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| <h1>Source code for airflow.providers.google.cloud.utils.mlengine_operator_utils</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1">#</span> |
| <span class="sd">"""This module contains helper functions for MLEngine operators."""</span> |
| |
| <span class="kn">import</span> <span class="nn">base64</span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">re</span> |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">TypeVar</span> |
| <span class="kn">from</span> <span class="nn">urllib.parse</span> <span class="kn">import</span> <span class="n">urlsplit</span> |
| |
| <span class="kn">import</span> <span class="nn">dill</span> |
| |
| <span class="kn">from</span> <span class="nn">airflow</span> <span class="kn">import</span> <span class="n">DAG</span> |
| <span class="kn">from</span> <span class="nn">airflow.exceptions</span> <span class="kn">import</span> <span class="n">AirflowException</span> |
| <span class="kn">from</span> <span class="nn">airflow.operators.python</span> <span class="kn">import</span> <span class="n">PythonOperator</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.cloud.hooks.gcs</span> <span class="kn">import</span> <span class="n">GCSHook</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.cloud.operators.dataflow</span> <span class="kn">import</span> <span class="n">DataflowCreatePythonJobOperator</span> |
| <span class="kn">from</span> <span class="nn">airflow.providers.google.cloud.operators.mlengine</span> <span class="kn">import</span> <span class="n">MLEngineStartBatchPredictionJobOperator</span> |
| |
| <div class="viewcode-block" id="T"><a class="viewcode-back" href="../../../../../../_api/airflow/providers/google/cloud/utils/mlengine_operator_utils/index.html#airflow.providers.google.cloud.utils.mlengine_operator_utils.T">[docs]</a><span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"T"</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="n">Callable</span><span class="p">)</span> <span class="c1"># pylint: disable=invalid-name</span></div> |
| |
| |
| <div class="viewcode-block" id="create_evaluate_ops"><a class="viewcode-back" href="../../../../../../_api/airflow/providers/google/cloud/utils/mlengine_operator_utils/index.html#airflow.providers.google.cloud.utils.mlengine_operator_utils.create_evaluate_ops">[docs]</a><span class="k">def</span> <span class="nf">create_evaluate_ops</span><span class="p">(</span> <span class="c1"># pylint: disable=too-many-arguments</span> |
| <span class="n">task_prefix</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">data_format</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">input_paths</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> |
| <span class="n">prediction_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> |
| <span class="n">metric_fn_and_keys</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">str</span><span class="p">]],</span> |
| <span class="n">validate_fn</span><span class="p">:</span> <span class="n">T</span><span class="p">,</span> |
| <span class="n">batch_prediction_job_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">region</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">project_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">dataflow_options</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_uri</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">model_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">version_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">dag</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DAG</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="n">py_interpreter</span><span class="o">=</span><span class="s2">"python3"</span><span class="p">,</span> |
| <span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> Creates Operators needed for model evaluation and returns.</span> |
| |
| <span class="sd"> It gets prediction over inputs via Cloud ML Engine BatchPrediction API by</span> |
| <span class="sd"> calling MLEngineBatchPredictionOperator, then summarize and validate</span> |
| <span class="sd"> the result via Cloud Dataflow using DataFlowPythonOperator.</span> |
| |
| <span class="sd"> For details and pricing about Batch prediction, please refer to the website</span> |
| <span class="sd"> https://cloud.google.com/ml-engine/docs/how-tos/batch-predict</span> |
| <span class="sd"> and for Cloud Dataflow, https://cloud.google.com/dataflow/docs/</span> |
| |
| <span class="sd"> It returns three chained operators for prediction, summary, and validation,</span> |
| <span class="sd"> named as ``<prefix>-prediction``, ``<prefix>-summary``, and ``<prefix>-validation``,</span> |
| <span class="sd"> respectively.</span> |
| <span class="sd"> (``<prefix>`` should contain only alphanumeric characters or hyphen.)</span> |
| |
| <span class="sd"> The upstream and downstream can be set accordingly like:</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> pred, _, val = create_evaluate_ops(...)</span> |
| <span class="sd"> pred.set_upstream(upstream_op)</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> downstream_op.set_upstream(val)</span> |
| |
| <span class="sd"> Callers will provide two python callables, metric_fn and validate_fn, in</span> |
| <span class="sd"> order to customize the evaluation behavior as they wish.</span> |
| |
| <span class="sd"> - metric_fn receives a dictionary per instance derived from json in the</span> |
| <span class="sd"> batch prediction result. The keys might vary depending on the model.</span> |
| <span class="sd"> It should return a tuple of metrics.</span> |
| <span class="sd"> - validation_fn receives a dictionary of the averaged metrics that metric_fn</span> |
| <span class="sd"> generated over all instances.</span> |
| <span class="sd"> The key/value of the dictionary matches to what's given by</span> |
| <span class="sd"> metric_fn_and_keys arg.</span> |
| <span class="sd"> The dictionary contains an additional metric, 'count' to represent the</span> |
| <span class="sd"> total number of instances received for evaluation.</span> |
| <span class="sd"> The function would raise an exception to mark the task as failed, in a</span> |
| <span class="sd"> case the validation result is not okay to proceed (i.e. to set the trained</span> |
| <span class="sd"> version as default).</span> |
| |
| <span class="sd"> Typical examples are like this:</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> def get_metric_fn_and_keys():</span> |
| <span class="sd"> import math # imports should be outside of the metric_fn below.</span> |
| <span class="sd"> def error_and_squared_error(inst):</span> |
| <span class="sd"> label = float(inst['input_label'])</span> |
| <span class="sd"> classes = float(inst['classes']) # 0 or 1</span> |
| <span class="sd"> err = abs(classes-label)</span> |
| <span class="sd"> squared_err = math.pow(classes-label, 2)</span> |
| <span class="sd"> return (err, squared_err) # returns a tuple.</span> |
| <span class="sd"> return error_and_squared_error, ['err', 'mse'] # key order must match.</span> |
| |
| <span class="sd"> def validate_err_and_count(summary):</span> |
| <span class="sd"> if summary['err'] > 0.2:</span> |
| <span class="sd"> raise ValueError('Too high err>0.2; summary=%s' % summary)</span> |
| <span class="sd"> if summary['mse'] > 0.05:</span> |
| <span class="sd"> raise ValueError('Too high mse>0.05; summary=%s' % summary)</span> |
| <span class="sd"> if summary['count'] < 1000:</span> |
| <span class="sd"> raise ValueError('Too few instances<1000; summary=%s' % summary)</span> |
| <span class="sd"> return summary</span> |
| |
| <span class="sd"> For the details on the other BatchPrediction-related arguments (project_id,</span> |
| <span class="sd"> job_id, region, data_format, input_paths, prediction_path, model_uri),</span> |
| <span class="sd"> please refer to MLEngineBatchPredictionOperator too.</span> |
| |
| <span class="sd"> :param task_prefix: a prefix for the tasks. Only alphanumeric characters and</span> |
| <span class="sd"> hyphen are allowed (no underscores), since this will be used as dataflow</span> |
| <span class="sd"> job name, which doesn't allow other characters.</span> |
| <span class="sd"> :type task_prefix: str</span> |
| |
| <span class="sd"> :param data_format: either of 'TEXT', 'TF_RECORD', 'TF_RECORD_GZIP'</span> |
| <span class="sd"> :type data_format: str</span> |
| |
| <span class="sd"> :param input_paths: a list of input paths to be sent to BatchPrediction.</span> |
| <span class="sd"> :type input_paths: list[str]</span> |
| |
| <span class="sd"> :param prediction_path: GCS path to put the prediction results in.</span> |
| <span class="sd"> :type prediction_path: str</span> |
| |
| <span class="sd"> :param metric_fn_and_keys: a tuple of metric_fn and metric_keys:</span> |
| |
| <span class="sd"> - metric_fn is a function that accepts a dictionary (for an instance),</span> |
| <span class="sd"> and returns a tuple of metric(s) that it calculates.</span> |
| |
| <span class="sd"> - metric_keys is a list of strings to denote the key of each metric.</span> |
| <span class="sd"> :type metric_fn_and_keys: tuple of a function and a list[str]</span> |
| |
| <span class="sd"> :param validate_fn: a function to validate whether the averaged metric(s) is</span> |
| <span class="sd"> good enough to push the model.</span> |
| <span class="sd"> :type validate_fn: function</span> |
| |
| <span class="sd"> :param batch_prediction_job_id: the id to use for the Cloud ML Batch</span> |
| <span class="sd"> prediction job. Passed directly to the MLEngineBatchPredictionOperator as</span> |
| <span class="sd"> the job_id argument.</span> |
| <span class="sd"> :type batch_prediction_job_id: str</span> |
| |
| <span class="sd"> :param project_id: the Google Cloud project id in which to execute</span> |
| <span class="sd"> Cloud ML Batch Prediction and Dataflow jobs. If None, then the `dag`'s</span> |
| <span class="sd"> `default_args['project_id']` will be used.</span> |
| <span class="sd"> :type project_id: str</span> |
| |
| <span class="sd"> :param region: the Google Cloud region in which to execute Cloud ML</span> |
| <span class="sd"> Batch Prediction and Dataflow jobs. If None, then the `dag`'s</span> |
| <span class="sd"> `default_args['region']` will be used.</span> |
| <span class="sd"> :type region: str</span> |
| |
| <span class="sd"> :param dataflow_options: options to run Dataflow jobs. If None, then the</span> |
| <span class="sd"> `dag`'s `default_args['dataflow_default_options']` will be used.</span> |
| <span class="sd"> :type dataflow_options: dictionary</span> |
| |
| <span class="sd"> :param model_uri: GCS path of the model exported by Tensorflow using</span> |
| <span class="sd"> ``tensorflow.estimator.export_savedmodel()``. It cannot be used with</span> |
| <span class="sd"> model_name or version_name below. See MLEngineBatchPredictionOperator for</span> |
| <span class="sd"> more detail.</span> |
| <span class="sd"> :type model_uri: str</span> |
| |
| <span class="sd"> :param model_name: Used to indicate a model to use for prediction. Can be</span> |
| <span class="sd"> used in combination with version_name, but cannot be used together with</span> |
| <span class="sd"> model_uri. See MLEngineBatchPredictionOperator for more detail. If None,</span> |
| <span class="sd"> then the `dag`'s `default_args['model_name']` will be used.</span> |
| <span class="sd"> :type model_name: str</span> |
| |
| <span class="sd"> :param version_name: Used to indicate a model version to use for prediction,</span> |
| <span class="sd"> in combination with model_name. Cannot be used together with model_uri.</span> |
| <span class="sd"> See MLEngineBatchPredictionOperator for more detail. If None, then the</span> |
| <span class="sd"> `dag`'s `default_args['version_name']` will be used.</span> |
| <span class="sd"> :type version_name: str</span> |
| |
| <span class="sd"> :param dag: The `DAG` to use for all Operators.</span> |
| <span class="sd"> :type dag: airflow.models.DAG</span> |
| |
| <span class="sd"> :param py_interpreter: Python version of the beam pipeline.</span> |
| <span class="sd"> If None, this defaults to the python3.</span> |
| <span class="sd"> To track python versions supported by beam and related</span> |
| <span class="sd"> issues check: https://issues.apache.org/jira/browse/BEAM-1251</span> |
| <span class="sd"> :type py_interpreter: str</span> |
| |
| <span class="sd"> :returns: a tuple of three operators, (prediction, summary, validation)</span> |
| <span class="sd"> :rtype: tuple(DataFlowPythonOperator, DataFlowPythonOperator,</span> |
| <span class="sd"> PythonOperator)</span> |
| <span class="sd"> """</span> |
| <span class="n">batch_prediction_job_id</span> <span class="o">=</span> <span class="n">batch_prediction_job_id</span> <span class="ow">or</span> <span class="s2">""</span> |
| <span class="n">dataflow_options</span> <span class="o">=</span> <span class="n">dataflow_options</span> <span class="ow">or</span> <span class="p">{}</span> |
| <span class="n">region</span> <span class="o">=</span> <span class="n">region</span> <span class="ow">or</span> <span class="s2">""</span> |
| |
| <span class="c1"># Verify that task_prefix doesn't have any special characters except hyphen</span> |
| <span class="c1"># '-', which is the only allowed non-alphanumeric character by Dataflow.</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">r</span><span class="s2">"^[a-zA-Z][-A-Za-z0-9]*$"</span><span class="p">,</span> <span class="n">task_prefix</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span> |
| <span class="s2">"Malformed task_id for DataFlowPythonOperator (only alphanumeric "</span> |
| <span class="s2">"and hyphens are allowed but got: "</span> <span class="o">+</span> <span class="n">task_prefix</span> |
| <span class="p">)</span> |
| |
| <span class="n">metric_fn</span><span class="p">,</span> <span class="n">metric_keys</span> <span class="o">=</span> <span class="n">metric_fn_and_keys</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">metric_fn</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"`metric_fn` param must be callable."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">validate_fn</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="n">AirflowException</span><span class="p">(</span><span class="s2">"`validate_fn` param must be callable."</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">dag</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dag</span><span class="o">.</span><span class="n">default_args</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">default_args</span> <span class="o">=</span> <span class="n">dag</span><span class="o">.</span><span class="n">default_args</span> |
| <span class="n">project_id</span> <span class="o">=</span> <span class="n">project_id</span> <span class="ow">or</span> <span class="n">default_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'project_id'</span><span class="p">)</span> |
| <span class="n">region</span> <span class="o">=</span> <span class="n">region</span> <span class="ow">or</span> <span class="n">default_args</span><span class="p">[</span><span class="s1">'region'</span><span class="p">]</span> |
| <span class="n">model_name</span> <span class="o">=</span> <span class="n">model_name</span> <span class="ow">or</span> <span class="n">default_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'model_name'</span><span class="p">)</span> |
| <span class="n">version_name</span> <span class="o">=</span> <span class="n">version_name</span> <span class="ow">or</span> <span class="n">default_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'version_name'</span><span class="p">)</span> |
| <span class="n">dataflow_options</span> <span class="o">=</span> <span class="n">dataflow_options</span> <span class="ow">or</span> <span class="n">default_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'dataflow_default_options'</span><span class="p">)</span> |
| |
| <span class="n">evaluate_prediction</span> <span class="o">=</span> <span class="n">MLEngineStartBatchPredictionJobOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="p">(</span><span class="n">task_prefix</span> <span class="o">+</span> <span class="s2">"-prediction"</span><span class="p">),</span> |
| <span class="n">project_id</span><span class="o">=</span><span class="n">project_id</span><span class="p">,</span> |
| <span class="n">job_id</span><span class="o">=</span><span class="n">batch_prediction_job_id</span><span class="p">,</span> |
| <span class="n">region</span><span class="o">=</span><span class="n">region</span><span class="p">,</span> |
| <span class="n">data_format</span><span class="o">=</span><span class="n">data_format</span><span class="p">,</span> |
| <span class="n">input_paths</span><span class="o">=</span><span class="n">input_paths</span><span class="p">,</span> |
| <span class="n">output_path</span><span class="o">=</span><span class="n">prediction_path</span><span class="p">,</span> |
| <span class="n">uri</span><span class="o">=</span><span class="n">model_uri</span><span class="p">,</span> |
| <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> |
| <span class="n">version_name</span><span class="o">=</span><span class="n">version_name</span><span class="p">,</span> |
| <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="n">metric_fn_encoded</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64encode</span><span class="p">(</span><span class="n">dill</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">metric_fn</span><span class="p">,</span> <span class="n">recurse</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> |
| <span class="n">evaluate_summary</span> <span class="o">=</span> <span class="n">DataflowCreatePythonJobOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="p">(</span><span class="n">task_prefix</span> <span class="o">+</span> <span class="s2">"-summary"</span><span class="p">),</span> |
| <span class="n">py_file</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">),</span> <span class="s1">'mlengine_prediction_summary.py'</span><span class="p">),</span> |
| <span class="n">dataflow_default_options</span><span class="o">=</span><span class="n">dataflow_options</span><span class="p">,</span> |
| <span class="n">options</span><span class="o">=</span><span class="p">{</span> |
| <span class="s2">"prediction_path"</span><span class="p">:</span> <span class="n">prediction_path</span><span class="p">,</span> |
| <span class="s2">"metric_fn_encoded"</span><span class="p">:</span> <span class="n">metric_fn_encoded</span><span class="p">,</span> |
| <span class="s2">"metric_keys"</span><span class="p">:</span> <span class="s1">','</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">metric_keys</span><span class="p">),</span> |
| <span class="p">},</span> |
| <span class="n">py_interpreter</span><span class="o">=</span><span class="n">py_interpreter</span><span class="p">,</span> |
| <span class="n">py_requirements</span><span class="o">=</span><span class="p">[</span><span class="s1">'apache-beam[gcp]>=2.14.0'</span><span class="p">],</span> |
| <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">evaluate_summary</span><span class="o">.</span><span class="n">set_upstream</span><span class="p">(</span><span class="n">evaluate_prediction</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">apply_validate_fn</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">templates_dict</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="n">prediction_path</span> <span class="o">=</span> <span class="n">templates_dict</span><span class="p">[</span><span class="s2">"prediction_path"</span><span class="p">]</span> |
| <span class="n">scheme</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="n">obj</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">urlsplit</span><span class="p">(</span><span class="n">prediction_path</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">scheme</span> <span class="o">!=</span> <span class="s2">"gs"</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">bucket</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">obj</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Wrong format prediction_path: </span><span class="si">{</span><span class="n">prediction_path</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="n">summary</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">"/"</span><span class="p">),</span> <span class="s2">"prediction.summary.json"</span><span class="p">)</span> |
| <span class="n">gcs_hook</span> <span class="o">=</span> <span class="n">GCSHook</span><span class="p">()</span> |
| <span class="n">summary</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">gcs_hook</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">bucket</span><span class="p">,</span> <span class="n">summary</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">validate_fn</span><span class="p">(</span><span class="n">summary</span><span class="p">)</span> |
| |
| <span class="n">evaluate_validation</span> <span class="o">=</span> <span class="n">PythonOperator</span><span class="p">(</span> |
| <span class="n">task_id</span><span class="o">=</span><span class="p">(</span><span class="n">task_prefix</span> <span class="o">+</span> <span class="s2">"-validation"</span><span class="p">),</span> |
| <span class="n">python_callable</span><span class="o">=</span><span class="n">apply_validate_fn</span><span class="p">,</span> |
| <span class="n">templates_dict</span><span class="o">=</span><span class="p">{</span><span class="s2">"prediction_path"</span><span class="p">:</span> <span class="n">prediction_path</span><span class="p">},</span> |
| <span class="n">dag</span><span class="o">=</span><span class="n">dag</span><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="n">evaluate_validation</span><span class="o">.</span><span class="n">set_upstream</span><span class="p">(</span><span class="n">evaluate_summary</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">evaluate_prediction</span><span class="p">,</span> <span class="n">evaluate_summary</span><span class="p">,</span> <span class="n">evaluate_validation</span></div> |
| </pre></div> |
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