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

alert_rule = fdl.AlertRule(
    name='Bank Churn Drift Hawaii Region',
    model_id=model.id,
    baseline_id=baseline.id,
    metric_id='jsd',
    priority=fdl.Priority.HIGH,
    compare_to=fdl.CompareTo.TIME_PERIOD,
    compare_bin_delta=1,
    condition=fdl.AlertCondition.GREATER,
    bin_size=fdl.BinSize.DAY,
    critical_threshold=0.5,
    warning_threshold=0.1,
    columns=['gender', 'creditscore'],
    segment_id=segment.id,
    evaluation_delay=1
).create()

Returns

Return Type	Description
	AlertRule instance.

get()

Get a single AlertRule.

Parameters

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

Usage

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

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

Returns

Return Type	Description
	AlertRule instance.

Raises

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

---

list()

Get a list of AlertRules .

Parameters

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

Usage

MODEL_ID = '299c7b40-b87c-4dad-bb94-251dbcd3cbdf'

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

Returns

Return Type	Description
	Iterator of AlertRule instances.

---

delete()

Delete an existing AlertRule.

Parameters

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

Usage

MODEL_ID = '299c7b40-b87c-4dad-bb94-251dbcd3cbdf'
ALERT_RULE_NAME = 'Testing Alert API'

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

for alert_rule in alert_rules:
    if alert_rule.name == ALERT_RULE_NAME:
        alert_rule.delete()
        break

Returns

No

Raises

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

---

enable_notifications()

Enable an AlertRule's notification.

Parameters

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

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
MODEL_ID = '299c7b40-b87c-4dad-bb94-251dbcd3cbdf'

alerts_list = fdl.AlertRule.list(model_id=MODEL_ID)
for alert_rule in alerts_list:
    if ALERT_NAME == alert.name:
        alert_rule.enable_notifications()
        break

Returns

None

Raises

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

---

disable_notifications()

Disable notifications for an AlertRule.

Parameters

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

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
MODEL_ID = '299c7b40-b87c-4dad-bb94-251dbcd3cbdf'

alerts_list = fdl.AlertRule.list(model_id=MODEL_ID)
for alert_rule in alerts_list:
    if ALERT_NAME == alert.name:
        alert_rule.disable_notifications()

Returns

None

Raises

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

---

Alert Notifications

Alert notifications for an AlertRule.

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

set_notification_config()

Set NotificationConfig for an AlertRule.

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

Usage

ALERT_RULE_ID = '72e8835b-cde2-4dd2-a435-a35d4b51196b'
rule = fdl.AlertRule.get(id_=ALERT_RULE_ID)

rule.set_notification_config(
  emails=['[e-mail]', '[e-mail]'],
  webhooks=['8b403d99-530a-4c5a-a519-89688d65ddc1'], # Webhook UUID
  PagerDuty_services = ['PagerDuty_service_1','PagerDuty_service_2'], # PagerDuty service names
  PagerDuty_severity = 'critical' # Only applies to PagerDuty, ignored otherwise
)

Returns

Return Type	Description
NotificationConfig	Alert notification settings for an AlertRule.

If PagerDuty_severity is passed without specifying 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 AlertRule.

Parameters

None

Usage

ALERT_RULE_ID = '72e8835b-cde2-4dd2-a435-a35d4b51196b'
rule = fdl.AlertRule.get(id_=ALERT_RULE_ID)

rule.get_notification_config()

Returns

Return Type	Description
NotificationConfig	Alert notification settings for an AlertRule.

Raises

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

---

Triggered Alerts

AlertRecord

An AlertRecord details an AlertRule's triggered alert.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the triggered AlertRule.
alert_rule_id	UUID	-	Unique identifier for the AlertRule which needs to be triggered.
alert_run_start_time	int	-	Timestamp of AlertRule 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 AlertRule 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	-
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 AlertRule was created.
updated_at	datetime	-	Latest time at which trigger AlertRule was updated.

list()

List AlertRecords triggered for an AlertRule.

Parameters

Parameter	Type	Default	Description
alert_rule_id	UUID	-	Unique identifier for the AlertRule 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 AlertRule fields to order by. Eg. [‘alert_time_bucket’] or [‘- alert_time_bucket’] for descending order.

Usage

ALERT_NAME = "YOUR_ALERT_NAME"
MODEL_ID = '299c7b40-b87c-4dad-bb94-251dbcd3cbdf'
triggered_alerts = None

alerts_list = fdl.AlertRule.list(model_id=MODEL_ID)
for alert_rule in alerts_list:
    if ALERT_NAME == alert.name:
        triggered_alerts = fdl.AlertRecord.list(
            alert_rule_id=ALERT_RULE_ID,
            start_time=datetime(2024, 9, 1), # optional
            end_time=datetime(2024, 9, 24), # optional
            ordering = ['alert_time_bucket'], # ['-alert_time_bucket'] for descending sort, optional.
        )

Returns

Return Type	Description
	Iterable of triggered AlertRule instances for an AlertRule.

---

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_		-	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		-	Details of the model.
project		-	Details of the project to which the baseline belongs.
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		-	Type of environment. Can either be PRE_PRODUCTION or PRODUCTION.
type_		-	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
window_bin_size	Optional[str]	None

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 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 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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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

Returns

Return Type	Description
	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 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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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.

---

Custom Metrics

User-defined metrics to extend Fiddler's built-in metrics.

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()

Initialize 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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

metric = fdl.CustomMetric(
    name=METRIC_NAME,
    model_id=MODEL_ID,
    definition="average(if(\"spend_amount\">1000, \"spend_amount\", 0))", #Use Fiddler Query Language (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

MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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

Returns

Return Type	Description
	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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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

Returns

Return Type	Description
	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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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

Returns

Return Type	Description
	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'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

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.

---

Dataset

Dataset object contains the below parameters.

Parameter	Type	Default	Description
id	UUID	-	Unique identifier for the dataset.
name	str	-	Dataset name.
row_count	int	None	Number of rows in dataset.
model_id		-	Unique identifier of the associated model
project_id		-	Unique identifier of the associated project

---

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 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

DATASET_NAME = 'YOUR_DATASET_NAME'
MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

dataset = fdl.Dataset.from_name(
    name=DATASET_NAME,
    model_id=MODEL_ID
)

Returns

Return Type	Description
	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

MODEL_ID = '4531bfd9-2ca2-4a7b-bb5a-136c8da09ca2'

datasets = fdl.Dataset.list(model_id=MODEL_ID)

Returns

Return Type	Description
	Iterable of all dataset objects.

Raises

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

---

Jobs

A Job is used to track asynchronous processes such as batch publishing of data.

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
	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
	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 = "69f846db-5aac-44fe-9fa5-14f40048e4b2"
job = fdl.Job.get(id_=JOB_ID)

for ijob in job.watch(interval=30, timeout=1200):
    print(f'Status: {ijob.status} - progress: {ijob.progress}')

Returns

Return Type	Description
	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 which can be used for monitoring, explainability, and more. 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.TABULAR	Input data type used by the model.
task		ModelTask.NOT_SET	Task the model is designed to address.
task_params		-	Task parameters given to a particular model.
schema		-	Model schema defines the details of each column.
version	Optional[str]	-	Unique version name within a model
spec		-	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		-	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	-
created_at	datetime	-	Time at which model was created.
updated_at	datetime	-	Latest time at which model was updated.
created_by		-	Details of the who created the model.
updated_by		-	Details of the who last updated the model.
project		-	Details of the project to which the model belongs.
organization		-	Details of the organization to which the model belongs.

constructor()

Initialize a new model instance.

Usage

model = fdl.Model(
    name='model_name',
    project_id=project.id,
    task=fdl.ModelTask.BINARY_CLASSIFICATION,
    task_params=fdl.ModelTaskParams(target_class_order=['no', 'yes']),
    schema=model_schema,
    spec=model_spec,
    event_id_col='column_name_1',
    event_ts_col='column_name_2',
)

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.TABULAR	Input data type used by the model.
task		ModelTask.NOT_SET	Task the model is designed to address.
schema		-	Model schema defines the details of each column.
spec		-	Model spec defines how model columns are used along with model task.
version	Optional[str]	-	Unique version name within a model
task_params		-	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		-	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.TABULAR	Input data type used by the model.
task		ModelTask.NOT_SET	Task the model is designed to address.
spec		-	Model spec defines how model columns are used along with model task.
version	Optional[str]	-	Unique version name within a model
task_params		-	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		-	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 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 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 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	Optional[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 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	UUID | str	-	Unique UUID of the project to which model is associated.
name	Optional[str]	-	Model name. Pass this to fetch all versions of a model.

Returns

Return Type	Description
	Iterable of model compact objects.

Errors

Error code	Issue
Forbidden	Current user may not have permission to the given project.

Usage example

PROJECT_NAME = 'YOUR_PROJECT_NAME'
MODEL_NAME = 'YOUR_MODEL_NAME'

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

models = fdl.Model.list(project_id=project.id)

Notes

Since Model contains a lot of information, list operations does not return all the fields of a model. Instead this method returns ModelCompact objects on which .fetch() can be called to get the complete Model instance. For most of the use-cases, ModelCompact objects are sufficient.

update()

Update an existing model. Only following fields are allowed to be updated, backend will ignore if any other field is updated on the instance.

Parameters

Parameter	Type	Default	Description
version	Optional[str]	None	Model version name
xai_params		None	Explainability parameters of the model.
description	Optional[str]	None	Description of the model.
event_id_col	Optional[str]	None	Column containing event id.
event_ts_col	Optional[str]	None	Column containing event timestamp.

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

Usage

model.description = 'YOUR_MODEL_DESCRIPTION'
model.update()

Returns

No

Raises

Error code	Issue
BadRequest	If field is not updatable.

duplicate()

Duplicate the model instance with the given version name.

This call will not save the model on Fiddler Platform. After making changes to the model instance, call .create() to add the model version to Fiddler Platform.

Added in version 3.1.0

Parameters

Parameter	Type	Default	Description
version	Optional[str]	None	Model version name

Usage

PROJECT_ID = '1531bfd9-2ca2-4a7b-bb5a-136c8da09ca1'
MODEL_NAME = 'test_model'