\n",
+ "\n",
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the drift table artifact\n",
+ "drift_table_plot_artifact = project.get_artifact(\"drift_table_plot\")\n",
+ "drift_table_plot_artifact.to_dataitem().show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "mlrun-extended",
+ "language": "python",
+ "name": "conda-env-mlrun-extended-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": "3.9.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/functions/development/batch_inference_v2/2.6.0/src/batch_inference_v2.py b/functions/development/batch_inference_v2/2.6.0/src/batch_inference_v2.py
new file mode 100644
index 00000000..78f9a709
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/src/batch_inference_v2.py
@@ -0,0 +1,253 @@
+# 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, Union, Optional
+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 _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
+ model_endpoint_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
+ model_artifact_feature_stats: dict,
+ feature_columns: Optional[List],
+ drop_columns: Optional[List],
+ label_columns: Optional[List]) -> Dict[str, Any]:
+ statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_artifact_feature_stats,
+ sample_set_columns=feature_columns,
+ sample_set_drop_columns=drop_columns,
+ sample_set_label_columns=label_columns)
+ get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
+ statics_function_input_dict = signature(get_sample_statics_function).parameters
+ # As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
+ # we will now send only the parameters it expects.
+ statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
+ if len(statistics_input_filtered) != len(statics_function_input_dict):
+ context.logger.warning(f"get_sample_set_statistics is in an older version; "
+ "some parameters will not be sent to the function."
+ f" Expected input: {list(statics_function_input_dict.keys())},"
+ f" actual input: {list(statistics_input_filtered.keys())}")
+ return statistics_input_filtered
+
+
+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,
+ 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_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
+ ] = None,
+ **predict_kwargs: Dict[str, Any],
+):
+ """
+ Perform a prediction on the provided dataset using the specified model.
+ Ensure that the model has already been logged under the current project.
+
+ If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
+ This will create a new model endpoint record under the specified model_endpoint_name.
+ Additionally, ensure that model monitoring is enabled at the project level by calling the
+ project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
+ endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
+ 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 prediction set result artifact.
+ :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.
+ :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_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.
+
+ raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
+ """
+
+ # 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)
+ statistics_input_filtered = _get_sample_set_statistics_parameters(
+ context=context,
+ model_endpoint_sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
+ feature_columns=feature_columns,
+ drop_columns=drop_columns,
+ label_columns=label_columns)
+ sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
+ 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,
+ )
diff --git a/functions/development/batch_inference_v2/2.6.0/src/function.yaml b/functions/development/batch_inference_v2/2.6.0/src/function.yaml
new file mode 100644
index 00000000..0db7b636
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/src/function.yaml
@@ -0,0 +1,128 @@
+kind: job
+verbose: false
+spec:
+ entry_points:
+ infer:
+ has_kwargs: true
+ lineno: 102
+ has_varargs: false
+ doc: 'Perform a prediction on the provided dataset using the specified model.
+
+ Ensure that the model has already been logged under the current project.
+
+
+ If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis
+ parameter to True.
+
+ This will create a new model endpoint record under the specified model_endpoint_name.
+
+ Additionally, ensure that model monitoring is enabled at the project level
+ by calling the
+
+ project.enable_model_monitoring() function. You can also apply monitoring
+ to an existing model by providing its
+
+ endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
+
+ 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 prediction set result artifact.
+ 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.
+ default: null
+ - 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_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: infer
+ description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+ allow_empty_resources: true
+ build:
+ with_mlrun: false
+ code_origin: ''
+ 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, Union, Optional
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 _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
                                          model_endpoint_sample_set: Union[
                                              mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
                                          model_artifact_feature_stats: dict,
                                          feature_columns: Optional[List],
                                          drop_columns: Optional[List],
                                          label_columns: Optional[List]) -> Dict[str, Any]:
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statistics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning(f"get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function."
                               f" Expected input: {list(statics_function_input_dict.keys())},"
                               f" actual input: {list(statistics_input_filtered.keys())}")
    return statistics_input_filtered


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,
        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_sample_set: Union[
            mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
        ] = None,
        **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on the provided dataset using the specified model.
    Ensure that the model has already been logged under the current project.

    If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
    This will create a new model endpoint record under the specified model_endpoint_name.
    Additionally, ensure that model monitoring is enabled at the project level by calling the
    project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
    endpoint id or name, and the monitoring tools will be applied to that endpoint.

    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 prediction set result artifact.
    :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.
    :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_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.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """

    # 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)
        statistics_input_filtered = _get_sample_set_statistics_parameters(
            context=context,
            model_endpoint_sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
            feature_columns=feature_columns,
            drop_columns=drop_columns,
            label_columns=label_columns)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
        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,
        )

+ origin_filename: ''
+ auto_build: false
+ default_handler: infer
+ image: mlrun/mlrun
+ disable_auto_mount: false
+ command: ''
+metadata:
+ tag: ''
+ categories:
+ - utils
+ - data-analysis
+ - monitoring
+ name: batch-inference-v2
diff --git a/functions/development/batch_inference_v2/2.6.0/src/item.yaml b/functions/development/batch_inference_v2/2.6.0/src/item.yaml
new file mode 100644
index 00000000..e995c770
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/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: 1.7.0-rc51
+name: batch_inference_v2
+platformVersion: 3.6.0
+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: 2.6.0
diff --git a/functions/development/batch_inference_v2/2.6.0/src/requirements.txt b/functions/development/batch_inference_v2/2.6.0/src/requirements.txt
new file mode 100644
index 00000000..c9fd306e
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/src/requirements.txt
@@ -0,0 +1,5 @@
+numpy
+pandas
+scikit-learn
+plotly
+xgboost
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.6.0/src/test_batch_inference_v2.py b/functions/development/batch_inference_v2/2.6.0/src/test_batch_inference_v2.py
new file mode 100644
index 00000000..6fa657a0
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/src/test_batch_inference_v2.py
@@ -0,0 +1,255 @@
+# 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 os
+import pickle
+import time
+import uuid
+import numpy as np
+import pandas as pd
+import pytest
+from sklearn.datasets import make_classification
+from sklearn.tree import DecisionTreeClassifier
+import datetime
+from sklearn.model_selection import train_test_split
+from xgboost import XGBClassifier
+from mlrun.frameworks.sklearn import apply_mlrun
+from mlrun.projects import get_or_create_project
+import mlrun
+import mlrun.common.schemas
+from batch_inference_v2 import infer
+import shutil
+from mlrun.model_monitoring.api import get_or_create_model_endpoint
+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)
+
+
+def assert_batch_predict(n_features, batch_inference_run, with_monitoring=False, project_name="batch-infer-test"):
+ # Check the logged results:
+ assert "batch_id" in batch_inference_run.status.results
+ assert len(batch_inference_run.status.artifacts) == 1
+ assert len(batch_inference_run.artifact("prediction").as_df().columns) == n_features + 1
+ if with_monitoring:
+ # Check that the drift analysis was performed:
+ time.sleep(60)
+ # Retrieve the model endpoint
+ project = get_or_create_project(project_name)
+ endpoint = get_or_create_model_endpoint(project=project.name, model_endpoint_name="my_cool_endpoint")
+
+ # Validate that the artifacts were logged in the project
+ artifacts = project.list_artifacts(
+ labels={
+ "mlrun/producer-type": "model-monitoring-app",
+ "mlrun/app-name": "histogram-data-drift",
+ "mlrun/endpoint-id": endpoint.metadata.uid,
+ }
+ )
+
+ assert len(artifacts) == 2
+
+ # Validate that the model endpoint has been updated as expected
+ assert endpoint.status.current_stats
+ assert endpoint.status.drift_status
+
+
+@pytest.mark.skipif(
+ condition=not _validate_environment_variables(),
+ reason="Project's environment variables are not set",
+)
+def test_batch_predict():
+ project = 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": "target_label",
+ "perform_drift_analysis": False,
+ },
+ local=True,
+ )
+
+ # Check the logged results:
+ assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run)
+
+ # Enable model monitoring
+ project.set_model_monitoring_credentials(
+ endpoint_store_connection="v3io",
+ tsdb_connection="v3io",
+ stream_path="v3io")
+
+ # Deploy model monitoring infrastructure
+ project.enable_model_monitoring(wait_for_deployment=True, base_period=1)
+
+ # Wait until the monitoring application is triggered
+ import time
+ time.sleep(60)
+
+ # Check the logged results:
+ assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run, with_monitoring=True)
+
+ # Clean resources
+ _delete_project(project=project.metadata.name)
+
+
+@pytest.mark.skipif(
+ condition=not _validate_environment_variables(),
+ reason="Project's environment variables are not set",
+)
+class TestBatchInferUnitTests:
+ @classmethod
+ def setup_class(cls):
+ cls.project_name = "batch-infer-v2-unit-test"
+ cls.infer_artifact_path = "./infer_test_result/"
+
+ def setup_method(self):
+ self.project = get_or_create_project(self.project_name)
+ current_datetime = datetime.datetime.now()
+ datetime_str = current_datetime.strftime("%Y%m%d_%H%M%S")
+ mlrun.runtimes.utils.global_context.set(None)
+ self.context = mlrun.get_or_create_ctx(datetime_str, project=self.project.metadata.name, upload_artifacts=True)
+ self.context.artifact_path = self.infer_artifact_path
+
+ def teardown_method(self):
+ mlrun.get_run_db().delete_project(
+ self.project.metadata.name,
+ deletion_strategy=mlrun.common.schemas.DeletionStrategy.cascade,
+ )
+ if os.path.exists(self.infer_artifact_path):
+ shutil.rmtree(self.infer_artifact_path)
+
+ def _get_model_endpoint_sample_set(self, sample_type, n_features: int = 20):
+ data = generate_data(n_samples=4, n_features=n_features)[0]
+ if sample_type == mlrun.DataItem:
+ artifact = self.project.log_dataset("infer_sample", df=data)
+ return artifact.to_dataitem()
+ elif sample_type == list:
+ return data.values.tolist()
+ elif sample_type == dict:
+ return data.to_dict(orient='list')
+ elif sample_type == pd.DataFrame:
+ return data
+ elif sample_type == np.ndarray:
+ return data.values
+
+ @pytest.mark.parametrize("sample_type", [mlrun.DataItem, list, dict, pd.DataFrame, np.ndarray])
+ def test_infer_sample_types(self, sample_type):
+ n_features = 10
+ training_set, prediction_set = generate_data(n_features=n_features)
+ clf = XGBClassifier(n_estimators=2, max_depth=2, learning_rate=1, objective="binary:logistic")
+ x, y = prediction_set, training_set['target_label']
+ x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2, random_state=0)
+ clf.fit(x_train, y_train)
+ train_set_to_log = x_train.join(y_train)
+ model = self.project.log_model(f"model-{uuid.uuid4()}", body=pickle.dumps(clf),
+ model_file=f"model-{uuid.uuid4()}.pkl", framework="xgboost",
+ training_set=train_set_to_log, label_column="target_label")
+
+ dataset = self.project.log_dataset(f"dataset-{uuid.uuid4()}", df=x_test)
+ z_test = train_set_to_log * 5
+ model_endpoint_sample_set = self.project.log_dataset(f"model-endpoint-sample-set{uuid.uuid4()}", df=z_test)
+
+ sample = self._get_model_endpoint_sample_set(
+ sample_type=sample_type, n_features=n_features)
+ infer(context=self.context,
+ dataset=dataset.to_dataitem().as_df(), model_path=model.uri,
+ model_endpoint_sample_set=sample,
+ feature_columns=list(model_endpoint_sample_set.to_dataitem().as_df().columns),
+ label_columns="target_label",
+ model_endpoint_name=f"model-endpoint-name-{uuid.uuid4()}",
+ trigger_monitoring_job=True,
+ perform_drift_analysis=True)
+ # a workaround until ML-4636 will be solved.
+ batch_inference_run = self.project.list_runs(name=self.context.name).to_objects()[0]
+ mlrun.get_run_db().update_run(updates={"status.state": "completed"}, uid=batch_inference_run.uid())
+ assert_batch_predict(n_features=n_features, batch_inference_run=batch_inference_run, project_name=self.project_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_v2/2.6.0/static/batch_inference_v2.html b/functions/development/batch_inference_v2/2.6.0/static/batch_inference_v2.html
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+batch_inference_v2.batch_inference_v2
+
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+
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.
+
+frominspectimportsignature
+fromtypingimportAny,Dict,List,Union,Optional
+importmlrun
+
+try:
+ importmlrun.model_monitoring.api
+exceptModuleNotFoundError:
+ raisemlrun.errors.MLRunNotFoundError(
+ f"Please update your `mlrun` version to >=1.5.0 or use an "
+ f"older version of the batch inference function."
+ )
+
+importnumpyasnp
+importpandasaspd
+frommlrun.frameworks.auto_mlrunimportAutoMLRun
+
+
+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=1iflen(y_pred.shape)==1elsey_pred.shape[1]
+ iflen(label_columns)==0:
+ # Add default label column names:
+ ifprediction_columns_amount==1:
+ label_columns=["predicted_label"]
+ else:
+ label_columns=[
+ f"predicted_label_{i}"foriinrange(prediction_columns_amount)
+ ]
+
+ # Validate the label columns:
+ ifprediction_columns_amount!=len(label_columns):
+ # No equality between provided label column names and outputs amount:
+ raisemlrun.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())
+ ifcommon_labels:
+ # Label column exist in the original inputs:
+ raisemlrun.errors.MLRunInvalidArgumentError(
+ f"The labels: {common_labels} are already existed in the given dataset."
+ )
+
+ returnpd.concat(
+ [x,pd.DataFrame(y_pred,columns=label_columns,index=x.index)],axis=1
+ )
+
+
+def_get_sample_set_statistics_parameters(context:mlrun.MLClientCtx,
+ model_endpoint_sample_set:Union[
+ mlrun.DataItem,list,dict,pd.DataFrame,pd.Series,np.ndarray],
+ model_artifact_feature_stats:dict,
+ feature_columns:Optional[List],
+ drop_columns:Optional[List],
+ label_columns:Optional[List])->Dict[str,Any]:
+ statics_input_full_dict=dict(sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_artifact_feature_stats,
+ sample_set_columns=feature_columns,
+ sample_set_drop_columns=drop_columns,
+ sample_set_label_columns=label_columns)
+ get_sample_statics_function=mlrun.model_monitoring.api.get_sample_set_statistics
+ statics_function_input_dict=signature(get_sample_statics_function).parameters
+ # As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
+ # we will now send only the parameters it expects.
+ statistics_input_filtered={key:statics_input_full_dict[key]forkeyinstatics_function_input_dict}
+ iflen(statistics_input_filtered)!=len(statics_function_input_dict):
+ context.logger.warning(f"get_sample_set_statistics is in an older version; "
+ "some parameters will not be sent to the function."
+ f" Expected input: {list(statics_function_input_dict.keys())},"
+ f" actual input: {list(statistics_input_filtered.keys())}")
+ returnstatistics_input_filtered
+
+
+
[docs]definfer(
+ 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,
+ 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_sample_set:Union[
+ mlrun.DataItem,list,dict,pd.DataFrame,pd.Series,np.ndarray
+ ]=None,
+ **predict_kwargs:Dict[str,Any],
+):
+"""
+ Perform a prediction on the provided dataset using the specified model.
+ Ensure that the model has already been logged under the current project.
+
+ If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
+ This will create a new model endpoint record under the specified model_endpoint_name.
+ Additionally, ensure that model monitoring is enabled at the project level by calling the
+ project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
+ endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
+ 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 prediction set result artifact.
+ :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.
+ :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_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.
+
+ raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
+ """
+
+ # Loading the model:
+ context.logger.info(f"Loading model...")
+ ifisinstance(model_path,mlrun.DataItem):
+ model_path=model_path.artifact_url
+ ifnotmlrun.datastore.is_store_uri(model_path):
+ raisemlrun.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)
+
+ iflabel_columnsisNone:
+ label_columns=[
+ output.nameforoutputinmodel_handler._model_artifact.spec.outputs
+ ]
+
+ iffeature_columnsisNone:
+ feature_columns=[
+ input.nameforinputinmodel_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:
+ iflog_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_analysisisNone
+ andmodel_handler._model_artifact.spec.feature_statsisnotNone
+ ):
+ perform_drift_analysis=True
+ ifperform_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)
+ statistics_input_filtered=_get_sample_set_statistics_parameters(
+ context=context,
+ model_endpoint_sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
+ feature_columns=feature_columns,
+ drop_columns=drop_columns,
+ label_columns=label_columns)
+ sample_set_statistics=mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
+ 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,
+ )
Perform a prediction on the provided dataset using the specified model.
+Ensure that the model has already been logged under the current project.
+
If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
+This will create a new model endpoint record under the specified model_endpoint_name.
+Additionally, ensure that model monitoring is enabled at the project level by calling the
+project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
+endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
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 prediction set result artifact.
+
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.
+
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_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.
+
+
+
+
raises MLRunInvalidArgumentError: if both model_path and endpoint_id are not provided
Perform a prediction on a given dataset with the given model. Using the default histogram data drift application, can perform drift analysis between the sample set
+statistics stored in the model to the current input data. The drift rule in this case is the value per-feature mean of the TVD
+and Hellinger scores. When §, 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 versions.
Can be passed in inputs as a dataset artifact / Feature vector URI.
+
Or, in parameters as a list, dictionary or numpy array.
+
+
+
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 of existing model endpoint is provided, make sure that it has a similar model store path, otherwise the drift analysis won’t be triggered.
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" for the i column.
+
+
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, it will be generated. Will be logged as a result of the run.
+
+
artifacts_tag: str = ""
+
Tag to use for all the artifacts resulted from the function. Defaulted to no tag.
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.
+
+
endpoint_id: str = ""
+
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.
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.
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 given as inputs was:
+
+
+
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 is True, the outputs of the results prediction will be:
+
+
The result set as described above.
+
The batch ID result - batch_id: str - a hashing result that is given by the user or generated randomly in case it was not provided to represent the batch that was being inferred.
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 - Generate a binary classification data. The data will be split into a 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 - Train a decision tree classifier on a given data.
We will use mlrun.code_to_function to turn this demo notebook into an MLRun function we can run.
+
We will use mlrun.import_function to import the batch_predict function .
+
+
+
+
# Create an MLRun function to run the notebook:
+demo_function=mlrun.code_to_function(name="batch-inference-demo",kind="job")
+
+# Import the `batch_inference_v2` function from the functions hub:
+batch_inference_function=mlrun.import_function('hub://batch_inference_v2')
+# you can import the function from the current directory as well:
+# batch_inference_function = mlrun.import_function("function.yaml")
+
+
+
+
+
Now, we will follow the demo steps as discussed above:
> 2024-10-08 10:23:40,584 [error] error getting build status: details: MLRunNotFoundError('Function tag not found batch-infer-demo/batch-inference-demo'), caused by: 404 Client Error: Not Found for url: http://mlrun-api:8080/api/v1/build/status?name=batch-inference-demo&project=batch-infer-demo&tag=&logs=no&offset=0&last_log_timestamp=0.0&verbose=no
+> 2024-10-08 10:23:40,586 [info] Storing function: {"db":"http://mlrun-api:8080","name":"batch-inference-demo-generate-data","uid":"4f68ba3fd9084e3e941ab3872ceb3635"}
+> 2024-10-08 10:23:40,881 [info] Job is running in the background, pod: batch-inference-demo-generate-data-52w8s
+> 2024-10-08 10:23:46,954 [info] To track results use the CLI: {"info_cmd":"mlrun get run 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo","logs_cmd":"mlrun logs 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo"}
+> 2024-10-08 10:23:46,954 [info] Or click for UI: {"ui_url":"https://dashboard.default-tenant.app.vmdev57.lab.iguazeng.com/mlprojects/batch-infer-demo/jobs/monitor/4f68ba3fd9084e3e941ab3872ceb3635/overview"}
+> 2024-10-08 10:23:46,955 [info] Run execution finished: {"name":"batch-inference-demo-generate-data","status":"completed"}
+
The data drift analysis in this demo is based on the histogram data drift application, and it 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: $TVD(x_S,x_I) = \frac{1}{2}\sum_{b_1}^{b_{20}} {|x_S - x_I|}$
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:
+
+
Drift table plot - The results are presented in a plotly table artifact named "drift_table_plot" that shows each feature’s statistics and its TVD, Hellinger and KLD (Kullback–Leibler divergence) results as follows:
+
+
+
+
+
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" where each key is a feature and its value is the feature’s metric value: Dict[str,float]
In addition, two results are being added to summarize the drift analysis:
+
+
drift_status: bool - A boolean value indicating whether a drift was found.
+
drift_metric: float - The mean of all the features drift metric value (the rule above):
+for $n$ features and metric rule $M(x_S,x_I)=\frac{H(x_S,x_I)+TVD(x_S,x_I)}{2}$, drift_metric $=\frac{1}{n}\sum_{x\in S}M(x_S,x_I)$
To calculate data drift and generate artifact results, we first need to define the model monitoring configuration. Next, we will deploy the model monitoring infrastructure, which includes the default histogram-based data drift application.
+
In this demo, we will use V3IO resources, but you can also provide your own. For more information on supported resources, please visit https://docs.mlrun.org/en/latest/model-monitoring/index.html#selecting-the-streaming-and-tsdb-platforms
+
+
4.1.1 Set credentials and deploy model monitoring infrastructure#
# Deploy model monitoring infrastructure
+project.enable_model_monitoring(wait_for_deployment=True,base_period=1)
+
+
+
+
+
> 2024-10-08 10:24:25,053 [warning] enable_model_monitoring: 'base_period' < 10 minutes is not supported in production environments: {"project":"batch-infer-demo"}
+2024-10-08 10:24:30 (info) Deploying function
+2024-10-08 10:24:30 (info) Building
+2024-10-08 10:24:30 (info) Staging files and preparing base images
+2024-10-08 10:24:30 (warn) Using user provided base image, runtime interpreter version is provided by the base image
+2024-10-08 10:24:30 (info) Building processor image
+2024-10-08 10:26:01 (info) Build complete
+2024-10-08 10:26:11 (info) Function deploy complete
+2024-10-08 10:24:25 (info) Deploying function
+2024-10-08 10:24:25 (info) Building
+2024-10-08 10:24:25 (info) Staging files and preparing base images
+2024-10-08 10:24:25 (warn) Using user provided base image, runtime interpreter version is provided by the base image
+2024-10-08 10:24:25 (info) Building processor image
+2024-10-08 10:26:06 (info) Build complete
+2024-10-08 10:27:07 (info) Function deploy complete
+2024-10-08 10:24:26 (info) Deploying function
+2024-10-08 10:24:26 (info) Building
+2024-10-08 10:24:28 (info) Staging files and preparing base images
+2024-10-08 10:24:28 (warn) Using user provided base image, runtime interpreter version is provided by the base image
+2024-10-08 10:24:28 (info) Building processor image
+2024-10-08 10:26:01 (info) Build complete
+2024-10-08 10:26:10 (info) Function deploy complete
+2024-10-08 10:24:32 (info) Deploying function
+2024-10-08 10:24:32 (info) Building
+2024-10-08 10:24:32 (info) Staging files and preparing base images
+2024-10-08 10:24:32 (warn) Using user provided base image, runtime interpreter version is provided by the base image
+2024-10-08 10:24:32 (info) Building processor image
+2024-10-08 10:26:01 (info) Build complete
+2024-10-08 10:26:12 (info) Function deploy complete
+
# Perform batch prediction, this time enable drift analysis
+batch_inference_run=batch_inference_function.run(
+ handler="infer",
+ inputs={"dataset":generate_data_run.outputs["prediction_set"]},
+ params={
+ "model_path":train_run.outputs["model"],
+ "label_columns":"label",
+ "perform_drift_analysis":True,
+ "model_endpoint_name":"my_cool_endpoint"# a display name for the model endpoint
+ },
+)
+
+
+
+
+
> 2024-10-08 10:27:29,954 [info] Storing function: {"db":"http://mlrun-api:8080","name":"batch-inference-v2-infer","uid":"ca26cf51bb984354aa2b6c42540af1f7"}
+> 2024-10-08 10:27:30,317 [info] Job is running in the background, pod: batch-inference-v2-infer-qmszk
+> 2024-10-08 10:27:34,169 [info] Loading model...
+> 2024-10-08 10:27:35,237 [info] Loading data...
+> 2024-10-08 10:27:35,277 [info] Calculating prediction...
+> 2024-10-08 10:27:35,281 [info] Logging result set (x | prediction)...
+> 2024-10-08 10:27:35,773 [info] Performing drift analysis...
+> 2024-10-08 10:27:37,335 [info] Bumping model endpoint last request time (EP without serving): {"bumped_last_request":"2024-10-08T10:29:35.991331+00:00","current_request":"2024-10-08T10:27:35.991331+00:00","endpoint_id":"cc285767a39c0455d050a8c0d7b0421c19394aba","last_request":"2024-10-08T10:27:35.806824+00:00","project":"batch-infer-demo"}
+> 2024-10-08 10:27:37,586 [info] To track results use the CLI: {"info_cmd":"mlrun get run ca26cf51bb984354aa2b6c42540af1f7 -p batch-infer-demo","logs_cmd":"mlrun logs ca26cf51bb984354aa2b6c42540af1f7 -p batch-infer-demo"}
+> 2024-10-08 10:27:37,586 [info] Or click for UI: {"ui_url":"https://dashboard.default-tenant.app.vmdev57.lab.iguazeng.com/mlprojects/batch-infer-demo/jobs/monitor/ca26cf51bb984354aa2b6c42540af1f7/overview"}
+> 2024-10-08 10:27:37,587 [info] Run execution finished: {"name":"batch-inference-v2-infer","status":"completed"}
+
# Wait until the monitoring application is triggered
+importtime
+time.sleep(60)
+
+
+
+
+
+
+
# Retrieve the model endpoint
+frommlrun.model_monitoring.apiimportget_or_create_model_endpoint
+endpoint=get_or_create_model_endpoint(project=project.name,model_endpoint_name="my_cool_endpoint")
+
+
+
+
+
+
+
# Validate that the artifacts were logged in the project
+artifacts=project.list_artifacts(
+ labels={
+ "mlrun/producer-type":"model-monitoring-app",
+ "mlrun/app-name":"histogram-data-drift",
+ "mlrun/endpoint-id":endpoint.metadata.uid,
+ }
+)
+
# Plot the drift results per feature and label
+features_drift_results_artifact=project.get_artifact("features_drift_results")
+features_drift_results_artifact.to_dataitem().show()
+
+
+
+
+
<IPython.core.display.JSON object>
+
+
+
+
+
+
+
# Plot the drift table artifact
+drift_table_plot_artifact=project.get_artifact("drift_table_plot")
+drift_table_plot_artifact.to_dataitem().show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.6.0/static/function.html b/functions/development/batch_inference_v2/2.6.0/static/function.html
new file mode 100644
index 00000000..f6938063
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/static/function.html
@@ -0,0 +1,150 @@
+
+
+
+
+
+
+
+
+
+
+ Source
+
+
+
+
+
+
+kind: job
+verbose: false
+spec:
+ entry_points:
+ infer:
+ has_kwargs: true
+ lineno: 102
+ has_varargs: false
+ doc: 'Perform a prediction on the provided dataset using the specified model.
+
+ Ensure that the model has already been logged under the current project.
+
+
+ If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis
+ parameter to True.
+
+ This will create a new model endpoint record under the specified model_endpoint_name.
+
+ Additionally, ensure that model monitoring is enabled at the project level
+ by calling the
+
+ project.enable_model_monitoring() function. You can also apply monitoring
+ to an existing model by providing its
+
+ endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
+
+ 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 prediction set result artifact.
+ 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.
+ default: null
+ - 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_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: infer
+ description: Batch inference (also knows as prediction) for the common ML frameworks
+ (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.
+ allow_empty_resources: true
+ build:
+ with_mlrun: false
+ code_origin: ''
+ 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, Union, Optional
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 _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
                                          model_endpoint_sample_set: Union[
                                              mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
                                          model_artifact_feature_stats: dict,
                                          feature_columns: Optional[List],
                                          drop_columns: Optional[List],
                                          label_columns: Optional[List]) -> Dict[str, Any]:
    statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
                                   model_artifact_feature_stats=model_artifact_feature_stats,
                                   sample_set_columns=feature_columns,
                                   sample_set_drop_columns=drop_columns,
                                   sample_set_label_columns=label_columns)
    get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
    statics_function_input_dict = signature(get_sample_statics_function).parameters
    #  As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
    #  we will now send only the parameters it expects.
    statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
    if len(statistics_input_filtered) != len(statics_function_input_dict):
        context.logger.warning(f"get_sample_set_statistics is in an older version; "
                               "some parameters will not be sent to the function."
                               f" Expected input: {list(statics_function_input_dict.keys())},"
                               f" actual input: {list(statistics_input_filtered.keys())}")
    return statistics_input_filtered


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,
        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_sample_set: Union[
            mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
        ] = None,
        **predict_kwargs: Dict[str, Any],
):
    """
    Perform a prediction on the provided dataset using the specified model.
    Ensure that the model has already been logged under the current project.

    If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
    This will create a new model endpoint record under the specified model_endpoint_name.
    Additionally, ensure that model monitoring is enabled at the project level by calling the
    project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
    endpoint id or name, and the monitoring tools will be applied to that endpoint.

    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 prediction set result artifact.
    :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.
    :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_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.

    raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
    """

    # 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)
        statistics_input_filtered = _get_sample_set_statistics_parameters(
            context=context,
            model_endpoint_sample_set=model_endpoint_sample_set,
            model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
            feature_columns=feature_columns,
            drop_columns=drop_columns,
            label_columns=label_columns)
        sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
        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,
        )

+ origin_filename: ''
+ auto_build: false
+ default_handler: infer
+ image: mlrun/mlrun
+ disable_auto_mount: false
+ command: ''
+metadata:
+ tag: ''
+ categories:
+ - utils
+ - data-analysis
+ - monitoring
+ name: batch-inference-v2
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.6.0/static/item.html b/functions/development/batch_inference_v2/2.6.0/static/item.html
new file mode 100644
index 00000000..d857a2c1
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/static/item.html
@@ -0,0 +1,54 @@
+
+
+
+
+
+
+
+
+
+
+ Source
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/2.6.0/static/source.html b/functions/development/batch_inference_v2/2.6.0/static/source.html
new file mode 100644
index 00000000..880ca8be
--- /dev/null
+++ b/functions/development/batch_inference_v2/2.6.0/static/source.html
@@ -0,0 +1,275 @@
+
+
+
+
+
+
+
+
+
+
+ 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, Union, Optional
+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 _get_sample_set_statistics_parameters(context: mlrun.MLClientCtx,
+ model_endpoint_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray],
+ model_artifact_feature_stats: dict,
+ feature_columns: Optional[List],
+ drop_columns: Optional[List],
+ label_columns: Optional[List]) -> Dict[str, Any]:
+ statics_input_full_dict = dict(sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_artifact_feature_stats,
+ sample_set_columns=feature_columns,
+ sample_set_drop_columns=drop_columns,
+ sample_set_label_columns=label_columns)
+ get_sample_statics_function = mlrun.model_monitoring.api.get_sample_set_statistics
+ statics_function_input_dict = signature(get_sample_statics_function).parameters
+ # As a result of changes to input parameters in the mlrun-get_sample_set_statistics function,
+ # we will now send only the parameters it expects.
+ statistics_input_filtered = {key: statics_input_full_dict[key] for key in statics_function_input_dict}
+ if len(statistics_input_filtered) != len(statics_function_input_dict):
+ context.logger.warning(f"get_sample_set_statistics is in an older version; "
+ "some parameters will not be sent to the function."
+ f" Expected input: {list(statics_function_input_dict.keys())},"
+ f" actual input: {list(statistics_input_filtered.keys())}")
+ return statistics_input_filtered
+
+
+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,
+ 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_sample_set: Union[
+ mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray
+ ] = None,
+ **predict_kwargs: Dict[str, Any],
+):
+ """
+ Perform a prediction on the provided dataset using the specified model.
+ Ensure that the model has already been logged under the current project.
+
+ If you wish to apply monitoring tools (e.g., drift analysis), set the perform_drift_analysis parameter to True.
+ This will create a new model endpoint record under the specified model_endpoint_name.
+ Additionally, ensure that model monitoring is enabled at the project level by calling the
+ project.enable_model_monitoring() function. You can also apply monitoring to an existing model by providing its
+ endpoint id or name, and the monitoring tools will be applied to that endpoint.
+
+ 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 prediction set result artifact.
+ :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.
+ :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_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.
+
+ raises MLRunInvalidArgumentError: if both `model_path` and `endpoint_id` are not provided
+ """
+
+ # 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)
+ statistics_input_filtered = _get_sample_set_statistics_parameters(
+ context=context,
+ model_endpoint_sample_set=model_endpoint_sample_set,
+ model_artifact_feature_stats=model_handler._model_artifact.spec.feature_stats,
+ feature_columns=feature_columns,
+ drop_columns=drop_columns,
+ label_columns=label_columns)
+ sample_set_statistics = mlrun.model_monitoring.api.get_sample_set_statistics(**statistics_input_filtered)
+ 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,
+ )
+
+
+
+
+
\ No newline at end of file
diff --git a/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb b/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb
index bb59221f..7f369fa5 100644
--- a/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb
+++ b/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb
@@ -10,14 +10,14 @@
"source": [
"# Batch Inference V2\n",
"\n",
- "A function for inferring given input through a given model while producing a **Result Set** and performing **Data Drift Analysis**.\n",
+ "A function for inferring given input through a given model while producing a **Result Set** and applying monitoring analysis.\n",
"\n",
"In this notebook we will go over the function's docs and outputs and see an end-to-end example of running it.\n",
"\n",
"1. [Documentation](#chapter1)\n",
"2. [Results Prediction](#chapter2)\n",
- "3. [Data Drift Analysis](#chapter3)\n",
- "4. [End-to-end Demo](#chapter4)"
+ "3. [End-to-end Demo](#chapter3)\n",
+ "4. [Data Drift Analysis](#chapter4)"
]
},
{
@@ -31,9 +31,9 @@
"\n",
"## 1. Documentation\n",
"\n",
- "Perform a prediction on a given dataset with the given model. Can perform drift analysis between the sample set\n",
- "statistics stored in the model to the current input data. The drift rule is the value per-feature mean of the TVD\n",
- "and Hellinger scores according to the thresholds configures here. When performing drift analysis, this function\n",
+ "Perform a prediction on a given dataset with the given model. Using the default histogram data drift application, can perform drift analysis between the sample set\n",
+ "statistics stored in the model to the current input data. The drift rule in this case is the value per-feature mean of the TVD\n",
+ "and Hellinger scores. When §, this function\n",
"either creates or update an existing model endpoint record (depends on the provided `endpoint_id`).\n",
"\n",
"At the moment, this function is supported for `mlrun>=1.5.0` versions."
@@ -104,19 +104,7 @@
" dataset given. By default, None, which means it will perform drift analysis if the\n",
" model already has feature stats that are considered as a reference sample set.\n",
" Performing drift analysis on a new endpoint id will generate a new model endpoint\n",
- " record. Please note that in order to trigger the drift analysis job, you need to\n",
- " set `trigger_monitoring_job=True`. Otherwise, the drift analysis will be triggered\n",
- " only as part the scheduled monitoring job (if exist in the current project) or\n",
- " if triggered manually by the user.\n",
- " \n",
- "* **trigger_monitoring_job**: `bool` = `False`\n",
- "\n",
- " Whether to trigger the batch drift analysis after the infer job.\n",
- "\n",
- "* **batch_image_job**: `str` = `mlrun/mlrun`\n",
- "\n",
- " The image that will be used for the monitoring batch job analysis. By default,\n",
- " the image is mlrun/mlrun\n",
+ " record.\n",
"\n",
"* **endpoint_id**: `str` = `\"\"`\n",
" \n",
@@ -130,13 +118,6 @@
" \n",
" If a new model endpoint is generated, the model name will be presented under this endpoint.\n",
"\n",
- "* **model_endpoint_drift_threshold**: `float` = `0.7`\n",
- " \n",
- " The threshold of which to mark drifts. Defaulted to 0.7.\n",
- "\n",
- "* **model_endpoint_possible_drift_threshold**: `float` = `0.5`\n",
- " \n",
- " The threshold of which to mark possible drifts. Defaulted to 0.5.\n",
" \n",
"* **model_endpoint_sample_set**: `Union[mlrun.DataItem, list, dict, pd.DataFrame, pd.Series, np.ndarray]` = `None`\n",
" \n",
@@ -155,15 +136,13 @@
"### 1.2. Outputs\n",
"\n",
"The outputs are split to two actions the functions can perform:\n",
- "* [**Results Prediction**](#chapter2) - Will log:\n",
+ "* [**Results Prediction**](#chapter3) - Will log:\n",
" * A dataset artifact named by the `result_set_name` parameter.\n",
" * A `str` result named `\"batch_id\"` of the given / generated batch ID.\n",
"\n",
- "* [**Data Drift Analysis**](#chapter3) - Will log:\n",
+ "* [**Data Drift Analysis**](#chapter4) - Will log:\n",
" * A `plotly` artifact named `\"data_drift_table\"` with a visualization of the drifts results and histograms.\n",
" * A json artifact named `\"features_drift_results\"` with all the features metric values.\n",
- " * A `bool` result named `\"drift_status\"` of the overall drift status (`True` if there was a drift and `False` otherwise).\n",
- " * A `float` result named `\"drift_score\"` of the overall drift metric score.\n",
"\n",
"For more details, see the next chapters."
]
@@ -225,62 +204,7 @@
},
"source": [
"\n",
- "## 3. Data Drift Analysis\n",
- "\n",
- "The data drift analysis is done per feature using two distance measure metrics for probability distributions.\n",
- "\n",
- "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:\n",
- "\n",
- "* Total Variance Distance: $TVD(x_S,x_I) = \\frac{1}{2}\\sum_{b_1}^{b_{20}} {|x_S - x_I|}$\n",
- "* Hellinger Distance: $H(x_S,x_I) = \\sqrt{1-{\\sum_{b_1}^{b_{20}}\\sqrt{x_S \\cdot x_I}}}$\n",
- "\n",
- "Our **rule** then is calculating for each $x\\in S: \\frac{H(x_S,x_I)+TVD(x_S,x_I)}{2} < $ given thresholds.\n",
- "\n",
- "The outputs of the analysis will be:\n",
- "* **Drift table plot** - The results are presented in a `plotly` table artifact named `\"drift_table_plot\"` that shows each feature's statistics and its TVD, Hellinger and KLD (Kullback–Leibler divergence) results as follows:\n",
- "\n",
- "| | Count | | Mean | | Std | | Min | | Max | | Tvd | Hellinger | Kld | Histograms |\n",
- "| ------ | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | --- | --------- | --- |------------|\n",
- "| | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | **Sample** | **Input** | | | | |\n",
- "| **x1** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
- "| **x2** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
- "| **x3** | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
- "\n",
- "* **Features drift results** - A rule metric per feature dictionary is saved in a json file named `\"features_drift_results\"` where each key is a feature and its value is the feature's metric value: `Dict[str, float]`\n",
- "\n",
- " ```python\n",
- " {\n",
- " \"x1\": 0.12,\n",
- " \"x2\": 0.345,\n",
- " \"x3\": 0.00678,\n",
- " ...\n",
- " }\n",
- " ```\n",
- "\n",
- "* In addition, two results are being added to summarize the drift analysis:\n",
- "\n",
- " * `drift_status`: `bool` - A boolean value indicating whether a drift was found.\n",
- " * `drift_metric`: `float` - The mean of all the features drift metric value (the rule above):\n",
- " for $n$ features and metric rule $M(x_S,x_I)=\\frac{H(x_S,x_I)+TVD(x_S,x_I)}{2}$, `drift_metric` $=\\frac{1}{n}\\sum_{x\\in S}M(x_S,x_I)$\n",
- "\n",
- " ```python\n",
- " {\n",
- " \"drift_status\": True,\n",
- " \"drift_metric\": 0.81234\n",
- " }\n",
- " ```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "\n",
- "## 4. End-to-end Demo\n",
+ "## 3. End-to-end Demo\n",
"\n",
"We will see an end-to-end example that follows the steps below:\n",
"1. Generate data.\n",
@@ -296,7 +220,7 @@
}
},
"source": [
- "### 4.1. Code review\n",
+ "### 3.1. Code review\n",
"\n",
"We are using a very simple example of training a decision tree on a binary classification problem. For that we wrote two functions:\n",
"* `generate_data` - Generate a binary classification data. The data will be split into a *training set* and *data for prediction*. The data for prediction will be drifted in half of its features to showcase the plot later on.\n",
@@ -312,10 +236,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "> 2023-08-29 11:13:44,649 [warning] Failed resolving version info. Ignoring and using defaults\n",
- "> 2023-08-29 11:13:46,598 [warning] Server or client version is unstable. Assuming compatible: {'server_version': '0.0.0+image-test', 'client_version': '0.0.0+unstable'}\n",
- "> 2023-08-29 11:13:46,667 [info] Loading project from path: {'project_name': 'batch-infer-demo', 'path': './'}\n",
- "> 2023-08-29 11:14:02,192 [info] Project loaded successfully: {'project_name': 'batch-infer-demo', 'path': './', 'stored_in_db': True}\n"
+ "> 2024-10-08 10:23:13,060 [info] Loading project from path: {\"path\":\"./\",\"project_name\":\"batch-infer-demo\",\"user_project\":false}\n",
+ "> 2024-10-08 10:23:28,490 [info] Project loaded successfully: {\"path\":\"./\",\"project_name\":\"batch-infer-demo\",\"stored_in_db\":true}\n"
]
}
],
@@ -332,6 +254,9 @@
"execution_count": 2,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -346,6 +271,9 @@
"execution_count": 3,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -367,6 +295,9 @@
"execution_count": 4,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -429,6 +360,9 @@
"execution_count": 5,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -446,7 +380,7 @@
}
},
"source": [
- "### 4.2. Run the Example with MLRun\n",
+ "### 3.2. Run the Example with MLRun\n",
"\n",
"First, we will prepare our MLRun functions:\n",
"1. We will use `mlrun.code_to_function` to turn this demo notebook into an MLRun function we can run.\n",
@@ -458,19 +392,14 @@
"execution_count": 6,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> 2023-08-29 11:14:04,182 [warning] Failed to add git metadata, ignore if path is not part of a git repo.: {'path': './', 'error': '/User/EYAL'}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Create an MLRun function to run the notebook:\n",
"demo_function = mlrun.code_to_function(name=\"batch-inference-demo\", kind=\"job\")\n",
@@ -494,9 +423,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {
"collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ },
"pycharm": {
"name": "#%%\n"
}
@@ -506,8 +438,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "> 2023-08-29 11:14:42,198 [warning] artifact/output path is not defined or is local and relative, artifacts will not be visible in the UI: {'output_path': './'}\n",
- "> 2023-08-29 11:14:42,198 [info] Storing function: {'name': 'batch-inference-demo-generate-data', 'uid': 'd04e9f978132472695774f01b2becb6c', 'db': None}\n"
+ "> 2024-10-08 10:23:40,584 [error] error getting build status: details: MLRunNotFoundError('Function tag not found batch-infer-demo/batch-inference-demo'), caused by: 404 Client Error: Not Found for url: http://mlrun-api:8080/api/v1/build/status?name=batch-inference-demo&project=batch-infer-demo&tag=&logs=no&offset=0&last_log_timestamp=0.0&verbose=no\n",
+ "> 2024-10-08 10:23:40,586 [info] Storing function: {\"db\":\"http://mlrun-api:8080\",\"name\":\"batch-inference-demo-generate-data\",\"uid\":\"4f68ba3fd9084e3e941ab3872ceb3635\"}\n",
+ "> 2024-10-08 10:23:40,881 [info] Job is running in the background, pod: batch-inference-demo-generate-data-52w8s\n",
+ "> 2024-10-08 10:23:46,954 [info] To track results use the CLI: {\"info_cmd\":\"mlrun get run 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo\",\"logs_cmd\":\"mlrun logs 4f68ba3fd9084e3e941ab3872ceb3635 -p batch-infer-demo\"}\n",
+ "> 2024-10-08 10:23:46,954 [info] Or click for UI: {\"ui_url\":\"https://dashboard.default-tenant.app.vmdev57.lab.iguazeng.com/mlprojects/batch-infer-demo/jobs/monitor/4f68ba3fd9084e3e941ab3872ceb3635/overview\"}\n",
+ "> 2024-10-08 10:23:46,955 [info] Run execution finished: {\"name\":\"batch-inference-demo-generate-data\",\"status\":\"completed\"}\n"
]
},
{
@@ -602,9 +538,14 @@
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" const panelName = \"#\" + el.getAttribute('paneName');\n",
- " console.log(el.title);\n",
"\n",
- " document.querySelector(panelName + \"-title\").innerHTML = el.title\n",
+ " // Get the base URL of the current notebook\n",
+ " var baseUrl = window.location.origin;\n",
+ "\n",
+ " // Construct the full URL\n",
+ " var fullUrl = new URL(el.title, baseUrl).href;\n",
+ "\n",
+ " document.querySelector(panelName + \"-title\").innerHTML = fullUrl\n",
" iframe = document.querySelector(panelName + \"-body\");\n",
"\n",
" const tblcss = `\n",
" \n",
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s=a._size,l=_.isVertical(i),u=_.isGrouped(i),h=\"fraction\"===i.entrywidthmode,f=i.borderwidth,d=2*f,m=p.itemGap,g=i.indentation+i.itemwidth+2*m,y=2*(f+m),v=I(i),x=i.y<0||0===i.y&&\"top\"===v,b=i.y>1||1===i.y&&\"bottom\"===v,w=i.tracegroupgap,T={};i._maxHeight=Math.max(x||b?a.height/2:s.h,30);var A=0;i._width=0,i._height=0;var M=function(t){var e=0,r=0,n=t.title.side;return n&&(-1!==n.indexOf(\"left\")&&(e=t._titleWidth),-1!==n.indexOf(\"top\")&&(r=t._titleHeight)),[e,r]}(i);if(l)r.each((function(t){var e=t[0].height;c.setTranslate(this,f+M[0],f+M[1]+i._height+e/2+m),i._height+=e,i._width=Math.max(i._width,t[0].width)})),A=g+i._width,i._width+=m+g+d,i._height+=y,u&&(e.each((function(t,e){c.setTranslate(this,0,e*i.tracegroupgap)})),i._height+=(i._lgroupsLength-1)*i.tracegroupgap);else{var S=L(i),E=i.x<0||0===i.x&&\"right\"===S,C=i.x>1||1===i.x&&\"left\"===S,z=b||x,O=a.width/2;i._maxWidth=Math.max(E?z&&\"left\"===S?s.l+s.w:O:C?z&&\"right\"===S?s.r+s.w:O:s.w,2*g);var D=0,R=0;r.each((function(t){var e=k(t,i,g);D=Math.max(D,e),R+=e})),A=null;var F=0;if(u){var B=0,N=0,j=0;e.each((function(){var t=0,e=0;n.select(this).selectAll(\"g.traces\").each((function(r){var n=k(r,i,g),a=r[0].height;c.setTranslate(this,M[0],M[1]+f+m+a/2+e),e+=a,t=Math.max(t,n),T[r[0].trace.legendgroup]=t}));var r=t+m;N>0&&r+f+N>i._maxWidth?(F=Math.max(F,N),N=0,j+=B+w,B=e):B=Math.max(B,e),c.setTranslate(this,N,j),N+=r})),i._width=Math.max(F,N)+f,i._height=j+B+y}else{var U=r.size(),V=R+d+(U-1)*m=i._maxWidth&&(F=Math.max(F,Z),H=0,G+=q,i._height+=q,q=0),c.setTranslate(this,M[0]+f+H,M[1]+f+G+e/2+m),Z=H+r+m,H+=n,q=Math.max(q,e)})),V?(i._width=H+d,i._height=q+y):(i._width=Math.max(F,Z)+d,i._height+=q+y)}}i._width=Math.ceil(Math.max(i._width+M[0],i._titleWidth+2*(f+p.titlePad))),i._height=Math.ceil(Math.max(i._height+M[1],i._titleHeight+2*(f+p.itemGap))),i._effHeight=Math.min(i._height,i._maxHeight);var W=t._context.edits,Y=W.legendText||W.legendPosition;r.each((function(t){var e=n.select(this).select(\".\"+o+\"toggle\"),r=t[0].height,a=t[0].trace.legendgroup,s=k(t,i,g);u&&\"\"!==a&&(s=T[a]);var f=Y?g:A||s;l||h||(f+=m/2),c.setRect(e,0,-r/2,f,r)}))}(t,Z,W,f)},function(){var e,u,v,x,_=d._size,b=f.borderwidth,k=\"paper\"===f.xref,M=\"paper\"===f.yref;if(H.text&&function(t,e,r){if(\"top center\"===e.title.side||\"top right\"===e.title.side){var n=e.title.font.size*m,i=0,a=t.node(),o=c.bBox(a).width;\"top center\"===e.title.side?i=.5*(e._width-2*r-2*p.titlePad-o):\"top right\"===e.title.side&&(i=e._width-2*r-2*p.titlePad-o),h.positionText(t,r+p.titlePad+i,r+n)}}(V,f,b),!T){var S,E;S=k?_.l+_.w*f.x-g[L(f)]*f._width:d.width*f.x-g[L(f)]*f._width,E=M?_.t+_.h*(1-f.y)-g[I(f)]*f._effHeight:d.height*(1-f.y)-g[I(f)]*f._effHeight;var C=function(t,e,r,n){var i=t._fullLayout,o=i[e],s=L(o),l=I(o),c=\"paper\"===o.xref,u=\"paper\"===o.yref;t._fullLayout._reservedMargin[e]={};var h=o.y<.5?\"b\":\"t\",f=o.x<.5?\"l\":\"r\",p={r:i.width-r,l:r+o._width,b:i.height-n,t:n+o._effHeight};if(c&&u)return a.autoMargin(t,e,{x:o.x,y:o.y,l:o._width*g[s],r:o._width*y[s],b:o._effHeight*y[l],t:o._effHeight*g[l]});c?t._fullLayout._reservedMargin[e][h]=p[h]:u||\"v\"===o.orientation?t._fullLayout._reservedMargin[e][f]=p[f]:t._fullLayout._reservedMargin[e][h]=p[h]}(t,w,S,E);if(C)return;if(d.margin.autoexpand){var P=S,z=E;S=k?i.constrain(S,0,d.width-f._width):P,E=M?i.constrain(E,0,d.height-f._effHeight):z,S!==P&&i.log(\"Constrain \"+w+\".x to make legend fit inside graph\"),E!==z&&i.log(\"Constrain \"+w+\".y to make legend fit inside graph\")}c.setTranslate(N,S,E)}if(G.on(\".drag\",null),N.on(\"wheel\",null),T||f._height<=f._maxHeight||t._context.staticPlot){var O=f._effHeight;T&&(O=f._height),U.attr({width:f._width-b,height:O-b,x:b/2,y:b/2}),c.setTranslate(q,0,0),j.select(\"rect\").attr({width:f._width-2*b,height:O-2*b,x:b,y:b}),c.setClipUrl(q,r,t),c.setRect(G,0,0,0,0),delete f._scrollY}else{var D,R,F,B=Math.max(p.scrollBarMinHeight,f._effHeight*f._effHeight/f._height),Z=f._effHeight-B-2*p.scrollBarMargin,W=f._height-f._effHeight,Y=Z/W,X=Math.min(f._scrollY||0,W);U.attr({width:f._width-2*b+p.scrollBarWidth+p.scrollBarMargin,height:f._effHeight-b,x:b/2,y:b/2}),j.select(\"rect\").attr({width:f._width-2*b+p.scrollBarWidth+p.scrollBarMargin,height:f._effHeight-2*b,x:b,y:b+X}),c.setClipUrl(q,r,t),K(X,B,Y),N.on(\"wheel\",(function(){K(X=i.constrain(f._scrollY+n.event.deltaY/Z*W,0,W),B,Y),0!==X&&X!==W&&n.event.preventDefault()}));var $=n.behavior.drag().on(\"dragstart\",(function(){var t=n.event.sourceEvent;D=\"touchstart\"===t.type?t.changedTouches[0].clientY:t.clientY,F=X})).on(\"drag\",(function(){var t=n.event.sourceEvent;2===t.buttons||t.ctrlKey||(R=\"touchmove\"===t.type?t.changedTouches[0].clientY:t.clientY,X=function(t,e,r){var n=(r-e)/Y+t;return i.constrain(n,0,W)}(F,D,R),K(X,B,Y))}));G.call($);var J=n.behavior.drag().on(\"dragstart\",(function(){var t=n.event.sourceEvent;\"touchstart\"===t.type&&(D=t.changedTouches[0].clientY,F=X)})).on(\"drag\",(function(){var t=n.event.sourceEvent;\"touchmove\"===t.type&&(R=t.changedTouches[0].clientY,X=function(t,e,r){var n=(e-r)/Y+t;return i.constrain(n,0,W)}(F,D,R),K(X,B,Y))}));q.call(J)}function K(e,r,n){f._scrollY=t._fullLayout[w]._scrollY=e,c.setTranslate(q,0,-e),c.setRect(G,f._width,p.scrollBarMargin+e*n,p.scrollBarWidth,r),j.select(\"rect\").attr(\"y\",b+e)}t._context.edits.legendPosition&&(N.classed(\"cursor-move\",!0),l.init({element:N.node(),gd:t,prepFn:function(t){if(t.target!==G.node()){var e=c.getTranslate(N);v=e.x,x=e.y}},moveFn:function(t,r){if(void 0!==v&&void 0!==x){var n=v+t,i=x+r;c.setTranslate(N,n,i),e=l.align(n,f._width,_.l,_.l+_.w,f.xanchor),u=l.align(i+f._height,-f._height,_.t+_.h,_.t,f.yanchor)}},doneFn:function(){if(void 0!==e&&void 0!==u){var r={};r[w+\".x\"]=e,r[w+\".y\"]=u,o.call(\"_guiRelayout\",t,r)}},clickFn:function(e,r){var n=s.selectAll(\"g.traces\").filter((function(){var t=this.getBoundingClientRect();return r.clientX>=t.left&&r.clientX<=t.right&&r.clientY>=t.top&&r.clientY<=t.bottom}));n.size()>0&&A(t,N,n,e,r)}}))}],t)}}function k(t,e,r){var n=t[0],i=n.width,a=e.entrywidthmode,o=n.trace.legendwidth||e.entrywidth;return\"fraction\"===a?e._maxWidth*o:r+(o||i)}function A(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label);var c=s.triggerHandler(t,\"plotly_legendclick\",l);if(1===n){if(!1===c)return;e._clickTimeout=setTimeout((function(){t._fullLayout&&f(r,t,n)}),t._context.doubleClickDelay)}else 2===n&&(e._clickTimeout&&clearTimeout(e._clickTimeout),t._legendMouseDownTime=0,!1!==s.triggerHandler(t,\"plotly_legenddoubleclick\",l)&&!1!==c&&f(r,t,n))}function M(t,e,r){var n,a,s=P(r),l=t.data()[0][0],u=l.trace,f=o.traceIs(u,\"pie-like\"),d=!r._inHover&&e._context.edits.legendText&&!f,m=r._maxNameLength;l.groupTitle?(n=l.groupTitle.text,a=l.groupTitle.font):(a=r.font,r.entries?n=l.text:(n=f?l.label:u.name,u._meta&&(n=i.templateString(n,u._meta))));var g=i.ensureSingle(t,\"text\",s+\"text\");g.attr(\"text-anchor\",\"start\").call(c.font,a).text(d?S(n,m):n);var y=r.indentation+r.itemwidth+2*p.itemGap;h.positionText(g,y,0),d?g.call(h.makeEditable,{gd:e,text:n}).call(C,t,e,r).on(\"edit\",(function(n){this.text(S(n,m)).call(C,t,e,r);var a=l.trace._fullInput||{},s={};if(o.hasTransform(a,\"groupby\")){var c=o.getTransformIndices(a,\"groupby\"),h=c[c.length-1],f=i.keyedContainer(a,\"transforms[\"+h+\"].styles\",\"target\",\"value.name\");f.set(l.trace._group,n),s=f.constructUpdate()}else s.name=n;return a._isShape?o.call(\"_guiRelayout\",e,\"shapes[\"+u.index+\"].name\",s.name):o.call(\"_guiRestyle\",e,s,u.index)})):C(g,t,e,r)}function S(t,e){var r=Math.max(4,e);if(t&&t.trim().length>=r/2)return t;for(var n=r-(t=t||\"\").length;n>0;n--)t+=\" \";return t}function E(t,e,r){var a,o=e._context.doubleClickDelay,s=1,l=i.ensureSingle(t,\"rect\",r+\"toggle\",(function(t){e._context.staticPlot||t.style(\"cursor\",\"pointer\").attr(\"pointer-events\",\"all\"),t.call(u.fill,\"rgba(0,0,0,0)\")}));e._context.staticPlot||(l.on(\"mousedown\",(function(){(a=(new Date).getTime())-e._legendMouseDownTimeo&&(s=Math.max(s-1,1)),A(e,i,t,s,n.event)}})))}function C(t,e,r,n,i){n._inHover&&t.attr(\"data-notex\",!0),h.convertToTspans(t,r,(function(){!function(t,e,r,n){var i=t.data()[0][0];if(r._inHover||!i||i.trace.showlegend){var a=t.select(\"g[class*=math-group]\"),o=a.node(),s=P(r);r||(r=e._fullLayout[s]);var l,u,f=r.borderwidth,d=(n===b?r.title.font:i.groupTitle?i.groupTitle.font:r.font).size*m;if(o){var g=c.bBox(o);l=g.height,u=g.width,n===b?c.setTranslate(a,f,f+.75*l):c.setTranslate(a,0,.25*l)}else{var y=\".\"+s+(n===b?\"title\":\"\")+\"text\",v=t.select(y),x=h.lineCount(v),_=v.node();if(l=d*x,u=_?c.bBox(_).width:0,n===b)\"left\"===r.title.side&&(u+=2*p.itemGap),h.positionText(v,f+p.titlePad,f+d);else{var w=2*p.itemGap+r.indentation+r.itemwidth;i.groupTitle&&(w=p.itemGap,u-=r.indentation+r.itemwidth),h.positionText(v,w,-d*((x-1)/2-.3))}}n===b?(r._titleWidth=u,r._titleHeight=l):(i.lineHeight=d,i.height=Math.max(l,16)+3,i.width=u)}else t.remove()}(e,r,n,i)}))}function L(t){return i.isRightAnchor(t)?\"right\":i.isCenterAnchor(t)?\"center\":\"left\"}function I(t){return i.isBottomAnchor(t)?\"bottom\":i.isMiddleAnchor(t)?\"middle\":\"top\"}function P(t){return t._id||\"legend\"}t.exports=function(t,e){if(e)T(t,e);else{var r=t._fullLayout,i=r._legends;r._infolayer.selectAll('[class^=\"legend\"]').each((function(){var t=n.select(this),e=t.attr(\"class\").split(\" \")[0];e.match(w)&&-1===i.indexOf(e)&&t.remove()}));for(var a=0;aS&&(M=S)}k[a][0]._groupMinRank=M,k[a][0]._preGroupSort=a}var E=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(k.forEach((function(t,e){t[0]._preGroupSort=e})),k.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),a=0;ar?r:t}t.exports=function(t,e,r){var y=e._fullLayout;r||(r=y.legend);var v=\"constant\"===r.itemsizing,x=r.itemwidth,_=(x+2*p.itemGap)/2,b=o(_,0),w=function(t,e,r,n){var i;if(t+1)i=t;else{if(!(e&&e.width>0))return 0;i=e.width}return v?n:Math.min(i,r)};function T(t,a,o){var u=t[0].trace,h=u.marker||{},f=h.line||{},p=h.cornerradius?\"M6,3a3,3,0,0,1-3,3H-3a3,3,0,0,1-3-3V-3a3,3,0,0,1,3-3H3a3,3,0,0,1,3,3Z\":\"M6,6H-6V-6H6Z\",d=o?u.visible&&u.type===o:i.traceIs(u,\"bar\"),m=n.select(a).select(\"g.legendpoints\").selectAll(\"path.legend\"+o).data(d?[t]:[]);m.enter().append(\"path\").classed(\"legend\"+o,!0).attr(\"d\",p).attr(\"transform\",b),m.exit().remove(),m.each((function(t){var i=n.select(this),a=t[0],o=w(a.mlw,h.line,5,2);i.style(\"stroke-width\",o+\"px\");var p=a.mcc;if(!r._inHover&&\"mc\"in a){var d=c(h),m=d.mid;void 0===m&&(m=(d.max+d.min)/2),p=s.tryColorscale(h,\"\")(m)}var y=p||a.mc||h.color,v=h.pattern,x=v&&s.getPatternAttr(v.shape,0,\"\");if(x){var _=s.getPatternAttr(v.bgcolor,0,null),b=s.getPatternAttr(v.fgcolor,0,null),T=v.fgopacity,k=g(v.size,8,10),A=g(v.solidity,.5,1),M=\"legend-\"+u.uid;i.call(s.pattern,\"legend\",e,M,x,k,A,p,v.fillmode,_,b,T)}else i.call(l.fill,y);o&&l.stroke(i,a.mlc||f.color)}))}function k(t,r,o){var s=t[0],l=s.trace,c=o?l.visible&&l.type===o:i.traceIs(l,o),u=n.select(r).select(\"g.legendpoints\").selectAll(\"path.legend\"+o).data(c?[t]:[]);if(u.enter().append(\"path\").classed(\"legend\"+o,!0).attr(\"d\",\"M6,6H-6V-6H6Z\").attr(\"transform\",b),u.exit().remove(),u.size()){var p=l.marker||{},d=w(f(p.line.width,s.pts),p.line,5,2),m=\"pieLike\",g=a.minExtend(l,{marker:{line:{width:d}}},m),y=a.minExtend(s,{trace:g},m);h(u,y,g,e)}}t.each((function(t){var e=n.select(this),i=a.ensureSingle(e,\"g\",\"layers\");i.style(\"opacity\",t[0].trace.opacity);var s=r.indentation,l=r.valign,c=t[0].lineHeight,u=t[0].height;if(\"middle\"===l&&0===s||!c||!u)i.attr(\"transform\",null);else{var h={top:1,bottom:-1}[l]*(.5*(c-u+3))||0,f=r.indentation;i.attr(\"transform\",o(f,h))}i.selectAll(\"g.legendfill\").data([t]).enter().append(\"g\").classed(\"legendfill\",!0),i.selectAll(\"g.legendlines\").data([t]).enter().append(\"g\").classed(\"legendlines\",!0);var 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r=e._dims,n=r.inputAreaLength-2*h.railInset,i=s.ensureSingle(t,\"rect\",h.railRectClass);i.attr({width:n,height:h.railWidth,rx:h.railRadius,ry:h.railRadius,\"shape-rendering\":\"crispEdges\"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\"),o.setTranslate(i,h.railInset,.5*(r.inputAreaWidth-h.railWidth)+r.currentValueTotalHeight)}t.exports=function(t){var e=t._context.staticPlot,r=t._fullLayout,a=function(t,e){for(var r=t[h.name],n=[],i=0;i0?[0]:[]);function l(e){e._commandObserver&&(e._commandObserver.remove(),delete e._commandObserver),i.autoMargin(t,g(e))}if(s.enter().append(\"g\").classed(h.containerClassName,!0).style(\"cursor\",e?null:\"ew-resize\"),s.exit().each((function(){n.select(this).selectAll(\"g.\"+h.groupClassName).each(l)})).remove(),0!==a.length){var c=s.selectAll(\"g.\"+h.groupClassName).data(a,y);c.enter().append(\"g\").classed(h.groupClassName,!0),c.exit().each(l).remove();for(var u=0;u0||T<0){var 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r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,\"projection.scale\":n.scale},e=t._isScoped?{\"center.lon\":r.lon,\"center.lat\":r.lat}:t._isClipped?{\"projection.rotation.lon\":i.lon,\"projection.rotation.lat\":i.lat}:{\"center.lon\":r.lon,\"center.lat\":r.lat,\"projection.rotation.lon\":i.lon},c.extendFlat(this.viewInitial,e)},S.render=function(t){this._hasMarkerAngles&&t?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()},S._render=function(){var t,e=this.projection,r=e.getPath();function n(t){var r=e(t.lonlat);return r?u(r[0],r[1]):null}function i(t){return e.isLonLatOverEdges(t.lonlat)?\"none\":null}for(t in 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r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},32412:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var h=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var f=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d/g,\" 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I=[];d.loneHover({trace:b,x:(.5+.5*v[0]/v[3])*s,y:(.5-.5*v[1]/v[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:x,name:u.name,color:d.castHoverOption(b,T,\"bgcolor\")||u.color,borderColor:d.castHoverOption(b,T,\"bordercolor\"),fontFamily:d.castHoverOption(b,T,\"font.family\"),fontSize:d.castHoverOption(b,T,\"font.size\"),fontColor:d.castHoverOption(b,T,\"font.color\"),nameLength:d.castHoverOption(b,T,\"namelength\"),textAlign:d.castHoverOption(b,T,\"align\"),hovertemplate:h.castOption(b,T,\"hovertemplate\"),hovertemplateLabels:h.extendFlat({},C,k),eventData:[C]},{container:n,gd:r,inOut_bbox:I}),C.bbox=I[0]}f.distance<5&&(f.buttons||w)?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},k.recoverContext=function(){var t=this;t.glplot.dispose();var 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R=[1,1,1];for(o=0;o<3;++o){var F=x[c=(l=u[M[o]]).type];R[o]=Math.pow(F.acc,1/F.count)/p[o]}g=\"data\"===O||Math.max.apply(null,R)/Math.min.apply(null,R)<=4?R:[1,1,1]}u.aspectratio.x=h.aspectratio.x=g[0],u.aspectratio.y=h.aspectratio.y=g[1],u.aspectratio.z=h.aspectratio.z=g[2],n.glplot.setAspectratio(u.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:u.aspectratio.x,y:u.aspectratio.y,z:u.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=u.aspectmode);var B=u.domain||null,N=e._size||null;if(B&&N){var j=n.container.style;j.position=\"absolute\",j.left=N.l+B.x[0]*N.w+\"px\",j.top=N.t+(1-B.y[1])*N.h+\"px\",j.width=N.w*(B.x[1]-B.x[0])+\"px\",j.height=N.h*(B.y[1]-B.y[0])+\"px\"}n.glplot.redraw()}},k.destroy=function(){var t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener(\"wheel\",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},k.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},k.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),\"orthographic\"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},k.isCameraChanged=function(t){var e=this.getCamera(),r=h.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},k.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=h.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},k.saveLayout=function(t){var e,r,n,i,a,o,s=this,l=s.fullLayout,c=s.isCameraChanged(t),f=s.isAspectChanged(t),p=c||f;if(p){var d={};c&&(e=s.getCamera(),n=(r=h.nestedProperty(t,s.id+\".camera\")).get(),d[s.id+\".camera\"]=n),f&&(i=s.glplot.getAspectratio(),o=(a=h.nestedProperty(t,s.id+\".aspectratio\")).get(),d[s.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,l._preGUI,d),c&&(r.set(e),h.nestedProperty(l,s.id+\".camera\").set(e)),f&&(a.set(i),h.nestedProperty(l,s.id+\".aspectratio\").set(i),s.glplot.redraw())}return 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l,c=s.getContext(\"2d\",{willReadFrequently:!0}),u=c.createImageData(i,a);switch(u.data.set(o),c.putImageData(u,0,0),t){case\"jpeg\":l=s.toDataURL(\"image/jpeg\");break;case\"webp\":l=s.toDataURL(\"image/webp\");break;default:l=s.toDataURL(\"image/png\")}return e.staticMode&&e.container.removeChild(n),l},k.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[M[t]];p.setConvert(e,this.fullLayout),e.setScale=h.noop}},k.make4thDimension=function(){var t=this,e=t.graphDiv._fullLayout;t._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(t._mockAxis,e)},t.exports=T},88239:function(t){\"use strict\";t.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;aOpenStreetMap 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n=r(34809),i=n.strTranslate,a=n.strScale,o=r(4173).fX,s=r(62972),l=r(45568),c=r(62203),u=r(30635),h=r(38793),f=\"map\";e.name=f,e.attr=\"subplot\",e.idRoot=f,e.idRegex=e.attrRegex=n.counterRegex(f),e.attributes={subplot:{valType:\"subplotid\",dflt:\"map\",editType:\"calc\"}},e.layoutAttributes=r(8257),e.supplyLayoutDefaults=r(97446),e.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,i=e._subplots[f],a=0;ax/2){var _=m.split(\"|\").join(\" \");y.text(_).attr(\"data-unformatted\",_).call(u.convertToTspans,t),v=c.bBox(y.node())}y.attr(\"transform\",i(-3,8-v.height)),g.insert(\"rect\",\".static-attribution\").attr({x:-v.width-6,y:-v.height-3,width:v.width+6,height:v.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var b=1;v.width+6>x&&(b=x/(v.width+6));var w=[n.l+n.w*p.x[1],n.t+n.h*(1-p.y[0])];g.attr(\"transform\",i(w[0],w[1])+a(b))}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[f],n=0;n0){for(var r=0;r