"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "batch_inference_run.artifact(\"drift_table_plot\").show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'batch_id': '3d5f6aa8a2d63cc0e84ebd95a0bc0000979a0989bbf4c211651a4e2a',\n",
+ " 'drift_status': True,\n",
+ " 'drift_metric': 0.4478631590778279}"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch_inference_run.status.results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlrun-base",
+ "language": "python",
+ "name": "conda-env-mlrun-base-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/functions/development/batch_inference_v2/2.3.0/src/batch_inference_v2.py b/functions/development/batch_inference_v2/2.3.0/src/batch_inference_v2.py
new file mode 100644
index 00000000..dfabf16e
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/src/batch_inference_v2.py
@@ -0,0 +1,276 @@
+# Copyright 2023 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from inspect import signature
+from typing import Any, Dict, List, Optional, Union
+
+import mlrun
+
+try:
+ import mlrun.model_monitoring.api
+except ModuleNotFoundError:
+ raise mlrun.errors.MLRunNotFoundError(
+ f"Please update your `mlrun` version to >=1.5.0 or use an "
+ f"older version of the batch inference function."
+ )
+
+
+import numpy as np
+import pandas as pd
+from mlrun.frameworks.auto_mlrun import AutoMLRun
+
+
+def _prepare_result_set(
+ x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray
+) -> pd.DataFrame:
+ """
+ Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
+ (x) and the model predictions (y_pred).
+
+ :param x: The inputs.
+ :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
+ will be used in case the list is empty (predicted_label_{i}).
+ :param y_pred: The model predictions on the inputs.
+
+ :returns: The result set.
+
+ raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
+ column already exists in the dataset.
+ """
+ # Prepare default target columns names if not provided:
+ prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
+ if len(label_columns) == 0:
+ # Add default label column names:
+ if prediction_columns_amount == 1:
+ label_columns = ["predicted_label"]
+ else:
+ label_columns = [
+ f"predicted_label_{i}" for i in range(prediction_columns_amount)
+ ]
+
+ # Validate the label columns:
+ if prediction_columns_amount != len(label_columns):
+ # No equality between provided label column names and outputs amount:
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The number of predicted labels: {prediction_columns_amount} "
+ f"is not equal to the given label columns: {len(label_columns)}"
+ )
+ common_labels = set(label_columns) & set(x.columns.tolist())
+ if common_labels:
+ # Label column exist in the original inputs:
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The labels: {common_labels} are already existed in the given dataset."
+ )
+
+ return pd.concat(
+ [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
+ )
+
+
+def _parse_record_results_kwarg(
+ last_in_batch_set: Optional[bool],
+) -> dict[str, bool]:
+ """
+ Check if `last_in_batch_set` is provided and expected as a parameter.
+ Return it as a dictionary.
+ """
+ kwarg = "last_in_batch_set"
+ if last_in_batch_set is None:
+ return {}
+ if (
+ signature(mlrun.model_monitoring.api.record_results).parameters.get(kwarg)
+ is None
+ ):
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"Unexpected parameter `{kwarg}` for function: "
+ "`mlrun.model_monitoring.api.record_results`. "
+ "Please make sure that you are using `mlrun>=1.6.0` version."
+ )
+ return {kwarg: last_in_batch_set}
+
+
+def infer(
+ context: mlrun.MLClientCtx,
+ dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
+ model_path: Union[str, mlrun.DataItem],
+ drop_columns: Union[str, List[str], int, List[int]] = None,
+ label_columns: Union[str, List[str]] = None,
+ feature_columns: Union[str, List[str]] = None,
+ log_result_set: bool = True,
+ result_set_name: str = "prediction",
+ batch_id: str = None,
+ artifacts_tag: str = "",
+ # Drift analysis parameters
+ perform_drift_analysis: bool = None,
+ trigger_monitoring_job: bool = False,
+ batch_image_job: str = "mlrun/mlrun",
+ endpoint_id: str = "",
+ # The following model endpoint parameters are relevant only if:
+ # perform drift analysis is not disabled
+ # a new model endpoint record is going to be generated
+ model_endpoint_name: str = "batch-infer",
+ model_endpoint_drift_threshold: float = 0.7,
+ model_endpoint_possible_drift_threshold: float = 0.5,
+ model_endpoint_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
+ ] = None,
+ last_in_batch_set: Optional[bool] = None,
+ **predict_kwargs: Dict[str, Any],
+):
+ """
+ Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
+ under the current project.
+ Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
+ drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
+ here. When performing drift analysis, this function either uses an existing model endpoint record or creates
+ a new one.
+ At the moment, this function is supported for `mlrun>=1.5.0` versions.
+
+ :param context: MLRun context.
+ :param dataset: The dataset to infer through the model. Provided as an input (DataItem)
+ that represents Dataset artifact / Feature vector URI.
+ If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
+ numpy array.
+ :param model_path: Model store uri (should start with store://). Provided as an input (DataItem).
+ If using MLRun SDK, `model_path` can also be provided as a parameter (string).
+ To generate a valid model store URI, please log the model before running this function.
+ If `endpoint_id` of existing model endpoint is provided, make sure
+ that it has a similar model store path, otherwise the drift analysis
+ won't be triggered.
+ :param drop_columns: A string / integer or a list of strings / integers that represent the column names
+ / indices to drop. When the dataset is a list or a numpy array this parameter must
+ be represented by integers.
+ :param label_columns: The target label(s) of the column(s) in the dataset for Regression or
+ Classification tasks. The label column can be accessed from the model object, or
+ the feature vector provided if available.
+ :param feature_columns: List of feature columns that will be used to build the dataframe when dataset is
+ from type list or numpy array.
+ :param log_result_set: Whether to log the result set - a DataFrame of the given inputs concatenated with
+ the predictions. Defaulted to True.
+ :param result_set_name: The db key to set name of the prediction result and the filename. Defaulted to
+ 'prediction'.
+ :param batch_id: The ID of the given batch (inference dataset). If `None`, it will be generated.
+ Will be logged as a result of the run.
+ :param artifacts_tag: Tag to use for all the artifacts resulted from the function (result set and
+ model monitoring artifacts)
+ :param perform_drift_analysis: Whether to perform drift analysis between the sample set of the model object to the
+ dataset given. By default, None, which means it will perform drift analysis if the
+ model already has feature stats that are considered as a reference sample set.
+ Performing drift analysis on a new endpoint id will generate a new model endpoint
+ record. Please note that in order to trigger the drift analysis job, you need to
+ set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
+ only as part the scheduled monitoring job (if exist in the current project) or
+ if triggered manually by the user.
+ :param trigger_monitoring_job: Whether to trigger the batch drift analysis after the infer job.
+ :param batch_image_job: The image that will be used to register the monitoring batch job if not exist.
+ By default, the image is mlrun/mlrun.
+ :param endpoint_id: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
+ will be used either to perform the analysis on existing model endpoint or to
+ generate a new model endpoint record.
+ :param model_endpoint_name: If a new model endpoint is generated, the model name will be presented under this
+ endpoint.
+ :param model_endpoint_drift_threshold: The threshold of which to mark drifts. Defaulted to 0.7.
+ :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
+ :param model_endpoint_sample_set: A sample dataset to give to compare the inputs in the drift analysis.
+ Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
+ The default chosen sample set will always be the one who is set in the model artifact itself.
+ :param last_in_batch_set: Relevant only when `perform_drift_analysis` is `True`.
+ This flag can (and should only) be used when the model endpoint does not have
+ model-monitoring set.
+ If set to `True` (the default), this flag marks the current monitoring window
+ (on this monitoring endpoint) as completed - the data inferred so far is assumed
+ to be the complete data for this monitoring window.
+ You may want to set this flag to `False` if you want to record multiple results in
+ close time proximity ("batch set"). In this case, set this flag to `False` on all
+ but the last batch in the set.
+ raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
+ provided for an unsupported `mlrun` version.
+ """
+
+ # Loading the model:
+ context.logger.info(f"Loading model...")
+ if isinstance(model_path, mlrun.DataItem):
+ model_path = model_path.artifact_url
+ if not mlrun.datastore.is_store_uri(model_path):
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The provided model path ({model_path}) is invalid - should start with `store://`. "
+ f"Please make sure that you have logged the model using `project.log_model()` "
+ f"which generates a unique store uri for the logged model."
+ )
+ model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+ if label_columns is None:
+ label_columns = [
+ output.name for output in model_handler._model_artifact.spec.outputs
+ ]
+
+ if feature_columns is None:
+ feature_columns = [
+ input.name for input in model_handler._model_artifact.spec.inputs
+ ]
+
+ # Get dataset by object, URL or by FeatureVector:
+ context.logger.info(f"Loading data...")
+ x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
+ dataset=dataset,
+ feature_columns=feature_columns,
+ label_columns=label_columns,
+ drop_columns=drop_columns,
+ )
+
+ # Predict:
+ context.logger.info(f"Calculating prediction...")
+ y_pred = model_handler.model.predict(x, **predict_kwargs)
+
+ # Prepare the result set:
+ result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)
+
+ # Check for logging the result set:
+ if log_result_set:
+ mlrun.model_monitoring.api.log_result(
+ context=context,
+ result_set_name=result_set_name,
+ result_set=result_set,
+ artifacts_tag=artifacts_tag,
+ batch_id=batch_id,
+ )
+
+ # Check for performing drift analysis
+ if (
+ perform_drift_analysis is None
+ and model_handler._model_artifact.spec.feature_stats is not None
+ ):
+ perform_drift_analysis = True
+ if perform_drift_analysis:
+ context.logger.info("Performing drift analysis...")
+ # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
+ sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(
+ sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
+ )
+ mlrun.model_monitoring.api.record_results(
+ project=context.project,
+ context=context,
+ endpoint_id=endpoint_id,
+ model_path=model_path,
+ model_endpoint_name=model_endpoint_name,
+ infer_results_df=result_set.copy(),
+ sample_set_statistics=sample_set_statistics,
+ drift_threshold=model_endpoint_drift_threshold,
+ possible_drift_threshold=model_endpoint_possible_drift_threshold,
+ artifacts_tag=artifacts_tag,
+ trigger_monitoring_job=trigger_monitoring_job,
+ default_batch_image=batch_image_job,
+ **_parse_record_results_kwarg(last_in_batch_set=last_in_batch_set),
+ )
diff --git a/functions/development/batch_inference_v2/2.3.0/src/function.yaml b/functions/development/batch_inference_v2/2.3.0/src/function.yaml
new file mode 100644
index 00000000..b04ddce9
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/src/function.yaml
@@ -0,0 +1,173 @@
+kind: job
+metadata:
+ name: batch-inference-v2
+ tag: ''
+ hash: 6d03260f9186d7b27651d6d0b42074fec54eb0f9
+ project: ''
+ labels:
+ author: eyald
+ categories:
+ - utils
+ - data-analysis
+ - monitoring
+spec:
+ command: ''
+ args: []
+ image: mlrun/mlrun
+ build:
+ functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Optional, Union

import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )


import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(
    x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray
) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _parse_record_results_kwarg(
    last_in_batch_set: Optional[bool],
) -> dict[str, bool]:
    """
    Check if `last_in_batch_set` is provided and expected as a parameter.
    Return it as a dictionary.
    """
    kwarg = "last_in_batch_set"
    if last_in_batch_set is None:
        return {}
    if (
        signature(mlrun.model_monitoring.api.record_results).parameters.get(kwarg)
        is None
    ):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"Unexpected parameter `{kwarg}` for function: "
            "`mlrun.model_monitoring.api.record_results`. "
            "Please make sure that you are using `mlrun>=1.6.0` version."
        )
    return {kwarg: last_in_batch_set}


def infer(
    context: mlrun.MLClientCtx,
    dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
    model_path: Union[str, mlrun.DataItem],
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Union[str, List[str]] = None,
    feature_columns: Union[str, List[str]] = None,
    log_result_set: bool = True,
    result_set_name: str = "prediction",
    batch_id: str = None,
    artifacts_tag: str = "",
    # Drift analysis parameters
    perform_drift_analysis: bool = None,
    trigger_monitoring_job: bool = False,
    batch_image_job: str = "mlrun/mlrun",
    endpoint_id: str = "",
    # The following model endpoint parameters are relevant only if:
    # perform drift analysis is not disabled
    # a new model endpoint record is going to be generated
    model_endpoint_name: str = "batch-infer",
    model_endpoint_drift_threshold: float = 0.7,
    model_endpoint_possible_drift_threshold: float = 0.5,
    model_endpoint_sample_set: Union[
        mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
    ] = None,
    last_in_batch_set: Optional[bool] = None,
    **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
    under the current project.
    Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
    drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
    here. When performing drift analysis, this function either uses an existing model endpoint record or creates
    a new one.
    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for all the artifacts resulted from the function (result set and
                                                    model monitoring artifacts)
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record. Please note that in order to trigger the drift analysis job, you need to
                                                    set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
                                                    only as part the scheduled monitoring job (if exist in the current project) or
                                                    if triggered manually by the user.
    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.
    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.
                                                    By default, the image is mlrun/mlrun.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.
    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.
    :param last_in_batch_set:                       Relevant only when `perform_drift_analysis` is `True`.
                                                    This flag can (and should only) be used when the model endpoint does not have
                                                    model-monitoring set.
                                                    If set to `True` (the default), this flag marks the current monitoring window
                                                    (on this monitoring endpoint) as completed - the data inferred so far is assumed
                                                    to be the complete data for this monitoring window.
                                                    You may want to set this flag to `False` if you want to record multiple results in
                                                    close time proximity ("batch set"). In this case, set this flag to `False` on all
                                                    but the last batch in the set.
    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
                                      provided for an unsupported `mlrun` version.
    """

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
        perform_drift_analysis is None
        and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(
            sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
        )
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
            drift_threshold=model_endpoint_drift_threshold,
            possible_drift_threshold=model_endpoint_possible_drift_threshold,
            artifacts_tag=artifacts_tag,
            trigger_monitoring_job=trigger_monitoring_job,
            default_batch_image=batch_image_job,
            **_parse_record_results_kwarg(last_in_batch_set=last_in_batch_set),
        )

+ commands: []
+ code_origin: ''
+ origin_filename: ''
+ with_mlrun: false
+ auto_build: false
+ requirements: []
+ entry_points:
+ infer:
+ name: infer
+ doc: 'Perform a prediction on a given dataset with the given model. Please make
+ sure that you have already logged the model
+
+ under the current project.
+
+ Can perform drift analysis between the sample set statistics stored in the
+ model to the current input data. The
+
+ drift rule is the value per-feature mean of the TVD and Hellinger scores according
+ to the thresholds configures
+
+ here. When performing drift analysis, this function either uses an existing
+ model endpoint record or creates
+
+ a new one.
+
+ At the moment, this function is supported for `mlrun>=1.5.0` versions.'
+ parameters:
+ - name: context
+ type: MLClientCtx
+ doc: MLRun context.
+ - name: dataset
+ type: Union[DataItem, list, dict, DataFrame, Series, ndarray]
+ doc: The dataset to infer through the model. Provided as an input (DataItem)
+ that represents Dataset artifact / Feature vector URI. If using MLRun SDK,
+ `dataset` can also be provided as a list, dictionary or numpy array.
+ - name: model_path
+ type: Union[str, DataItem]
+ doc: Model store uri (should start with store://). Provided as an input (DataItem).
+ If using MLRun SDK, `model_path` can also be provided as a parameter (string).
+ To generate a valid model store URI, please log the model before running
+ this function. If `endpoint_id` of existing model endpoint is provided,
+ make sure that it has a similar model store path, otherwise the drift analysis
+ won't be triggered.
+ - name: drop_columns
+ type: Union[str, List[str], int, List[int]]
+ doc: A string / integer or a list of strings / integers that represent the
+ column names / indices to drop. When the dataset is a list or a numpy array
+ this parameter must be represented by integers.
+ default: null
+ - name: label_columns
+ type: Union[str, List[str]]
+ doc: The target label(s) of the column(s) in the dataset for Regression or
+ Classification tasks. The label column can be accessed from the model object,
+ or the feature vector provided if available.
+ default: null
+ - name: feature_columns
+ type: Union[str, List[str]]
+ doc: List of feature columns that will be used to build the dataframe when
+ dataset is from type list or numpy array.
+ default: null
+ - name: log_result_set
+ type: bool
+ doc: Whether to log the result set - a DataFrame of the given inputs concatenated
+ with the predictions. Defaulted to True.
+ default: true
+ - name: result_set_name
+ type: str
+ doc: The db key to set name of the prediction result and the filename. Defaulted
+ to 'prediction'.
+ default: prediction
+ - name: batch_id
+ type: str
+ doc: The ID of the given batch (inference dataset). If `None`, it will be
+ generated. Will be logged as a result of the run.
+ default: null
+ - name: artifacts_tag
+ type: str
+ doc: Tag to use for all the artifacts resulted from the function (result set
+ and model monitoring artifacts)
+ default: ''
+ - name: perform_drift_analysis
+ type: bool
+ doc: Whether to perform drift analysis between the sample set of the model
+ object to the dataset given. By default, None, which means it will perform
+ drift analysis if the model already has feature stats that are considered
+ as a reference sample set. Performing drift analysis on a new endpoint id
+ will generate a new model endpoint record. Please note that in order to
+ trigger the drift analysis job, you need to set `trigger_monitoring_job=True`.
+ Otherwise, the drift analysis will be triggered only as part the scheduled
+ monitoring job (if exist in the current project) or if triggered manually
+ by the user.
+ default: null
+ - name: trigger_monitoring_job
+ type: bool
+ doc: Whether to trigger the batch drift analysis after the infer job.
+ default: false
+ - name: batch_image_job
+ type: str
+ doc: The image that will be used to register the monitoring batch job if not
+ exist. By default, the image is mlrun/mlrun.
+ default: mlrun/mlrun
+ - name: endpoint_id
+ type: str
+ doc: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
+ will be used either to perform the analysis on existing model endpoint or
+ to generate a new model endpoint record.
+ default: ''
+ - name: model_endpoint_name
+ type: str
+ doc: If a new model endpoint is generated, the model name will be presented
+ under this endpoint.
+ default: batch-infer
+ - name: model_endpoint_drift_threshold
+ type: float
+ doc: The threshold of which to mark drifts. Defaulted to 0.7.
+ default: 0.7
+ - name: model_endpoint_possible_drift_threshold
+ type: float
+ doc: The threshold of which to mark possible drifts. Defaulted to 0.5.
+ default: 0.5
+ - name: model_endpoint_sample_set
+ type: Union[DataItem, list, dict, DataFrame, Series, ndarray]
+ doc: A sample dataset to give to compare the inputs in the drift analysis.
+ Can be provided as an input (DataItem) or as a parameter (e.g. string, list,
+ DataFrame). The default chosen sample set will always be the one who is
+ set in the model artifact itself.
+ default: null
+ - name: last_in_batch_set
+ type: Optional[bool]
+ doc: Relevant only when `perform_drift_analysis` is `True`. This flag can
+ (and should only) be used when the model endpoint does not have model-monitoring
+ set. If set to `True` (the default), this flag marks the current monitoring
+ window (on this monitoring endpoint) as completed - the data inferred so
+ far is assumed to be the complete data for this monitoring window. You may
+ want to set this flag to `False` if you want to record multiple results
+ in close time proximity ("batch set"). In this case, set this flag to `False`
+ on all but the last batch in the set.
+ default: null
+ outputs:
+ - default: ''
+ lineno: 103
+ has_kwargs: true
+ description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+ default_handler: infer
+ disable_auto_mount: false
+ allow_empty_resources: true
+ clone_target_dir: ''
+ env: []
+ priority_class_name: ''
+ preemption_mode: prevent
+ affinity: null
+ tolerations: null
+ security_context: {}
+verbose: false
diff --git a/functions/development/batch_inference_v2/2.3.0/src/item.yaml b/functions/development/batch_inference_v2/2.3.0/src/item.yaml
new file mode 100644
index 00000000..0467c42c
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/src/item.yaml
@@ -0,0 +1,32 @@
+apiVersion: v1
+categories:
+- utils
+- data-analysis
+- monitoring
+description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+doc: ''
+example: batch_inference_v2.ipynb
+generationDate: 2023-08-07:12-25
+hidden: false
+icon: ''
+labels:
+ author: eyald
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.5.0
+name: batch_inference_v2
+platformVersion: 3.5.3
+spec:
+ extra_spec:
+ allow_empty_resources: true
+ build:
+ auto_build: false
+ with_mlrun: false
+ filename: batch_inference_v2.py
+ handler: infer
+ image: mlrun/mlrun
+ kind: job
+ requirements: null
+url: ''
+version: 2.3.0
diff --git a/functions/development/batch_inference_v2/2.3.0/src/requirements.txt b/functions/development/batch_inference_v2/2.3.0/src/requirements.txt
new file mode 100644
index 00000000..c120cd84
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/src/requirements.txt
@@ -0,0 +1,4 @@
+numpy
+pandas
+scikit-learn
+plotly
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.3.0/src/test_batch_inference_v2.py b/functions/development/batch_inference_v2/2.3.0/src/test_batch_inference_v2.py
new file mode 100644
index 00000000..cc247a29
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/src/test_batch_inference_v2.py
@@ -0,0 +1,151 @@
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import json
+import os
+
+import mlrun
+import mlrun.common.schemas
+import numpy as np
+import pandas as pd
+import pytest
+from mlrun.frameworks.sklearn import apply_mlrun
+from sklearn.datasets import make_classification
+from sklearn.tree import DecisionTreeClassifier
+
+REQUIRED_ENV_VARS = [
+ "MLRUN_DBPATH",
+ "V3IO_USERNAME",
+ "V3IO_API",
+ "V3IO_ACCESS_KEY",
+]
+
+
+def _validate_environment_variables() -> bool:
+ """
+ Checks that all required Environment variables are set.
+ """
+ environment_keys = os.environ.keys()
+ return all(key in environment_keys for key in REQUIRED_ENV_VARS)
+
+
+def generate_data(n_samples: int = 5000, n_features: int = 20):
+ # Generate a classification data:
+ x, y = make_classification(n_samples=n_samples, n_features=n_features, n_classes=2)
+
+ # Split the data into a training set and a prediction set:
+ x_train, x_prediction = x[: n_samples // 2], x[n_samples // 2 :]
+ y_train = y[: n_samples // 2]
+
+ # Randomly drift some features:
+ x_prediction += np.random.uniform(
+ low=2, high=4, size=x_train.shape
+ ) * np.random.randint(low=0, high=2, size=x_train.shape[1], dtype=int)
+
+ # Initialize dataframes:
+ features = [f"feature_{i}" for i in range(n_features)]
+ training_set = pd.DataFrame(data=x_train, columns=features)
+ training_set.insert(
+ loc=n_features, column="target_label", value=y_train, allow_duplicates=True
+ )
+ prediction_set = pd.DataFrame(data=x_prediction, columns=features)
+
+ return training_set, prediction_set
+
+
+def train(training_set: pd.DataFrame):
+ # Get the data into x, y:
+ labels = pd.DataFrame(training_set["target_label"])
+ training_set.drop(columns=["target_label"], inplace=True)
+
+ # Initialize a model:
+ model = DecisionTreeClassifier()
+
+ # Apply MLRun:
+ apply_mlrun(model=model, model_name="model")
+
+ # Train:
+ model.fit(training_set, labels)
+
+
+@pytest.mark.skipif(
+ condition=not _validate_environment_variables(),
+ reason="Project's environment variables are not set",
+)
+def test_batch_predict():
+ project = mlrun.get_or_create_project(
+ "batch-infer-test", context="./", user_project=True
+ )
+
+ # Configure test:
+ n_samples = 5000
+ n_features = 20
+
+ # Create the function and run:
+ test_function = mlrun.code_to_function(filename=__file__, kind="job")
+ generate_data_run = test_function.run(
+ handler="generate_data",
+ params={"n_samples": n_samples, "n_features": n_features},
+ returns=["training_set : dataset", "prediction_set : dataset"],
+ local=True,
+ )
+ train_run = test_function.run(
+ handler="train",
+ inputs={"training_set": generate_data_run.outputs["training_set"]},
+ local=True,
+ )
+
+ batch_predict_function = mlrun.import_function("function.yaml")
+ batch_inference_run = batch_predict_function.run(
+ handler="infer",
+ inputs={"dataset": generate_data_run.outputs["prediction_set"]},
+ params={
+ "model_path": train_run.outputs["model"],
+ "label_columns": "label",
+ "trigger_monitoring_job": True,
+ "perform_drift_analysis": True,
+ "model_endpoint_drift_threshold": 0.2,
+ "model_endpoint_possible_drift_threshold": 0.1,
+ },
+ )
+
+ # Check the logged results:
+ assert "batch_id" in batch_inference_run.status.results
+ assert "drift_metric" in batch_inference_run.status.results
+ assert batch_inference_run.status.results["drift_status"] is True
+
+ # Check that 3 artifacts were generated
+ assert len(batch_inference_run.status.artifacts) == 3
+
+ # Check drift table artifact url
+ assert (
+ batch_inference_run.artifact("drift_table_plot").artifact_url
+ == batch_inference_run.outputs["drift_table_plot"]
+ )
+
+ # Check the features drift results json:
+ drift_results_file = batch_inference_run.artifact("features_drift_results").local()
+ with open(drift_results_file, "r") as json_file:
+ drift_results = json.load(json_file)
+ assert len(drift_results) == n_features + 1
+
+ # Clean resources
+ _delete_project(project=project.metadata.name)
+
+
+def _delete_project(project: str):
+ mlrun.get_run_db().delete_project(
+ project,
+ deletion_strategy=mlrun.common.schemas.DeletionStrategy.cascading,
+ )
diff --git a/functions/development/batch_inference_v2/2.3.0/static/batch_inference_v2.html b/functions/development/batch_inference_v2/2.3.0/static/batch_inference_v2.html
new file mode 100644
index 00000000..2568c0bd
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/static/batch_inference_v2.html
@@ -0,0 +1,416 @@
+
+
+
+
+
+batch_inference_v2.batch_inference_v2
+
+
+Toggle navigation sidebar
+
+
+
+
+Toggle in-page Table of Contents
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Source code for batch_inference_v2.batch_inference_v2
+# Copyright 2023 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from inspect import signature
+from typing import Any , Dict , List , Optional , Union
+
+import mlrun
+
+try :
+ import mlrun.model_monitoring.api
+except ModuleNotFoundError :
+ raise mlrun . errors . MLRunNotFoundError (
+ f "Please update your `mlrun` version to >=1.5.0 or use an "
+ f "older version of the batch inference function."
+ )
+
+
+import numpy as np
+import pandas as pd
+from mlrun.frameworks.auto_mlrun import AutoMLRun
+
+
+def _prepare_result_set (
+ x : pd . DataFrame , label_columns : List [ str ], y_pred : np . ndarray
+) -> pd . DataFrame :
+ """
+ Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
+ (x) and the model predictions (y_pred).
+
+ :param x: The inputs.
+ :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
+ will be used in case the list is empty (predicted_label_{i}).
+ :param y_pred: The model predictions on the inputs.
+
+ :returns: The result set.
+
+ raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
+ column already exists in the dataset.
+ """
+ # Prepare default target columns names if not provided:
+ prediction_columns_amount = 1 if len ( y_pred . shape ) == 1 else y_pred . shape [ 1 ]
+ if len ( label_columns ) == 0 :
+ # Add default label column names:
+ if prediction_columns_amount == 1 :
+ label_columns = [ "predicted_label" ]
+ else :
+ label_columns = [
+ f "predicted_label_ { i } " for i in range ( prediction_columns_amount )
+ ]
+
+ # Validate the label columns:
+ if prediction_columns_amount != len ( label_columns ):
+ # No equality between provided label column names and outputs amount:
+ raise mlrun . errors . MLRunInvalidArgumentError (
+ f "The number of predicted labels: { prediction_columns_amount } "
+ f "is not equal to the given label columns: { len ( label_columns ) } "
+ )
+ common_labels = set ( label_columns ) & set ( x . columns . tolist ())
+ if common_labels :
+ # Label column exist in the original inputs:
+ raise mlrun . errors . MLRunInvalidArgumentError (
+ f "The labels: { common_labels } are already existed in the given dataset."
+ )
+
+ return pd . concat (
+ [ x , pd . DataFrame ( y_pred , columns = label_columns , index = x . index )], axis = 1
+ )
+
+
+def _parse_record_results_kwarg (
+ last_in_batch_set : Optional [ bool ],
+) -> dict [ str , bool ]:
+ """
+ Check if `last_in_batch_set` is provided and expected as a parameter.
+ Return it as a dictionary.
+ """
+ kwarg = "last_in_batch_set"
+ if last_in_batch_set is None :
+ return {}
+ if (
+ signature ( mlrun . model_monitoring . api . record_results ) . parameters . get ( kwarg )
+ is None
+ ):
+ raise mlrun . errors . MLRunInvalidArgumentError (
+ f "Unexpected parameter ` { kwarg } ` for function: "
+ "`mlrun.model_monitoring.api.record_results`. "
+ "Please make sure that you are using `mlrun>=1.6.0` version."
+ )
+ return { kwarg : last_in_batch_set }
+
+
+[docs] def infer (
+
context : mlrun . MLClientCtx ,
+
dataset : Union [ mlrun . DataItem , list , dict , pd . DataFrame , pd . Series , np . ndarray ],
+
model_path : Union [ str , mlrun . DataItem ],
+
drop_columns : Union [ str , List [ str ], int , List [ int ]] = None ,
+
label_columns : Union [ str , List [ str ]] = None ,
+
feature_columns : Union [ str , List [ str ]] = None ,
+
log_result_set : bool = True ,
+
result_set_name : str = "prediction" ,
+
batch_id : str = None ,
+
artifacts_tag : str = "" ,
+
# Drift analysis parameters
+
perform_drift_analysis : bool = None ,
+
trigger_monitoring_job : bool = False ,
+
batch_image_job : str = "mlrun/mlrun" ,
+
endpoint_id : str = "" ,
+
# The following model endpoint parameters are relevant only if:
+
# perform drift analysis is not disabled
+
# a new model endpoint record is going to be generated
+
model_endpoint_name : str = "batch-infer" ,
+
model_endpoint_drift_threshold : float = 0.7 ,
+
model_endpoint_possible_drift_threshold : float = 0.5 ,
+
model_endpoint_sample_set : Union [
+
mlrun . DataItem , list , dict , pd . DataFrame , pd . Series , np . ndarray
+
] = None ,
+
last_in_batch_set : Optional [ bool ] = None ,
+
** predict_kwargs : Dict [ str , Any ],
+
):
+
"""
+
Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
+
under the current project.
+
Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
+
drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
+
here. When performing drift analysis, this function either uses an existing model endpoint record or creates
+
a new one.
+
At the moment, this function is supported for `mlrun>=1.5.0` versions.
+
+
:param context: MLRun context.
+
:param dataset: The dataset to infer through the model. Provided as an input (DataItem)
+
that represents Dataset artifact / Feature vector URI.
+
If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
+
numpy array.
+
:param model_path: Model store uri (should start with store://). Provided as an input (DataItem).
+
If using MLRun SDK, `model_path` can also be provided as a parameter (string).
+
To generate a valid model store URI, please log the model before running this function.
+
If `endpoint_id` of existing model endpoint is provided, make sure
+
that it has a similar model store path, otherwise the drift analysis
+
won't be triggered.
+
:param drop_columns: A string / integer or a list of strings / integers that represent the column names
+
/ indices to drop. When the dataset is a list or a numpy array this parameter must
+
be represented by integers.
+
:param label_columns: The target label(s) of the column(s) in the dataset for Regression or
+
Classification tasks. The label column can be accessed from the model object, or
+
the feature vector provided if available.
+
:param feature_columns: List of feature columns that will be used to build the dataframe when dataset is
+
from type list or numpy array.
+
:param log_result_set: Whether to log the result set - a DataFrame of the given inputs concatenated with
+
the predictions. Defaulted to True.
+
:param result_set_name: The db key to set name of the prediction result and the filename. Defaulted to
+
'prediction'.
+
:param batch_id: The ID of the given batch (inference dataset). If `None`, it will be generated.
+
Will be logged as a result of the run.
+
:param artifacts_tag: Tag to use for all the artifacts resulted from the function (result set and
+
model monitoring artifacts)
+
:param perform_drift_analysis: Whether to perform drift analysis between the sample set of the model object to the
+
dataset given. By default, None, which means it will perform drift analysis if the
+
model already has feature stats that are considered as a reference sample set.
+
Performing drift analysis on a new endpoint id will generate a new model endpoint
+
record. Please note that in order to trigger the drift analysis job, you need to
+
set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
+
only as part the scheduled monitoring job (if exist in the current project) or
+
if triggered manually by the user.
+
:param trigger_monitoring_job: Whether to trigger the batch drift analysis after the infer job.
+
:param batch_image_job: The image that will be used to register the monitoring batch job if not exist.
+
By default, the image is mlrun/mlrun.
+
:param endpoint_id: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
+
will be used either to perform the analysis on existing model endpoint or to
+
generate a new model endpoint record.
+
:param model_endpoint_name: If a new model endpoint is generated, the model name will be presented under this
+
endpoint.
+
:param model_endpoint_drift_threshold: The threshold of which to mark drifts. Defaulted to 0.7.
+
:param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
+
:param model_endpoint_sample_set: A sample dataset to give to compare the inputs in the drift analysis.
+
Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
+
The default chosen sample set will always be the one who is set in the model artifact itself.
+
:param last_in_batch_set: Relevant only when `perform_drift_analysis` is `True`.
+
This flag can (and should only) be used when the model endpoint does not have
+
model-monitoring set.
+
If set to `True` (the default), this flag marks the current monitoring window
+
(on this monitoring endpoint) as completed - the data inferred so far is assumed
+
to be the complete data for this monitoring window.
+
You may want to set this flag to `False` if you want to record multiple results in
+
close time proximity ("batch set"). In this case, set this flag to `False` on all
+
but the last batch in the set.
+
raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
+
provided for an unsupported `mlrun` version.
+
"""
+
+
# Loading the model:
+
context . logger . info ( f "Loading model..." )
+
if isinstance ( model_path , mlrun . DataItem ):
+
model_path = model_path . artifact_url
+
if not mlrun . datastore . is_store_uri ( model_path ):
+
raise mlrun . errors . MLRunInvalidArgumentError (
+
f "The provided model path ( { model_path } ) is invalid - should start with `store://`. "
+
f "Please make sure that you have logged the model using `project.log_model()` "
+
f "which generates a unique store uri for the logged model."
+
)
+
model_handler = AutoMLRun . load_model ( model_path = model_path , context = context )
+
+
if label_columns is None :
+
label_columns = [
+
output . name for output in model_handler . _model_artifact . spec . outputs
+
]
+
+
if feature_columns is None :
+
feature_columns = [
+
input . name for input in model_handler . _model_artifact . spec . inputs
+
]
+
+
# Get dataset by object, URL or by FeatureVector:
+
context . logger . info ( f "Loading data..." )
+
x , label_columns = mlrun . model_monitoring . api . read_dataset_as_dataframe (
+
dataset = dataset ,
+
feature_columns = feature_columns ,
+
label_columns = label_columns ,
+
drop_columns = drop_columns ,
+
)
+
+
# Predict:
+
context . logger . info ( f "Calculating prediction..." )
+
y_pred = model_handler . model . predict ( x , ** predict_kwargs )
+
+
# Prepare the result set:
+
result_set = _prepare_result_set ( x = x , label_columns = label_columns , y_pred = y_pred )
+
+
# Check for logging the result set:
+
if log_result_set :
+
mlrun . model_monitoring . api . log_result (
+
context = context ,
+
result_set_name = result_set_name ,
+
result_set = result_set ,
+
artifacts_tag = artifacts_tag ,
+
batch_id = batch_id ,
+
)
+
+
# Check for performing drift analysis
+
if (
+
perform_drift_analysis is None
+
and model_handler . _model_artifact . spec . feature_stats is not None
+
):
+
perform_drift_analysis = True
+
if perform_drift_analysis :
+
context . logger . info ( "Performing drift analysis..." )
+
# Get the sample set statistics (either from the sample set or from the statistics logged with the model)
+
sample_set_statistics = mlrun . model_monitoring . api . get_sample_set_statistics (
+
sample_set = model_endpoint_sample_set ,
+
model_artifact_feature_stats = model_handler . _model_artifact . spec . feature_stats ,
+
)
+
mlrun . model_monitoring . api . record_results (
+
project = context . project ,
+
context = context ,
+
endpoint_id = endpoint_id ,
+
model_path = model_path ,
+
model_endpoint_name = model_endpoint_name ,
+
infer_results_df = result_set . copy (),
+
sample_set_statistics = sample_set_statistics ,
+
drift_threshold = model_endpoint_drift_threshold ,
+
possible_drift_threshold = model_endpoint_possible_drift_threshold ,
+
artifacts_tag = artifacts_tag ,
+
trigger_monitoring_job = trigger_monitoring_job ,
+
default_batch_image = batch_image_job ,
+
** _parse_record_results_kwarg ( last_in_batch_set = last_in_batch_set ),
+
)
+
+
+
+
+
+
+
+
batch_inference_v2 package
+
+
+Toggle navigation sidebar
+
+
+
+
+Toggle in-page Table of Contents
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
batch_inference_v2 package
+
+
+
+
+
+
+batch_inference_v2 package
+
+
+batch_inference_v2.batch_inference_v2 module
+
+
+batch_inference_v2.batch_inference_v2. infer ( context : mlrun.execution.MLClientCtx dataset : Union [ mlrun.datastore.base.DataItem , list , dict , pandas.core.frame.DataFrame , pandas.core.series.Series , numpy.ndarray ] model_path : Union [ str , mlrun.datastore.base.DataItem ] drop_columns : Optional [ Union [ str , List [ str ] , int , List [ int ] ] ] = None label_columns : Optional [ Union [ str , List [ str ] ] ] = None feature_columns : Optional [ Union [ str , List [ str ] ] ] = None log_result_set : bool = True result_set_name : str = 'prediction' batch_id : Optional [ str ] = None artifacts_tag : str = '' perform_drift_analysis : Optional [ bool ] = None trigger_monitoring_job : bool = False batch_image_job : str = 'mlrun/mlrun' endpoint_id : str = '' model_endpoint_name : str = 'batch-infer' model_endpoint_drift_threshold : float = 0.7 model_endpoint_possible_drift_threshold : float = 0.5 model_endpoint_sample_set : Optional [ Union [ mlrun.datastore.base.DataItem , list , dict , pandas.core.frame.DataFrame , pandas.core.series.Series , numpy.ndarray ] ] = None last_in_batch_set : Optional [ bool ] = None ** predict_kwargs : Dict [ str , Any ] ) [source]
+Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
+under the current project.
+Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
+drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
+here. When performing drift analysis, this function either uses an existing model endpoint record or creates
+a new one.
+At the moment, this function is supported for mlrun>=1.5.0 versions.
+
+Parameters
+
+context – MLRun context.
dataset – The dataset to infer through the model. Provided as an input (DataItem)
+that represents Dataset artifact / Feature vector URI.
+If using MLRun SDK, dataset can also be provided as a list, dictionary or
+numpy array.
model_path – Model store uri (should start with store://). Provided as an input (DataItem).
+If using MLRun SDK, model_path can also be provided as a parameter (string).
+To generate a valid model store URI, please log the model before running this function.
+If endpoint_id of existing model endpoint is provided, make sure
+that it has a similar model store path, otherwise the drift analysis
+won’t be triggered.
drop_columns – A string / integer or a list of strings / integers that represent the column names
+/ indices to drop. When the dataset is a list or a numpy array this parameter must
+be represented by integers.
label_columns – The target label(s) of the column(s) in the dataset for Regression or
+Classification tasks. The label column can be accessed from the model object, or
+the feature vector provided if available.
feature_columns – List of feature columns that will be used to build the dataframe when dataset is
+from type list or numpy array.
log_result_set – Whether to log the result set - a DataFrame of the given inputs concatenated with
+the predictions. Defaulted to True.
result_set_name – The db key to set name of the prediction result and the filename. Defaulted to
+‘prediction’.
batch_id – The ID of the given batch (inference dataset). If None , it will be generated.
+Will be logged as a result of the run.
artifacts_tag – Tag to use for all the artifacts resulted from the function (result set and
+model monitoring artifacts)
perform_drift_analysis – Whether to perform drift analysis between the sample set of the model object to the
+dataset given. By default, None, which means it will perform drift analysis if the
+model already has feature stats that are considered as a reference sample set.
+Performing drift analysis on a new endpoint id will generate a new model endpoint
+record. Please note that in order to trigger the drift analysis job, you need to
+set trigger_monitoring_job=True . Otherwise, the drift analysis will be triggered
+only as part the scheduled monitoring job (if exist in the current project) or
+if triggered manually by the user.
trigger_monitoring_job – Whether to trigger the batch drift analysis after the infer job.
batch_image_job – The image that will be used to register the monitoring batch job if not exist.
+By default, the image is mlrun/mlrun.
endpoint_id – Model endpoint unique ID. If perform_drift_analysis was set, the endpoint_id
+will be used either to perform the analysis on existing model endpoint or to
+generate a new model endpoint record.
model_endpoint_name – If a new model endpoint is generated, the model name will be presented under this
+endpoint.
model_endpoint_drift_threshold – The threshold of which to mark drifts. Defaulted to 0.7.
model_endpoint_possible_drift_threshold – The threshold of which to mark possible drifts. Defaulted to 0.5.
model_endpoint_sample_set – A sample dataset to give to compare the inputs in the drift analysis.
+Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
+The default chosen sample set will always be the one who is set in the model artifact itself.
last_in_batch_set – Relevant only when perform_drift_analysis is True .
+This flag can (and should only) be used when the model endpoint does not have
+model-monitoring set.
+If set to True (the default), this flag marks the current monitoring window
+(on this monitoring endpoint) as completed - the data inferred so far is assumed
+to be the complete data for this monitoring window.
+You may want to set this flag to False if you want to record multiple results in
+close time proximity (“batch set”). In this case, set this flag to False on all
+but the last batch in the set.
+
+
+raises MLRunInvalidArgumentError: if both model_path and endpoint_id are not provided, or if last_in_batch_set is provided for an unsupported mlrun version.
+
+
+
+
+
+
+
+
+
+
+
+
+
Batch Inference V2
+
+
+Toggle navigation sidebar
+
+
+
+
+Toggle in-page Table of Contents
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Batch Inference V2
+
+
+
+
+
+
+Batch Inference V2
+A function for inferring given input through a given model while producing a Result Set and performing Data Drift Analysis .
+In this notebook we will go over the function’s docs and outputs and see an end-to-end example of running it.
+
+Documentation
Results Prediction
Data Drift Analysis
End-to-end Demo
+
+
+1. Documentation
+Perform a prediction on a given dataset with the given model. Can perform drift analysis between the sample set
+statistics stored in the model to the current input data. The drift rule is the value per-feature mean of the TVD
+and Hellinger scores according to the thresholds configures here. When performing drift analysis, this function
+either creates or update an existing model endpoint record (depends on the provided endpoint_id
+At the moment, this function is supported for mlrun>=1.5.0
+
+1.1. Parameters:
+
+1.1.1 Batch Infer Parameters:
+
+context : mlrun.MLClientCtx
+An MLRun context.
+dataset : Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray]
+The dataset to infer through the model.
+
+Can be passed in inputs
Or, in parameters
+model_path : Union[str, mlrun.DataItem]
+Model store uri (should start with store://). Provided as an input (DataItem). To generate a valid model store URI, please log the model before running this function. If endpoint_id
+drop_columns : Union[str, List[str], int, List[int]] None
+A string / integer or a list of strings / integers that represent the column names / indices to drop. When the dataset is a list or a numpy array this parameter must be represented by integers.
+label_columns : Union[str, List[str]] None
+The target label(s) of the column(s) in the dataset. These names will be used as the column names for the predictions. The label column can be accessed from the model object, or the feature vector provided if available. The default name is "predicted_label_i" i
+feature_columns : Union[str, List[str]] None
+List of feature columns that will be used to build the dataframe when dataset is
+from type list or numpy array.
+log_result_set : str True
+Whether to log the result set - a DataFrame of the given inputs concatenated with the predictions. Defaulted to True
+result_set_name : str "prediction"
+The db key to set name of the prediction result and the filename. Defaulted to "prediction"
+batch_id : str None
+The ID of the given batch (inference dataset). If None
+artifacts_tag : str ""
+Tag to use for all the artifacts resulted from the function. Defaulted to no tag.
+
+
+
+1.1.2 Drift Analysis Parameters:
+
+perform_drift_analysis : bool None
+Whether to perform drift analysis between the sample set of the model object to the
+dataset given. By default, None, which means it will perform drift analysis if the
+model already has feature stats that are considered as a reference sample set.
+Performing drift analysis on a new endpoint id will generate a new model endpoint
+record. Please note that in order to trigger the drift analysis job, you need to
+set trigger_monitoring_job=True
+trigger_monitoring_job : bool False
+Whether to trigger the batch drift analysis after the infer job.
+batch_image_job : str mlrun/mlrun
+The image that will be used for the monitoring batch job analysis. By default,
+the image is mlrun/mlrun
+endpoint_id : str ""
+Model endpoint unique ID. If perform_drift_analysis
+
+
+
+1.1.3 New Model Endpoint Parameters:
+
+model_endpoint_name : str "batch-infer"
+If a new model endpoint is generated, the model name will be presented under this endpoint.
+model_endpoint_drift_threshold : float 0.7
+The threshold of which to mark drifts. Defaulted to 0.7.
+model_endpoint_possible_drift_threshold : float 0.5
+The threshold of which to mark possible drifts. Defaulted to 0.5.
+model_endpoint_sample_set : Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray] None
+A sample dataset to give to compare the inputs in the drift analysis. Can be provided as an input or as a parameter. The default chosen sample
+set will always be the one who is set in the model artifact itself.
+
+
+
+
+1.2. Outputs
+The outputs are split to two actions the functions can perform:
+
+For more details, see the next chapters.
+
+
+
+
+2. Results Prediction
+The result set is a concatenated dataset of the inputs ($X$) provided and the predictions ($Y$) yielded by the model, so it will be $X | Y$.
+For example, if the dataset
+
+
+x1
x2
x3
x4
x5
+
+…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
+
+And the outputs yielded by the model’s prediction was:
+
+
+y1
y2
+
+…
…
…
…
…
…
+
+Then the result set will be:
+
+
+x1
x2
x3
x4
x5
y1
y2
+
+…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
+
+In case the parameter log_result_set True
+
+The result set as described above.
The batch ID result - batch_id str
+{
+ "batch_id" : "884a0cb00d8ae16d132dd8259aac29aa78f50a9245d0e4bd58cfbf77" ,
+}
+
+
+
+
+
+3. Data Drift Analysis
+The data drift analysis is done per feature using two distance measure metrics for probability distributions.
+Let us mark our sample set as $S$ and our inputs as $I$. We will look at one feature $x$ out of $n$ features. Assuming the histograms of feature $x$ is split into 20 bins: $b_1,b_2,…,b_{20}$, we will match the feature $x$ histogram of the inputs $I$ ($x_I$) into the same bins (meaning to $x_S$) and compare their distributions using:
+
+Total Variance Distance: $TVD(x_S,x_I) = \frac{1}{2}\sum_{b_1}^{b_{20}} {|x_S - x_I|}$
Hellinger Distance: $H(x_S,x_I) = \sqrt{1-{\sum_{b_1}^{b_{20}}\sqrt{x_S \cdot x_I}}}$
+Our rule then is calculating for each $x\in S: \frac{H(x_S,x_I)+TVD(x_S,x_I)}{2} < $ given thresholds.
+The outputs of the analysis will be:
+
+
+
+
Count
Mean
Std
Min
Max
Tvd
Hellinger
Kld
Histograms
+
+
Sample
Input
Sample
Input
Sample
Input
Sample
Input
Sample
Input
x1
…
…
…
…
…
…
…
…
…
…
…
…
…
…
x2
…
…
…
…
…
…
…
…
…
…
…
…
…
…
x3
…
…
…
…
…
…
…
…
…
…
…
…
…
…
+
+
+Features drift results - A rule metric per feature dictionary is saved in a json file named "features_drift_results" Dict[str, float]
+{
+ "x1" : 0.12 ,
+ "x2" : 0.345 ,
+ "x3" : 0.00678 ,
+ ...
+}
+
+
In addition, two results are being added to summarize the drift analysis:
+
+drift_status bool
drift_metric float drift_metric
+{
+ "drift_status" : True ,
+ "drift_metric" : 0.81234
+}
+
+
+
+
+
+4. End-to-end Demo
+We will see an end-to-end example that follows the steps below:
+
+Generate data.
Train a model.
Infer data through the model using batch_predict
+
+4.1. Code review
+We are using a very simple example of training a decision tree on a binary classification problem. For that we wrote two functions:
+
+generate_data training set and data for prediction . The data for prediction will be drifted in half of its features to showcase the plot later on.
train
+
+
+
+
+
+
+
+
+4.2. Run the Example with MLRun
+First, we will prepare our MLRun functions:
+
+We will use mlrun.code_to_function
We will use mlrun.import_function batch_predict
+
+
+
+
+
+
+
Now, we will follow the demo steps as discussed above:
+
+
+
+
+
+
+
+
+
+
+
+
+project
+uid
+iter
+start
+state
+name
+labels
+inputs
+parameters
+results
+artifacts
+
+
+
+
+batch-infer-demo
+0
+Aug 29 11:14:42
+completed
+batch-inference-demo-generate-data
+v3io_user=iguazio
kind=
owner=iguazio
host=jupyter-69ff7bc987-9fmj4
training_set
prediction_set
+
+
+
+
+
+
+
+
+
+
> to track results use the .show() or .logs() methods or click here to open in UI
+
+
+
+
+
+
+
+
+project
+uid
+iter
+start
+state
+name
+labels
+inputs
+parameters
+results
+artifacts
+
+
+
+
+batch-infer-demo
+0
+Aug 29 11:14:45
+completed
+batch-inference-demo-train
+v3io_user=iguazio
kind=
owner=iguazio
host=jupyter-69ff7bc987-9fmj4
training_set
model
+
+
+
+
+
+
+
+
+
+
> to track results use the .show() or .logs() methods or click here to open in UI
+
+
+
+
+
+
+
+
+project
+uid
+iter
+start
+state
+name
+labels
+inputs
+parameters
+results
+artifacts
+
+
+
+
+batch-infer-demo
+0
+Aug 29 11:15:03
+completed
+model-monitoring-batch
+v3io_user=iguazio
kind=job
owner=iguazio
mlrun/client_version=0.0.0+unstable
mlrun/client_python_version=3.9.16
host=model-monitoring-batch-klkxx
model_endpoints=['80daa6376087b987a2e8d97b4850772fee6ff503']
batch_intervals_dict={'minutes': 0, 'hours': 1, 'days': 0}
+
+
+
+
+
+
+
+
+
+
> to track results use the .show() or .logs() methods or click here to open in UI
+
+
+
+
+
+
+
+
+
+
+project
+uid
+iter
+start
+state
+name
+labels
+inputs
+parameters
+results
+artifacts
+
+
+
+
+batch-infer-demo
+0
+Aug 29 11:14:46
+completed
+batch-inference-demo-infer
+v3io_user=iguazio
kind=
owner=iguazio
host=jupyter-69ff7bc987-9fmj4
dataset
model_path=store://artifacts/batch-infer-demo/model:c0a23b01a8ac4b36b78c6066780df84d
label_columns=label
trigger_monitoring_job=True
perform_drift_analysis=True
model_endpoint_drift_threshold=0.2
model_endpoint_possible_drift_threshold=0.1
batch_image_job=eyaligu/mlrun-api:image-test
batch_id=3d5f6aa8a2d63cc0e84ebd95a0bc0000979a0989bbf4c211651a4e2a
drift_status=True
drift_metric=0.4478631590778279
prediction
drift_table_plot
features_drift_results
+
+
+
+
+
+
+
+
+
+
> to track results use the .show() or .logs() methods or click here to open in UI
+
+
+
+
+4.3. Review Outputs
+We will review the outputs as explained in the notebook above.
+
+4.3.1. Results Prediction
+First we will showcase the Result Set . As we didn’t send any name, it’s default name will be "prediction"
+
+
+
+
+
+
+
+
+feature_0
+feature_1
+feature_2
+feature_3
+feature_4
+feature_5
+feature_6
+feature_7
+feature_8
+feature_9
+...
+feature_11
+feature_12
+feature_13
+feature_14
+feature_15
+feature_16
+feature_17
+feature_18
+feature_19
+label
+
+
+
+
+0
+-0.839322
+1.333380
+0.096353
+-1.406629
+2.400648
+-1.290373
+3.977797
+3.313780
+5.283433
+-1.052645
+...
+2.453055
+3.461347
+2.907938
+-0.101736
+0.525861
+3.303174
+2.662573
+-0.156935
+3.249469
+0
+
+
+1
+-1.396684
+2.160555
+-2.079091
+-0.202079
+2.884480
+2.034316
+4.885637
+4.221477
+2.573911
+0.180173
+...
+4.962888
+1.372881
+2.562159
+0.662939
+-0.805594
+3.533896
+3.790616
+-0.154599
+4.930846
+0
+
+
+2
+1.040872
+5.255454
+-0.147940
+0.294196
+0.597649
+-1.235295
+4.313004
+5.238071
+5.605235
+0.012568
+...
+2.174434
+3.278299
+3.462563
+-0.409624
+0.322454
+4.029577
+2.600332
+-1.107307
+2.887775
+0
+
+
+3
+0.997923
+2.140354
+-0.010764
+0.354853
+1.915891
+-1.041988
+2.193550
+3.467318
+2.996250
+0.242412
+...
+3.236173
+2.916559
+4.634331
+-0.348694
+-2.413849
+2.261911
+1.594445
+-0.301463
+2.758082
+0
+
+
+4
+0.201835
+2.702231
+0.350186
+1.007252
+3.745162
+1.618354
+5.198454
+4.954427
+4.045311
+-0.155478
+...
+3.502037
+5.419818
+0.721887
+0.780405
+-1.332553
+2.386323
+3.399030
+0.805458
+5.311518
+0
+
+
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+...
+
+
+2495
+-1.612271
+1.136322
+0.439253
+-1.661940
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+-0.445082
+2.862625
+2.228701
+3.714258
+-1.834704
+...
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+...
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+
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+...
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+2.124600
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+-0.444126
+2.429726
+0
+
+
+
+
2500 rows × 21 columns
+
+
+
+4.3.2. Data Drift Analysis
+Second we will review the data drift table plot and the drift results:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Source
+
+
+
+
+
+
+kind: job
+metadata:
+ name: batch-inference-v2
+ tag: ''
+ hash: 6d03260f9186d7b27651d6d0b42074fec54eb0f9
+ project: ''
+ labels:
+ author: eyald
+ categories:
+ - utils
+ - data-analysis
+ - monitoring
+spec:
+ command: ''
+ args: []
+ image: mlrun/mlrun
+ build:
+ functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from inspect import signature
from typing import Any, Dict, List, Optional, Union

import mlrun

try:
    import mlrun.model_monitoring.api
except ModuleNotFoundError:
    raise mlrun.errors.MLRunNotFoundError(
        f"Please update your `mlrun` version to >=1.5.0 or use an "
        f"older version of the batch inference function."
    )


import numpy as np
import pandas as pd
from mlrun.frameworks.auto_mlrun import AutoMLRun


def _prepare_result_set(
    x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray
) -> pd.DataFrame:
    """
    Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
    (x) and the model predictions (y_pred).

    :param x:             The inputs.
    :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
                          will be used in case the list is empty (predicted_label_{i}).
    :param y_pred:        The model predictions on the inputs.

    :returns: The result set.

    raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
                                       column already exists in the dataset.
    """
    # Prepare default target columns names if not provided:
    prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
    if len(label_columns) == 0:
        # Add default label column names:
        if prediction_columns_amount == 1:
            label_columns = ["predicted_label"]
        else:
            label_columns = [
                f"predicted_label_{i}" for i in range(prediction_columns_amount)
            ]

    # Validate the label columns:
    if prediction_columns_amount != len(label_columns):
        # No equality between provided label column names and outputs amount:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The number of predicted labels: {prediction_columns_amount} "
            f"is not equal to the given label columns: {len(label_columns)}"
        )
    common_labels = set(label_columns) & set(x.columns.tolist())
    if common_labels:
        # Label column exist in the original inputs:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The labels: {common_labels} are already existed in the given dataset."
        )

    return pd.concat(
        [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
    )


def _parse_record_results_kwarg(
    last_in_batch_set: Optional[bool],
) -> dict[str, bool]:
    """
    Check if `last_in_batch_set` is provided and expected as a parameter.
    Return it as a dictionary.
    """
    kwarg = "last_in_batch_set"
    if last_in_batch_set is None:
        return {}
    if (
        signature(mlrun.model_monitoring.api.record_results).parameters.get(kwarg)
        is None
    ):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"Unexpected parameter `{kwarg}` for function: "
            "`mlrun.model_monitoring.api.record_results`. "
            "Please make sure that you are using `mlrun>=1.6.0` version."
        )
    return {kwarg: last_in_batch_set}


def infer(
    context: mlrun.MLClientCtx,
    dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
    model_path: Union[str, mlrun.DataItem],
    drop_columns: Union[str, List[str], int, List[int]] = None,
    label_columns: Union[str, List[str]] = None,
    feature_columns: Union[str, List[str]] = None,
    log_result_set: bool = True,
    result_set_name: str = "prediction",
    batch_id: str = None,
    artifacts_tag: str = "",
    # Drift analysis parameters
    perform_drift_analysis: bool = None,
    trigger_monitoring_job: bool = False,
    batch_image_job: str = "mlrun/mlrun",
    endpoint_id: str = "",
    # The following model endpoint parameters are relevant only if:
    # perform drift analysis is not disabled
    # a new model endpoint record is going to be generated
    model_endpoint_name: str = "batch-infer",
    model_endpoint_drift_threshold: float = 0.7,
    model_endpoint_possible_drift_threshold: float = 0.5,
    model_endpoint_sample_set: Union[
        mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
    ] = None,
    last_in_batch_set: Optional[bool] = None,
    **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
    under the current project.
    Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
    drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
    here. When performing drift analysis, this function either uses an existing model endpoint record or creates
    a new one.
    At the moment, this function is supported for `mlrun>=1.5.0` versions.

    :param context:                                 MLRun context.
    :param dataset:                                 The dataset to infer through the model. Provided as an input (DataItem)
                                                    that represents Dataset artifact / Feature vector URI.
                                                    If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
                                                    numpy array.
    :param model_path:                              Model store uri (should start with store://). Provided as an input (DataItem).
                                                    If using MLRun SDK, `model_path` can also be provided as a parameter (string).
                                                    To generate a valid model store URI, please log the model before running this function.
                                                    If `endpoint_id` of existing model endpoint is provided, make sure
                                                    that it has a similar model store path, otherwise the drift analysis
                                                    won't be triggered.
    :param drop_columns:                            A string / integer or a list of strings / integers that represent the column names
                                                    / indices to drop. When the dataset is a list or a numpy array this parameter must
                                                    be represented by integers.
    :param label_columns:                           The target label(s) of the column(s) in the dataset for Regression or
                                                    Classification tasks. The label column can be accessed from the model object, or
                                                    the feature vector provided if available.
    :param feature_columns:                         List of feature columns that will be used to build the dataframe when dataset is
                                                    from type list or numpy array.
    :param log_result_set:                          Whether to log the result set - a DataFrame of the given inputs concatenated with
                                                    the predictions. Defaulted to True.
    :param result_set_name:                         The db key to set name of the prediction result and the filename. Defaulted to
                                                    'prediction'.
    :param batch_id:                                The ID of the given batch (inference dataset). If `None`, it will be generated.
                                                    Will be logged as a result of the run.
    :param artifacts_tag:                           Tag to use for all the artifacts resulted from the function (result set and
                                                    model monitoring artifacts)
    :param perform_drift_analysis:                  Whether to perform drift analysis between the sample set of the model object to the
                                                    dataset given. By default, None, which means it will perform drift analysis if the
                                                    model already has feature stats that are considered as a reference sample set.
                                                    Performing drift analysis on a new endpoint id will generate a new model endpoint
                                                    record. Please note that in order to trigger the drift analysis job, you need to
                                                    set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
                                                    only as part the scheduled monitoring job (if exist in the current project) or
                                                    if triggered manually by the user.
    :param trigger_monitoring_job:                  Whether to trigger the batch drift analysis after the infer job.
    :param batch_image_job:                         The image that will be used to register the monitoring batch job if not exist.
                                                    By default, the image is mlrun/mlrun.
    :param endpoint_id:                             Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
                                                    will be used either to perform the analysis on existing model endpoint or to
                                                    generate a new model endpoint record.
    :param model_endpoint_name:                     If a new model endpoint is generated, the model name will be presented under this
                                                    endpoint.
    :param model_endpoint_drift_threshold:          The threshold of which to mark drifts. Defaulted to 0.7.
    :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
    :param model_endpoint_sample_set:               A sample dataset to give to compare the inputs in the drift analysis.
                                                    Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
                                                    The default chosen sample set will always be the one who is set in the model artifact itself.
    :param last_in_batch_set:                       Relevant only when `perform_drift_analysis` is `True`.
                                                    This flag can (and should only) be used when the model endpoint does not have
                                                    model-monitoring set.
                                                    If set to `True` (the default), this flag marks the current monitoring window
                                                    (on this monitoring endpoint) as completed - the data inferred so far is assumed
                                                    to be the complete data for this monitoring window.
                                                    You may want to set this flag to `False` if you want to record multiple results in
                                                    close time proximity ("batch set"). In this case, set this flag to `False` on all
                                                    but the last batch in the set.
    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
                                      provided for an unsupported `mlrun` version.
    """

    # Loading the model:
    context.logger.info(f"Loading model...")
    if isinstance(model_path, mlrun.DataItem):
        model_path = model_path.artifact_url
    if not mlrun.datastore.is_store_uri(model_path):
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The provided model path ({model_path}) is invalid - should start with `store://`. "
            f"Please make sure that you have logged the model using `project.log_model()` "
            f"which generates a unique store uri for the logged model."
        )
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    if label_columns is None:
        label_columns = [
            output.name for output in model_handler._model_artifact.spec.outputs
        ]

    if feature_columns is None:
        feature_columns = [
            input.name for input in model_handler._model_artifact.spec.inputs
        ]

    # Get dataset by object, URL or by FeatureVector:
    context.logger.info(f"Loading data...")
    x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
        dataset=dataset,
        feature_columns=feature_columns,
        label_columns=label_columns,
        drop_columns=drop_columns,
    )

    # Predict:
    context.logger.info(f"Calculating prediction...")
    y_pred = model_handler.model.predict(x, **predict_kwargs)

    # Prepare the result set:
    result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)

    # Check for logging the result set:
    if log_result_set:
        mlrun.model_monitoring.api.log_result(
            context=context,
            result_set_name=result_set_name,
            result_set=result_set,
            artifacts_tag=artifacts_tag,
            batch_id=batch_id,
        )

    # Check for performing drift analysis
    if (
        perform_drift_analysis is None
        and model_handler._model_artifact.spec.feature_stats is not None
    ):
        perform_drift_analysis = True
    if perform_drift_analysis:
        context.logger.info("Performing drift analysis...")
        # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(
            sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
        )
        mlrun.model_monitoring.api.record_results(
            project=context.project,
            context=context,
            endpoint_id=endpoint_id,
            model_path=model_path,
            model_endpoint_name=model_endpoint_name,
            infer_results_df=result_set.copy(),
            sample_set_statistics=sample_set_statistics,
            drift_threshold=model_endpoint_drift_threshold,
            possible_drift_threshold=model_endpoint_possible_drift_threshold,
            artifacts_tag=artifacts_tag,
            trigger_monitoring_job=trigger_monitoring_job,
            default_batch_image=batch_image_job,
            **_parse_record_results_kwarg(last_in_batch_set=last_in_batch_set),
        )

+ commands: []
+ code_origin: ''
+ origin_filename: ''
+ with_mlrun: false
+ auto_build: false
+ requirements: []
+ entry_points:
+ infer:
+ name: infer
+ doc: 'Perform a prediction on a given dataset with the given model. Please make
+ sure that you have already logged the model
+
+ under the current project.
+
+ Can perform drift analysis between the sample set statistics stored in the
+ model to the current input data. The
+
+ drift rule is the value per-feature mean of the TVD and Hellinger scores according
+ to the thresholds configures
+
+ here. When performing drift analysis, this function either uses an existing
+ model endpoint record or creates
+
+ a new one.
+
+ At the moment, this function is supported for `mlrun>=1.5.0` versions.'
+ parameters:
+ - name: context
+ type: MLClientCtx
+ doc: MLRun context.
+ - name: dataset
+ type: Union[DataItem, list, dict, DataFrame, Series, ndarray]
+ doc: The dataset to infer through the model. Provided as an input (DataItem)
+ that represents Dataset artifact / Feature vector URI. If using MLRun SDK,
+ `dataset` can also be provided as a list, dictionary or numpy array.
+ - name: model_path
+ type: Union[str, DataItem]
+ doc: Model store uri (should start with store://). Provided as an input (DataItem).
+ If using MLRun SDK, `model_path` can also be provided as a parameter (string).
+ To generate a valid model store URI, please log the model before running
+ this function. If `endpoint_id` of existing model endpoint is provided,
+ make sure that it has a similar model store path, otherwise the drift analysis
+ won't be triggered.
+ - name: drop_columns
+ type: Union[str, List[str], int, List[int]]
+ doc: A string / integer or a list of strings / integers that represent the
+ column names / indices to drop. When the dataset is a list or a numpy array
+ this parameter must be represented by integers.
+ default: null
+ - name: label_columns
+ type: Union[str, List[str]]
+ doc: The target label(s) of the column(s) in the dataset for Regression or
+ Classification tasks. The label column can be accessed from the model object,
+ or the feature vector provided if available.
+ default: null
+ - name: feature_columns
+ type: Union[str, List[str]]
+ doc: List of feature columns that will be used to build the dataframe when
+ dataset is from type list or numpy array.
+ default: null
+ - name: log_result_set
+ type: bool
+ doc: Whether to log the result set - a DataFrame of the given inputs concatenated
+ with the predictions. Defaulted to True.
+ default: true
+ - name: result_set_name
+ type: str
+ doc: The db key to set name of the prediction result and the filename. Defaulted
+ to 'prediction'.
+ default: prediction
+ - name: batch_id
+ type: str
+ doc: The ID of the given batch (inference dataset). If `None`, it will be
+ generated. Will be logged as a result of the run.
+ default: null
+ - name: artifacts_tag
+ type: str
+ doc: Tag to use for all the artifacts resulted from the function (result set
+ and model monitoring artifacts)
+ default: ''
+ - name: perform_drift_analysis
+ type: bool
+ doc: Whether to perform drift analysis between the sample set of the model
+ object to the dataset given. By default, None, which means it will perform
+ drift analysis if the model already has feature stats that are considered
+ as a reference sample set. Performing drift analysis on a new endpoint id
+ will generate a new model endpoint record. Please note that in order to
+ trigger the drift analysis job, you need to set `trigger_monitoring_job=True`.
+ Otherwise, the drift analysis will be triggered only as part the scheduled
+ monitoring job (if exist in the current project) or if triggered manually
+ by the user.
+ default: null
+ - name: trigger_monitoring_job
+ type: bool
+ doc: Whether to trigger the batch drift analysis after the infer job.
+ default: false
+ - name: batch_image_job
+ type: str
+ doc: The image that will be used to register the monitoring batch job if not
+ exist. By default, the image is mlrun/mlrun.
+ default: mlrun/mlrun
+ - name: endpoint_id
+ type: str
+ doc: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
+ will be used either to perform the analysis on existing model endpoint or
+ to generate a new model endpoint record.
+ default: ''
+ - name: model_endpoint_name
+ type: str
+ doc: If a new model endpoint is generated, the model name will be presented
+ under this endpoint.
+ default: batch-infer
+ - name: model_endpoint_drift_threshold
+ type: float
+ doc: The threshold of which to mark drifts. Defaulted to 0.7.
+ default: 0.7
+ - name: model_endpoint_possible_drift_threshold
+ type: float
+ doc: The threshold of which to mark possible drifts. Defaulted to 0.5.
+ default: 0.5
+ - name: model_endpoint_sample_set
+ type: Union[DataItem, list, dict, DataFrame, Series, ndarray]
+ doc: A sample dataset to give to compare the inputs in the drift analysis.
+ Can be provided as an input (DataItem) or as a parameter (e.g. string, list,
+ DataFrame). The default chosen sample set will always be the one who is
+ set in the model artifact itself.
+ default: null
+ - name: last_in_batch_set
+ type: Optional[bool]
+ doc: Relevant only when `perform_drift_analysis` is `True`. This flag can
+ (and should only) be used when the model endpoint does not have model-monitoring
+ set. If set to `True` (the default), this flag marks the current monitoring
+ window (on this monitoring endpoint) as completed - the data inferred so
+ far is assumed to be the complete data for this monitoring window. You may
+ want to set this flag to `False` if you want to record multiple results
+ in close time proximity ("batch set"). In this case, set this flag to `False`
+ on all but the last batch in the set.
+ default: null
+ outputs:
+ - default: ''
+ lineno: 103
+ has_kwargs: true
+ description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+ default_handler: infer
+ disable_auto_mount: false
+ allow_empty_resources: true
+ clone_target_dir: ''
+ env: []
+ priority_class_name: ''
+ preemption_mode: prevent
+ affinity: null
+ tolerations: null
+ security_context: {}
+verbose: false
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.3.0/static/item.html b/functions/development/batch_inference_v2/2.3.0/static/item.html
new file mode 100644
index 00000000..4e513597
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/static/item.html
@@ -0,0 +1,54 @@
+
+
+
+
+ Source
+
+
+
+
+
+
+apiVersion: v1
+categories:
+- utils
+- data-analysis
+- monitoring
+description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+doc: ''
+example: batch_inference_v2.ipynb
+generationDate: 2023-08-07:12-25
+hidden: false
+icon: ''
+labels:
+ author: eyald
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.5.0
+name: batch_inference_v2
+platformVersion: 3.5.3
+spec:
+ extra_spec:
+ allow_empty_resources: true
+ build:
+ auto_build: false
+ with_mlrun: false
+ filename: batch_inference_v2.py
+ handler: infer
+ image: mlrun/mlrun
+ kind: job
+ requirements: null
+url: ''
+version: 2.3.0
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.3.0/static/source.html b/functions/development/batch_inference_v2/2.3.0/static/source.html
new file mode 100644
index 00000000..e716c999
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.3.0/static/source.html
@@ -0,0 +1,298 @@
+
+
+
+
+ Source
+
+
+
+
+
+
+# Copyright 2023 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from inspect import signature
+from typing import Any, Dict, List, Optional, Union
+
+import mlrun
+
+try:
+ import mlrun.model_monitoring.api
+except ModuleNotFoundError:
+ raise mlrun.errors.MLRunNotFoundError(
+ f"Please update your `mlrun` version to >=1.5.0 or use an "
+ f"older version of the batch inference function."
+ )
+
+
+import numpy as np
+import pandas as pd
+from mlrun.frameworks.auto_mlrun import AutoMLRun
+
+
+def _prepare_result_set(
+ x: pd.DataFrame, label_columns: List[str], y_pred: np.ndarray
+) -> pd.DataFrame:
+ """
+ Set default label column names and validate given names to prepare the result set - a concatenation of the inputs
+ (x) and the model predictions (y_pred).
+
+ :param x: The inputs.
+ :param label_columns: A list of strings representing the target column names to add to the predictions. Default name
+ will be used in case the list is empty (predicted_label_{i}).
+ :param y_pred: The model predictions on the inputs.
+
+ :returns: The result set.
+
+ raises MLRunInvalidArgumentError: If the labels columns amount do not match the outputs or if one of the label
+ column already exists in the dataset.
+ """
+ # Prepare default target columns names if not provided:
+ prediction_columns_amount = 1 if len(y_pred.shape) == 1 else y_pred.shape[1]
+ if len(label_columns) == 0:
+ # Add default label column names:
+ if prediction_columns_amount == 1:
+ label_columns = ["predicted_label"]
+ else:
+ label_columns = [
+ f"predicted_label_{i}" for i in range(prediction_columns_amount)
+ ]
+
+ # Validate the label columns:
+ if prediction_columns_amount != len(label_columns):
+ # No equality between provided label column names and outputs amount:
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The number of predicted labels: {prediction_columns_amount} "
+ f"is not equal to the given label columns: {len(label_columns)}"
+ )
+ common_labels = set(label_columns) & set(x.columns.tolist())
+ if common_labels:
+ # Label column exist in the original inputs:
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The labels: {common_labels} are already existed in the given dataset."
+ )
+
+ return pd.concat(
+ [x, pd.DataFrame(y_pred, columns=label_columns, index=x.index)], axis=1
+ )
+
+
+def _parse_record_results_kwarg(
+ last_in_batch_set: Optional[bool],
+) -> dict[str, bool]:
+ """
+ Check if `last_in_batch_set` is provided and expected as a parameter.
+ Return it as a dictionary.
+ """
+ kwarg = "last_in_batch_set"
+ if last_in_batch_set is None:
+ return {}
+ if (
+ signature(mlrun.model_monitoring.api.record_results).parameters.get(kwarg)
+ is None
+ ):
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"Unexpected parameter `{kwarg}` for function: "
+ "`mlrun.model_monitoring.api.record_results`. "
+ "Please make sure that you are using `mlrun>=1.6.0` version."
+ )
+ return {kwarg: last_in_batch_set}
+
+
+def infer(
+ context: mlrun.MLClientCtx,
+ dataset: Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
+ model_path: Union[str, mlrun.DataItem],
+ drop_columns: Union[str, List[str], int, List[int]] = None,
+ label_columns: Union[str, List[str]] = None,
+ feature_columns: Union[str, List[str]] = None,
+ log_result_set: bool = True,
+ result_set_name: str = "prediction",
+ batch_id: str = None,
+ artifacts_tag: str = "",
+ # Drift analysis parameters
+ perform_drift_analysis: bool = None,
+ trigger_monitoring_job: bool = False,
+ batch_image_job: str = "mlrun/mlrun",
+ endpoint_id: str = "",
+ # The following model endpoint parameters are relevant only if:
+ # perform drift analysis is not disabled
+ # a new model endpoint record is going to be generated
+ model_endpoint_name: str = "batch-infer",
+ model_endpoint_drift_threshold: float = 0.7,
+ model_endpoint_possible_drift_threshold: float = 0.5,
+ model_endpoint_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
+ ] = None,
+ last_in_batch_set: Optional[bool] = None,
+ **predict_kwargs: Dict[str, Any],
+):
+ """
+ Perform a prediction on a given dataset with the given model. Please make sure that you have already logged the model
+ under the current project.
+ Can perform drift analysis between the sample set statistics stored in the model to the current input data. The
+ drift rule is the value per-feature mean of the TVD and Hellinger scores according to the thresholds configures
+ here. When performing drift analysis, this function either uses an existing model endpoint record or creates
+ a new one.
+ At the moment, this function is supported for `mlrun>=1.5.0` versions.
+
+ :param context: MLRun context.
+ :param dataset: The dataset to infer through the model. Provided as an input (DataItem)
+ that represents Dataset artifact / Feature vector URI.
+ If using MLRun SDK, `dataset` can also be provided as a list, dictionary or
+ numpy array.
+ :param model_path: Model store uri (should start with store://). Provided as an input (DataItem).
+ If using MLRun SDK, `model_path` can also be provided as a parameter (string).
+ To generate a valid model store URI, please log the model before running this function.
+ If `endpoint_id` of existing model endpoint is provided, make sure
+ that it has a similar model store path, otherwise the drift analysis
+ won't be triggered.
+ :param drop_columns: A string / integer or a list of strings / integers that represent the column names
+ / indices to drop. When the dataset is a list or a numpy array this parameter must
+ be represented by integers.
+ :param label_columns: The target label(s) of the column(s) in the dataset for Regression or
+ Classification tasks. The label column can be accessed from the model object, or
+ the feature vector provided if available.
+ :param feature_columns: List of feature columns that will be used to build the dataframe when dataset is
+ from type list or numpy array.
+ :param log_result_set: Whether to log the result set - a DataFrame of the given inputs concatenated with
+ the predictions. Defaulted to True.
+ :param result_set_name: The db key to set name of the prediction result and the filename. Defaulted to
+ 'prediction'.
+ :param batch_id: The ID of the given batch (inference dataset). If `None`, it will be generated.
+ Will be logged as a result of the run.
+ :param artifacts_tag: Tag to use for all the artifacts resulted from the function (result set and
+ model monitoring artifacts)
+ :param perform_drift_analysis: Whether to perform drift analysis between the sample set of the model object to the
+ dataset given. By default, None, which means it will perform drift analysis if the
+ model already has feature stats that are considered as a reference sample set.
+ Performing drift analysis on a new endpoint id will generate a new model endpoint
+ record. Please note that in order to trigger the drift analysis job, you need to
+ set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered
+ only as part the scheduled monitoring job (if exist in the current project) or
+ if triggered manually by the user.
+ :param trigger_monitoring_job: Whether to trigger the batch drift analysis after the infer job.
+ :param batch_image_job: The image that will be used to register the monitoring batch job if not exist.
+ By default, the image is mlrun/mlrun.
+ :param endpoint_id: Model endpoint unique ID. If `perform_drift_analysis` was set, the endpoint_id
+ will be used either to perform the analysis on existing model endpoint or to
+ generate a new model endpoint record.
+ :param model_endpoint_name: If a new model endpoint is generated, the model name will be presented under this
+ endpoint.
+ :param model_endpoint_drift_threshold: The threshold of which to mark drifts. Defaulted to 0.7.
+ :param model_endpoint_possible_drift_threshold: The threshold of which to mark possible drifts. Defaulted to 0.5.
+ :param model_endpoint_sample_set: A sample dataset to give to compare the inputs in the drift analysis.
+ Can be provided as an input (DataItem) or as a parameter (e.g. string, list, DataFrame).
+ The default chosen sample set will always be the one who is set in the model artifact itself.
+ :param last_in_batch_set: Relevant only when `perform_drift_analysis` is `True`.
+ This flag can (and should only) be used when the model endpoint does not have
+ model-monitoring set.
+ If set to `True` (the default), this flag marks the current monitoring window
+ (on this monitoring endpoint) as completed - the data inferred so far is assumed
+ to be the complete data for this monitoring window.
+ You may want to set this flag to `False` if you want to record multiple results in
+ close time proximity ("batch set"). In this case, set this flag to `False` on all
+ but the last batch in the set.
+ raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided, or if `last_in_batch_set` is
+ provided for an unsupported `mlrun` version.
+ """
+
+ # Loading the model:
+ context.logger.info(f"Loading model...")
+ if isinstance(model_path, mlrun.DataItem):
+ model_path = model_path.artifact_url
+ if not mlrun.datastore.is_store_uri(model_path):
+ raise mlrun.errors.MLRunInvalidArgumentError(
+ f"The provided model path ({model_path}) is invalid - should start with `store://`. "
+ f"Please make sure that you have logged the model using `project.log_model()` "
+ f"which generates a unique store uri for the logged model."
+ )
+ model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+ if label_columns is None:
+ label_columns = [
+ output.name for output in model_handler._model_artifact.spec.outputs
+ ]
+
+ if feature_columns is None:
+ feature_columns = [
+ input.name for input in model_handler._model_artifact.spec.inputs
+ ]
+
+ # Get dataset by object, URL or by FeatureVector:
+ context.logger.info(f"Loading data...")
+ x, label_columns = mlrun.model_monitoring.api.read_dataset_as_dataframe(
+ dataset=dataset,
+ feature_columns=feature_columns,
+ label_columns=label_columns,
+ drop_columns=drop_columns,
+ )
+
+ # Predict:
+ context.logger.info(f"Calculating prediction...")
+ y_pred = model_handler.model.predict(x, **predict_kwargs)
+
+ # Prepare the result set:
+ result_set = _prepare_result_set(x=x, label_columns=label_columns, y_pred=y_pred)
+
+ # Check for logging the result set:
+ if log_result_set:
+ mlrun.model_monitoring.api.log_result(
+ context=context,
+ result_set_name=result_set_name,
+ result_set=result_set,
+ artifacts_tag=artifacts_tag,
+ batch_id=batch_id,
+ )
+
+ # Check for performing drift analysis
+ if (
+ perform_drift_analysis is None
+ and model_handler._model_artifact.spec.feature_stats is not None
+ ):
+ perform_drift_analysis = True
+ if perform_drift_analysis:
+ context.logger.info("Performing drift analysis...")
+ # Get the sample set statistics (either from the sample set or from the statistics logged with the model)
+ sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(
+ sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
+ )
+ mlrun.model_monitoring.api.record_results(
+ project=context.project,
+ context=context,
+ endpoint_id=endpoint_id,
+ model_path=model_path,
+ model_endpoint_name=model_endpoint_name,
+ infer_results_df=result_set.copy(),
+ sample_set_statistics=sample_set_statistics,
+ drift_threshold=model_endpoint_drift_threshold,
+ possible_drift_threshold=model_endpoint_possible_drift_threshold,
+ artifacts_tag=artifacts_tag,
+ trigger_monitoring_job=trigger_monitoring_job,
+ default_batch_image=batch_image_job,
+ **_parse_record_results_kwarg(last_in_batch_set=last_in_batch_set),
+ )
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/latest/src/function.yaml b/functions/development/batch_inference_v2/latest/src/function.yaml
index 0f81e3c6..b04ddce9 100644
--- a/functions/development/batch_inference_v2/latest/src/function.yaml
+++ b/functions/development/batch_inference_v2/latest/src/function.yaml
@@ -2,7 +2,7 @@ kind: job
metadata:
name: batch-inference-v2
tag: ''
- hash: 1bb938adfe2b006dad2c70d5fec32e88b22bde4e
+ hash: 6d03260f9186d7b27651d6d0b42074fec54eb0f9
project: ''
labels:
author: eyald
@@ -157,6 +157,7 @@ spec:
outputs:
- default: ''
lineno: 103
+ has_kwargs: true
description: Batch inference (also knows as prediction) for the common ML frameworks
(SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
default_handler: infer
diff --git a/functions/development/batch_inference_v2/latest/src/item.yaml b/functions/development/batch_inference_v2/latest/src/item.yaml
index 78804fe9..0467c42c 100644
--- a/functions/development/batch_inference_v2/latest/src/item.yaml
+++ b/functions/development/batch_inference_v2/latest/src/item.yaml
@@ -29,4 +29,4 @@ spec:
kind: job
requirements: null
url: ''
-version: 2.2.0
+version: 2.3.0
diff --git a/functions/development/batch_inference_v2/latest/static/function.html b/functions/development/batch_inference_v2/latest/static/function.html
index 300b5d34..a678ec94 100644
--- a/functions/development/batch_inference_v2/latest/static/function.html
+++ b/functions/development/batch_inference_v2/latest/static/function.html
@@ -19,7 +19,7 @@
metadata:
name: batch-inference-v2
tag: ''
- hash: 1bb938adfe2b006dad2c70d5fec32e88b22bde4e
+ hash: 6d03260f9186d7b27651d6d0b42074fec54eb0f9
project: ''
labels:
author: eyald
@@ -174,6 +174,7 @@
outputs:
- default: ''
lineno: 103
+ has_kwargs: true
description: Batch inference (also knows as prediction) for the common ML frameworks
(SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
default_handler: infer
diff --git a/functions/development/batch_inference_v2/latest/static/item.html b/functions/development/batch_inference_v2/latest/static/item.html
index b701ebb5..4e513597 100644
--- a/functions/development/batch_inference_v2/latest/static/item.html
+++ b/functions/development/batch_inference_v2/latest/static/item.html
@@ -46,7 +46,7 @@
kind: job
requirements: null
url: ''
-version: 2.2.0
+version: 2.3.0
diff --git a/functions/development/catalog.json b/functions/development/catalog.json
index d471c471..fe5a4d24 100644
--- a/functions/development/catalog.json
+++ b/functions/development/catalog.json
@@ -1 +1 @@
-{"ingest": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0", "assets": {"example": "src/ingest.ipynb", "source": "src/ingest.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "ingest", "platformVersion": "", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0", "assets": {"example": "src/ingest.ipynb", "source": "src/ingest.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "ingest", "platformVersion": "3.5.0", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0", "assets": {"example": "src/ingest.ipynb", "source": "src/ingest.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "data-analysis", "feature-store"], "description": "Feature Store ingest function that runs the transformation graph on the source of the featureset.", "doc": "", "example": "ingest.ipynb", "generationDate": "2021-11-13:00-15", "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "ingest", "platformVersion": "", "spec": {"filename": "ingest.py", "handler": "ingest", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1", "assets": {"example": "src/ingest.ipynb", "source": "src/ingest.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}}, "load_dataset": {"latest": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0", "assets": {"example": "src/load_dataset.ipynb", "source": "src/load_dataset.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.9.0", "assets": {"example": "src/load_dataset.ipynb", "source": "src/load_dataset.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "1.1.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "load-dataset", "platformVersion": "3.5.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "1.1.0", "assets": {"example": "src/load_dataset.ipynb", "source": "src/load_dataset.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.8.0": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "load-dataset", "platformVersion": "3.2.0", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.8.0", "assets": {"example": "src/load_dataset.ipynb", "source": "src/load_dataset.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation"], "description": "load a toy dataset from scikit-learn", "doc": "README.md", "example": "load_dataset.ipynb", "generationDate": "2021-05-19:23-13", "icon": "", "labels": {"author": "yjb", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "", "name": "load-dataset", "platformVersion": "", "spec": {"filename": "load_dataset.py", "handler": "load_dataset", "image": "mlrun/ml-models", "kind": "job", "requirements": []}, "url": "", "version": "0.0.1", "assets": {"example": "src/load_dataset.ipynb", "source": "src/load_dataset.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}}, "concept_drift": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/concept_drift.ipynb", "source": "src/concept_drift.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "concept-drift", "platformVersion": "3.2.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "0.9.0", "assets": {"example": "src/concept_drift.ipynb", "source": "src/concept_drift.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "1.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-serving"], "description": "Deploy a streaming Concept Drift detector on a labeled stream", "doc": "", "example": "concept_drift.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "orz", "framework": "sklearn"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "concept-drift", "platformVersion": "3.5.0", "spec": {"filename": "concept_drift.py", "handler": "concept_drift_deployer", "image": "mlrun/ml-models", "kind": "job", "requirements": ["scikit-multiflow"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/concept_drift.ipynb", "source": "src/concept_drift.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.0.2": 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\ No newline at end of file
diff --git a/functions/development/tags.json b/functions/development/tags.json
index 2f96c41d..cfd36fb7 100644
--- a/functions/development/tags.json
+++ b/functions/development/tags.json
@@ -1 +1 @@
-{"kind": ["nuclio", "nuclio:serving", "dask", "serving", "job"], "categories": ["utils", "feature-store", "Data Generation", "model-training", "GenAI", "Audio", "data-preparation", "Data Preparation", "NLP", "data-validation", "monitoring", "data-analysis", "deep-learning", "machine-learning", "Deep Learning", "model-testing", "PyTorch", "Huggingface", "model-serving", "etl"]}
\ No newline at end of file
+{"categories": ["etl", "machine-learning", "model-testing", "Audio", "model-serving", "PyTorch", "data-preparation", "utils", "NLP", "data-analysis", "Data Generation", "data-validation", "Huggingface", "model-training", "Data Preparation", "GenAI", "deep-learning", "feature-store", "monitoring", "Deep Learning"], "kind": ["nuclio", "nuclio:serving", "serving", "job", "dask"]}
\ No newline at end of file