From 2d87b5a0980e478203fc4fee0a9bed2d44b9c2eb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Tue, 5 Oct 2021 18:29:21 +0200
Subject: [PATCH 01/11] Rename get_full_info to get_evaluation_full_info Add
 getters for properties of DSSModelEvaluationFullInfo Document data drift
 parameters and results

---
 dataikuapi/dss/modelevaluationstore.py | 434 ++++++++++++++++++++++++-
 1 file changed, 426 insertions(+), 8 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index 4d2d2900..f0f2ae3e 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -5,8 +5,6 @@
 from .discussion import DSSObjectDiscussions
 from .future import DSSFuture
 
-from requests import utils
-
 try:
     basestring
 except NameError:
@@ -271,9 +269,11 @@ def __init__(self, model_evaluation_store, run_id):
         self.project_key = model_evaluation_store.project_key
         self.mes_id = model_evaluation_store.mes_id
 
-    def get_full_info(self):
+    def get_evaluation_full_info(self):
         """
         Retrieve the model evaluation with its performance data
+
+        :return: the model evaluation full info, as a :class:`dataikuapi.dss.DSSModelEvaluationInfo`
         """
         data = self.client._perform_json(
             "GET", "/projects/%s/modelevaluationstores/%s/runs/%s" % (self.project_key, self.mes_id, self.run_id))
@@ -301,21 +301,25 @@ def compute_data_drift(self, reference=None, data_drift_params=None, wait=True):
         :param reference: saved model version (full ID or DSSTrainedPredictionModelDetails)
                 or model evaluation (full ID or DSSModelEvaluation) to use as reference (optional)
         :type reference: Union[str, DSSModelEvaluation, DSSTrainedPredictionModelDetails]
-        :param data_drift_params: data drift computation settings (optional)
+        :param data_drift_params: data drift computation settings as a :class:`dataikuapi.dss.modelevaluationstore.DataDriftParams` (optional)
+        :type data_drift_params: DataDriftParams
         :param wait: data drift computation settings (optional)
-        :returns: a `dict` containing data drift analysis results if `wait` is `True`, or a :class:`~dataikuapi.dss.future.DSSFuture` handle otherwise
+        :returns: a :class:`dataikuapi.dss.modelevaluationstore.DataDriftResult` containing data drift analysis results if `wait` is `True`, or a :class:`~dataikuapi.dss.future.DSSFuture` handle otherwise
         """
 
         if hasattr(reference, 'full_id'):
             reference = reference.full_id
 
+        if data_drift_params:
+            data_drift_params = data_drift_params.data
+
         future_response = self.client._perform_json(
             "POST", "/projects/%s/modelevaluationstores/%s/runs/%s/computeDataDrift" % (self.project_key, self.mes_id, self.run_id),
             body={
                 "referenceId": reference,
                 "dataDriftParams": data_drift_params
             })
-        future = DSSFuture(self.client, future_response.get('jobId', None), future_response)
+        future = DSSFuture(self.client, future_response.get('jobId', None), future_response, result_wrapper=DataDriftResult)
         return future.wait_for_result() if wait else future
 
     def get_metrics(self):
@@ -356,7 +360,7 @@ class DSSModelEvaluationFullInfo:
     Includes information such as the full id of the evaluated model, the evaluation params,
     the performance and drift metrics, if any, etc.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.DSSModelEvaluation.get_full_info`
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.DSSModelEvaluation.get_evaluation_full_info`
     """
     def __init__(self, model_evaluation, full_info):
         self.model_evaluation = model_evaluation
@@ -396,4 +400,418 @@ def get_creation_date(self):
 
         :return: the date and time, as an epoch
         """
-        return self.full_info["evaluation"]["created"]
\ No newline at end of file
+        return self.full_info["evaluation"]["created"]
+
+    def get_full_id(self):
+        """
+        Returns the full id of the Model Evaluation.
+
+        :return: the full id, as a string
+        """
+        return self.full_info["evaluation"]["ref"]["fullId"]
+
+    def get_model_full_id(self):
+        """
+        Returns the full id of the evaluated model.
+
+        :return: the model full id, as a string
+        """
+        return self.full_info["evaluation"]["modelRef"]["fullId"]
+
+    def get_model_type(self):
+        """
+        Returns the evaluated model type.
+
+        :return: the model type, as a string
+        """
+        return self.full_info["evaluation"]["modelType"]
+
+    def get_model_parameters(self):
+        """
+        Returns the evaluated model params.
+
+        :return: the model params, as a dict
+        """
+        return self.full_info["evaluation"]["modelParams"]
+
+    def get_prediction_type(self):
+        """
+        Returns the prediction type of the evaluated model.
+
+        :return: the prediction type, as a string
+        """
+        return self.full_info["evaluation"]["predictionType"]
+
+    def get_prediction_variable(self):
+        """
+        Returns the prediction variable used for evaluation.
+
+        :return: the prediction variable, as a string
+        """
+        return self.full_info["evaluation"]["predictionVariable"]
+
+    def get_target_variable(self):
+        """
+        Returns the target variable used for evaluation.
+
+        :return: the target variable, as a string
+        """
+        return self.full_info["evaluation"]["targetVariable"]
+
+
+class DataDriftParams(object):
+    """
+    Object that represents parameters for data drift computation.
+    Do not create this object directly, use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftParams.from_params` instead.
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def __repr__(self):
+        return u"{}({})".format(self.__class__.__name__, self.data)
+
+    @staticmethod
+    def from_params(columns, nb_bins=10, compute_histograms=True, confidence_level=0.95):
+        """
+        Creates parameters for data drift computation from columns, number of bins, compute histograms and confidence level
+
+        :param dict columns: A dict representing the per column settings.
+        You should use a :class:`~dataikuapi.dss.modelevaluationstore.PerColumnDriftParamBuilder` to build it.
+        :param int nb_bins: (optional) Nb. bins in histograms (apply to all columns) - default: 10
+        :param bool compute_histograms: (optional) Enable/disable histograms - default: True
+        :param float confidence_level: (optional) Used to compute confidence interval on drift's model accuracy - default: 0.95
+
+        :rtype: :class:`dataikuapi.dss.modelevaluationstore.DataDriftParams`
+        """
+        return DataDriftParams({
+            "columns": columns,
+            "nbBins": nb_bins,
+            "computeHistograms": compute_histograms,
+            "confidenceLevel": confidence_level
+        })
+
+
+class PerColumnDriftParamBuilder(object):
+    """
+    Builder for a map of per column drift params settings.
+    Used as a helper before computing data drift to build columns param expected in dataikuapi.dss.modelevaluationstore.DataDriftParams.from_params.
+    """
+    def __init__(self):
+        self.columns = {}
+
+    def build(self):
+        """Returns the built dict for per column drift params settings"""
+        return self.columns
+
+    def with_column_drift_param(self, name, handling="AUTO", enabled=False):
+        """
+        Sets the drift params settings for given column name.
+
+        :param: string name: The name of the column
+        :param: string handling: (optional) The column type, should be either NUMERICAL, CATEGORICAL or AUTO (default: AUTO)
+        :param: bool name: (optional) If the column should be enabled (default: False)
+        """
+        self.columns[name] = {
+            "handling": handling,
+            "enabled": enabled
+        }
+        return self
+
+
+class DataDriftResult(object):
+    """
+    A handle on the data drift result of a model evaluation.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.DSSModelEvaluation.compute_data_drift`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw data drift result.
+
+        :return: the raw data drift result
+        :rtype: dict
+        """
+        return self.data
+
+    def get_drift_model_result(self):
+        """
+        Get the drift analysis based on drift modeling.
+
+        :return: A handle on the drift model result
+        :rtype: :class:`dataikuapi.dss.modelevaluationstore.DriftModelResult`
+        """
+        return DriftModelResult(self.data["driftModelResult"])
+
+    def get_univariate_drift_result(self):
+        """
+        Get a per-column drift analysis based on comparison of distributions.
+
+        :return: A handle on the univariate drift result
+        :rtype: :class:`dataikuapi.dss.modelevaluationstore.UnivariateDriftResult`
+        """
+        return UnivariateDriftResult(self.data["univariateDriftResult"])
+
+    def get_per_column_report(self):
+        """
+        Get the information about column handling that has been used (errors, types, etc).
+
+        :return: A list of handles on column reports
+        :rtype: list of :class:`dataikuapi.dss.modelevaluationstore.ColumnReport`
+        """
+        return map(ColumnReport, self.data["perColumnReport"])
+
+    def get_reference_sample_size(self):
+        """
+        Get the size of the reference ME sample.
+
+        :return: the size of the reference sample
+        :rtype: int
+        """
+        return self.data["referenceSampleSize"]
+
+    def get_current_sample_size(self):
+        """
+        Size of the current ME sample.
+
+        :return: the size of the current ME
+        :rtype: int
+        """
+        return self.data["currentSampleSize"]
+
+
+class DriftModelResult(object):
+    """
+    A handle on the drift model result.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_drift_model_result`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw drift model result.
+
+        :return: the raw drift model result
+        :rtype: dict
+        """
+        return self.data
+
+    def get_reference_sample_size(self):
+        """
+        Get the number of rows coming from reference ME in the drift model trainset.
+
+        :return: the number of rows coming from reference ME
+        :rtype: int
+        """
+        return self.data["referenceSampleSize"]
+
+    def get_current_sample_size(self):
+        """
+        Get the number of rows coming from current ME in the drift model trainset.
+
+        :return: the number of rows coming from current ME
+        :rtype: int
+        """
+        return self.data["currentSampleSize"]
+
+    def get_drift_model_accuracy(self):
+        """
+        Get the drift model accuracy information.
+
+        :return: A handle to interact with the drift model accuracy information
+        :rtype: :class:`dataiku.dss.modelevaluationstore.DriftModelAccuracy`
+        """
+        return DriftModelAccuracy(self.data["driftModelAccuracy"])
+
+    def get_feature_drift_importance(self):
+        """
+        Get the feature drift importance chart data.
+
+        :return: A handle on feature drift importance chart data
+        :rtype: :class:`dataiku.dss.modelevaluationstore.DriftVersusImportanceChart`
+        """
+        return DriftVersusImportanceChart(self.data["driftVersusImportance"])
+
+
+class UnivariateDriftResult(object):
+    """
+    A handle on the univariate data drift.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_univariate_drift_result`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw univariate data drift.
+        It consists of a map with column name as keys, and a `dict ` with drift data as value.
+
+        :return: the raw univariate data drift
+        :rtype: dict
+        """
+        return self.data["columns"]
+
+
+class ColumnReport(object):
+    """
+    A handle on column handling information.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_per_column_report`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw column handling information.
+
+        :return: the raw column handling information
+        :rtype: dict
+        """
+        return self.data
+
+    def get_name(self):
+        """
+        Get the column name.
+
+        :return: the column name
+        :rtype: string
+        """
+        return self.data["name"]
+
+    def get_actual_column_handling(self):
+        """
+        Get the actual column handling (either forced via drift params or inferred from ME preprocessings).
+        It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
+
+        :return: the actual column handling
+        :rtype: string
+        """
+        return self.data["actualHandling"]
+
+    def get_default_column_handling(self):
+        """
+        Get the default column handling (based on ME preprocessing only).
+        It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
+
+        :return: the default column handling
+        :rtype: string
+        """
+        return self.data["defaultHandling"]
+
+    def get_error_message(self):
+        """
+        Get the error message.
+        For example: "could not treat column as numerical" (in this case, the column handling is forced to 'IGNORED').
+
+        :return: the error message, if there is any
+        :rtype: string
+        """
+        return self.data["errorMessage"]
+
+
+class DriftModelAccuracy(object):
+    """
+    A handle on the drift model accuracy.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DriftModelResult.get_drift_model_accuracy`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw drift model accuracy data.
+
+        :return: the drift model accuracy data
+        :rtype: dict
+        """
+        return self.data
+
+    def get_value(self):
+        """
+        Get the drift model accuracy value.
+
+        :return: the accuracy value
+        :rtype: float
+        """
+        return self.data["value"]
+
+    def get_lower_confidence_interval(self):
+        """
+        Get the drift model accuracy lower confidence interval.
+
+        :return: the lower confidence interval
+        :rtype: float
+        """
+        return self.data["lower"]
+
+    def get_upper_confidence_interval(self):
+        """
+        Get the drift model accuracy upper confidence interval.
+
+        :return: the upper confidence interval
+        :rtype: float
+        """
+        return self.data["upper"]
+
+    def get_pvalue(self):
+        """
+        Get the drift model accuracy pvalue for H0: "there is no drift".
+
+        :return: the accuracy pvalue for H0: "there is no drift"
+        :rtype: float
+        """
+        return self.data["pvalue"]
+
+
+class DriftVersusImportanceChart(object):
+    """
+    A handle on the feature drift importance chart data.
+
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DriftModelResult.get_feature_drift_importance`
+    """
+    def __init__(self, data):
+        self.data = data
+
+    def get_raw(self):
+        """
+        Get the raw feature drift importance chart data.
+
+        :return: the feature drift importance chart data
+        :rtype: dict
+        """
+        return self.data
+
+    def get_column_names(self):
+        """
+        Get the column names.
+
+        :return: a `list` of the column names
+        :rtype: list of string
+        """
+        return self.data["columns"]
+
+    def get_column_drift_scores(self):
+        """
+        Get the importance of the columns in the drift model.
+
+        :return: the `list` of the importance of the columns in the drift model
+        :rtype: list of float
+        """
+        return self.data["columnDriftScores"]
+
+    def get_column_original_scores(self):
+        """
+        Get the importance of the columns in the original model.
+        Returns None when it can't be computed.
+
+        :return: the `list` of the importance of the columns in the original model
+        :rtype: list of float
+        """
+        return self.data["columnImportanceScores"]

From a5b7aa4c22cfb291da76a9e01a745d17045a6934 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Thu, 7 Oct 2021 20:10:15 +0200
Subject: [PATCH 02/11] Rollback renaming of get_full_info to
 get_evaluation_full_info

---
 dataikuapi/dss/modelevaluationstore.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index f0f2ae3e..e630ad5c 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -269,7 +269,7 @@ def __init__(self, model_evaluation_store, run_id):
         self.project_key = model_evaluation_store.project_key
         self.mes_id = model_evaluation_store.mes_id
 
-    def get_evaluation_full_info(self):
+    def get_full_info(self):
         """
         Retrieve the model evaluation with its performance data
 
@@ -360,7 +360,7 @@ class DSSModelEvaluationFullInfo:
     Includes information such as the full id of the evaluated model, the evaluation params,
     the performance and drift metrics, if any, etc.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.DSSModelEvaluation.get_evaluation_full_info`
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.DSSModelEvaluation.get_full_info`
     """
     def __init__(self, model_evaluation, full_info):
         self.model_evaluation = model_evaluation

From e1c23129422313e3a16b1f6d2cba604460f14f5b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Fri, 8 Oct 2021 17:33:41 +0200
Subject: [PATCH 03/11] Add consistency to naming styles in the documentation

---
 dataikuapi/dss/modelevaluationstore.py | 32 +++++++++++++-------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index e630ad5c..29eabfc2 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -167,7 +167,7 @@ def delete_model_evaluations(self, evaluations):
 
     def build(self, job_type="NON_RECURSIVE_FORCED_BUILD", wait=True, no_fail=False):
         """
-        Starts a new job to build this Model Evaluation Store and wait for it to complete.
+        Starts a new job to build this model evaluation store and wait for it to complete.
         Raises if the job failed.
 
         .. code-block:: python
@@ -379,7 +379,7 @@ def get_metrics(self):
 
     def get_labels(self):
         """
-        Get the labels of the Model Evaluation
+        Get the labels of the model evaluation
 
         :return: a dict containing the labels
         """
@@ -396,7 +396,7 @@ def get_evaluation_parameters(self):
 
     def get_creation_date(self):
         """
-        Return the date and time of the creation of the Model Evaluation
+        Return the date and time of the creation of the model evaluation
 
         :return: the date and time, as an epoch
         """
@@ -404,7 +404,7 @@ def get_creation_date(self):
 
     def get_full_id(self):
         """
-        Returns the full id of the Model Evaluation.
+        Returns the full id of the model evaluation.
 
         :return: the full id, as a string
         """
@@ -565,18 +565,18 @@ def get_per_column_report(self):
 
     def get_reference_sample_size(self):
         """
-        Get the size of the reference ME sample.
+        Get the size of the reference model evaluation sample.
 
-        :return: the size of the reference sample
+        :return: the size of the reference model evaluation sample
         :rtype: int
         """
         return self.data["referenceSampleSize"]
 
     def get_current_sample_size(self):
         """
-        Size of the current ME sample.
+        Size of the current model evaluation sample.
 
-        :return: the size of the current ME
+        :return: the size of the current model evaluation sample
         :rtype: int
         """
         return self.data["currentSampleSize"]
@@ -602,18 +602,18 @@ def get_raw(self):
 
     def get_reference_sample_size(self):
         """
-        Get the number of rows coming from reference ME in the drift model trainset.
+        Get the number of rows coming from reference model evaluation in the drift model trainset.
 
-        :return: the number of rows coming from reference ME
+        :return: the number of rows coming from reference model evaluation
         :rtype: int
         """
         return self.data["referenceSampleSize"]
 
     def get_current_sample_size(self):
         """
-        Get the number of rows coming from current ME in the drift model trainset.
+        Get the number of rows coming from current model evaluation in the drift model trainset.
 
-        :return: the number of rows coming from current ME
+        :return: the number of rows coming from current model evaluation
         :rtype: int
         """
         return self.data["currentSampleSize"]
@@ -686,7 +686,7 @@ def get_name(self):
 
     def get_actual_column_handling(self):
         """
-        Get the actual column handling (either forced via drift params or inferred from ME preprocessings).
+        Get the actual column handling (either forced via drift params or inferred from model evaluation preprocessings).
         It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
 
         :return: the actual column handling
@@ -696,7 +696,7 @@ def get_actual_column_handling(self):
 
     def get_default_column_handling(self):
         """
-        Get the default column handling (based on ME preprocessing only).
+        Get the default column handling (based on model evaluation preprocessing only).
         It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
 
         :return: the default column handling
@@ -762,9 +762,9 @@ def get_upper_confidence_interval(self):
 
     def get_pvalue(self):
         """
-        Get the drift model accuracy pvalue for H0: "there is no drift".
+        Get the drift model accuracy pvalue for null hypothesis: "there is no drift".
 
-        :return: the accuracy pvalue for H0: "there is no drift"
+        :return: the accuracy pvalue for null hypothesis
         :rtype: float
         """
         return self.data["pvalue"]

From fe26f3f1238d2d8def0250fdfb2609efbe391baa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Tue, 12 Oct 2021 11:02:26 +0200
Subject: [PATCH 04/11] Rename ColumnReport to ColumnSettings

---
 dataikuapi/dss/modelevaluationstore.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index adb31339..0694176e 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -554,14 +554,14 @@ def get_univariate_drift_result(self):
         """
         return UnivariateDriftResult(self.data["univariateDriftResult"])
 
-    def get_per_column_report(self):
+    def get_per_column_settings(self):
         """
         Get the information about column handling that has been used (errors, types, etc).
 
-        :return: A list of handles on column reports
-        :rtype: list of :class:`dataikuapi.dss.modelevaluationstore.ColumnReport`
+        :return: A list of handles on column settings
+        :rtype: list of :class:`dataikuapi.dss.modelevaluationstore.ColumnSettings`
         """
-        return map(ColumnReport, self.data["perColumnReport"])
+        return map(ColumnSettings, self.data["perColumnSettings"])
 
     def get_reference_sample_size(self):
         """
@@ -657,11 +657,11 @@ def get_raw(self):
         return self.data["columns"]
 
 
-class ColumnReport(object):
+class ColumnSettings(object):
     """
     A handle on column handling information.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_per_column_report`
+    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_per_column_settings`
     """
     def __init__(self, data):
         self.data = data

From fbc1217b51ea1e3dcfbd8cad6329a9f1a18318ca Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Tue, 12 Oct 2021 12:30:54 +0200
Subject: [PATCH 05/11] Change some getters to properties for primitive types
 Return raw data for get_raw in UnivariateDriftResult and add a property for
 getting per column data drift info.

---
 dataikuapi/dss/modelevaluationstore.py | 90 ++++++++++++++++++--------
 1 file changed, 63 insertions(+), 27 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index 0694176e..c7ddd334 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -369,7 +369,8 @@ def __init__(self, model_evaluation, full_info):
     def get_raw(self):
         return self.full_info
 
-    def get_metrics(self):
+    @property
+    def metrics(self):
         """
         Get the metrics evaluated, if any.
 
@@ -377,7 +378,8 @@ def get_metrics(self):
         """
         return self.full_info["metrics"]
 
-    def get_labels(self):
+    @property
+    def labels(self):
         """
         Get the labels of the model evaluation
 
@@ -385,7 +387,8 @@ def get_labels(self):
         """
         return self.full_info["evaluation"]["labels"]
 
-    def get_evaluation_parameters(self):
+    @property
+    def evaluation_parameters(self):
         """
         Get info on the evaluation parameters, most noticeably the evaluation metric (evaluationMetric field
         of the returned dict)
@@ -394,7 +397,8 @@ def get_evaluation_parameters(self):
         """
         return self.full_info["evaluation"]["metricParams"]
 
-    def get_creation_date(self):
+    @property
+    def creation_date(self):
         """
         Return the date and time of the creation of the model evaluation
 
@@ -402,7 +406,8 @@ def get_creation_date(self):
         """
         return self.full_info["evaluation"]["created"]
 
-    def get_full_id(self):
+    @property
+    def full_id(self):
         """
         Returns the full id of the model evaluation.
 
@@ -410,7 +415,8 @@ def get_full_id(self):
         """
         return self.full_info["evaluation"]["ref"]["fullId"]
 
-    def get_model_full_id(self):
+    @property
+    def model_full_id(self):
         """
         Returns the full id of the evaluated model.
 
@@ -418,7 +424,8 @@ def get_model_full_id(self):
         """
         return self.full_info["evaluation"]["modelRef"]["fullId"]
 
-    def get_model_type(self):
+    @property
+    def model_type(self):
         """
         Returns the evaluated model type.
 
@@ -426,7 +433,8 @@ def get_model_type(self):
         """
         return self.full_info["evaluation"]["modelType"]
 
-    def get_model_parameters(self):
+    @property
+    def model_parameters(self):
         """
         Returns the evaluated model params.
 
@@ -434,7 +442,8 @@ def get_model_parameters(self):
         """
         return self.full_info["evaluation"]["modelParams"]
 
-    def get_prediction_type(self):
+    @property
+    def prediction_type(self):
         """
         Returns the prediction type of the evaluated model.
 
@@ -442,7 +451,8 @@ def get_prediction_type(self):
         """
         return self.full_info["evaluation"]["predictionType"]
 
-    def get_prediction_variable(self):
+    @property
+    def prediction_variable(self):
         """
         Returns the prediction variable used for evaluation.
 
@@ -450,7 +460,8 @@ def get_prediction_variable(self):
         """
         return self.full_info["evaluation"]["predictionVariable"]
 
-    def get_target_variable(self):
+    @property
+    def target_variable(self):
         """
         Returns the target variable used for evaluation.
 
@@ -563,7 +574,8 @@ def get_per_column_settings(self):
         """
         return map(ColumnSettings, self.data["perColumnSettings"])
 
-    def get_reference_sample_size(self):
+    @property
+    def reference_sample_size(self):
         """
         Get the size of the reference model evaluation sample.
 
@@ -572,7 +584,8 @@ def get_reference_sample_size(self):
         """
         return self.data["referenceSampleSize"]
 
-    def get_current_sample_size(self):
+    @property
+    def current_sample_size(self):
         """
         Size of the current model evaluation sample.
 
@@ -600,7 +613,8 @@ def get_raw(self):
         """
         return self.data
 
-    def get_reference_sample_size(self):
+    @property
+    def reference_sample_size(self):
         """
         Get the number of rows coming from reference model evaluation in the drift model trainset.
 
@@ -609,7 +623,8 @@ def get_reference_sample_size(self):
         """
         return self.data["referenceSampleSize"]
 
-    def get_current_sample_size(self):
+    @property
+    def current_sample_size(self):
         """
         Get the number of rows coming from current model evaluation in the drift model trainset.
 
@@ -649,11 +664,21 @@ def __init__(self, data):
     def get_raw(self):
         """
         Get the raw univariate data drift.
-        It consists of a map with column name as keys, and a `dict ` with drift data as value.
 
         :return: the raw univariate data drift
         :rtype: dict
         """
+        return self.data
+
+    @property
+    def drift_data_per_column(self):
+        """
+        Get the drift data per column.
+        It consists of a map with column name as keys, and a `dict ` with drift data as value.
+
+        :return: the drift data per column
+        :rtype: dict
+        """
         return self.data["columns"]
 
 
@@ -675,7 +700,8 @@ def get_raw(self):
         """
         return self.data
 
-    def get_name(self):
+    @property
+    def name(self):
         """
         Get the column name.
 
@@ -684,7 +710,8 @@ def get_name(self):
         """
         return self.data["name"]
 
-    def get_actual_column_handling(self):
+    @property
+    def actual_column_handling(self):
         """
         Get the actual column handling (either forced via drift params or inferred from model evaluation preprocessings).
         It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
@@ -694,7 +721,8 @@ def get_actual_column_handling(self):
         """
         return self.data["actualHandling"]
 
-    def get_default_column_handling(self):
+    @property
+    def default_column_handling(self):
         """
         Get the default column handling (based on model evaluation preprocessing only).
         It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
@@ -704,7 +732,8 @@ def get_default_column_handling(self):
         """
         return self.data["defaultHandling"]
 
-    def get_error_message(self):
+    @property
+    def error_message(self):
         """
         Get the error message.
         For example: "could not treat column as numerical" (in this case, the column handling is forced to 'IGNORED').
@@ -733,7 +762,8 @@ def get_raw(self):
         """
         return self.data
 
-    def get_value(self):
+    @property
+    def value(self):
         """
         Get the drift model accuracy value.
 
@@ -742,7 +772,8 @@ def get_value(self):
         """
         return self.data["value"]
 
-    def get_lower_confidence_interval(self):
+    @property
+    def lower_confidence_interval(self):
         """
         Get the drift model accuracy lower confidence interval.
 
@@ -751,7 +782,8 @@ def get_lower_confidence_interval(self):
         """
         return self.data["lower"]
 
-    def get_upper_confidence_interval(self):
+    @property
+    def upper_confidence_interval(self):
         """
         Get the drift model accuracy upper confidence interval.
 
@@ -760,7 +792,8 @@ def get_upper_confidence_interval(self):
         """
         return self.data["upper"]
 
-    def get_pvalue(self):
+    @property
+    def pvalue(self):
         """
         Get the drift model accuracy pvalue for null hypothesis: "there is no drift".
 
@@ -788,7 +821,8 @@ def get_raw(self):
         """
         return self.data
 
-    def get_column_names(self):
+    @property
+    def column_names(self):
         """
         Get the column names.
 
@@ -797,7 +831,8 @@ def get_column_names(self):
         """
         return self.data["columns"]
 
-    def get_column_drift_scores(self):
+    @property
+    def column_drift_scores(self):
         """
         Get the importance of the columns in the drift model.
 
@@ -806,7 +841,8 @@ def get_column_drift_scores(self):
         """
         return self.data["columnDriftScores"]
 
-    def get_column_original_scores(self):
+    @property
+    def column_original_scores(self):
         """
         Get the importance of the columns in the original model.
         Returns None when it can't be computed.

From 10c6f67e7766516f29f50732461bede7ef474979 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Mon, 18 Oct 2021 12:26:29 +0200
Subject: [PATCH 06/11] Taking into account PR review

---
 dataikuapi/dss/modelevaluationstore.py | 360 ++++---------------------
 1 file changed, 51 insertions(+), 309 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index c7ddd334..af3d6b15 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -365,109 +365,30 @@ class DSSModelEvaluationFullInfo:
     def __init__(self, model_evaluation, full_info):
         self.model_evaluation = model_evaluation
         self.full_info = full_info
+        self.metrics = self.full_info["metrics"]  # type: dict
+        """The performance and data drift metric, if any."""
+        self.evaluation_parameters = self.full_info["evaluation"]["metricParams"]  # type: dict
+        """Information on the evaluation parameters, most noticeably the evaluation metric (evaluationMetric field of the returned dict)."""
+        self.creation_date = self.full_info["evaluation"]["created"]  # type: int
+        """The date and time of the creation of the model evaluation, as an epoch."""
+        self.full_id = self.full_info["evaluation"]["ref"]["fullId"]  # type: str
+        self.model_full_id = self.full_info["evaluation"]["modelRef"]["fullId"]  # type: str
+        self.model_type = self.full_info["evaluation"]["modelType"]  # type: str
+        self.model_parameters = self.full_info["evaluation"]["modelParams"]
+        self.prediction_type = self.full_info["evaluation"]["predictionType"]  # type: str
+        self.prediction_variable = self.full_info["evaluation"]["predictionVariable"]  # type: str
+        self.target_variable = self.full_info["evaluation"]["targetVariable"]  # type: str
+        self.user_meta = self.full_info["evaluation"]["userMeta"]  # type: dict
+        """The user-accessible metadata (name, labels)
+        Returns the original object, not a copy. Changes to the returned object are persisted to DSS by calling :meth:`save_user_meta`."""
 
     def get_raw(self):
         return self.full_info
 
-    @property
-    def metrics(self):
-        """
-        Get the metrics evaluated, if any.
-
-        :return: a dict containing the performance and data drift metric, if any
-        """
-        return self.full_info["metrics"]
-
-    @property
-    def labels(self):
-        """
-        Get the labels of the model evaluation
-
-        :return: a dict containing the labels
-        """
-        return self.full_info["evaluation"]["labels"]
-
-    @property
-    def evaluation_parameters(self):
-        """
-        Get info on the evaluation parameters, most noticeably the evaluation metric (evaluationMetric field
-        of the returned dict)
-
-        :return: a dict
-        """
-        return self.full_info["evaluation"]["metricParams"]
-
-    @property
-    def creation_date(self):
-        """
-        Return the date and time of the creation of the model evaluation
-
-        :return: the date and time, as an epoch
-        """
-        return self.full_info["evaluation"]["created"]
-
-    @property
-    def full_id(self):
-        """
-        Returns the full id of the model evaluation.
-
-        :return: the full id, as a string
-        """
-        return self.full_info["evaluation"]["ref"]["fullId"]
-
-    @property
-    def model_full_id(self):
-        """
-        Returns the full id of the evaluated model.
-
-        :return: the model full id, as a string
-        """
-        return self.full_info["evaluation"]["modelRef"]["fullId"]
-
-    @property
-    def model_type(self):
-        """
-        Returns the evaluated model type.
-
-        :return: the model type, as a string
-        """
-        return self.full_info["evaluation"]["modelType"]
-
-    @property
-    def model_parameters(self):
-        """
-        Returns the evaluated model params.
-
-        :return: the model params, as a dict
-        """
-        return self.full_info["evaluation"]["modelParams"]
-
-    @property
-    def prediction_type(self):
-        """
-        Returns the prediction type of the evaluated model.
-
-        :return: the prediction type, as a string
-        """
-        return self.full_info["evaluation"]["predictionType"]
-
-    @property
-    def prediction_variable(self):
-        """
-        Returns the prediction variable used for evaluation.
-
-        :return: the prediction variable, as a string
-        """
-        return self.full_info["evaluation"]["predictionVariable"]
-
-    @property
-    def target_variable(self):
-        """
-        Returns the target variable used for evaluation.
-
-        :return: the target variable, as a string
-        """
-        return self.full_info["evaluation"]["targetVariable"]
+    def save_user_meta(self):
+        return self.model_evaluation.client._perform_text(
+                "PUT", "/projects/%s/modelevaluationstores/%s/evaluations/%s/user-meta" %
+                       (self.model_evaluation.project_key, self.model_evaluation.mes_id, self.model_evaluation.evaluation_id), body=self.user_meta)
 
 
 class DataDriftParams(object):
@@ -537,6 +458,14 @@ class DataDriftResult(object):
     """
     def __init__(self, data):
         self.data = data
+        self.drift_model_result = DriftModelResult(self.data["driftModelResult"])
+        """Drift analysis based on drift modeling."""
+        self.univariate_drift_result = UnivariateDriftResult(self.data["univariateDriftResult"])
+        """Per-column drift analysis based on comparison of distributions."""
+        self.per_column_settings = list(map(ColumnSettings, self.data["perColumnSettings"]))
+        """Information about column handling that has been used (errors, types, etc)."""
+        self.reference_sample_size = self.data["referenceSampleSize"]  # type: int
+        self.current_sample_size = self.data["currentSampleSize"]  # type: int
 
     def get_raw(self):
         """
@@ -547,53 +476,6 @@ def get_raw(self):
         """
         return self.data
 
-    def get_drift_model_result(self):
-        """
-        Get the drift analysis based on drift modeling.
-
-        :return: A handle on the drift model result
-        :rtype: :class:`dataikuapi.dss.modelevaluationstore.DriftModelResult`
-        """
-        return DriftModelResult(self.data["driftModelResult"])
-
-    def get_univariate_drift_result(self):
-        """
-        Get a per-column drift analysis based on comparison of distributions.
-
-        :return: A handle on the univariate drift result
-        :rtype: :class:`dataikuapi.dss.modelevaluationstore.UnivariateDriftResult`
-        """
-        return UnivariateDriftResult(self.data["univariateDriftResult"])
-
-    def get_per_column_settings(self):
-        """
-        Get the information about column handling that has been used (errors, types, etc).
-
-        :return: A list of handles on column settings
-        :rtype: list of :class:`dataikuapi.dss.modelevaluationstore.ColumnSettings`
-        """
-        return map(ColumnSettings, self.data["perColumnSettings"])
-
-    @property
-    def reference_sample_size(self):
-        """
-        Get the size of the reference model evaluation sample.
-
-        :return: the size of the reference model evaluation sample
-        :rtype: int
-        """
-        return self.data["referenceSampleSize"]
-
-    @property
-    def current_sample_size(self):
-        """
-        Size of the current model evaluation sample.
-
-        :return: the size of the current model evaluation sample
-        :rtype: int
-        """
-        return self.data["currentSampleSize"]
-
 
 class DriftModelResult(object):
     """
@@ -603,6 +485,12 @@ class DriftModelResult(object):
     """
     def __init__(self, data):
         self.data = data
+        self.reference_sample_size = self.data["referenceSampleSize"]  # type: int
+        """Number of rows coming from reference model evaluation in the drift model trainset."""
+        self.current_sample_size = self.data["currentSampleSize"]  # type: int
+        """Number of rows coming from current model evaluation in the drift model trainset."""
+        self.drift_model_accuracy = DriftModelAccuracy(self.data["driftModelAccuracy"])
+        self.feature_drift_importance = DriftVersusImportanceChart(self.data["driftVersusImportance"])
 
     def get_raw(self):
         """
@@ -613,44 +501,6 @@ def get_raw(self):
         """
         return self.data
 
-    @property
-    def reference_sample_size(self):
-        """
-        Get the number of rows coming from reference model evaluation in the drift model trainset.
-
-        :return: the number of rows coming from reference model evaluation
-        :rtype: int
-        """
-        return self.data["referenceSampleSize"]
-
-    @property
-    def current_sample_size(self):
-        """
-        Get the number of rows coming from current model evaluation in the drift model trainset.
-
-        :return: the number of rows coming from current model evaluation
-        :rtype: int
-        """
-        return self.data["currentSampleSize"]
-
-    def get_drift_model_accuracy(self):
-        """
-        Get the drift model accuracy information.
-
-        :return: A handle to interact with the drift model accuracy information
-        :rtype: :class:`dataiku.dss.modelevaluationstore.DriftModelAccuracy`
-        """
-        return DriftModelAccuracy(self.data["driftModelAccuracy"])
-
-    def get_feature_drift_importance(self):
-        """
-        Get the feature drift importance chart data.
-
-        :return: A handle on feature drift importance chart data
-        :rtype: :class:`dataiku.dss.modelevaluationstore.DriftVersusImportanceChart`
-        """
-        return DriftVersusImportanceChart(self.data["driftVersusImportance"])
-
 
 class UnivariateDriftResult(object):
     """
@@ -660,6 +510,8 @@ class UnivariateDriftResult(object):
     """
     def __init__(self, data):
         self.data = data
+        self.per_column_drift_data = self.data["columns"]  # type: dict
+        """Drift data per column, as a dict of column name -> drift data."""
 
     def get_raw(self):
         """
@@ -670,17 +522,6 @@ def get_raw(self):
         """
         return self.data
 
-    @property
-    def drift_data_per_column(self):
-        """
-        Get the drift data per column.
-        It consists of a map with column name as keys, and a `dict ` with drift data as value.
-
-        :return: the drift data per column
-        :rtype: dict
-        """
-        return self.data["columns"]
-
 
 class ColumnSettings(object):
     """
@@ -690,6 +531,14 @@ class ColumnSettings(object):
     """
     def __init__(self, data):
         self.data = data
+        self.name = self.data["name"]  # type: str
+        self.actual_column_handling = self.data["actualHandling"]  # type: str
+        """The actual column handling (either forced via drift params or inferred from model evaluation preprocessings).
+        It can be any of NUMERICAL, CATEGORICAL, or IGNORED."""
+        self.default_column_handling = self.data["defaultHandling"]  # type: str
+        """The default column handling (based on model evaluation preprocessing only).
+        It can be any of NUMERICAL, CATEGORICAL, or IGNORED."""
+        self.error_message = self.data.get("errorMessage", None)
 
     def get_raw(self):
         """
@@ -700,49 +549,6 @@ def get_raw(self):
         """
         return self.data
 
-    @property
-    def name(self):
-        """
-        Get the column name.
-
-        :return: the column name
-        :rtype: string
-        """
-        return self.data["name"]
-
-    @property
-    def actual_column_handling(self):
-        """
-        Get the actual column handling (either forced via drift params or inferred from model evaluation preprocessings).
-        It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
-
-        :return: the actual column handling
-        :rtype: string
-        """
-        return self.data["actualHandling"]
-
-    @property
-    def default_column_handling(self):
-        """
-        Get the default column handling (based on model evaluation preprocessing only).
-        It can be any of NUMERICAL, CATEGORICAL, or IGNORED.
-
-        :return: the default column handling
-        :rtype: string
-        """
-        return self.data["defaultHandling"]
-
-    @property
-    def error_message(self):
-        """
-        Get the error message.
-        For example: "could not treat column as numerical" (in this case, the column handling is forced to 'IGNORED').
-
-        :return: the error message, if there is any
-        :rtype: string
-        """
-        return self.data["errorMessage"]
-
 
 class DriftModelAccuracy(object):
     """
@@ -752,6 +558,10 @@ class DriftModelAccuracy(object):
     """
     def __init__(self, data):
         self.data = data
+        self.value = self.data["value"]  # type: float
+        self.lower_confidence_interval = self.data["lower"]  # type: float
+        self.upper_confidence_interval = self.data["upper"]  # type: float
+        self.pvalue = self.data["pvalue"]  # type: float
 
     def get_raw(self):
         """
@@ -762,46 +572,6 @@ def get_raw(self):
         """
         return self.data
 
-    @property
-    def value(self):
-        """
-        Get the drift model accuracy value.
-
-        :return: the accuracy value
-        :rtype: float
-        """
-        return self.data["value"]
-
-    @property
-    def lower_confidence_interval(self):
-        """
-        Get the drift model accuracy lower confidence interval.
-
-        :return: the lower confidence interval
-        :rtype: float
-        """
-        return self.data["lower"]
-
-    @property
-    def upper_confidence_interval(self):
-        """
-        Get the drift model accuracy upper confidence interval.
-
-        :return: the upper confidence interval
-        :rtype: float
-        """
-        return self.data["upper"]
-
-    @property
-    def pvalue(self):
-        """
-        Get the drift model accuracy pvalue for null hypothesis: "there is no drift".
-
-        :return: the accuracy pvalue for null hypothesis
-        :rtype: float
-        """
-        return self.data["pvalue"]
-
 
 class DriftVersusImportanceChart(object):
     """
@@ -811,6 +581,9 @@ class DriftVersusImportanceChart(object):
     """
     def __init__(self, data):
         self.data = data
+        self.column_names = self.data["columns"]  # type: list
+        self.column_drift_scores = self.data["columnDriftScores"]  # type: list
+        self.column_original_scores = self.data["columnImportanceScores"]  # type: list
 
     def get_raw(self):
         """
@@ -820,34 +593,3 @@ def get_raw(self):
         :rtype: dict
         """
         return self.data
-
-    @property
-    def column_names(self):
-        """
-        Get the column names.
-
-        :return: a `list` of the column names
-        :rtype: list of string
-        """
-        return self.data["columns"]
-
-    @property
-    def column_drift_scores(self):
-        """
-        Get the importance of the columns in the drift model.
-
-        :return: the `list` of the importance of the columns in the drift model
-        :rtype: list of float
-        """
-        return self.data["columnDriftScores"]
-
-    @property
-    def column_original_scores(self):
-        """
-        Get the importance of the columns in the original model.
-        Returns None when it can't be computed.
-
-        :return: the `list` of the importance of the columns in the original model
-        :rtype: list of float
-        """
-        return self.data["columnImportanceScores"]

From 7efb7f7d3df5260322bf472fcfcfcf282be49071 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Mon, 18 Oct 2021 14:50:39 +0200
Subject: [PATCH 07/11] Removing unimportant helpers

---
 dataikuapi/dss/modelevaluationstore.py | 34 +++-----------------------
 1 file changed, 4 insertions(+), 30 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index af3d6b15..3b5b0e5a 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -367,14 +367,10 @@ def __init__(self, model_evaluation, full_info):
         self.full_info = full_info
         self.metrics = self.full_info["metrics"]  # type: dict
         """The performance and data drift metric, if any."""
-        self.evaluation_parameters = self.full_info["evaluation"]["metricParams"]  # type: dict
-        """Information on the evaluation parameters, most noticeably the evaluation metric (evaluationMetric field of the returned dict)."""
         self.creation_date = self.full_info["evaluation"]["created"]  # type: int
         """The date and time of the creation of the model evaluation, as an epoch."""
         self.full_id = self.full_info["evaluation"]["ref"]["fullId"]  # type: str
         self.model_full_id = self.full_info["evaluation"]["modelRef"]["fullId"]  # type: str
-        self.model_type = self.full_info["evaluation"]["modelType"]  # type: str
-        self.model_parameters = self.full_info["evaluation"]["modelParams"]
         self.prediction_type = self.full_info["evaluation"]["predictionType"]  # type: str
         self.prediction_variable = self.full_info["evaluation"]["predictionVariable"]  # type: str
         self.target_variable = self.full_info["evaluation"]["targetVariable"]  # type: str
@@ -481,7 +477,7 @@ class DriftModelResult(object):
     """
     A handle on the drift model result.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_drift_model_result`
+    Do not create this class directly, instead use :attr:`dataikuapi.dss.modelevaluationstore.DataDriftResult.drift_model_result`
     """
     def __init__(self, data):
         self.data = data
@@ -490,7 +486,7 @@ def __init__(self, data):
         self.current_sample_size = self.data["currentSampleSize"]  # type: int
         """Number of rows coming from current model evaluation in the drift model trainset."""
         self.drift_model_accuracy = DriftModelAccuracy(self.data["driftModelAccuracy"])
-        self.feature_drift_importance = DriftVersusImportanceChart(self.data["driftVersusImportance"])
+        self.feature_drift_importance = self.data["driftVersusImportance"]  # type: dict
 
     def get_raw(self):
         """
@@ -506,7 +502,7 @@ class UnivariateDriftResult(object):
     """
     A handle on the univariate data drift.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DataDriftResult.get_univariate_drift_result`
+    Do not create this class directly, instead use :attr:`dataikuapi.dss.modelevaluationstore.DataDriftResult.univariate_drift_result`
     """
     def __init__(self, data):
         self.data = data
@@ -554,7 +550,7 @@ class DriftModelAccuracy(object):
     """
     A handle on the drift model accuracy.
 
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DriftModelResult.get_drift_model_accuracy`
+    Do not create this class directly, instead use :attr:`dataikuapi.dss.modelevaluationstore.DriftModelResult.drift_model_accuracy`
     """
     def __init__(self, data):
         self.data = data
@@ -571,25 +567,3 @@ def get_raw(self):
         :rtype: dict
         """
         return self.data
-
-
-class DriftVersusImportanceChart(object):
-    """
-    A handle on the feature drift importance chart data.
-
-    Do not create this class directly, instead use :meth:`dataikuapi.dss.modelevaluationstore.DriftModelResult.get_feature_drift_importance`
-    """
-    def __init__(self, data):
-        self.data = data
-        self.column_names = self.data["columns"]  # type: list
-        self.column_drift_scores = self.data["columnDriftScores"]  # type: list
-        self.column_original_scores = self.data["columnImportanceScores"]  # type: list
-
-    def get_raw(self):
-        """
-        Get the raw feature drift importance chart data.
-
-        :return: the feature drift importance chart data
-        :rtype: dict
-        """
-        return self.data

From 671d763e88e58ef79e9666a2832c2abae6d363ee Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Mon, 18 Oct 2021 14:51:00 +0200
Subject: [PATCH 08/11] Use a list comprehension instead of a `list(map())` for
 wider compatibility

---
 dataikuapi/dss/modelevaluationstore.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index 3b5b0e5a..da9184fa 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -458,7 +458,7 @@ def __init__(self, data):
         """Drift analysis based on drift modeling."""
         self.univariate_drift_result = UnivariateDriftResult(self.data["univariateDriftResult"])
         """Per-column drift analysis based on comparison of distributions."""
-        self.per_column_settings = list(map(ColumnSettings, self.data["perColumnSettings"]))
+        self.per_column_settings = [ColumnSettings(cs) for cs in self.data["perColumnSettings"]]
         """Information about column handling that has been used (errors, types, etc)."""
         self.reference_sample_size = self.data["referenceSampleSize"]  # type: int
         self.current_sample_size = self.data["currentSampleSize"]  # type: int

From 0636feb0d2ccf25bbe2badbe1175d71d67a8b5bb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Mon, 18 Oct 2021 15:55:48 +0200
Subject: [PATCH 09/11] Remove _sample_size attributes

---
 dataikuapi/dss/modelevaluationstore.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index da9184fa..1b42e35f 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -460,8 +460,6 @@ def __init__(self, data):
         """Per-column drift analysis based on comparison of distributions."""
         self.per_column_settings = [ColumnSettings(cs) for cs in self.data["perColumnSettings"]]
         """Information about column handling that has been used (errors, types, etc)."""
-        self.reference_sample_size = self.data["referenceSampleSize"]  # type: int
-        self.current_sample_size = self.data["currentSampleSize"]  # type: int
 
     def get_raw(self):
         """
@@ -481,10 +479,6 @@ class DriftModelResult(object):
     """
     def __init__(self, data):
         self.data = data
-        self.reference_sample_size = self.data["referenceSampleSize"]  # type: int
-        """Number of rows coming from reference model evaluation in the drift model trainset."""
-        self.current_sample_size = self.data["currentSampleSize"]  # type: int
-        """Number of rows coming from current model evaluation in the drift model trainset."""
         self.drift_model_accuracy = DriftModelAccuracy(self.data["driftModelAccuracy"])
         self.feature_drift_importance = self.data["driftVersusImportance"]  # type: dict
 

From 29851f6e3447c775d6c771adea466fd414999691 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Tue, 19 Oct 2021 15:04:26 +0200
Subject: [PATCH 10/11] Fix parameter naming & doc

---
 dataikuapi/dss/modelevaluationstore.py | 25 ++++++++-----------------
 1 file changed, 8 insertions(+), 17 deletions(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index 1b42e35f..5b8b5637 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -399,11 +399,11 @@ def __repr__(self):
         return u"{}({})".format(self.__class__.__name__, self.data)
 
     @staticmethod
-    def from_params(columns, nb_bins=10, compute_histograms=True, confidence_level=0.95):
+    def from_params(per_column_settings, nb_bins=10, compute_histograms=True, confidence_level=0.95):
         """
         Creates parameters for data drift computation from columns, number of bins, compute histograms and confidence level
 
-        :param dict columns: A dict representing the per column settings.
+        :param dict per_column_settings: A dict representing the per column settings.
         You should use a :class:`~dataikuapi.dss.modelevaluationstore.PerColumnDriftParamBuilder` to build it.
         :param int nb_bins: (optional) Nb. bins in histograms (apply to all columns) - default: 10
         :param bool compute_histograms: (optional) Enable/disable histograms - default: True
@@ -412,7 +412,7 @@ def from_params(columns, nb_bins=10, compute_histograms=True, confidence_level=0
         :rtype: :class:`dataikuapi.dss.modelevaluationstore.DataDriftParams`
         """
         return DataDriftParams({
-            "columns": columns,
+            "columns": per_column_settings,
             "nbBins": nb_bins,
             "computeHistograms": compute_histograms,
             "confidenceLevel": confidence_level
@@ -422,7 +422,8 @@ def from_params(columns, nb_bins=10, compute_histograms=True, confidence_level=0
 class PerColumnDriftParamBuilder(object):
     """
     Builder for a map of per column drift params settings.
-    Used as a helper before computing data drift to build columns param expected in dataikuapi.dss.modelevaluationstore.DataDriftParams.from_params.
+    Used as a helper before computing data drift to build columns param expected in
+    :meth:`dataikuapi.dss.modelevaluationstore.DataDriftParams.from_params`.
     """
     def __init__(self):
         self.columns = {}
@@ -431,13 +432,13 @@ def build(self):
         """Returns the built dict for per column drift params settings"""
         return self.columns
 
-    def with_column_drift_param(self, name, handling="AUTO", enabled=False):
+    def with_column_drift_param(self, name, handling="AUTO", enabled=True):
         """
         Sets the drift params settings for given column name.
 
         :param: string name: The name of the column
         :param: string handling: (optional) The column type, should be either NUMERICAL, CATEGORICAL or AUTO (default: AUTO)
-        :param: bool name: (optional) If the column should be enabled (default: False)
+        :param: bool enabled: (optional) If the column should be enabled in drift computation (default: True)
         """
         self.columns[name] = {
             "handling": handling,
@@ -457,14 +458,12 @@ def __init__(self, data):
         self.drift_model_result = DriftModelResult(self.data["driftModelResult"])
         """Drift analysis based on drift modeling."""
         self.univariate_drift_result = UnivariateDriftResult(self.data["univariateDriftResult"])
-        """Per-column drift analysis based on comparison of distributions."""
+        """Per-column drift analysis based on pairwise comparison of distributions."""
         self.per_column_settings = [ColumnSettings(cs) for cs in self.data["perColumnSettings"]]
         """Information about column handling that has been used (errors, types, etc)."""
 
     def get_raw(self):
         """
-        Get the raw data drift result.
-
         :return: the raw data drift result
         :rtype: dict
         """
@@ -484,8 +483,6 @@ def __init__(self, data):
 
     def get_raw(self):
         """
-        Get the raw drift model result.
-
         :return: the raw drift model result
         :rtype: dict
         """
@@ -505,8 +502,6 @@ def __init__(self, data):
 
     def get_raw(self):
         """
-        Get the raw univariate data drift.
-
         :return: the raw univariate data drift
         :rtype: dict
         """
@@ -532,8 +527,6 @@ def __init__(self, data):
 
     def get_raw(self):
         """
-        Get the raw column handling information.
-
         :return: the raw column handling information
         :rtype: dict
         """
@@ -555,8 +548,6 @@ def __init__(self, data):
 
     def get_raw(self):
         """
-        Get the raw drift model accuracy data.
-
         :return: the drift model accuracy data
         :rtype: dict
         """

From 27e711f0fad2cc8d8d056717c368d75e3f561dc6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C3=ABl=20Bourhis?= <michael.bourhis@dataiku.com>
Date: Tue, 19 Oct 2021 16:08:03 +0200
Subject: [PATCH 11/11] Better doc for `enabled` param of
 `PerColumnDriftParamBuilder.with_column_drift_param`

---
 dataikuapi/dss/modelevaluationstore.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dataikuapi/dss/modelevaluationstore.py b/dataikuapi/dss/modelevaluationstore.py
index 5b8b5637..9ab69a23 100644
--- a/dataikuapi/dss/modelevaluationstore.py
+++ b/dataikuapi/dss/modelevaluationstore.py
@@ -438,7 +438,7 @@ def with_column_drift_param(self, name, handling="AUTO", enabled=True):
 
         :param: string name: The name of the column
         :param: string handling: (optional) The column type, should be either NUMERICAL, CATEGORICAL or AUTO (default: AUTO)
-        :param: bool enabled: (optional) If the column should be enabled in drift computation (default: True)
+        :param: bool enabled: (optional) False means the column is ignored in drift computation (default: True)
         """
         self.columns[name] = {
             "handling": handling,