Add unit test values for COD, PRD, and PRB based on known good IAAO values

README.md

@@ -6,7 +6,7 @@
 
 AssessPy is a software package for Python developed by the Cook County Assessor’s (CCAO)
 Data Department. It contains many of the functions necessary to perform a standard
-[sales ratio study](https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf).
+[sales ratio study](https://www.iaao.org/wp-content/uploads/Standard_on_Ratio_Studies.pdf).
 
 For assessors, we believe that this package will reduce the complexity of calculating
 ratio statistics and detecting sales chasing. We also hope that reporters, taxpayers,

assesspy/__init__.py

@@ -4,7 +4,10 @@
     prb_ci,
     prd_ci,
 )
-from .load_data import ccao_sample, quintos_sample
+from .load_data import (
+    ccao_sample,
+    quintos_sample,
+)
 from .metrics import (
     cod,
     cod_met,

assesspy/tests/conftest.py

@@ -1,9 +1,13 @@
+import pathlib
+
 import numpy as np
 import pandas as pd
 import pytest as pt
 
 import assesspy as ap
 
+FIXTURE_DIR = pathlib.Path(__file__).parent / "fixtures"
+
 
 @pt.fixture(autouse=True, scope="function")
 def set_seed() -> None:
@@ -23,6 +27,17 @@ def quintos_data() -> tuple:
     return sample.estimate, sample.sale_price
 
 
+@pt.fixture(scope="session", params=["1_1", "1_4", "d_1", "d_2"])
+def iaao_data_name(request):
+    return request.param
+
+
+@pt.fixture(scope="session")
+def iaao_data(iaao_data_name) -> tuple:
+    sample = pd.read_csv(FIXTURE_DIR / f"iaao_table_{iaao_data_name}.csv")
+    return sample.estimate, sample.sale_price
+
+
 @pt.fixture(
     scope="session",
     params=[

assesspy/tests/fixtures/iaao_table_1_1.csv

@@ -0,0 +1,37 @@
+estimate,sale_price
+48000,138000
+28800,59250
+78400,157500
+39840,74400
+68160,114900
+94400,159000
+67200,111900
+56960,93000
+87200,138720
+38240,59700
+96320,146400
+67680,99000
+32960,47400
+50560,70500
+61360,78000
+47360,60000
+58080,69000
+47040,55500
+136000,154500
+103200,109500
+59040,60000
+168000,168000
+128000,124500
+132000,127500
+160000,150000
+160000,141000
+200000,171900
+184000,157500
+160000,129600
+157200,126000
+99200,77700
+200000,153000
+64000,48750
+192000,144000
+190400,141000
+65440,48000

assesspy/tests/fixtures/iaao_table_1_4.csv

@@ -0,0 +1,18 @@
+estimate,sale_price
+87200,138720
+38240,59700
+96320,146400
+68610,99000
+32960,47400
+50560,70500
+61360,78000
+47360,60000
+56580,69000
+47040,55500
+136000,154500
+98000,109500
+56000,60000
+159100,168000
+128000,124500
+132000,127500
+160000,150000

assesspy/tests/fixtures/iaao_table_d_1.csv

@@ -0,0 +1,26 @@
+estimate,sale_price
+116700,114500
+130300,121000
+130200,133900
+145500,139000
+134100,145000
+153900,156500
+143400,161100
+156900,169500
+169000,175000
+149200,181000
+160100,188900
+191400,205000
+177200,216150
+205500,219000
+206500,235000
+243800,249000
+211600,258900
+242500,263000
+258400,305900
+265900,312500
+305700,336000
+291600,360000
+312800,399900
+352200,418500
+354900,459000

assesspy/tests/fixtures/iaao_table_d_2.csv

@@ -0,0 +1,17 @@
+estimate,sale_price
+45000,50000
+100000,100000
+165000,150000
+180000,200000
+250000,250000
+330000,300000
+315000,350000
+400000,400000
+495000,450000
+450000,500000
+550000,550000
+660000,600000
+585000,650000
+700000,700000
+825000,750000
+1875000,2500000

assesspy/tests/test_metrics.py

@@ -14,15 +14,48 @@ def metric_val(self, metric, ccao_data, quintos_data):
             return getattr(ap, metric)(*quintos_data)
         return getattr(ap, metric)(*ccao_data)
 
-    def test_metric_value_is_correct(self, metric, metric_val):
+    def test_metric_value_is_correct_ccao(self, metric, metric_val):
         expected = {
             "cod": 17.81456901196891,
             "prd": 1.0484192615223522,
             "prb": 0.0024757,
             "mki": 0.794,
             "ki": -0.06,
         }
-        assert pt.approx(metric_val, rel=0.02) == expected[metric]
+        assert pt.approx(metric_val, rel=0.01) == expected[metric]
+
+    def test_metric_value_is_correct_iaao(
+        self, metric, iaao_data_name, iaao_data
+    ):
+        if metric in ["mki", "ki"]:
+            return None
+        else:
+            result = getattr(ap, metric)(*iaao_data)
+            expected = {
+                "1_1": {
+                    "cod": 29.8,
+                    "prd": 0.98,
+                    "prb": 0.232,
+                },
+                "1_4": {
+                    "cod": 14.5,
+                    "prd": 0.98,
+                    "prb": 0.135,
+                },
+                "d_1": {
+                    "cod": 7.5,
+                    "prd": 1.027,
+                    "prb": -0.120,
+                },
+                "d_2": {
+                    "cod": 7.8,
+                    "prd": 1.056,
+                    "prb": -0.011,
+                },
+            }
+            assert (
+                pt.approx(result, rel=0.02) == expected[iaao_data_name][metric]
+            )
 
     def test_metric_has_numeric_output(self, metric_val):
         assert type(metric_val) is float
@@ -38,7 +71,7 @@ def test_metric_succeeds_on_good_input(self, metric, good_input):
 
     def test_metric_met_function_thresholds(self, metric, metric_val):
         if metric == "ki":
-            pt.skip("Skipping test for 'ki' metric (ki_met does not exist)")
+            return None
         expected = {
             "cod": False,
             "prd": False,

assesspy/tests/test_sales_chasing.py

@@ -13,33 +13,37 @@ def sample_dist(self, ccao_data):
         return ratio
 
     @pt.fixture(params=["normal", "chased", "sample"])
-    def distribution(self, request, ccao_data, sample_dist):
-        return request.param, {
+    def distribution_name(self, request):
+        return request.param
+
+    @pt.fixture
+    def distribution(self, distribution_name, ccao_data, sample_dist):
+        return {
             "normal": np.random.normal(1, size=1000).tolist(),
             "chased": np.append(
                 np.random.normal(1, 0.15, 900), [1] * 100
             ).tolist(),
             "sample": sample_dist,
-        }[request.param]
+        }[distribution_name]
 
     @pt.fixture(params=["cdf", "dist", "both"])
     def method(self, request):
         return request.param
 
     def test_is_sales_chased_output_is_boolean(self, distribution, method):
-        dist_name, dist_data = distribution
-        assert isinstance(ap.is_sales_chased(dist_data, method), bool)
+        assert isinstance(ap.is_sales_chased(distribution, method), bool)
 
-    def test_is_sales_chased_has_expected_output(self, distribution, method):
-        dist_name, dist_data = distribution
+    def test_is_sales_chased_has_expected_output(
+        self, distribution_name, distribution, method
+    ):
         expected = {
             "normal": {"cdf": False, "dist": False, "both": False},
             "chased": {"cdf": True, "dist": True, "both": True},
             "sample": {"cdf": False, "dist": True, "both": False},
         }
         assert (
-            ap.is_sales_chased(dist_data, method)
-            == expected[dist_name][method]
+            ap.is_sales_chased(distribution, method)
+            == expected[distribution_name][method]
         )
 
     @pt.mark.parametrize(
@@ -61,13 +65,11 @@ def test_is_sales_chased_raises_on_invalid_values(
         self, input_data, distribution, method
     ):
         with pt.raises(Exception):
-            dist_name, dist_data = distribution
-            ap.is_outlier(input_data(dist_data), method)
+            ap.is_outlier(input_data(distribution), method)
 
     def test_is_sales_chased_raises_on_invalid_method(self, distribution):
         with pt.raises(Exception):
-            dist_name, dist_data = distribution
-            ap.is_sales_chased(dist_data, method="hug")
+            ap.is_sales_chased(distribution, method="hug")
 
     def test_is_sales_chased_warns_on_small_sample(self):
         with pt.warns(UserWarning):

docs/source/index.rst

@@ -22,16 +22,14 @@ AssessPy package
    Source Code <https://github.com/ccao-data/assesspy>
 
 AssessPy is a software package for Python developed by the Cook County
-Assessor's (CCAO) Data Department. The codebase for the CCAO's CAMA system
-uses a wide range of functions regularly, and packaging these functions
-streamlines and standardizes their use. The CCAO is publishing this package
-to make it available to assessors, reporters, and citizens everywhere.
-
-For assessors, we believe that this package will reduce the complexity
-of calculating ratio statistics and detecting sales chasing. We also
-believe that reporters, taxpayers, and members of academia will find
-this package helpful in monitoring the performance of local assessors
-and conducting research.
+Assessor’s (CCAO) Data Department. It contains many of the functions necessary
+to perform a standard
+`sales ratio study <https://www.iaao.org/wp-content/uploads/Standard_on_Ratio_Studies.pdf>`_
+
+For assessors, we believe that this package will reduce the complexity of
+calculating ratio statistics and detecting sales chasing. We also hope that
+reporters, taxpayers, and members of academia will find this package helpful
+in monitoring the performance of local assessors and conducting research.
 
 For detailed documentation on included functions and data, :doc:`visit the
 full reference list <reference>`.