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+ }, + { + "dataMap": { + "LABEL": "", + "LEVEL": "INTERVAL", + "ROLE": "INPUT", + "RelativeImportance": "0.05943719597225", + "Variable": "YOJ" + }, + "rowNumber": 10 + } + ], + "xInteger": false, + "yInteger": false +} \ No newline at end of file diff --git a/examples/score_testing_example.ipynb b/examples/score_testing_example.ipynb new file mode 100644 index 00000000..cfc172e7 --- /dev/null +++ b/examples/score_testing_example.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***\n", + "# Implementing Score Testing\n", + "\n", + "_**Note:** Before running this example, you will need to run an example that creates a model on a Viya server and copy the UUID. This will be used as a create_score_definition function parameter._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "import sys\n", + "\n", + "from sasctl._services.score_definitions import ScoreDefinitions as sd # Importing ScoreDefinitions service\n", + "from sasctl._services.score_execution import ScoreExecution as se # Importing ScoreExecution service" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating the score definition for this model using the model UUID generated two steps before\n", + "score_definition = sd.create_score_definition(\n", + " \"example_score_def_name\", # Name of the score_definition, which can be any string\n", + " \"model_id\", # Use Model UUID generated two steps before\n", + " \"table_name\", # Table name for input data, which must exist in host server or it will throw an HTTP error and prompt you to upload a data file\n", + " # True, # Uncomment 'True' if your Viya version is compatible with CAS Gateway\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Executing the score definition\n", + "score_execution = se.create_score_execution(\n", + " score_definition.get(\"id\") # Score definition id created in the previous cell\n", + ")\n", + "\n", + "# Prints score_execution_id\n", + "print(score_execution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can view our scored model information within Model Manager under Projects -> Choose your model -> Scoring. \n", + "***" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/sasctl/_services/score_definitions.py b/src/sasctl/_services/score_definitions.py new file mode 100644 index 00000000..6146878e --- /dev/null +++ b/src/sasctl/_services/score_definitions.py @@ -0,0 +1,162 @@ +import requests +from requests import HTTPError +import sys + +from pathlib import Path +import json +from typing import Union, Optional + +from ..core import current_session, delete, get, sasctl_command, RestObj +from .cas_management import CASManagement +from .model_repository import ModelRepository +from .service import Service + + +class ScoreDefinitions(Service): + """ + Used for creating and maintaining score definitions. + + The Score Definitions API is used for creating and maintaining score definitions. + + See Also + -------- + `REST Documentation ` + + """ + + _SERVICE_ROOT = "/scoreDefinitions" + _cas_management = CASManagement() + _model_repository = ModelRepository() + + ( + list_definitions, + get_definition, + update_definition, + delete_definition, + ) = Service._crud_funcs("/definitions", "definition") + + @classmethod + def create_score_definition( + cls, + score_def_name: str, + model: Union[str, dict], + table_name: str, + use_cas_gateway: Optional[bool] = False, + table_file: Union[str, Path] = None, + description: str = "", + server_name: str = "cas-shared-default", + library_name: str = "Public", + model_version: str = "latest", + ): + """Creates the score definition service. + + Parameters + -------- + score_def_name: str + Name of score definition. + model : str or dict + The name or id of the model, or a dictionary representation of the model. + table_name: str + A user-inputted table name in CAS Management. + use_cas_gateway: bool, optional + Determines whether object uses CAS Gateway or not. + table_file: str or Path, optional + A user-provided path to an uploadable file. Defaults to None. + description: str, optional + Description of score definition. Defaults to an empty string. + server_name: str, optional + The server within CAS that the table is in. Defaults to "cas-shared-default". + library_name: str, optional + The library within the CAS server the table exists in. Defaults to "Public". + model_version: str, optional + The user-chosen version of the model with the specified model_id. Defaults to "latest". + + Returns + ------- + RestObj + + """ + # Changes object descriptor type to either use or not use CAS Gateway + object_descriptor_type: str + if use_cas_gateway: + object_descriptor_type = "sas.models.model.python" + else: + object_descriptor_type = "sas.models.model.ds2" + + model = cls._model_repository.get_model(model) + + if not model: + raise HTTPError( + { + f"This model may not exist in a project or the model may not exist at all." + } + ) + model_id = model.id + model_project_id = model.get("projectId") + model_project_version_id = model.get("projectVersionId") + model_name = model.get("name") + # Checking if the model exists and if it's in a project + + try: + inputMapping = [] + for input_item in model.get("inputVariables"): + var = { + "mappingValue": input_item["name"], + "mappingType": "datasource", + "variableName": input_item["name"], + } + inputMapping.append(var) + + except: + print("This model does not have the optional 'inputVariables' parameter.") + + # Optional mapping - Maps the variables in the data to the variables of the score object. It's not necessary to create a score definition. + + table = cls._cas_management.get_table(table_name, library_name, server_name) + if not table and not table_file: + raise HTTPError( + f"This table may not exist in CAS. Please include the `table_file` argument in the function call if it doesn't exist." + ) + elif not table and table_file: + cls._cas_management.upload_file( + str(table_file), table_name + ) # do I need to add a check if the file doesn't exist or does upload_file take care of that? + table = cls._cas_management.get_table(table_name, library_name, server_name) + if not table: + raise HTTPError( + f"The file failed to upload properly or another error occurred." + ) + # Checks if the inputted table exists, and if not, uploads a file to create a new table + + save_score_def = { + "name": model_name, # used to be score_def_name + "description": description, + "objectDescriptor": { + "uri": f"/modelManagement/models/{model_id}", + "name": f"{model_name}({model_version})", + "type": f"{object_descriptor_type}", + }, + "inputData": { + "type": "CASTable", + "serverName": server_name, + "libraryName": library_name, + "tableName": table_name, + }, + "properties": { + "tableBaseName": "", + "modelOrigUri": f"/modelRepository/models/{model_id}", + "projectUri": f"/modelRepository/projects/{model_project_id}", + "projectVersionUri": f"/modelRepository/projects/{model_project_id}/projectVersions/{model_project_version_id}", + "publishDestination": "", + "versionedModel": f"{model_name}({model_version})", + }, + "mappings": inputMapping, + } + # Consolidating all of the model and table information to create the score definition information + + headers_score_def = {"Content-Type": "application/json"} + + return cls.post( + "/definitions", data=json.dumps(save_score_def), headers=headers_score_def + ) + # The response information of the score definition can be seen as a JSON as well as a RestOBJ diff --git a/src/sasctl/_services/score_execution.py b/src/sasctl/_services/score_execution.py new file mode 100644 index 00000000..eeac893e --- /dev/null +++ b/src/sasctl/_services/score_execution.py @@ -0,0 +1,250 @@ +import json +import time +import warnings +from distutils.version import StrictVersion +from typing import Union + +import pandas as pd +from requests import HTTPError + +from .cas_management import CASManagement +from ..core import current_session +from .score_definitions import ScoreDefinitions +from .service import Service + + +class ScoreExecution(Service): + """ + The Score Execution API is used to produce a score by + executing the mapped code generated by score objects using the score definition. + + See Also + -------- + `REST Documentation ` + + """ + + _SERVICE_ROOT = "/scoreExecution" + _cas_management = CASManagement() + _score_definitions = ScoreDefinitions() + _services = Service() + + ( + list_executions, + get_execution, + update_execution, + delete_execution, + ) = Service._crud_funcs("/executions", "execution") + + @classmethod + def create_score_execution( + cls, + score_definition_id: str, + description: str = "", + output_server_name: str = "cas-shared-default", + output_library_name: str = "Public", + output_table_name: str = "", + ): + """Creates the score definition service. + + Parameters + -------- + score_definition_id: str + A score definition id representing score definition existing on the server that needs to be executed. + description: str, optional + Description of score execution. Defaults to an empty string. + output_server_name: str, optional + The name of the output server the output table and output library is stored in. Defaults to "cas-shared-default". + output_library_name: str, optional + The name of the output library the output table is stored in. Defaults to "Public". + output_table_name: str, optional + The name of the output table the score execution or analysis output will be stored in. Defaults to an empty string. + + Returns + ------- + RestObj + + """ + + # Gets information about the scoring object from the score definition + score_definition = cls._score_definitions.get_definition(score_definition_id) + if not score_definition: + raise HTTPError + score_exec_name = score_definition.get("name") + model_uuid = score_definition.get("objectDescriptor").get("uri").split("/")[-1] + model_uri = f"/modelManagement/models/{model_uuid}" + model_name = score_definition.get("objectDescriptor").get("name") + model_input_library = score_definition.get("inputData").get("libraryName") + model_table_name = score_definition.get("inputData").get("tableName") + + # Defining a default output table name if none is provided + if not output_table_name: + output_table_name = f"{model_name}_{score_definition_id}" + + headers_score_exec = {"Content-Type": "application/json"} + + create_score_exec = { + "name": f"{score_exec_name}", + "description": description, + "hints": { + "objectURI": f"{model_uri}", + "inputTableName": f"{model_table_name}", + "inputLibraryName": f"{model_input_library}", + }, + "scoreDefinitionId": f"{score_definition_id}", + "outputTable": { + "tableName": output_table_name, + "libraryName": output_library_name, + "serverName": output_server_name, + }, + } + + # Creating the score execution + score_execution = cls.post( + "executions", + data=json.dumps(create_score_exec), + headers=headers_score_exec, + ) + + return score_execution + + @classmethod + def poll_score_execution_state( + cls, score_execution: Union[dict, str], timeout: int = 300 + ): + """Checks the state of the score execution. + + Parameters + -------- + score_execution: str or dict + A running score_execution. + timeout: int + Time limit for checking the score_execution state. + + Returns + ------- + String + + """ + if type(score_execution) is str: + exec_id = score_execution + else: + exec_id = score_execution.get("id") + + start_poll = time.time() + while time.time() - start_poll < timeout: + score_execution_state = cls.get(f"executions/{exec_id}/state") + if score_execution_state == "completed": + print("Score execution state is 'completed'") + return "completed" + elif score_execution_state == "failed": + # TODO: Grab score execution logs and return those + print("The score execution state is failed.") + return "failed" + elif time.time() - start_poll > timeout: + print("The score execution is still running, but polling time ran out.") + return "timeout" + + @classmethod + def get_score_execution_results( + cls, + score_execution: Union[dict, str], + ): + """Generates an output table for the score_execution results. + + Parameters + -------- + score_execution: str or dict + A running score_execution. + + Returns + ------- + Table reference + + """ + try: + import swat + except ImportError: + swat = None + + if type(score_execution) is str: + score_execution = cls.get_execution(score_execution) + + server_name = score_execution.get("outputTable").get("serverName") + library_name = score_execution.get("outputTable").get("libraryName") + table_name = score_execution.get("outputTable").get("tableName") + + # If swat is not available, then + if not swat: + output_table = cls._no_gateway_get_results( + server_name, library_name, table_name + ) + return output_table + else: + session = current_session() + cas = session.as_swat() + response = cas.loadActionSet("gateway") + if not response: + output_table = cls._no_gateway_get_results( + server_name, library_name, table_name + ) + return output_table + else: + gateway_code = f""" +import pandas as pd +import numpy as np + +table = gateway.read_table({{"caslib": "{library_name}", "name": "{table_name}"}}) + +gateway.return_table("Execution Results", df = table, label = "label", title = "title")""" + + output_table = cas.gateway.runlang( + code=gateway_code, single=True, timeout_millis=10000 + ) + output_table = pd.DataFrame(output_table["Execution Results"]) + return output_table + + @classmethod + def _no_gateway_get_results(cls, server_name, library_name, table_name): + """Helper method that builds the output table. + + Parameters + -------- + server_name: str + CAS server where original table is stored. + library_name: CAS library where original table is stored. + table_name: Table that contains row and columns information to build the output table + + Returns + ------- + Pandas Dataframe + + """ + if pd.__version__ >= StrictVersion("1.0.3"): + from pandas import json_normalize + else: + from pandas.io.json import json_normalize + + warnings.warn( + "Without swat installed, the amount of rows from the output table that " + "can be collected are memory limited by the CAS worker." + ) + + output_columns = cls._cas_management.get( + f"servers/{server_name}/" + f"caslibs/{library_name}/" + f"tables/{table_name}/columns?limit=10000" + ) + columns = json_normalize(output_columns.json(), "items") + column_names = columns["names"].to_list() + + output_rows = cls._services.get( + f"casRowSets/servers/{server_name}" + f"caslibs/{library_name}" + f"tables/{table_name}/rows?limit=10000" + ) + output_table = pd.DataFrame( + json_normalize(output_rows.json()["items"])["cells"].to_list(), + columns=column_names, + ) + return output_table diff --git a/src/sasctl/services.py b/src/sasctl/services.py index 6b93c80e..38e67bdb 100644 --- a/src/sasctl/services.py +++ b/src/sasctl/services.py @@ -21,6 +21,8 @@ from ._services.report_images import ReportImages as report_images from ._services.reports import Reports as reports from ._services.saslogon import SASLogon as saslogon +from ._services.score_definitions import ScoreDefinitions as score_definitions +from ._services.score_execution import ScoreExecution as score_execution from ._services.sentiment_analysis import SentimentAnalysis as sentiment_analysis from ._services.text_categorization import TextCategorization as text_categorization from ._services.text_parsing import TextParsing as text_parsing diff --git a/src/sasctl/tasks.py b/src/sasctl/tasks.py index 5324a06b..1da7d3fb 100644 --- a/src/sasctl/tasks.py +++ b/src/sasctl/tasks.py @@ -12,6 +12,8 @@ import os import re import sys +from pathlib import Path +from typing import Union from warnings import warn import pandas as pd @@ -29,7 +31,10 @@ from .services import model_management as mm from .services import model_publish as mp from .services import model_repository as mr - +from .services import score_definitions as sd +from .services import score_execution as se +from .utils.misc import installed_packages +from .utils.pymas import from_pickle logger = logging.getLogger(__name__) @@ -964,3 +969,32 @@ def get_project_kpis( kpiTableDf = kpiTableDf.apply(lambda x: x.str.strip()).replace([".", ""], None) return kpiTableDf + + +def score_model_with_cas( + score_def_name: str, + model: Union[str, dict], + table_name: str, + table_file: Union[str, Path] = None, + description: str = "", + server_name: str = "cas-shared-default", + library_name: str = "Public", + model_version: str = "latest", + use_cas_gateway: bool = False, +): + score_definition = sd.create_score_definition( + score_def_name, + model, + table_name, + table_file=table_file, + description=description, + server_name=server_name, + library_name=library_name, + model_version=model_version, + use_cas_gateway=use_cas_gateway, + ) + score_execution = se.create_score_execution(score_definition.id) + score_execution_poll = se.poll_score_execution_state(score_execution) + print(score_execution_poll) + score_results = se.get_score_execution_results(score_execution) + return score_results diff --git a/tests/unit/test_score_definitions.py b/tests/unit/test_score_definitions.py new file mode 100644 index 00000000..8a75294a --- /dev/null +++ b/tests/unit/test_score_definitions.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright © 2024, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +import json +import os +import pickle +import random +import tempfile +import unittest +import copy +from pathlib import Path +from unittest import mock + +import numpy as np +import pandas as pd +import pytest +from requests import HTTPError + +import sasctl.pzmm as pzmm +from sasctl import current_session +from sasctl.core import RestObj, VersionInfo, request +from sasctl._services.score_definitions import ScoreDefinitions as sd + + +class CustomMock: + def __init__(self, json_info, id=""): + self.json_info = json_info + self.id = id + + def get(self, key1, key2=None, key3=None): + if key2 is None and key3 is None: + return self.json_info[key1] + else: + return self.json_info[key1][key2][key3] + + +def test_create_score_definition(): + """ + Test Cases: + - Valid model id with input mapping and valid table name + - Invalid model id + - Valid table name without input mapping + - Invalid table name with invalid file + - Invalid table name with valid file + - Invalid table name without table file argument + + """ + # Mocking a session to allow the post call to go through + with mock.patch("sasctl.core.Session._get_authorization_token"): + current_session("example.com", "username", "password") + + # Mocking the REST API calls and methods + with mock.patch( + "sasctl._services.model_repository.ModelRepository.get_model" + ) as get_model: + with mock.patch( + "sasctl._services.cas_management.CASManagement.get_table" + ) as get_table: + with mock.patch( + "sasctl._services.cas_management.CASManagement.upload_file" + ) as upload_file: + with mock.patch( + "sasctl._services.score_definitions.ScoreDefinitions.post" + ) as post: + # Invalid model id test case + get_model.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + + # Valid model id but invalid table name with no table_file argument test case + get_model_mock = CustomMock( + json_info={ + "id": "12345", + "projectId": "54321", + "projectVersionId": "67890", + "name": "test_model", + }, + ) + get_model.return_value = get_model_mock + get_table.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + + # Invalid table name with a table_file argument that doesn't work test case + get_table.return_value = None + upload_file.return_value = None + get_table.return_value = None + with pytest.raises(HTTPError): + sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + + # Valid table_file argument that successfully creates a table test case + get_table.return_value = None + upload_file.return_value = RestObj + get_table_mock = CustomMock( + json_info={"tableName": "test_table"}, + ) + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + assert response + + # Valid table_name argument test case + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + table_file="test_path", + ) + assert response + + # Checking response with inputVariables in model elements + get_model_mock = CustomMock( + json_info={ + "id": "12345", + "projectId": "54321", + "projectVersionId": "67890", + "name": "test_model", + "inputVariables": [ + {"name": "first"}, + {"name": "second"}, + {"name": "third"}, + ], + }, + ) + get_model.return_value = get_model_mock + get_table.return_value = get_table_mock + response = sd.create_score_definition( + score_def_name="test_create_sd", + model="12345", + table_name="test_table", + ) + assert response + assert post.call_count == 3 + + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert json_data["mappings"] != [] diff --git a/tests/unit/test_score_execution.py b/tests/unit/test_score_execution.py new file mode 100644 index 00000000..f0090155 --- /dev/null +++ b/tests/unit/test_score_execution.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright © 2024, SAS Institute Inc., Cary, NC, USA. All Rights Reserved. +# SPDX-License-Identifier: Apache-2.0 + +import json +import os +import pickle +import random +import tempfile +import unittest +import copy +from pathlib import Path +from unittest import mock +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd +import pytest +from requests import HTTPError + +import sasctl.pzmm as pzmm +from sasctl import current_session +from sasctl.core import RestObj, VersionInfo +from sasctl._services.score_definitions import ScoreDefinitions as sd +from sasctl._services.score_execution import ScoreExecution as se + + +# Creating a CustomMock for list_executions to avoid a TypeError when comparing status code from mock with >= 400 in score_execution +class CustomMock: + def __init__(self, json_info): + self.json_info = json_info + + def get(self, key1, key2=None, key3=None): + if key2 is None and key3 is None: + return self.json_info[key1] + elif key2 and not key3: + return self.json_info[key1][key2] + else: + return self.json_info[key1][key2][key3] + + +def test_create_score_execution(): + """ + Test Cases: + - Valid score definition id and invalid list_executions argument + - Invalid score definition id + - Valid list_executions argument with execution already running but invalid delete_execution argument + - Valid list_executions argument with execution already running but valid delete_execution argument + - Valid list_executions argument without execution already running + - With output table specified within create_score_execution arguments + + """ + + # Mocking a session to allow the post call to go through + with mock.patch("sasctl.core.Session._get_authorization_token"): + current_session("example.com", "username", "password") + # Mocking what a successful score execution post would be if the user specified an output table + TARGET = {"outputTable": {"tableName": "example_table"}} + # Creating a target to compare post call responses to + target = copy.deepcopy(TARGET) + + # Mocking the REST API calls and functions + with mock.patch( + "sasctl._services.score_definitions.ScoreDefinitions.get_definition" + ) as get_definition: + with mock.patch("sasctl._services.score_execution.ScoreExecution.post") as post: + # Invalid score definition id test case + get_definition.return_value = None + with pytest.raises(HTTPError): + se.create_score_execution(score_definition_id="12345") + + # Valid score definition id and invalid list_executions argument test case + get_definition_mock = CustomMock( + json_info={ + "inputData": { + "libraryName": "cas-shared-default", + "tableName": "test_table", + }, + "name": "score_def_name", + "objectDescriptor": { + "name": "test_model", + "type": "sas.publish.example", + "uri": "/modelPublish/models/example", + }, + }, + ) + get_definition.return_value = get_definition_mock + response = se.create_score_execution(score_definition_id="3456") + assert response + + # Checking whether the output table name remained the default empty string or the default changed as writted in score_execution + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert json_data["outputTable"]["tableName"] != "" + + # With output table specified within create_score_execution arguments test case + response = se.create_score_execution( + score_definition_id="12345", output_table_name="example_table" + ) + assert response + assert post.call_count == 2 + + # Checking whether specified output table name or the default output table name is in the response + data = post.call_args + json_data = json.loads(data.kwargs["data"]) + assert ( + target["outputTable"]["tableName"] + == json_data["outputTable"]["tableName"] + )