Replace _require_numeric with _cast_to_numeric

Whereas _require_numeric simply checked that the values in a DataFrame
could be interpreted as numbers, _cast_to_numeric actually performs the
cast and returns the result.

Signed-off-by: John Pennycook <[email protected]>

p3analysis/_utils.py

@@ -19,14 +19,15 @@ def _require_columns(df, columns):
             raise ValueError(msg % (column, str(columns)))
 
 
-def _require_numeric(df, columns):
+def _cast_to_numeric(df, columns):
     """
-    Check that the named columns are numeric.
+    Check that the named columns are numeric, and cast them.
     """
-
+    result = df.copy(deep=True)
     for column in columns:
         try:
-            pd.to_numeric(df[column])
+            result[column] = pd.to_numeric(df[column])
         except Exception:
             msg = "Column '%s' must contain only numeric values."
             raise TypeError(msg % (column))
+    return result

p3analysis/metrics/_efficiency.py

@@ -3,7 +3,7 @@
 
 import numpy
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def application_efficiency(df, foms="lower"):
@@ -45,7 +45,7 @@ def application_efficiency(df, foms="lower"):
     """
     required_columns = ["problem", "platform", "application", "fom"]
     _require_columns(df, required_columns)
-    _require_numeric(df, ["fom"])
+    df = _cast_to_numeric(df, ["fom"])
 
     if foms not in ["lower", "higher"]:
         raise ValueError("FOM interpretation must be 'lower' or 'higher'")

p3analysis/metrics/_pp.py

@@ -6,7 +6,7 @@
 
 import pandas as pd
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def _hmean(series):
@@ -79,7 +79,7 @@ def pp(df):
     if len(efficiencies) == 0:
         msg = "DataFrame must contain a column named 'arch eff' or 'app eff'."
         raise ValueError(msg)
-    _require_numeric(df, efficiencies)
+    df = _cast_to_numeric(df, efficiencies)
 
     # Check that efficiencies are not given in percentages
     for eff in efficiencies:

p3analysis/plot/_cascade.py

@@ -6,7 +6,7 @@
 # Copyright (c) 2020 Performance Portability authors
 # SPDX-License-Identifier: MIT
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def cascade(df, eff=None, size=None, **kwargs):
@@ -111,7 +111,7 @@ def cascade(df, eff=None, size=None, **kwargs):
         if eff_column not in df:
             msg = "DataFrame does not contain an '%s' column."
             raise ValueError(msg % (eff_column))
-    _require_numeric(df, [eff_column])
+    df = _cast_to_numeric(df, [eff_column])
 
     # Check there is only one entry per (application, platform) pair.
     grouped = df.groupby(["platform", "application"])

p3analysis/plot/_navchart.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2022-2023 Intel Corporation
 # SPDX-License-Identifier: MIT
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def navchart(pp, cd, eff=None, size=None, goal=None, **kwargs):
@@ -80,7 +80,7 @@ def navchart(pp, cd, eff=None, size=None, goal=None, **kwargs):
 
     _require_columns(pp, ["problem", "application"])
     _require_columns(cd, ["problem", "application", "divergence"])
-    _require_numeric(cd, ["divergence"])
+    cd = _cast_to_numeric(cd, ["divergence"])
 
     if len(cd["problem"].unique()) > 1:
         raise NotImplementedError(

p3analysis/plot/backend/matplotlib.py

@@ -15,7 +15,7 @@
 from matplotlib.path import Path
 
 import p3analysis.metrics
-from p3analysis._utils import _require_numeric
+from p3analysis._utils import _cast_to_numeric
 from p3analysis.plot._common import ApplicationStyle, Legend, PlatformStyle
 from p3analysis.plot.backend import CascadePlot, NavChart
 
@@ -471,7 +471,7 @@ def __init__(
             if pp_column not in pp:
                 msg = "DataFrame does not contain an '%s' column."
                 raise ValueError(msg % (pp_column))
-        _require_numeric(pp, [pp_column])
+        pp = _cast_to_numeric(pp, [pp_column])
 
         # If the size is unset, default to 5 x 5
         if not size:

p3analysis/plot/backend/pgfplots.py

@@ -14,7 +14,7 @@
 import pandas as pd
 
 import p3analysis.metrics
-from p3analysis._utils import _require_numeric
+from p3analysis._utils import _cast_to_numeric
 from p3analysis.plot._common import ApplicationStyle, Legend, PlatformStyle
 from p3analysis.plot.backend import CascadePlot, NavChart
 
@@ -293,7 +293,7 @@ def __init__(
             if pp_column not in pp:
                 msg = "DataFrame does not contain an '%s' column."
                 raise ValueError(msg % (pp_column))
-        _require_numeric(pp, [pp_column])
+        pp = _cast_to_numeric(pp, [pp_column])
 
         # If the size is unset, default to 200pt x 200pt, otherwise set size
         plotwidth = "200pt"

tests/metrics/test_efficiency.py

@@ -5,6 +5,7 @@
 
 import pandas as pd
 
+from p3analysis._utils import _cast_to_numeric
 from p3analysis.metrics import application_efficiency
 
 
@@ -141,6 +142,7 @@ def test_non_numeric(self):
         expected_data.update(eff_data)
         expected_df = pd.DataFrame(expected_data)
 
+        expected_df = _cast_to_numeric(expected_df, ["fom", "app eff"])
         pd.testing.assert_frame_equal(result, expected_df)