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8c204aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import pytest
import os
import sys
import pandas as pd
backend_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../backend'))
if backend_dir not in sys.path:
sys.path.insert(0, backend_dir)
from dataset_analytics import get_dataset_profile, get_paginated_rows
@pytest.fixture(scope="module")
def sample_dataset(tmp_path_factory):
fn = tmp_path_factory.mktemp("data") / "test_corpus.csv"
df = pd.DataFrame({
'Patient Number': list(range(1, 101)),
'Heart Rate (bpm)': [70 + (i % 50) for i in range(100)],
'SpO2 Level (%)': [95 + (i % 5) for i in range(100)],
'Systolic Blood Pressure (mmHg)': [110 + (i % 60) for i in range(100)],
'Diastolic Blood Pressure (mmHg)': [70 + (i % 30) for i in range(100)],
'Body Temperature (°C)': [36.5 + ((i % 15) * 0.1) for i in range(100)],
'Fall Detection': ['No' if i % 10 != 0 else 'Yes' for i in range(100)],
'Predicted Disease': [
'Healthy' if i % 4 == 0 else ('Heart Disease' if i % 4 == 1 else ('Hypertension' if i % 4 == 2 else 'Asthma'))
for i in range(100)
],
'Data Accuracy (%)': [98 for _ in range(100)],
'Heart Rate Alert': ['NORMAL' for _ in range(100)],
'SpO2 Level Alert': ['NORMAL' for _ in range(100)],
'Blood Pressure Alert': ['NORMAL' for _ in range(100)],
'Temperature Alert': ['NORMAL' for _ in range(100)]
})
df.to_csv(fn, index=False)
return str(fn)
def test_get_dataset_profile_valid(sample_dataset):
profile = get_dataset_profile(sample_dataset)
assert profile is not None
assert isinstance(profile, dict)
# Required keys
assert "summary" in profile
assert profile["summary"]["total_records"] == 100
assert "target_distribution" in profile
assert len(profile["target_distribution"]) == 4
assert "correlation_matrix" in profile
assert "labels" in profile["correlation_matrix"]
assert "matrix" in profile["correlation_matrix"]
assert "feature_stats" in profile
def test_get_dataset_profile_caching(sample_dataset):
# Verify in-memory caching returns identical object reference
p1 = get_dataset_profile(sample_dataset)
p2 = get_dataset_profile(sample_dataset)
assert p1 is p2
def test_get_dataset_profile_nonexistent():
prof = get_dataset_profile("non_existent_path_xyz.csv")
assert prof is None
def test_get_paginated_rows_default(sample_dataset):
res = get_paginated_rows(sample_dataset, page=1, limit=25)
assert res["total"] == 100
assert res["page"] == 1
assert res["limit"] == 25
assert res["total_pages"] == 4
assert len(res["rows"]) == 25
def test_get_paginated_rows_boundary_clamps(sample_dataset):
# Page <= 0 should clamp to 1
res_zero = get_paginated_rows(sample_dataset, page=0, limit=10)
assert res_zero["page"] == 1
assert len(res_zero["rows"]) == 10
res_neg = get_paginated_rows(sample_dataset, page=-5, limit=10)
assert res_neg["page"] == 1
assert len(res_neg["rows"]) == 10
def test_get_paginated_rows_out_of_bounds_page(sample_dataset):
res = get_paginated_rows(sample_dataset, page=9999, limit=25)
assert res["total"] == 100
assert res["rows"] == []
def test_get_paginated_rows_disease_filtering(sample_dataset):
# Case insensitive exact match
res_hd = get_paginated_rows(sample_dataset, page=1, limit=50, disease="heart disease")
assert res_hd["total"] == 25
for r in res_hd["rows"]:
assert r["Predicted Disease"].lower() == "heart disease"
# Non-matching disease
res_unknown = get_paginated_rows(sample_dataset, page=1, limit=50, disease="alien syndrome")
assert res_unknown["total"] == 0
assert res_unknown["rows"] == []
def test_get_paginated_rows_search_filtering(sample_dataset):
# Search by patient ID substring
res_search = get_paginated_rows(sample_dataset, page=1, limit=50, search="42")
assert res_search["total"] >= 1
assert any(r["Patient Number"] == 42 for r in res_search["rows"])
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