orca_sdk.classification_model#

ClassificationModel #

A handle to a classification model in OrcaCloud

Attributes:

id (str) –

Unique identifier for the model
name (str) –

Unique name of the model
memoryset (LabeledMemoryset) –

Memoryset that the model uses
head_type (RACHeadType) –

Classification head type of the model
num_classes (int) –

Number of distinct classes the model can predict
memory_lookup_count (int) –

Number of memories the model uses for each prediction
weigh_memories (bool | None) –

If using a KNN head, whether the model weighs memories by their lookup score
min_memory_weight (float | None) –

If using a KNN head, minimum lookup score memories have to be over to not be ignored
created_at (datetime) –

When the model was created

last_prediction `property` #

last_prediction

Last prediction made by the model

Note

If the last prediction was part of a batch prediction, the last prediction from the batch is returned. If no prediction has been made yet, a LookupError is raised.

create `classmethod` #

create(
    name,
    memoryset,
    head_type="KNN",
    *,
    num_classes=None,
    memory_lookup_count=None,
    weigh_memories=True,
    min_memory_weight=None,
    if_exists="error"
)

Create a new classification model

Parameters:

name (str) –

Name for the new model (must be unique)
memoryset (LabeledMemoryset) –

Memoryset to attach the model to
head_type (Literal['BMMOE', 'FF', 'KNN', 'MMOE'], default: 'KNN' ) –

Type of model head to use
num_classes (int | None, default: None ) –

Number of classes this model can predict, will be inferred from memoryset if not specified
memory_lookup_count (int | None, default: None ) –

Number of memories to lookup for each prediction, by default the system uses a simple heuristic to choose a number of memories that works well in most cases
weigh_memories (bool, default: True ) –

If using a KNN head, whether the model weighs memories by their lookup score
min_memory_weight (float | None, default: None ) –

If using a KNN head, minimum lookup score memories have to be over to not be ignored
if_exists (CreateMode, default: 'error' ) –

What to do if a model with the same name already exists, defaults to "error". Other option is "open" to open the existing model.

Returns:

ClassificationModel –

Handle to the new model in the OrcaCloud

Raises:

ValueError –

If the model already exists and if_exists is "error" or if it is "open" and the existing model has different attributes.

Examples:

Create a new model using default options:

>>> model = ClassificationModel.create(
...    "my_model",
...    LabeledMemoryset.open("my_memoryset"),
... )

Create a new model with non-default model head and options:

>>> model = ClassificationModel.create(
...     name="my_model",
...     memoryset=LabeledMemoryset.open("my_memoryset"),
...     head_type=RACHeadType.MMOE,
...     num_classes=5,
...     memory_lookup_count=20,
... )

open `classmethod` #

open(name)

Get a handle to a classification model in the OrcaCloud

Parameters:

name (str) –

Name or unique identifier of the classification model

Returns:

ClassificationModel –

Handle to the existing classification model in the OrcaCloud

Raises:

LookupError –

If the classification model does not exist

exists `classmethod` #

exists(name_or_id)

Check if a classification model exists in the OrcaCloud

Parameters:

name_or_id (str) –

Name or id of the classification model

Returns:

bool –

True if the classification model exists, False otherwise

all `classmethod` #

all()

Get a list of handles to all classification models in the OrcaCloud

Returns:

list[ClassificationModel] –

List of handles to all classification models in the OrcaCloud

drop `classmethod` #

drop(name_or_id, if_not_exists='error')

Delete a classification model from the OrcaCloud

Warning

This will delete the model and all associated data, including predictions, evaluations, and feedback.

Parameters:

name_or_id (str) –

Name or id of the classification model
if_not_exists (DropMode, default: 'error' ) –

What to do if the classification model does not exist, defaults to "error". Other option is "ignore" to do nothing if the classification model does not exist.

Raises:

LookupError –

If the classification model does not exist and if_not_exists is "error"

predict #

predict(
    value: list[str],
    expected_labels: list[int] | None = None,
    tags: set[str] = set(),
) -> list[LabelPrediction]

predict(
    value: str,
    expected_labels: int | None = None,
    tags: set[str] = set(),
) -> LabelPrediction

predict(value, expected_labels=None, tags=set())

Predict label(s) for the given input value(s) grounded in similar memories

Parameters:

value (list[str] | str) –

Value(s) to get predict the labels of
expected_labels (list[int] | int | None, default: None ) –

Expected label(s) for the given input to record for model evaluation
tags (set[str], default: set() ) –

Tags to add to the prediction(s)

Returns:

list[LabelPrediction] | LabelPrediction –

Label prediction or list of label predictions

Examples:

Predict the label for a single value:

>>> prediction = model.predict("I am happy", tags={"test"})
LabelPrediction({label: <positive: 1>, confidence: 0.95, input_value: 'I am happy' })

Predict the labels for a list of values:

>>> predictions = model.predict(["I am happy", "I am sad"], expected_labels=[1, 0])
[
    LabelPrediction({label: <positive: 1>, confidence: 0.95, input_value: 'I am happy'}),
    LabelPrediction({label: <negative: 0>, confidence: 0.05, input_value: 'I am sad'}),
]

predictions #

predictions(limit=100, offset=0, tag=None, sort=[])

Get a list of predictions made by this model

Parameters:

limit (int, default: 100 ) –

Optional maximum number of predictions to return
offset (int, default: 0 ) –

Optional offset of the first prediction to return
tag (str | None, default: None ) –

Optional tag to filter predictions by
sort (list[tuple[ListPredictionsRequestSortItemItemType0, ListPredictionsRequestSortItemItemType1]], default: [] ) –

Optional list of columns and directions to sort the predictions by. Predictions can be sorted by timestamp or confidence.

Returns:

list[LabelPrediction] –

List of label predictions

Examples:

Get the last 3 predictions:

>>> predictions = model.predictions(limit=3, sort=[("timestamp", "desc")])
[
    LabeledPrediction({label: <positive: 1>, confidence: 0.95, input_value: 'I am happy'}),
    LabeledPrediction({label: <negative: 0>, confidence: 0.05, input_value: 'I am sad'}),
    LabeledPrediction({label: <positive: 1>, confidence: 0.90, input_value: 'I am ecstatic'}),
]

Get second most confident prediction:

>>> predictions = model.predictions(sort=[("confidence", "desc")], offset=1, limit=1)
[LabeledPrediction({label: <positive: 1>, confidence: 0.90, input_value: 'I am having a good day'})]

evaluate #

evaluate(
    datasource,
    value_column="value",
    label_column="label",
    record_predictions=False,
    tags=None,
)

Evaluate the classification model on a given datasource

Parameters:

datasource (Datasource) –

Datasource to evaluate the model on
value_column (str, default: 'value' ) –

Name of the column that contains the input values to the model
label_column (str, default: 'label' ) –

Name of the column containing the expected labels
record_predictions (bool, default: False ) –

Whether to record LabelPredictions for analysis
tags (set[str] | None, default: None ) –

Optional tags to add to the recorded LabelPredictions

Returns:

dict[str, float] –

Dictionary with evaluation metrics

Examples:

>>> model.evaluate(datasource, value_column="text", label_column="airline_sentiment")
{ "f1_score": 0.85, "roc_auc": 0.85, "pr_auc": 0.85, "accuracy": 0.85, "loss": 0.35 }

use_memoryset #

use_memoryset(memoryset_override)

Temporarily override the memoryset used by the model for predictions

Parameters:

memoryset_override (LabeledMemoryset) –

Memoryset to override the default memoryset with

Examples:

>>> with model.use_memoryset(LabeledMemoryset.open("my_other_memoryset")):
...     predictions = model.predict("I am happy")

record_feedback #

record_feedback(feedback: dict[str, Any]) -> None

record_feedback(feedback: Iterable[dict[str, Any]]) -> None

record_feedback(feedback)

Record feedback for a list of predictions.

We support recording feedback in several categories for each prediction. A FeedbackCategory is created automatically, the first time feedback with a new name is recorded. Categories are global across models. The value type of the category is inferred from the first recorded value. Subsequent feedback for the same category must be of the same type.

Parameters:

feedback (Iterable[dict[str, Any]] | dict[str, Any]) –
Feedback to record, this should be dictionaries with the following keys:
- category: Name of the category under which to record the feedback.
- value: Feedback value to record, should be True for positive feedback and False for negative feedback or a float between -1.0 and +1.0 where negative values indicate negative feedback and positive values indicate positive feedback.
- comment: Optional comment to record with the feedback.

Examples:

Record whether predictions were correct or incorrect:

>>> model.record_feedback({
...     "prediction": p.prediction_id,
...     "category": "correct",
...     "value": p.label == p.expected_label,
... } for p in predictions)

Record star rating as normalized continuous score between -1.0 and +1.0:

>>> model.record_feedback({
...     "prediction": "123e4567-e89b-12d3-a456-426614174000",
...     "category": "rating",
...     "value": -0.5,
...     "comment": "2 stars"
... })

Raises:

ValueError –

If the value does not match previous value types for the category, or is a float that is not between -1.0 and +1.0.

orca_sdk.classification_model#

ClassificationModel #

last_prediction property #

create classmethod #

open classmethod #

exists classmethod #

all classmethod #

drop classmethod #

predict #

predictions #

evaluate #

use_memoryset #

record_feedback #

last_prediction `property` #

create `classmethod` #

open `classmethod` #

exists `classmethod` #

all `classmethod` #

drop `classmethod` #