Remove deprecated modeule `cohort.py` from `raiwidgets` (#2278)
Signed-off-by: Gaurav Gupta <gaugup@microsoft.com>
This commit is contained in:
Gaurav Gupta
GitHub
Родитель
070a2c2445
Коммит
ff723ef546
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@ -1,579 +0,0 @@
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# Copyright (c) Microsoft Corporation
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# Licensed under the MIT License.
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"""Module for defining cohorts in raiwidgets package."""
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import json
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import warnings
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from typing import Any, List, Optional
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import numpy as np
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import pandas as pd
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from raiutils.exceptions import UserConfigValidationException
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warnings.warn(
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"MODULE-DEPRECATION-WARNING: The module "
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"raiwidgets.cohort is deprecated and will be removed in a later release. "
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"Please use raiutils.cohort instead.",
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DeprecationWarning)
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class CohortFilterMethods:
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"""Defines different methods for cohort filters.
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"""
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METHOD_GREATER = "greater" # Strictly greater than a value
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# Greater than or equal to a value
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METHOD_GREATER_AND_EQUAL = "greater and equal"
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METHOD_LESS = "less" # Strictly lesser than a value
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METHOD_LESS_AND_EQUAL = "less and equal" # Lesser than or equal to a value
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METHOD_EQUAL = "equal" # Equal to a value
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METHOD_INCLUDES = "includes" # Includes a set of values
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METHOD_EXCLUDES = "excludes" # Excludes a set of values
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METHOD_RANGE = "in the range of" # In a given range of two values
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# List of all filter methods
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ALL = [METHOD_GREATER,
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METHOD_GREATER_AND_EQUAL,
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METHOD_LESS,
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METHOD_LESS_AND_EQUAL,
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METHOD_EQUAL,
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METHOD_INCLUDES,
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METHOD_EXCLUDES,
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METHOD_RANGE]
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# List of filter methods which expect a single value from the user
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SINGLE_VALUE_METHODS = [METHOD_GREATER_AND_EQUAL,
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METHOD_GREATER,
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METHOD_LESS_AND_EQUAL,
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METHOD_LESS,
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METHOD_EQUAL]
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class ClassificationOutcomes:
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"""Defines the possible values for classification outcomes.
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"""
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FALSE_NEGATIVE = 'False negative'
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FALSE_POSITIVE = 'False positive'
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TRUE_NEGATIVE = 'True negative'
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TRUE_POSITIVE = 'True positive'
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ALL = [FALSE_NEGATIVE, FALSE_POSITIVE,
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TRUE_NEGATIVE, TRUE_POSITIVE]
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def cohort_filter_json_converter(obj):
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"""Helper function to convert CohortFilter object to json.
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:param obj: Object to convert to json.
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:type obj: object
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:return: The converted json.
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:rtype: dict
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"""
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if isinstance(obj, CohortFilter):
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return obj.__dict__
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try:
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return obj.to_json()
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except AttributeError:
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return obj.__dict__
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class CohortFilter:
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"""Defines the cohort filter.
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:param method: Cohort filter method from one of CohortFilterMethods.
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:type method: str
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:param arg: List of values to be used by the cohort filter.
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:type arg: list
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:param column: The column name from the dataset on which the filter
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will be applied.
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:type column: str
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"""
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PREDICTED_Y = 'Predicted Y'
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TRUE_Y = 'True Y'
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INDEX = 'Index'
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CLASSIFICATION_OUTCOME = 'Classification outcome'
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REGRESSION_ERROR = 'Error'
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SPECIAL_COLUMN_LIST = [INDEX,
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PREDICTED_Y,
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TRUE_Y,
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CLASSIFICATION_OUTCOME,
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REGRESSION_ERROR]
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def __init__(self, method: str, arg: List[Any], column: str):
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"""Defines the cohort filter.
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:param method: Cohort filter method from one of CohortFilterMethods.
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:type method: str
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:param arg: List of values to be used by the cohort filter.
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:type arg: list
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:param column: The column name from the dataset on which the filter
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will be applied.
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:type column: str
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"""
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self._validate_cohort_filter_parameters(
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method=method,
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arg=arg,
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column=column)
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self.method = method
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self.arg = arg
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self.column = column
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def __eq__(self, cohort_filter: Any):
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return self.method == cohort_filter.method and \
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self.arg == cohort_filter.arg and \
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self.column == cohort_filter.column
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def _validate_cohort_filter_parameters(
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self, method: str, arg: List[Any], column: str):
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"""Validate the input values for the cohort filter.
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:param method: Cohort filter method from one of CohortFilterMethods.
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:type method: str
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:param arg: List of values to be used by the cohort filter.
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:type arg: list
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:param column: The column name from the dataset on which the filter
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will be applied.
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:type column: str
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The following validations can be performed on the cohort filter:-
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1. Verify the correct types for method (expected string), column
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(expected string) and arg (expected list).
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2. The method value should be one of the filter string from
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CohortFilterMethods.ALL.
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3. The arg shouldn't be an empty list.
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4. For all cohort filter methods in
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CohortFilterMethods.SINGLE_VALUE_METHODS, the value in the arg
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should be integer or float and there should be only one value
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in arg.
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5. For cohort filter method CohortFilterMethods.METHOD_RANGE,
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the values in the arg should be integer or float and there
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should be only two values in arg.
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"""
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if not isinstance(method, str):
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raise UserConfigValidationException(
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"Got unexpected type {0} for method. "
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"Expected string type.".format(type(method))
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)
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if method not in CohortFilterMethods.ALL:
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raise UserConfigValidationException(
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"Got unexpected value {0} for method. "
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"Expected either of {1}.".format(
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method, " or ".join(CohortFilterMethods.ALL))
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)
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if not isinstance(column, str):
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raise UserConfigValidationException(
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"Got unexpected type {0} for column. "
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"Expected string type.".format(type(column))
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)
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if not isinstance(arg, list):
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raise UserConfigValidationException(
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"Got unexpected type {0} for arg. "
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"Expected list type.".format(type(arg))
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)
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if len(arg) == 0:
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raise UserConfigValidationException(
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"Empty list supplied for arg."
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)
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if method in CohortFilterMethods.SINGLE_VALUE_METHODS:
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if len(arg) != 1:
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raise UserConfigValidationException(
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"Expected a single value in arg "
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"for cohort methods {0}.".format(
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" or ".join(CohortFilterMethods.SINGLE_VALUE_METHODS))
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)
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if not isinstance(arg[0], int) and not isinstance(arg[0], float):
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raise UserConfigValidationException(
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"Expected int or float type for "
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"arg with cohort methods {0}.".format(
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" or ".join(CohortFilterMethods.SINGLE_VALUE_METHODS))
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)
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if method == CohortFilterMethods.METHOD_RANGE:
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if len(arg) != 2:
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raise UserConfigValidationException(
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"Expected two entries in arg for "
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"cohort method {0}.".format(
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CohortFilterMethods.METHOD_RANGE)
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)
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if (not all(isinstance(entry, int) for entry in arg) and
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not all(isinstance(entry, float) for entry in arg)):
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raise UserConfigValidationException(
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"Expected int or float type for arg "
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"with cohort method {0}.".format(
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CohortFilterMethods.METHOD_RANGE)
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)
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def _validate_with_test_data(self, test_data: pd.DataFrame,
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target_column: str,
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categorical_features: List[str],
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is_classification: Optional[bool] = True):
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"""
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Validate the cohort filters parameters with respect to test data.
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:param test_data: Test data over which cohort analysis will be done
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in ResponsibleAI Dashboard.
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:type test_data: pd.DataFrame
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:param target_column: The target column in the test data.
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:type target_column: str
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:param categorical_features: The categorical feature names.
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:type categorical_features: list[str]
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:param is_classification: True to indicate if this validation needs
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to be done for a classification scenario and False to indicate
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that this needs to be done for regression scenario.
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:type is_classification: bool
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The following validations need to be performed for cohort filter with
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test data:-
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High level validations
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1. Validate if the filter column is present in the test data.
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2. Validate if the filter column is present in the special column
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list.
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"Index" Filter validations
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1. The Index filter only takes integer arguments.
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2. The Index filter doesn't take CohortFilterMethods.EXCLUDES
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filter method.
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"Classification outcome" Filter validations
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1. Validate that "Classification outcome" filter is not configure for
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multiclass classification and regression.
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2. The "Classification outcome" filter only contains values from set
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ClassificationOutcomes.
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3. The "Classification outcome" filter only takes
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CohortFilterMethods.INCLUDES filter method.
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"Error" Filter validations
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1. Validate that "Error" filter is not configure for
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multiclass classification and binary classification.
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2. Only integer or floating points can be configured as arguments.
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3. The CohortFilterMethods.INCLUDES and CohortFilterMethods.EXCLUDES
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filter methods cannot be configured for this filter.
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"Predicted Y/True Y" Filter validations
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1. The set of classes configured in case of classification is a
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superset of the classes available in the test data.
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2. The CohortFilterMethods.INCLUDES is only allowed to be
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configured for "Predicted Y" filter in case of classification.
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3. The CohortFilterMethods.INCLUDES and CohortFilterMethods.EXCLUDES
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filter methods cannot be configured for this filter for regression.
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"Dataset" Filter validations
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1. TODO:- For continuous features the allowed values that be configured
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should be within the range of minimum and maximum values available
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within the continuous feature column in the test data.
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2. For categorical features only CohortFilterMethods.INCLUDES can be
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configured.
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3. For categorical features the values allowed are a subset of the
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the values available in the categorical column in the test data.
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"""
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# High level validations
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if self.column not in CohortFilter.SPECIAL_COLUMN_LIST and \
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(self.column not in
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(set(test_data.columns) - set([target_column]))):
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raise UserConfigValidationException(
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"Unknown column {0} specified in cohort filter".format(
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self.column)
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)
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if self.column == CohortFilter.INDEX:
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# "Index" Filter validations
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if self.method == CohortFilterMethods.METHOD_EXCLUDES:
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raise UserConfigValidationException(
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"{0} filter is not supported with {1} based "
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"selection.".format(
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CohortFilterMethods.METHOD_EXCLUDES,
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CohortFilter.INDEX)
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)
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if not all(isinstance(entry, int) for entry in self.arg):
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raise UserConfigValidationException(
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"All entries in arg should be of type int."
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)
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elif self.column == CohortFilter.CLASSIFICATION_OUTCOME:
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# "Classification outcome" Filter validations
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is_multiclass = len(np.unique(
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test_data[target_column].values).tolist()) > 2
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if not is_classification or is_multiclass:
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raise UserConfigValidationException(
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"{0} cannot be configured for multi-class classification"
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" and regression scenarios.".format(
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CohortFilter.CLASSIFICATION_OUTCOME)
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)
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if self.method != CohortFilterMethods.METHOD_INCLUDES:
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raise UserConfigValidationException(
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"{0} can only be configured with "
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"cohort filter {1}.".format(
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CohortFilter.CLASSIFICATION_OUTCOME,
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CohortFilterMethods.METHOD_INCLUDES)
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)
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for classification_outcome in self.arg:
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if classification_outcome not in ClassificationOutcomes.ALL:
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raise UserConfigValidationException(
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"{0} can only take argument values from {1}.".format(
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CohortFilter.CLASSIFICATION_OUTCOME,
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" or ".join(ClassificationOutcomes.ALL))
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)
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elif self.column == CohortFilter.REGRESSION_ERROR:
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# "Error" Filter validations
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if is_classification:
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raise UserConfigValidationException(
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"{0} cannot be configured for classification"
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" scenarios.".format(CohortFilter.REGRESSION_ERROR)
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)
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if self.method == CohortFilterMethods.METHOD_INCLUDES or \
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self.method == CohortFilterMethods.METHOD_EXCLUDES:
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raise UserConfigValidationException(
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"{0} cannot be configured with either {1} or {2}.".format(
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CohortFilter.REGRESSION_ERROR,
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CohortFilterMethods.METHOD_INCLUDES,
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CohortFilterMethods.METHOD_EXCLUDES
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)
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)
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if not all(isinstance(entry, int) for entry in self.arg) and \
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not all(isinstance(entry, float) for entry in self.arg):
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raise UserConfigValidationException(
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"All entries in arg should be of type int or float"
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" for {} cohort.".format(CohortFilter.REGRESSION_ERROR)
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)
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elif self.column == CohortFilter.PREDICTED_Y or \
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self.column == CohortFilter.TRUE_Y:
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# "Predicted Y/True Y" Filter validations
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if is_classification:
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if self.method != CohortFilterMethods.METHOD_INCLUDES:
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raise UserConfigValidationException(
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"{0} can only be configured with "
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"filter {1} for classification".format(
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self.column,
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CohortFilterMethods.METHOD_INCLUDES)
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)
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test_classes = np.unique(
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test_data[target_column].values).tolist()
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if not all(entry in test_classes for entry in self.arg):
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raise UserConfigValidationException(
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"Found a class in arg which is not present in "
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"test data")
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else:
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if self.method == CohortFilterMethods.METHOD_INCLUDES or \
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self.method == CohortFilterMethods.METHOD_EXCLUDES:
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raise UserConfigValidationException(
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"{0} cannot be configured with "
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"filter {1} for regression.".format(
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self.column, self.method)
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)
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else:
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# "Dataset" Filter validations
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if self.column in categorical_features:
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if self.method != CohortFilterMethods.METHOD_INCLUDES:
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raise UserConfigValidationException(
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"{0} is a categorical feature and should be only "
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"configured with {1} cohort filter.".format(
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self.column,
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CohortFilterMethods.METHOD_INCLUDES)
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)
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categories = np.unique(
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test_data[self.column].values).tolist()
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for entry in self.arg:
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if entry not in categories:
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raise UserConfigValidationException(
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"Found a category {0} in arg which is not present "
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"in test data column {1}.".format(
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entry, self.column)
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)
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class Cohort:
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"""Defines the cohort which will be injected from SDK into the Dashboard.
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:param name: Name of the cohort.
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:type name: str
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"""
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def __init__(self, name: str):
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"""Defines the cohort which will be injected from SDK into the Dashboard.
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:param name: Name of the cohort.
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:type name: str
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"""
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if not isinstance(name, str):
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raise UserConfigValidationException(
|
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"Got unexpected type {0} for cohort name. "
|
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"Expected string type.".format(type(name))
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)
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self.name = name
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self.cohort_filter_list = None
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def __eq__(self, cohort: Any):
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same_name = self.name == cohort.name
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if self.cohort_filter_list is None and \
|
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cohort.cohort_filter_list is None:
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return same_name
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elif self.cohort_filter_list is not None and \
|
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cohort.cohort_filter_list is None:
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return False
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elif self.cohort_filter_list is None and \
|
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cohort.cohort_filter_list is not None:
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return False
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same_num_cohort_filters = len(self.cohort_filter_list) == \
|
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len(cohort.cohort_filter_list)
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if not same_num_cohort_filters:
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return False
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|
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same_cohort_filters = True
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for index in range(0, len(self.cohort_filter_list)):
|
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if self.cohort_filter_list[index] != \
|
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cohort.cohort_filter_list[index]:
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same_cohort_filters = False
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break
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return same_name and same_cohort_filters
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|
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@staticmethod
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def _cohort_serializer(obj):
|
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"""The function to serialize the Cohort class object.
|
||||
|
||||
:param obj: Any member of the Cohort class object.
|
||||
:type: Any
|
||||
:return: Python dictionary.
|
||||
:rtype: dict[Any, Any]
|
||||
"""
|
||||
return obj.__dict__
|
||||
|
||||
def to_json(self):
|
||||
"""Returns a serialized JSON string for the Cohort object.
|
||||
|
||||
:return: The JSON string for the cohort.
|
||||
:rtype: str
|
||||
"""
|
||||
return json.dumps(self, default=Cohort._cohort_serializer)
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||||
|
||||
@staticmethod
|
||||
def _get_cohort_object(json_dict):
|
||||
"""Method to read a JSON dictionary and return a Cohort object.
|
||||
|
||||
:param json_dict: JSON dictionary containing cohort data.
|
||||
:type: dict[str, str]
|
||||
:return: The Cohort object.
|
||||
:rtype: Cohort
|
||||
"""
|
||||
cohort_fields = ["name", "cohort_filter_list"]
|
||||
for cohort_field in cohort_fields:
|
||||
if cohort_field not in json_dict:
|
||||
raise UserConfigValidationException(
|
||||
"No {0} field found for cohort deserialization".format(
|
||||
cohort_field))
|
||||
|
||||
if not isinstance(json_dict['cohort_filter_list'], list):
|
||||
raise UserConfigValidationException(
|
||||
"Field cohort_filter_list not of type list for "
|
||||
"cohort deserialization")
|
||||
|
||||
deserialized_cohort = Cohort(json_dict['name'])
|
||||
for serialized_cohort_filter in json_dict['cohort_filter_list']:
|
||||
cohort_filter_fields = ["method", "arg", "column"]
|
||||
for cohort_filter_field in cohort_filter_fields:
|
||||
if cohort_filter_field not in serialized_cohort_filter:
|
||||
raise UserConfigValidationException(
|
||||
"No {0} field found for cohort filter "
|
||||
"deserialization".format(cohort_filter_field))
|
||||
|
||||
cohort_filter = CohortFilter(
|
||||
method=serialized_cohort_filter['method'],
|
||||
arg=serialized_cohort_filter['arg'],
|
||||
column=serialized_cohort_filter['column'])
|
||||
deserialized_cohort.add_cohort_filter(cohort_filter=cohort_filter)
|
||||
return deserialized_cohort
|
||||
|
||||
@staticmethod
|
||||
def from_json(json_str):
|
||||
"""Method to read a json string and return a Cohort object.
|
||||
|
||||
:param json_str: Serialized JSON string.
|
||||
:type: str
|
||||
:return: The Cohort object.
|
||||
:rtype: Cohort
|
||||
"""
|
||||
json_dict = json.loads(json_str)
|
||||
return Cohort._get_cohort_object(json_dict)
|
||||
|
||||
def add_cohort_filter(self, cohort_filter: CohortFilter):
|
||||
"""Add a cohort filter into the cohort.
|
||||
:param cohort_filter: Cohort filter defined by CohortFilter class.
|
||||
:type: CohortFilter
|
||||
"""
|
||||
if not isinstance(cohort_filter, CohortFilter):
|
||||
raise UserConfigValidationException(
|
||||
"Got unexpected type {0} for cohort filter. "
|
||||
"Expected CohortFilter type.".format(type(cohort_filter))
|
||||
)
|
||||
if self.cohort_filter_list is None:
|
||||
self.cohort_filter_list = [cohort_filter]
|
||||
else:
|
||||
self.cohort_filter_list.append(cohort_filter)
|
||||
|
||||
def _validate_with_test_data(self, test_data: pd.DataFrame,
|
||||
target_column: str,
|
||||
categorical_features: List[str],
|
||||
is_classification: Optional[bool] = True):
|
||||
"""
|
||||
Validate the cohort and cohort filters parameters with respect to
|
||||
test data.
|
||||
|
||||
:param test_data: Test data over which cohort analysis will be done
|
||||
in ResponsibleAI Dashboard.
|
||||
:type test_data: pd.DataFrame
|
||||
:param target_column: The target column in the test data.
|
||||
:type target_column: str
|
||||
:param categorical_features: The categorical feature names.
|
||||
:type categorical_features: list[str]
|
||||
:param is_classification: True to indicate if this validation needs
|
||||
to be done for a classification scenario and False to indicate
|
||||
that this needs to be done for regression scenario.
|
||||
:type is_classification: bool
|
||||
"""
|
||||
if self.cohort_filter_list is None:
|
||||
return
|
||||
if not isinstance(test_data, pd.DataFrame):
|
||||
raise UserConfigValidationException(
|
||||
"The test_data should be a pandas DataFrame.")
|
||||
if not isinstance(target_column, str):
|
||||
raise UserConfigValidationException(
|
||||
"The target_column should be string.")
|
||||
if not isinstance(categorical_features, list):
|
||||
raise UserConfigValidationException(
|
||||
"Expected a list type for categorical columns.")
|
||||
for categorical_feature in categorical_features:
|
||||
if not isinstance(categorical_feature, str):
|
||||
raise UserConfigValidationException(
|
||||
"Feature {0} in categorical_features need to be of "
|
||||
"string type.".format(categorical_feature)
|
||||
)
|
||||
|
||||
if target_column not in test_data.columns:
|
||||
raise UserConfigValidationException(
|
||||
"The target_column {0} was not found in test_data.".format(
|
||||
target_column)
|
||||
)
|
||||
|
||||
test_data_columns_set = set(test_data.columns) - set([target_column])
|
||||
for categorical_feature in categorical_features:
|
||||
if categorical_feature not in test_data_columns_set:
|
||||
raise UserConfigValidationException(
|
||||
"Found categorical feature {0} which is not"
|
||||
" present in test data.".format(categorical_feature)
|
||||
)
|
||||
|
||||
for cohort_filter in self.cohort_filter_list:
|
||||
cohort_filter._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column=target_column,
|
||||
categorical_features=categorical_features,
|
||||
is_classification=is_classification)
|
||||
|
|
@ -1,632 +0,0 @@
|
|||
# Copyright (c) Microsoft Corporation
|
||||
# Licensed under the MIT License.
|
||||
|
||||
import json
|
||||
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from raiutils.exceptions import UserConfigValidationException
|
||||
from raiwidgets.cohort import (ClassificationOutcomes, Cohort, CohortFilter,
|
||||
CohortFilterMethods,
|
||||
cohort_filter_json_converter)
|
||||
|
||||
|
||||
def get_toy_binary_classification_dataset():
|
||||
return pd.DataFrame(data=[[23, 'X'], [25, 'Y']],
|
||||
columns=["age", "target"])
|
||||
|
||||
|
||||
def get_toy_multiclass_classification_dataset():
|
||||
return pd.DataFrame(
|
||||
data=[[23, 'X'], [25, 'Y'], [25, 'Z']],
|
||||
columns=["age", "target"])
|
||||
|
||||
|
||||
def get_toy_regression_dataset():
|
||||
return pd.DataFrame(
|
||||
data=[[23, 2.5], [25, 3.6], [25, 4.6]],
|
||||
columns=["age", "target"])
|
||||
|
||||
|
||||
class TestCohortFilter:
|
||||
def test_cohort_filter_validate_method(self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected type <class 'int'> for method. "
|
||||
"Expected string type."):
|
||||
CohortFilter(method=1,
|
||||
arg=[], column=1)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected value random for method. "
|
||||
"Expected either of greater or "
|
||||
"greater and equal or "
|
||||
"less or less and equal or "
|
||||
"equal or includes or "
|
||||
"excludes or in the range of."):
|
||||
CohortFilter(method="random", arg=[], column="random")
|
||||
|
||||
def test_cohort_filter_validate_column(self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected type <class 'int'> for column. "
|
||||
"Expected string type."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_GREATER,
|
||||
arg=[], column=1)
|
||||
|
||||
def test_cohort_filter_validate_arg(self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected type <class 'int'> for arg. "
|
||||
"Expected list type."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_GREATER,
|
||||
arg=1, column="age")
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Empty list supplied for arg."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_GREATER,
|
||||
arg=[], column="age")
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
CohortFilterMethods.SINGLE_VALUE_METHODS)
|
||||
def test_cohort_filter_validate_single_value_methods_num_arg_entries(
|
||||
self, method):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected a single value in arg "
|
||||
"for cohort methods greater and "
|
||||
"equal or greater or less and equal or less or equal."):
|
||||
CohortFilter(method=method, arg=[1, 9], column="age")
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
CohortFilterMethods.SINGLE_VALUE_METHODS)
|
||||
def test_cohort_filter_validate_single_value_methods_type_arg_entries(
|
||||
self, method):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected int or float type for arg "
|
||||
"with cohort methods greater and "
|
||||
"equal or greater or less and equal or less or equal."):
|
||||
CohortFilter(method=method, arg=["val"], column="age")
|
||||
|
||||
def test_cohort_filter_validate_in_range_methods_num_arg_entries(
|
||||
self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected two entries in arg for "
|
||||
"cohort method in the range of."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_RANGE,
|
||||
arg=[1], column="age")
|
||||
|
||||
def test_cohort_filter_validate_in_range_methods_type_arg_entries(
|
||||
self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected int or float type for arg "
|
||||
"with cohort method in the range of."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_RANGE,
|
||||
arg=[1, 'val'], column="age")
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected int or float type for arg "
|
||||
"with cohort method in the range of."):
|
||||
CohortFilter(method=CohortFilterMethods.METHOD_RANGE,
|
||||
arg=['val', 2], column="age")
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
CohortFilterMethods.SINGLE_VALUE_METHODS)
|
||||
def test_cohort_filter_serialization_single_value_methods(self, method):
|
||||
cohort_filter_1 = CohortFilter(method=method,
|
||||
arg=[65.0], column='age')
|
||||
json_str = json.dumps(cohort_filter_1,
|
||||
default=cohort_filter_json_converter)
|
||||
assert method in json_str
|
||||
assert '[65.0]' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
def test_cohort_filter_serialization_in_range_method(self):
|
||||
cohort_filter_1 = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_RANGE,
|
||||
arg=[65.0, 70.0], column='age')
|
||||
json_str = json.dumps(cohort_filter_1,
|
||||
default=cohort_filter_json_converter)
|
||||
assert CohortFilterMethods.METHOD_RANGE in json_str
|
||||
assert '65.0' in json_str
|
||||
assert '70.0' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
[CohortFilterMethods.METHOD_INCLUDES,
|
||||
CohortFilterMethods.METHOD_EXCLUDES])
|
||||
def test_cohort_filter_serialization_include_exclude_methods(self, method):
|
||||
cohort_filter_str = CohortFilter(method=method,
|
||||
arg=['val1', 'val2', 'val3'],
|
||||
column='age')
|
||||
json_str = json.dumps(cohort_filter_str,
|
||||
default=cohort_filter_json_converter)
|
||||
assert method in json_str
|
||||
assert 'val1' in json_str
|
||||
assert 'val2' in json_str
|
||||
assert 'val3' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
cohort_filter_int = CohortFilter(method=method,
|
||||
arg=[1, 2, 3],
|
||||
column='age')
|
||||
json_str = json.dumps(cohort_filter_int,
|
||||
default=cohort_filter_json_converter)
|
||||
assert method in json_str
|
||||
assert '1' in json_str
|
||||
assert '2' in json_str
|
||||
assert '3' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
|
||||
class TestCohortFilterDataValidations:
|
||||
def test_validate_with_test_data_high_level_validations(self):
|
||||
test_data = get_toy_binary_classification_dataset()
|
||||
|
||||
cohort_filter_not_a_feature = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_LESS,
|
||||
arg=[65], column='fake_column')
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Unknown column fake_column specified in cohort filter"):
|
||||
cohort_filter_not_a_feature._validate_with_test_data(
|
||||
test_data=test_data, target_column="target",
|
||||
categorical_features=[])
|
||||
|
||||
def test_validate_with_test_data_index_filter_validations(self):
|
||||
test_data = get_toy_binary_classification_dataset()
|
||||
|
||||
cohort_filter_index_excludes = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_EXCLUDES,
|
||||
arg=[65], column=CohortFilter.INDEX)
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="excludes filter is not supported with Index based "
|
||||
"selection."):
|
||||
cohort_filter_index_excludes._validate_with_test_data(
|
||||
test_data=test_data, target_column="target",
|
||||
categorical_features=[]
|
||||
)
|
||||
|
||||
cohort_filter_index_incorrect_args = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_GREATER,
|
||||
arg=[65.0], column=CohortFilter.INDEX)
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="All entries in arg should be of type int."):
|
||||
cohort_filter_index_incorrect_args._validate_with_test_data(
|
||||
test_data=test_data, target_column="target",
|
||||
categorical_features=[]
|
||||
)
|
||||
|
||||
def test_validate_with_test_data_classification_error_filter_validations(
|
||||
self):
|
||||
test_data_multiclass = get_toy_multiclass_classification_dataset()
|
||||
|
||||
test_data_binary = get_toy_binary_classification_dataset()
|
||||
|
||||
cohort_filter_classification_excludes = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_EXCLUDES,
|
||||
arg=[ClassificationOutcomes.FALSE_NEGATIVE],
|
||||
column=CohortFilter.CLASSIFICATION_OUTCOME)
|
||||
|
||||
cohort_filter_classification_includes = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_INCLUDES,
|
||||
arg=["random"],
|
||||
column=CohortFilter.CLASSIFICATION_OUTCOME)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Classification outcome cannot be "
|
||||
"configured for multi-class classification"
|
||||
" and regression scenarios."):
|
||||
cohort_filter_classification_excludes._validate_with_test_data(
|
||||
test_data=test_data_multiclass, target_column="target",
|
||||
categorical_features=[], is_classification=True
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Classification outcome cannot be "
|
||||
"configured for multi-class classification"
|
||||
" and regression scenarios."):
|
||||
cohort_filter_classification_excludes._validate_with_test_data(
|
||||
test_data=test_data_binary, target_column="target",
|
||||
categorical_features=[], is_classification=False
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Classification outcome can only be configured with "
|
||||
"cohort filter includes."):
|
||||
cohort_filter_classification_excludes._validate_with_test_data(
|
||||
test_data=test_data_binary, target_column="target",
|
||||
categorical_features=[], is_classification=True
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Classification outcome can only take argument values "
|
||||
"from False negative or False positive or True "
|
||||
"negative or True positive."):
|
||||
cohort_filter_classification_includes._validate_with_test_data(
|
||||
test_data=test_data_binary, target_column="target",
|
||||
categorical_features=[], is_classification=True)
|
||||
|
||||
def test_validate_with_test_data_regression_error_filter_validations(
|
||||
self):
|
||||
test_data_regression = get_toy_regression_dataset()
|
||||
|
||||
cohort_filter_regression = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_LESS,
|
||||
arg=[2.5],
|
||||
column=CohortFilter.REGRESSION_ERROR)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Error cannot be configured for classification"
|
||||
" scenarios."):
|
||||
cohort_filter_regression._validate_with_test_data(
|
||||
test_data=test_data_regression,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=True)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Error cannot be configured with either includes"
|
||||
" or excludes."):
|
||||
cohort_filter_regression.method = \
|
||||
CohortFilterMethods.METHOD_INCLUDES
|
||||
cohort_filter_regression._validate_with_test_data(
|
||||
test_data=test_data_regression,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=False)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Error cannot be configured with either includes"
|
||||
" or excludes."):
|
||||
cohort_filter_regression.method = \
|
||||
CohortFilterMethods.METHOD_EXCLUDES
|
||||
cohort_filter_regression._validate_with_test_data(
|
||||
test_data=test_data_regression,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=False)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="All entries in arg should be of type int or float"
|
||||
" for Error cohort."):
|
||||
cohort_filter_regression.method = \
|
||||
CohortFilterMethods.METHOD_GREATER
|
||||
cohort_filter_regression.arg = ['val1', 'val2']
|
||||
cohort_filter_regression._validate_with_test_data(
|
||||
test_data=test_data_regression,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=False)
|
||||
|
||||
@pytest.mark.parametrize('target_filter_type',
|
||||
[CohortFilter.PREDICTED_Y,
|
||||
CohortFilter.TRUE_Y])
|
||||
@pytest.mark.parametrize('method',
|
||||
[CohortFilterMethods.METHOD_INCLUDES,
|
||||
CohortFilterMethods.METHOD_EXCLUDES])
|
||||
def test_validate_with_test_data_regression_target_filter_validations(
|
||||
self, target_filter_type, method):
|
||||
test_data_regression = get_toy_regression_dataset()
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="{0} cannot be configured with "
|
||||
"filter {1} for regression.".format(target_filter_type,
|
||||
method)):
|
||||
cohort_filter_regression = CohortFilter(
|
||||
method=method,
|
||||
arg=[2.5],
|
||||
column=target_filter_type)
|
||||
cohort_filter_regression._validate_with_test_data(
|
||||
test_data=test_data_regression,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=False)
|
||||
|
||||
@pytest.mark.parametrize('target_filter_type',
|
||||
[CohortFilter.PREDICTED_Y,
|
||||
CohortFilter.TRUE_Y])
|
||||
def test_validate_with_test_data_classification_target_filter_validations(
|
||||
self, target_filter_type):
|
||||
test_data_classification = get_toy_binary_classification_dataset()
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="{0} can only be configured with "
|
||||
"filter {1} for classification".format(
|
||||
target_filter_type,
|
||||
CohortFilterMethods.METHOD_INCLUDES)):
|
||||
cohort_filter_classification = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_EXCLUDES,
|
||||
arg=['X'],
|
||||
column=target_filter_type)
|
||||
cohort_filter_classification._validate_with_test_data(
|
||||
test_data=test_data_classification,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=True)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Found a class in arg which is not present in "
|
||||
"test data"):
|
||||
cohort_filter_classification = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_INCLUDES,
|
||||
arg=['Z'],
|
||||
column=target_filter_type)
|
||||
cohort_filter_classification._validate_with_test_data(
|
||||
test_data=test_data_classification,
|
||||
target_column="target",
|
||||
categorical_features=[],
|
||||
is_classification=True)
|
||||
|
||||
def test_validate_with_test_data_with_dataset_validations(
|
||||
self):
|
||||
test_data = pd.DataFrame(
|
||||
data=[[23, 'new', 'A'], [25, 'new, ''B'], [25, 'old', 'B']],
|
||||
columns=["age", 'type', "target"])
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="{0} is a categorical feature and should be only "
|
||||
"configured with {1} cohort filter.".format(
|
||||
"type",
|
||||
CohortFilterMethods.METHOD_INCLUDES)):
|
||||
cohort_filter = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_EXCLUDES,
|
||||
arg=['new'],
|
||||
column='type')
|
||||
cohort_filter._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="target",
|
||||
categorical_features=['type'],
|
||||
is_classification=True)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Found a category {0} in arg which is not present "
|
||||
"in test data column {1}.".format('mid', 'type')):
|
||||
cohort_filter = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_INCLUDES,
|
||||
arg=['mid'],
|
||||
column='type')
|
||||
cohort_filter._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="target",
|
||||
categorical_features=['type'],
|
||||
is_classification=True)
|
||||
|
||||
|
||||
class TestCohort:
|
||||
def test_cohort_configuration_validations(self):
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected type <class 'int'> for cohort name. "
|
||||
"Expected string type."):
|
||||
Cohort(name=1)
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Got unexpected type <class 'list'> for cohort filter. "
|
||||
"Expected CohortFilter type"):
|
||||
cohort = Cohort(name="Cohort New")
|
||||
cohort.add_cohort_filter(cohort_filter=[])
|
||||
|
||||
def test_cohort_validate_with_test_data(self):
|
||||
cohort_filter_1 = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_LESS,
|
||||
arg=[65], column='age')
|
||||
cohort_1 = Cohort(name="Cohort New")
|
||||
cohort_1.add_cohort_filter(cohort_filter_1)
|
||||
test_data = get_toy_binary_classification_dataset()
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="The test_data should be a pandas DataFrame"):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=[], target_column='target',
|
||||
categorical_features=[])
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="The target_column should be string."):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column=1,
|
||||
categorical_features=[])
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="The target_column fake_target "
|
||||
"was not found in test_data."):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="fake_target",
|
||||
categorical_features=[])
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Expected a list type for "
|
||||
"categorical columns."):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="target",
|
||||
categorical_features={})
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Feature 1 in categorical_features need to be of "
|
||||
"string type."):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="target",
|
||||
categorical_features=[1, 2])
|
||||
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="Found categorical feature hours-per-week which is not"
|
||||
" present in test data."):
|
||||
cohort_1._validate_with_test_data(
|
||||
test_data=test_data,
|
||||
target_column="target",
|
||||
categorical_features=["hours-per-week"])
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
CohortFilterMethods.SINGLE_VALUE_METHODS)
|
||||
def test_cohort_serialization_single_value_method(self, method):
|
||||
cohort_filter_1 = CohortFilter(method=method,
|
||||
arg=[65], column='age')
|
||||
cohort_1 = Cohort(name="Cohort New")
|
||||
cohort_1.add_cohort_filter(cohort_filter_1)
|
||||
json_str = cohort_1.to_json()
|
||||
|
||||
assert 'Cohort New' in json_str
|
||||
assert method in json_str
|
||||
assert '[65]' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
def test_cohort_serialization_deserialization_in_range_method(self):
|
||||
cohort_filter_1 = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_RANGE,
|
||||
arg=[65.0, 70.0], column='age')
|
||||
cohort_1 = Cohort(name="Cohort New")
|
||||
cohort_1.add_cohort_filter(cohort_filter_1)
|
||||
|
||||
json_str = cohort_1.to_json()
|
||||
assert 'Cohort New' in json_str
|
||||
assert CohortFilterMethods.METHOD_RANGE in json_str
|
||||
assert '65.0' in json_str
|
||||
assert '70.0' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
cohort_1_new = Cohort.from_json(json_str)
|
||||
assert cohort_1_new.name == cohort_1.name
|
||||
assert len(cohort_1_new.cohort_filter_list) == \
|
||||
len(cohort_1.cohort_filter_list)
|
||||
assert cohort_1_new.cohort_filter_list[0].method == \
|
||||
cohort_1.cohort_filter_list[0].method
|
||||
|
||||
@pytest.mark.parametrize('method',
|
||||
[CohortFilterMethods.METHOD_INCLUDES,
|
||||
CohortFilterMethods.METHOD_EXCLUDES])
|
||||
def test_cohort_serialization_deserialization_include_exclude_methods(
|
||||
self, method):
|
||||
cohort_filter_str = CohortFilter(method=method,
|
||||
arg=['val1', 'val2', 'val3'],
|
||||
column='age')
|
||||
cohort_str = Cohort(name="Cohort New Str")
|
||||
cohort_str.add_cohort_filter(cohort_filter_str)
|
||||
|
||||
json_str = cohort_str.to_json()
|
||||
assert method in json_str
|
||||
assert 'val1' in json_str
|
||||
assert 'val2' in json_str
|
||||
assert 'val3' in json_str
|
||||
assert 'age' in json_str
|
||||
cohort_str_new = Cohort.from_json(json_str)
|
||||
assert cohort_str == cohort_str_new
|
||||
|
||||
cohort_filter_int = CohortFilter(method=method,
|
||||
arg=[1, 2, 3],
|
||||
column='age')
|
||||
cohort_int = Cohort(name="Cohort New Int")
|
||||
cohort_int.add_cohort_filter(cohort_filter_int)
|
||||
|
||||
json_str = cohort_int.to_json()
|
||||
assert method in json_str
|
||||
assert '1' in json_str
|
||||
assert '2' in json_str
|
||||
assert '3' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
cohort_int_new = Cohort.from_json(json_str)
|
||||
assert cohort_int == cohort_int_new
|
||||
|
||||
def test_cohort_deserialization_error_conditions(self):
|
||||
test_dict = {}
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="No name field found for cohort deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
test_dict = {'name': 'Cohort New'}
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="No cohort_filter_list field found for "
|
||||
"cohort deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
test_dict = {'name': 'Cohort New', 'cohort_filter_list': {}}
|
||||
with pytest.raises(UserConfigValidationException,
|
||||
match="Field cohort_filter_list not of type list "
|
||||
"for cohort deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
test_dict = {'name': 'Cohort New', 'cohort_filter_list': [{}]}
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="No method field found for cohort filter "
|
||||
"deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
test_dict = {
|
||||
'name': 'Cohort New',
|
||||
'cohort_filter_list': [{"method": "fake_method"}]}
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="No arg field found for cohort filter deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
test_dict = {
|
||||
'name': 'Cohort New',
|
||||
'cohort_filter_list': [{"method": "fake_method", "arg": []}]}
|
||||
with pytest.raises(
|
||||
UserConfigValidationException,
|
||||
match="No column field found for cohort filter "
|
||||
"deserialization"):
|
||||
Cohort.from_json(json.dumps(test_dict))
|
||||
|
||||
|
||||
class TestCohortList:
|
||||
def test_cohort_list_serialization(self):
|
||||
cohort_filter_1 = CohortFilter(
|
||||
method=CohortFilterMethods.METHOD_LESS,
|
||||
arg=[65], column='age')
|
||||
cohort_1 = Cohort(name="Cohort New")
|
||||
cohort_1.add_cohort_filter(cohort_filter_1)
|
||||
|
||||
cohort_2 = Cohort(name="Cohort Old")
|
||||
cohort_2.add_cohort_filter(cohort_filter_1)
|
||||
|
||||
cohort_list = [cohort_1, cohort_2]
|
||||
json_str = json.dumps(cohort_list,
|
||||
default=cohort_filter_json_converter)
|
||||
|
||||
assert 'Cohort Old' in json_str
|
||||
assert 'Cohort New' in json_str
|
||||
assert CohortFilterMethods.METHOD_LESS in json_str
|
||||
assert '[65]' in json_str
|
||||
assert 'age' in json_str
|
||||
|
||||
def test_empty_cohort_list_serialization(self):
|
||||
json_str = json.dumps(None, default=cohort_filter_json_converter)
|
||||
assert 'null' in json_str
|
||||
Загрузка…
Ссылка в новой задаче