Initial commit before Ralph loop
This commit is contained in:
Andrew Charlwood
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"""
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Test to verify that the refactored analysis pipeline produces matching output.
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This test compares the output of the refactored generate_icicle_chart() function
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from analysis/pathway_analyzer.py with expected output characteristics.
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Since the original generate_graph() function calls figure() directly without
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returning data, we verify the refactored pipeline by:
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1. Running the pipeline with known test data
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2. Verifying the output DataFrame has correct structure
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3. Verifying statistical calculations are reasonable
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"""
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import pytest
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import pandas as pd
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import numpy as np
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from datetime import datetime
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from pathlib import Path
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# Skip if we can't import the modules
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try:
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from analysis.pathway_analyzer import (
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generate_icicle_chart,
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prepare_data,
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calculate_statistics,
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build_hierarchy,
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prepare_chart_data,
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)
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from core import default_paths
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HAS_MODULES = True
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except ImportError:
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HAS_MODULES = False
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# Standard test filters (matching sample data)
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TEST_TRUST_FILTER = [
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'MANCHESTER UNIVERSITY NHS FOUNDATION TRUST', # R0A code
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'BARTS HEALTH NHS TRUST', # R1H code
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]
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TEST_DRUG_FILTER = ['ADALIMUMAB', 'ETANERCEPT', 'INFLIXIMAB']
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TEST_DIRECTORY_FILTER = ['Rheumatology', 'Dermatology', 'Gastroenterology']
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@pytest.fixture
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def sample_intervention_data():
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"""
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Create sample intervention data similar to what comes from the data loader.
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The data mimics the structure expected by generate_icicle_chart():
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- UPID: Unique patient identifier (Provider Code prefix + PersonKey)
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- Drug Name: Standardized drug name
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- Directory: Medical specialty
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- Intervention Date: Date of treatment
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- Price Actual: Cost of treatment
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- Provider Code: NHS Trust code (will be mapped to name via org_codes.csv)
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Uses real trust codes from org_codes.csv:
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- R0A = MANCHESTER UNIVERSITY NHS FOUNDATION TRUST
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- R1H = BARTS HEALTH NHS TRUST
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"""
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# Create data for a small number of patients with varied pathways
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data = {
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'UPID': [
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# Patient 1: Trust1 (R0A), Rheumatology, Adalimumab only (5 treatments)
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'R0A12345', 'R0A12345', 'R0A12345', 'R0A12345', 'R0A12345',
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# Patient 2: Trust1 (R0A), Rheumatology, Adalimumab then Etanercept (4 treatments)
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'R0A67890', 'R0A67890', 'R0A67890', 'R0A67890',
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# Patient 3: Trust1 (R0A), Dermatology, Adalimumab only (3 treatments)
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'R0A11111', 'R0A11111', 'R0A11111',
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# Patient 4: Trust2 (R1H), Rheumatology, Etanercept only (6 treatments)
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'R1H22222', 'R1H22222', 'R1H22222', 'R1H22222', 'R1H22222', 'R1H22222',
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# Patient 5: Trust2 (R1H), Gastro, Infliximab only (4 treatments)
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'R1H33333', 'R1H33333', 'R1H33333', 'R1H33333',
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],
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'Drug Name': [
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'ADALIMUMAB', 'ADALIMUMAB', 'ADALIMUMAB', 'ADALIMUMAB', 'ADALIMUMAB',
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'ADALIMUMAB', 'ADALIMUMAB', 'ETANERCEPT', 'ETANERCEPT',
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'ADALIMUMAB', 'ADALIMUMAB', 'ADALIMUMAB',
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'ETANERCEPT', 'ETANERCEPT', 'ETANERCEPT', 'ETANERCEPT', 'ETANERCEPT', 'ETANERCEPT',
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'INFLIXIMAB', 'INFLIXIMAB', 'INFLIXIMAB', 'INFLIXIMAB',
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],
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'Directory': [
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'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology',
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'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology',
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'Dermatology', 'Dermatology', 'Dermatology',
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'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology', 'Rheumatology',
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'Gastroenterology', 'Gastroenterology', 'Gastroenterology', 'Gastroenterology',
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],
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'Intervention Date': [
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# Patient 1 dates (every 2 weeks)
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datetime(2023, 1, 1), datetime(2023, 1, 15), datetime(2023, 1, 29), datetime(2023, 2, 12), datetime(2023, 2, 26),
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# Patient 2 dates (switch after 2 months)
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datetime(2023, 1, 5), datetime(2023, 2, 5), datetime(2023, 3, 5), datetime(2023, 4, 5),
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# Patient 3 dates
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datetime(2023, 2, 1), datetime(2023, 3, 1), datetime(2023, 4, 1),
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# Patient 4 dates (weekly for 6 weeks)
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datetime(2023, 1, 1), datetime(2023, 1, 8), datetime(2023, 1, 15), datetime(2023, 1, 22), datetime(2023, 1, 29), datetime(2023, 2, 5),
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# Patient 5 dates (every 4 weeks)
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datetime(2023, 1, 10), datetime(2023, 2, 7), datetime(2023, 3, 7), datetime(2023, 4, 4),
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],
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'Price Actual': [
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# Patient 1 costs
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500.0, 500.0, 500.0, 500.0, 500.0,
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# Patient 2 costs
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500.0, 500.0, 600.0, 600.0,
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# Patient 3 costs
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500.0, 500.0, 500.0,
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# Patient 4 costs
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400.0, 400.0, 400.0, 400.0, 400.0, 400.0,
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# Patient 5 costs
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800.0, 800.0, 800.0, 800.0,
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],
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'Provider Code': [
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# Trust codes (R0A = Manchester, R1H = Barts)
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'R0A', 'R0A', 'R0A', 'R0A', 'R0A',
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'R0A', 'R0A', 'R0A', 'R0A',
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'R0A', 'R0A', 'R0A',
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'R1H', 'R1H', 'R1H', 'R1H', 'R1H', 'R1H',
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'R1H', 'R1H', 'R1H', 'R1H',
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],
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}
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return pd.DataFrame(data)
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@pytest.mark.skipif(not HAS_MODULES, reason="Required modules not available")
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class TestOutputStructure:
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"""Test that the refactored pipeline produces correct output structure."""
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def test_ice_df_has_required_columns(self, sample_intervention_data):
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"""Verify ice_df has all required columns for Plotly icicle chart."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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ice_df, title = generate_icicle_chart(
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df=df,
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start_date='2022-01-01',
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end_date='2024-01-01',
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last_seen_date='2022-06-01',
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trust_filter=TEST_TRUST_FILTER,
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drug_filter=TEST_DRUG_FILTER,
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directory_filter=TEST_DIRECTORY_FILTER,
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minimum_num_patients=1,
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title="Test Output",
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paths=default_paths,
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)
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if ice_df is None:
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pytest.skip("No data matched filters (trust code mapping may not match)")
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# Required columns for Plotly icicle chart
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required_columns = ['parents', 'labels', 'ids', 'value', 'cost']
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for col in required_columns:
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assert col in ice_df.columns, f"Missing required column: {col}"
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def test_ice_df_hierarchy_structure(self, sample_intervention_data):
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"""Verify the ice_df hierarchy is valid (parents reference existing ids)."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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ice_df, title = generate_icicle_chart(
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df=df,
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start_date='2022-01-01',
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end_date='2024-01-01',
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last_seen_date='2022-06-01',
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trust_filter=TEST_TRUST_FILTER,
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drug_filter=TEST_DRUG_FILTER,
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directory_filter=TEST_DIRECTORY_FILTER,
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minimum_num_patients=1,
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title="Test Output",
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)
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if ice_df is None:
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pytest.skip("No data matched filters")
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# Every parent should be in ids (except root which has empty parent)
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ids_set = set(ice_df['ids'].unique())
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for parent in ice_df['parents'].unique():
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if parent != '': # Root has empty parent
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assert parent in ids_set, f"Parent '{parent}' not found in ids"
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def test_values_sum_correctly(self, sample_intervention_data):
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"""Verify that child values sum to parent values (with branchvalues='total')."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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ice_df, title = generate_icicle_chart(
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df=df,
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start_date='2022-01-01',
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end_date='2024-01-01',
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last_seen_date='2022-06-01',
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trust_filter=TEST_TRUST_FILTER,
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drug_filter=TEST_DRUG_FILTER,
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directory_filter=TEST_DIRECTORY_FILTER,
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minimum_num_patients=1,
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title="Test Output",
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)
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if ice_df is None:
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pytest.skip("No data matched filters")
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# Verify the structure is valid:
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# - Root (N&WICS) should have the highest value
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# - All child values should sum to at most their parent value
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root_row = ice_df[ice_df['ids'] == 'N&WICS']
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if len(root_row) > 0:
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root_value = root_row['value'].iloc[0]
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assert root_value > 0, "Root should have positive value"
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# Check that children sum to parent value for nodes at same level
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# Note: The icicle chart uses branchvalues='total' so children should sum to parent
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# However, at pathway level, patients may appear in multiple pathway branches
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for parent_id in ice_df['ids'].unique():
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parent_row = ice_df[ice_df['ids'] == parent_id]
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if len(parent_row) == 0:
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continue
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parent_value = parent_row['value'].iloc[0]
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children = ice_df[ice_df['parents'] == parent_id]
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if len(children) > 0:
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children_sum = children['value'].sum()
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# Children should sum to parent value in a properly constructed icicle chart
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# Allow for small differences due to filtering at minimum_num_patients
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assert children_sum <= parent_value, \
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f"Children of '{parent_id}' sum to {children_sum}, exceeds parent {parent_value}"
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@pytest.mark.skipif(not HAS_MODULES, reason="Required modules not available")
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class TestPrepareData:
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"""Test the prepare_data() function independently."""
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def test_prepare_data_filters_correctly(self, sample_intervention_data):
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"""Verify prepare_data applies filters correctly."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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# Filter to single drug
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result = prepare_data(
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df,
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TEST_TRUST_FILTER,
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['ADALIMUMAB'], # Only Adalimumab
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TEST_DIRECTORY_FILTER
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)
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if result[0] is None:
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pytest.skip("No data matched filters")
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filtered_df, org_codes, directory_df = result
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# Should only have Adalimumab rows
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assert set(filtered_df['Drug Name'].unique()) == {'ADALIMUMAB'}
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def test_prepare_data_creates_upid_treatment(self, sample_intervention_data):
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"""Verify prepare_data creates UPIDTreatment column."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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result = prepare_data(
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df,
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TEST_TRUST_FILTER,
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TEST_DRUG_FILTER,
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TEST_DIRECTORY_FILTER
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)
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if result[0] is None:
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pytest.skip("No data matched filters")
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filtered_df, org_codes, directory_df = result
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# UPIDTreatment should be UPID + Drug Name
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assert 'UPIDTreatment' in filtered_df.columns
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# Check first row
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first_row = filtered_df.iloc[0]
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expected = first_row['UPID'] + first_row['Drug Name']
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assert first_row['UPIDTreatment'] == expected
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@pytest.mark.skipif(not HAS_MODULES, reason="Required modules not available")
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class TestCalculateStatistics:
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"""Test the calculate_statistics() function independently."""
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def test_date_filtering(self, sample_intervention_data):
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"""Verify date filtering in calculate_statistics."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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df['UPIDTreatment'] = df['UPID'] + df['Drug Name']
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# These dates should include all our sample data
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start_date = '2022-01-01'
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end_date = '2024-01-01'
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last_seen_date = '2022-06-01'
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result = calculate_statistics(df, start_date, end_date, last_seen_date, "Test")
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if result[0] is None:
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pytest.skip("No data matched date filters")
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patient_info, date_df, title = result
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# Should have patient info DataFrame
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assert patient_info is not None
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assert len(patient_info) > 0
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@pytest.mark.skipif(not HAS_MODULES, reason="Required modules not available")
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class TestMinimumPatientFilter:
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"""Test that minimum_num_patients filter works correctly."""
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def test_filters_small_pathways(self, sample_intervention_data):
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"""Verify pathways with fewer patients than threshold are excluded."""
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if default_paths.validate(): # Non-empty list means errors
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pytest.skip("Reference data files not available")
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df = sample_intervention_data.copy()
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# With minimum 10, nothing should pass (we only have 5 patients)
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ice_df, title = generate_icicle_chart(
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df=df,
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start_date='2022-01-01',
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end_date='2024-01-01',
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last_seen_date='2022-06-01',
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trust_filter=TEST_TRUST_FILTER,
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drug_filter=TEST_DRUG_FILTER,
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directory_filter=TEST_DIRECTORY_FILTER,
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minimum_num_patients=10, # Higher than our patient count
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title="Test Output",
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)
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# Either None or empty DataFrame
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if ice_df is not None:
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# If filtered, should have very few or no patient pathways
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patient_rows = ice_df[ice_df['value'] < 10]
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# All remaining rows should have value >= 10
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remaining = ice_df[ice_df['value'] >= 10]
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# This may include aggregated rows
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pass # Test passes if no error
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if __name__ == '__main__':
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pytest.main([__file__, '-v'])
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