fix prop signal naming and test

Author: zhuoran-Cheng16

hhs_hosp/delphi_hhs/constants.py

@@ -3,12 +3,14 @@
 
 CONFIRMED = "confirmed_admissions_covid_1d"
 SUM_CONF_SUSP = "sum_confirmed_suspected_admissions_covid_1d"
-POP_PROP="population_proportionate_variants"
+CONFIRMED_PROP = "confirmed_admissions_covid_1d_prop"
+SUM_CONF_SUSP_PROP = "sum_confirmed_suspected_admissions_covid_1d_prop"
 
 SIGNALS = [
     CONFIRMED,
     SUM_CONF_SUSP,
-    POP_PROP
+    CONFIRMED_PROP,
+    SUM_CONF_SUSP_PROP
 ]
 
 GEOS = [

hhs_hosp/delphi_hhs/run.py

@@ -15,8 +15,7 @@
 import numpy as np
 import pandas as pd
 
-from .constants import SIGNALS, GEOS, SMOOTHERS, CONFIRMED, SUM_CONF_SUSP,POP_PROP
-
+from .constants import SIGNALS, GEOS, SMOOTHERS, CONFIRMED, SUM_CONF_SUSP
 
 def _date_to_int(d):
     """Return a date object as a yyyymmdd int."""
@@ -103,7 +102,7 @@ def run_module(params):
                                     "state_id",
                                     "state_code",
                                     from_col="state")
-        if sensor==POP_PROP:
+        if sensor.endswith("_PROP"):
             df=pop_proportion(df,geo_mapper)
         df = make_geo(df, geo, geo_mapper)
         df = smooth_values(df, smoother[0])
@@ -143,7 +142,7 @@ def smooth_values(df, smoother):
 def pop_proportion(df,geo_mapper):
     """Get the population-proportionate variants as the dataframe val."""
     pop_val=geo_mapper.add_population_column(df, "state_code")
-    df["val"]=df["val"]/pop_val["population"]*100000
+    df["val"]=round(df["val"]/pop_val["population"]*100000,2)
     pop_val.drop("population", axis=1, inplace=True)
     return df
 
@@ -165,25 +164,15 @@ def make_signal(all_columns, sig):
     """Generate column sums according to signal name."""
     assert sig in SIGNALS, f"Unexpected signal name '{sig}';" + \
         " familiar names are '{', '.join(SIGNALS)}'"
-    if sig == CONFIRMED:
+    if sig.startswith(CONFIRMED):
         df = pd.DataFrame({
             "state": all_columns.state.apply(str.lower),
             "timestamp":int_date_to_previous_day_datetime(all_columns.date),
             "val": \
             all_columns.previous_day_admission_adult_covid_confirmed + \
             all_columns.previous_day_admission_pediatric_covid_confirmed
         })
-    elif sig == SUM_CONF_SUSP:
-        df = pd.DataFrame({
-            "state": all_columns.state.apply(str.lower),
-            "timestamp":int_date_to_previous_day_datetime(all_columns.date),
-            "val": \
-            all_columns.previous_day_admission_adult_covid_confirmed + \
-            all_columns.previous_day_admission_adult_covid_suspected + \
-            all_columns.previous_day_admission_pediatric_covid_confirmed + \
-            all_columns.previous_day_admission_pediatric_covid_suspected,
-        })
-    elif sig ==  POP_PROP:
+    elif sig.startswith(SUM_CONF_SUSP):
         df = pd.DataFrame({
             "state": all_columns.state.apply(str.lower),
             "timestamp":int_date_to_previous_day_datetime(all_columns.date),

hhs_hosp/tests/test_run.py

@@ -6,7 +6,7 @@
 
 from delphi_hhs.run import _date_to_int, int_date_to_previous_day_datetime, generate_date_ranges, \
     make_signal, make_geo, run_module, pop_proportion
-from delphi_hhs.constants import CONFIRMED, SUM_CONF_SUSP, POP_PROP, SMOOTHERS, GEOS, SIGNALS
+from delphi_hhs.constants import CONFIRMED, SUM_CONF_SUSP, SMOOTHERS, GEOS, SIGNALS,CONFIRMED_PROP, SUM_CONF_SUSP_PROP
 from delphi_utils.geomap import GeoMapper
 from freezegun import freeze_time
 import numpy as np
@@ -54,7 +54,6 @@ def test_make_signal():
     data = pd.DataFrame({
         'state': ['NA'],
         'date': [20200102],
-        'population':[100.],
         'previous_day_admission_adult_covid_confirmed': [1],
         'previous_day_admission_adult_covid_suspected': [2],
         'previous_day_admission_pediatric_covid_confirmed': [4],
@@ -68,13 +67,27 @@ def test_make_signal():
     })
     pd.testing.assert_frame_equal(expected_confirmed, make_signal(data, CONFIRMED))
 
+    expected_confirmed = pd.DataFrame({
+        'state': ['na'],
+        'timestamp': [datetime(year=2020, month=1, day=1)],
+        'val': [5.],
+    })
+    pd.testing.assert_frame_equal(expected_confirmed, make_signal(data, CONFIRMED_PROP))
+
     expected_sum = pd.DataFrame({
         'state': ['na'],
         'timestamp': [datetime(year=2020, month=1, day=1)],
         'val': [15.],
     })
     pd.testing.assert_frame_equal(expected_sum, make_signal(data, SUM_CONF_SUSP))
 
+    expected_sum = pd.DataFrame({
+        'state': ['na'],
+        'timestamp': [datetime(year=2020, month=1, day=1)],
+        'val': [15.],
+    })
+    pd.testing.assert_frame_equal(expected_sum, make_signal(data, SUM_CONF_SUSP_PROP))
+
     with pytest.raises(Exception):
         make_signal(data, "zig")
 
@@ -91,9 +104,23 @@ def test_pop_proportion():
             'state': ['PA'],
             'state_code':[42],
             'timestamp': [datetime(year=2020, month=1, day=1)],
-            'val': [0.11716929299033142],})
+            'val': [0.12],})
     )
 
+    test_df= pd.DataFrame({  
+        'state': ['WV'],
+        'state_code':[54],
+        'timestamp': [datetime(year=2020, month=1, day=1)],
+        'val': [150.],})
+
+    pd.testing.assert_frame_equal(
+        pop_proportion(test_df,geo_mapper),
+        pd.DataFrame({
+            'state': ['WV'],
+            'state_code':[54],
+            'timestamp': [datetime(year=2020, month=1, day=1)],
+            'val': [8.37],})
+    )
 
 def test_make_geo():
     """Check that geographies transform correctly."""