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discovery_test.py
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discovery_test.py
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import pandas as pd
from prioritization_discovery.config import DEFAULT_CSV_IDS
from prioritization_discovery.discovery import _discover_prioritized_instances, _split_to_individual_observations, discover_priority_rules
def test_discover_prioritized_instances():
# Read event log
event_log = pd.read_csv("./tests/assets/event_log_1.csv")
event_log[DEFAULT_CSV_IDS.enabled_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.enabled_time], utc=True)
event_log[DEFAULT_CSV_IDS.start_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.start_time], utc=True)
event_log[DEFAULT_CSV_IDS.end_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.end_time], utc=True)
# Discover prioritization
attributes = [DEFAULT_CSV_IDS.activity]
prioritizations = _discover_prioritized_instances(event_log, attributes)
prioritizations.sort_values(['Activity'], inplace=True)
assert prioritizations.equals(
pd.DataFrame(
data=[
['B', 0], ['B', 0], ['B', 0],
['B', 0], ['B', 0], ['B', 0],
['C', 1], ['C', 1], ['C', 1],
['C', 1], ['C', 1], ['C', 1]
],
index=[0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5],
columns=['Activity', 'outcome']
)
)
def test_discover_prioritized_instances_with_extra_attribute():
# Read event log
event_log = pd.read_csv("./tests/assets/event_log_2.csv")
event_log[DEFAULT_CSV_IDS.enabled_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.enabled_time], utc=True)
event_log[DEFAULT_CSV_IDS.start_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.start_time], utc=True)
event_log[DEFAULT_CSV_IDS.end_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.end_time], utc=True)
# Discover prioritization
attributes = [DEFAULT_CSV_IDS.activity, 'loan_amount']
prioritizations = _discover_prioritized_instances(event_log, attributes)
prioritizations.sort_values(['Activity', 'loan_amount', 'outcome'], inplace=True)
assert prioritizations.equals(
pd.DataFrame(
data=[
['A', 500, 0],
['A', 500, 0],
['A', 500, 1],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['B', 500, 1],
['B', 1000, 1],
['B', 1000, 1],
['C', 100, 0],
['C', 500, 1],
['C', 500, 1],
['C', 1000, 1],
['C', 1000, 1]
],
index=[0, 1, 2, 2, 3, 4, 5, 6, 4, 0, 3, 7, 6, 7, 1, 5],
columns=['Activity', 'loan_amount', 'outcome']
)
)
def test__split_to_individual_observations():
# Create simple prioritizations with only the activity
prioritizations = pd.DataFrame(
[["B", "C"], ["B", "C"], ["B", "C"], ["B", "C"], ["B", "C"], ["B", "C"]],
columns=['delayed_Activity', 'prioritized_Activity']
)
# Split the prioritizations to the individual delayed/prioritized instances
prioritized_instances = _split_to_individual_observations(
prioritizations,
['delayed_Activity'],
['prioritized_Activity'],
'outcome'
)
# Assert that the split was done correctly, even maintaining the indexes
assert prioritized_instances.equals(
pd.DataFrame(
data=[
['B', 0], ['B', 0], ['B', 0],
['B', 0], ['B', 0], ['B', 0],
['C', 1], ['C', 1], ['C', 1],
['C', 1], ['C', 1], ['C', 1]
],
index=[0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5],
columns=['Activity', 'outcome']
)
)
def test__split_to_individual_observations_with_extra_attribute():
# Create simple prioritizations with only the activity
prioritizations = pd.DataFrame(
[
['A', 500, 'B', 1000],
['A', 500, 'C', 1000],
['B', 100, 'A', 500],
['B', 100, 'B', 500],
['B', 100, 'B', 1000],
['B', 100, 'C', 500],
['B', 100, 'C', 1000],
['C', 100, 'C', 500]
],
columns=[
'delayed_Activity',
'delayed_loan_amount',
'prioritized_Activity',
'prioritized_loan_amount'
]
)
# Split the prioritizations to the individual delayed/prioritized instances
prioritized_instances = _split_to_individual_observations(
prioritizations,
['delayed_Activity', 'delayed_loan_amount'],
['prioritized_Activity', 'prioritized_loan_amount'],
'outcome'
)
# Assert that the split was done correctly, even maintaining the indexes
assert prioritized_instances.equals(
pd.DataFrame(
data=[
['A', 500, 0],
['A', 500, 0],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['B', 100, 0],
['C', 100, 0],
['B', 1000, 1],
['C', 1000, 1],
['A', 500, 1],
['B', 500, 1],
['B', 1000, 1],
['C', 500, 1],
['C', 1000, 1],
['C', 500, 1]
],
index=[0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7],
columns=['Activity', 'loan_amount', 'outcome']
)
)
def test_discover_priority_rules_naive():
# Read event log
event_log = pd.read_csv("./tests/assets/event_log_3.csv")
event_log[DEFAULT_CSV_IDS.enabled_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.enabled_time], utc=True)
event_log[DEFAULT_CSV_IDS.start_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.start_time], utc=True)
event_log[DEFAULT_CSV_IDS.end_time] = pd.to_datetime(event_log[DEFAULT_CSV_IDS.end_time], utc=True)
# Discover prioritization
attributes = ['urgency']
# Get priority levels and their rules
prioritization_levels = discover_priority_rules(event_log, attributes)
# Assert expected levels and rules
assert prioritization_levels == [
{
'priority_level': 0,
'rules': [
[
{
'attribute': 'urgency',
'condition': '=',
'value': 'high'
}
]
]
},
{
'priority_level': 1,
'rules': [
[
{
'attribute': 'urgency',
'condition': '=',
'value': 'medium'
}
]
]
}
]