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getml.pipeline.Plots 

Plots(name: str)

Custom class for handling the plots generated by the pipeline.

PARAMETER DESCRIPTION
name

The id of the pipeline the plots are associated with.

TYPE: str

Example 
recall, precision = my_pipeline.plots.precision_recall_curve()
fpr, tpr = my_pipeline.plots.roc_curve()
Source code in getml/pipeline/plots.py 
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def __init__(self, name: str) -> None:
    if not isinstance(name, str):
        raise ValueError("'name' must be a str.")

    self.name = name

lift_curve 

lift_curve(target_num: int = 0) -> Tuple[ndarray, ndarray]

Returns the data for the lift curve, as displayed in the getML Monitor.

This requires that you call score first. The data used for the curve will always be the data from the last time you called score.

PARAMETER DESCRIPTION
target_num

Indicates for which target you want to plot the lift curve. (Pipelines can have more than one target.)

TYPE: int DEFAULT: 0

RETURNS DESCRIPTION
Tuple[ndarray, ndarray]

The first array is the proportion of samples, usually displayed on the x-axis. The second array is the lift, usually displayed on the y-axis.
Source code in getml/pipeline/plots.py 
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def lift_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:
    """
    Returns the data for the lift curve, as displayed in the getML Monitor.

    This requires that you call
    [`score`][getml.Pipeline.score] first. The data used
    for the curve will always be the data from the *last* time
    you called [`score`][getml.Pipeline.score].

    Args:
        target_num:
            Indicates for which target you want to plot the lift
            curve. (Pipelines can have more than one target.)

    Returns:
        The first array is the proportion of samples, usually displayed on the x-axis.
            The second array is the lift, usually displayed on the y-axis.
    """

    cmd: Dict[str, Any] = {}

    cmd["type_"] = "Pipeline.lift_curve"
    cmd["name_"] = self.name

    cmd["target_num_"] = target_num

    with comm.send_and_get_socket(cmd) as sock:
        msg = comm.recv_string(sock)
        if msg != "Success!":
            comm.handle_engine_exception(msg)
        msg = comm.recv_string(sock)

    json_obj = json.loads(msg)

    return (np.asarray(json_obj["proportion_"]), np.asarray(json_obj["lift_"]))

precision_recall_curve 

precision_recall_curve(
    target_num: int = 0,
) -> Tuple[ndarray, ndarray]

Returns the data for the precision-recall curve, as displayed in the getML Monitor.

This requires that you call score first. The data used for the curve will always be the data from the last time you called score.

PARAMETER DESCRIPTION
target_num

Indicates for which target you want to plot the lift curve. (Pipelines can have more than one target.)

TYPE: int DEFAULT: 0

RETURNS DESCRIPTION
Tuple[ndarray, ndarray]

The first array is the recall (a.k.a. true positive rate), usually displayed on the x-axis. The second array is the precision, usually displayed on the y-axis.
Source code in getml/pipeline/plots.py 
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def precision_recall_curve(
    self, target_num: int = 0
) -> Tuple[np.ndarray, np.ndarray]:
    """
    Returns the data for the precision-recall curve, as displayed in the getML
    Monitor.

    This requires that you call
    [`score`][getml.Pipeline.score] first. The data used
    for the curve will always be the data from the *last* time
    you called [`score`][getml.Pipeline.score].

    Args:
        target_num:
            Indicates for which target you want to plot the lift
            curve. (Pipelines can have more than one target.)

    Returns:
        The first array is the recall (a.k.a. true positive rate), usually displayed on the x-axis.
            The second array is the precision, usually displayed on the y-axis.
    """

    cmd: Dict[str, Any] = {}

    cmd["type_"] = "Pipeline.precision_recall_curve"
    cmd["name_"] = self.name

    cmd["target_num_"] = target_num

    with comm.send_and_get_socket(cmd) as sock:
        msg = comm.recv_string(sock)
        if msg != "Success!":
            comm.handle_engine_exception(msg)
        msg = comm.recv_string(sock)

    json_obj = json.loads(msg)

    return (np.asarray(json_obj["tpr_"]), np.asarray(json_obj["precision_"]))

roc_curve 

roc_curve(target_num: int = 0) -> Tuple[ndarray, ndarray]

Returns the data for the ROC curve, as displayed in the getML Monitor.

This requires that you call score first. The data used for the curve will always be the data from the last time you called score.

PARAMETER DESCRIPTION
target_num

Indicates for which target you want to plot the lift curve. (Pipelines can have more than one target.)

TYPE: int DEFAULT: 0

RETURNS DESCRIPTION
Tuple[ndarray, ndarray]

The first array is the false positive rate, usually displayed on the x-axis. The second array is the true positive rate, usually displayed on the y-axis.
Source code in getml/pipeline/plots.py 
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def roc_curve(self, target_num: int = 0) -> Tuple[np.ndarray, np.ndarray]:
    """
    Returns the data for the ROC curve, as displayed in the getML Monitor.

    This requires that you call
    [`score`][getml.Pipeline.score] first. The data used
    for the curve will always be the data from the *last* time
    you called [`score`][getml.Pipeline.score].

    Args:
        target_num:
            Indicates for which target you want to plot the lift
            curve. (Pipelines can have more than one target.)

    Returns:
        The first array is the false positive rate, usually displayed on the x-axis.
            The second array is the true positive rate, usually displayed on the y-axis.
    """

    cmd: Dict[str, Any] = {}

    cmd["type_"] = "Pipeline.roc_curve"
    cmd["name_"] = self.name

    cmd["target_num_"] = target_num

    with comm.send_and_get_socket(cmd) as sock:
        msg = comm.recv_string(sock)
        if msg != "Success!":
            comm.handle_engine_exception(msg)
        msg = comm.recv_string(sock)

    json_obj = json.loads(msg)

    return (np.asarray(json_obj["fpr_"]), np.asarray(json_obj["tpr_"]))