Merge pull request #69 from Pennycook/cast-to-numeric

Fix validation of numeric types

p3analysis/_utils.py

@@ -19,14 +19,16 @@ 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.
     """
-
+    _require_columns(df, columns)
+    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

@@ -8,7 +8,7 @@
 
 import copy
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def cascade(
@@ -115,7 +115,7 @@ def cascade(
         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

@@ -3,7 +3,7 @@
 
 import copy
 
-from p3analysis._utils import _require_columns, _require_numeric
+from p3analysis._utils import _cast_to_numeric, _require_columns
 
 
 def navchart(
@@ -86,7 +86,7 @@ def navchart(
 
     _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
 
 
@@ -20,6 +21,19 @@ def test_required_columns(self):
         with self.assertRaises(ValueError):
             application_efficiency(df)
 
+    def test_required_column_types(self):
+        """Check that application_efficiency() validates column types."""
+        data = {
+            "problem": ["problem"],
+            "platform": ["platform"],
+            "application": ["application"],
+            "fom": ["non-numeric"],
+        }
+        df = pd.DataFrame(data)
+
+        with self.assertRaises(TypeError):
+            application_efficiency(df)
+
     def test_foms(self):
         """p3analysis.data.efficiency.foms"""
         data = {"problem": [], "platform": [], "application": [], "fom": []}
@@ -108,6 +122,29 @@ def test_efficiency(self):
         with self.assertRaises(TypeError):
             application_efficiency(df, foms="higher")
 
+    def test_non_numeric(self):
+        """Check that non-numeric data is correctly cast to numeric."""
+        # This is the same correctness test as above, but values are strings.
+        data = {
+            "problem": ["test"] * 10,
+            "platform": ["A", "B", "C", "D", "E"] * 2,
+            "application": ["latest"] * 5 + ["best"] * 5,
+            "fom": ["4", "8", "4", 0, "20"] + ["4", "10", "8", "20", "100"],
+        }
+        df = pd.DataFrame(data)
+
+        result = application_efficiency(df, foms="higher")
+
+        eff_data = {
+            "app eff": [1.0, 0.8, 0.5, 0.0, 0.2] + [1.0, 1.0, 1.0, 1.0, 1.0],
+        }
+        expected_data = data.copy()
+        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)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/metrics/test_pp.py

@@ -20,6 +20,19 @@ def test_required_columns(self):
         with self.assertRaises(ValueError):
             pp(df)
 
+    def test_required_column_types(self):
+        """Check that pp() validates column types."""
+        data = {
+            "problem": ["problem"],
+            "platform": ["platform"],
+            "application": ["application"],
+            "arch eff": ["non-numeric"],
+        }
+        df = pd.DataFrame(data)
+
+        with self.assertRaises(TypeError):
+            pp(df)
+
     def test_effs(self):
         """p3analysis.data.pp.effs"""
         data = {

tests/plot/test_cascade.py

@@ -31,6 +31,19 @@ def test_required_columns(self):
         with self.assertRaises(ValueError):
             cascade(df, eff="invalid")
 
+    def test_required_column_types(self):
+        """Check that cascade() validates column types."""
+        data = {
+            "problem": ["problem"],
+            "platform": ["platform"],
+            "application": ["application"],
+            "app eff": ["non-numeric"],
+        }
+        df = pd.DataFrame(data)
+
+        with self.assertRaises(TypeError):
+            cascade(df, eff="app")
+
     def test_options(self):
         """p3analysis.plot.cascade.options"""
 

tests/plot/test_navchart.py

@@ -47,6 +47,30 @@ def test_required_columns(self):
         })
         navchart(pp, cd)
 
+    def test_required_column_types(self):
+        """Check that navchart() validates column types."""
+        pp = pd.DataFrame({
+            "problem": ["problem"],
+            "platform": ["platform"],
+            "application": ["application"],
+            "app pp": [1.0],
+        })
+        cd = pd.DataFrame({
+            "problem": ["problem"],
+            "application": ["application"],
+            "divergence": [0.0],
+        })
+
+        with self.assertRaises(TypeError):
+            tmp = pp
+            tmp["app pp"] = "non-numeric"
+            navchart(tmp, cd)
+
+        with self.assertRaises(TypeError):
+            tmp = cd
+            tmp["divergence"] = "non-numeric"
+            navchart(pp, tmp)
+
     def test_options(self):
         """p3analysis.plot.navchart.options"""