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api for analyses
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‎dataikuapi/dss/analysis.py‎

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from ..utils import DataikuException
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from ..utils import DataikuUTF8CSVReader
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from ..utils import DataikuStreamedHttpUTF8CSVReader
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import json
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import time
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from .metrics import ComputedMetrics
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from .ml import DSSMLTask
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class DSSAnalysis(object):
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"""A handle to interact with a DSS visual analysis"""
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def __init__(self, client, project_key, analysis_id):
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self.client = client
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self.project_key = project_key
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self.analysis_id = analysis_id
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########################################################
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# Analysis deletion
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########################################################
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def delete(self, drop_data=False):
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"""
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Delete the dataset
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:param bool drop_data: Should the data of the dataset be dropped
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"""
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return self.client._perform_empty("DELETE", "/projects/%s/lab/%s/" % (self.project_key, self.analysis_id))
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########################################################
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# Analysis definition
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########################################################
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def get_definition(self):
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"""
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Get the definition of the analysis
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Returns:
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the definition, as a JSON object
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"""
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return self.client._perform_json("GET", "/projects/%s/lab/%s/" % (self.project_key, self.analysis_id))
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def set_definition(self, definition):
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"""
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Set the definition of the analysis
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Args:
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definition: the definition, as a JSON object. You should only set a definition object
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that has been retrieved using the get_definition call.
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"""
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return self.client._perform_json("PUT", "/projects/%s/lab/%s/" % (self.project_key, self.analysis_id), body=definition)
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########################################################
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# ML
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########################################################
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def create_prediction_ml_task(self, target_variable,
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ml_backend_type = "PY_MEMORY",
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guess_policy = "DEFAULT"):
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"""Creates a new prediction task in this visual analysis lab
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for a dataset.
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The returned ML task will be in 'guessing' state, i.e. analyzing
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the input dataset to determine feature handling and algorithms.
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You should wait for the guessing to be completed by calling
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``wait_guess_complete`` on the returned object before doing anything
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else (in particular calling ``train`` or ``get_settings``)
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:param string ml_backend_type: ML backend to use, one of PY_MEMORY, MLLIB or H2O
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:param string guess_policy: Policy to use for setting the default parameters. Valid values are: DEFAULT, SIMPLE_FORMULA, DECISION_TREE, EXPLANATORY and PERFORMANCE
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"""
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obj = {
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"taskType" : "PREDICTION",
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"targetVariable" : target_variable,
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"backendType": ml_backend_type,
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"guessPolicy": guess_policy
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}
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ref = self.client._perform_json("POST", "/projects/%s/lab/%s/models/" % (self.project_key, self.analysis_id), body=obj)
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return DSSMLTask(self.client, self.project_key, self.analysis_id, ref["mlTaskId"])
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def create_clustering_ml_task(self,
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ml_backend_type = "PY_MEMORY",
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guess_policy = "KMEANS"):
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"""Creates a new clustering task in a new visual analysis lab
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for a dataset.
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The returned ML task will be in 'guessing' state, i.e. analyzing
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the input dataset to determine feature handling and algorithms.
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You should wait for the guessing to be completed by calling
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``wait_guess_complete`` on the returned object before doing anything
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else (in particular calling ``train`` or ``get_settings``)
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:param string ml_backend_type: ML backend to use, one of PY_MEMORY, MLLIB or H2O
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:param string guess_policy: Policy to use for setting the default parameters. Valid values are: KMEANS and ANOMALY_DETECTION
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"""
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obj = {
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"taskType" : "CLUSTERING",
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"backendType": ml_backend_type,
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"guessPolicy": guess_policy
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}
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ref = self.client._perform_json("POST", "/projects/%s/lab/%s/models/" % (self.project_key, self.analysis_id), body=obj)
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return DSSMLTask(self.client, self.project_key, self.analysis_id, ref["mlTaskId"])
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def list_ml_tasks(self):
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"""
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List the ML tasks in this visual analysis
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Returns:
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the list of the ML tasks summaries, each one as a JSON object
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"""
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return self.client._perform_json("GET", "/projects/%s/lab/%s/models/" % (self.project_key, self.analysis_id))
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def get_ml_task(self, mltask_id):
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"""
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Get a handle to interact with a specific ML task
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Args:
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mltask_id: the identifier of the desired ML task
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Returns:
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A :class:`dataikuapi.dss.ml.DSSMLTask` ML task handle
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"""
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return DSSMLTask(self.client, self.project_key, self.analysis_id, mltask_id)
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‎dataikuapi/dss/project.py‎

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from .notebook import DSSNotebook
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from .macro import DSSMacro
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from .ml import DSSMLTask
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from .analysis import DSSAnalysis
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from dataikuapi.utils import DataikuException
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ref = self.client._perform_json("POST", "/projects/%s/models/lab/" % self.project_key, body=obj)
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return DSSMLTask(self.client, self.project_key, ref["analysisId"], ref["mlTaskId"])
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def list_ml_tasks(self):
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"""
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List the ML tasks in this project
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Returns:
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the list of the ML tasks summaries, each one as a JSON object
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"""
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return self.client._perform_json("GET", "/projects/%s/models/lab/" % self.project_key)
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def get_ml_task(self, analysis_id, mltask_id):
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"""
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Get a handle to interact with a specific ML task
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Args:
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analysis_id: the identifier of the visual analysis containing the desired ML task
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mltask_id: the identifier of the desired ML task
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Returns:
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A :class:`dataikuapi.dss.ml.DSSMLTask` ML task handle
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"""
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return DSSMLTask(self.client, self.project_key, analysis_id, mltask_id)
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def create_analysis(self, input_dataset):
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"""
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Creates a new visual analysis lab for a dataset.
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"""
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obj = {
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"inputDataset" : input_dataset
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}
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ref = self.client._perform_json("POST", "/projects/%s/lab/" % self.project_key, body=obj)
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return DSSAnalysis(self.client, self.project_key, ref["analysisId"])
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def list_analyses(self):
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"""
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List the visual analyses in this project
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Returns:
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the list of the visual analyses summaries, each one as a JSON object
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"""
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return self.client._perform_json("GET", "/projects/%s/lab/" % self.project_key)
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def get_analysis(self, analysis_id):
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"""
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Get a handle to interact with a specific visual analysis
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Args:
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analysis_id: the identifier of the desired visual analysis
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Returns:
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A :class:`dataikuapi.dss.analysis.DSSAnalysis` visual analysis handle
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"""
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return DSSAnalysis(self.client, self.project_key, analysis_id)
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########################################################
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# Saved models
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########################################################

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