analysis: Improve log statements

ema_workbench/analysis/b_and_w_plotting.py

@@ -333,7 +333,7 @@ def set_fig_to_bw(fig, style=HATCHING, line_style="continuous"):
 
     if len(all_colors) > len(bw_mapping):
         mapping_cycle = itertools.cycle(bw_mapping)
-        _logger.warning("more colors used than provided in B&W mapping, cycling over mapping")
+        _logger.warning(f"more colors ({len(all_colors)}) used than provided in B&W mapping ({len(bw_mapping)}), cycling over mapping")
     else:
         mapping_cycle = bw_mapping
     colormap = dict(zip(all_colors, mapping_cycle))

ema_workbench/analysis/pairs_plotting.py

@@ -548,7 +548,7 @@ def do_text_ticks_labels(ax, i, j, field1, field2, ylabels, outcomes_to_show):
             try:
                 ax.set_xlabel(ylabels.get(field2))
             except KeyError:
-                _logger.info("no label specified for " + field2)
+                _logger.info(f"no label specified for {field2}")
         else:
             ax.set_xlabel(field2)
 
@@ -561,6 +561,6 @@ def do_text_ticks_labels(ax, i, j, field1, field2, ylabels, outcomes_to_show):
             try:
                 ax.set_ylabel(ylabels.get(field1))
             except KeyError:
-                _logger.info("no label specified for " + field1)
+                _logger.info(f"no label specified for {field1}")
         else:
             ax.set_ylabel(field1)

ema_workbench/analysis/plotting.py

@@ -203,7 +203,7 @@ def group_by_envelopes(outcomes, outcome_to_plot, time, density, ax, ax_d, fill,
         try:
             plot_envelope(ax, j, time, value, fill)
         except ValueError:
-            _logger.exception(f"value error when plotting for {key}")
+            _logger.exception(f"ValueError when plotting for {key}")
             raise
 
     if density:

ema_workbench/analysis/plotting_util.py

@@ -483,7 +483,7 @@ def determine_kde(data, size_kde=1000, ymin=None, ymax=None):
     #         best_kde = grid.best_estimator_
     #         kde_x = np.exp(best_kde.score_samples(kde_y[:, np.newaxis]))
     except Exception as e:
-        _logger.warning(e)
+        _logger.warning(f"error in determine_kde: {e}")
         kde_x = np.zeros(kde_y.shape)
 
     return kde_x, kde_y
@@ -508,7 +508,7 @@ def filter_scalar_outcomes(outcomes):
     temp = {}
     for key, value in outcomes.items():
         if value.ndim < 2:
-            _logger.info(("{} not shown because it is " "not time series data").format(key))
+            _logger.info(f"outcome {key} not shown because it is not time series data")
         else:
             temp[key] = value
     return temp
@@ -814,7 +814,7 @@ def do_titles(ax, titles, outcome):
             try:
                 ax.set_title(titles[outcome])
             except KeyError:
-                _logger.warning(f"key error in do_titles, no title provided for `{outcome}`")
+                _logger.warning(f"KeyError in do_titles, no title provided for outcome `{outcome}`")
                 ax.set_title(outcome)
 
 
@@ -839,7 +839,7 @@ def do_ylabels(ax, ylabels, outcome):
             try:
                 ax.set_ylabel(ylabels[outcome])
             except KeyError:
-                _logger.warning(f"key error in do_ylabels, no ylabel provided for `{outcome}`")
+                _logger.warning(f"KeyError in do_ylabels, no ylabel provided for outcome `{outcome}`")
                 ax.set_ylabel(outcome)
 
 

ema_workbench/analysis/prim.py

@@ -27,7 +27,7 @@
     import altair as alt
 except ImportError:
     alt = None
-    warnings.warn(("altair based interactive " "inspection not available"), ImportWarning)
+    warnings.warn("altair based interactive inspection not available", ImportWarning)
 
 from ..util import EMAError, temporary_filter, INFO, get_module_logger
 from . import scenario_discovery_util as sdutil
@@ -108,7 +108,9 @@ def pca_preprocess(experiments, y, subsets=None, exclude=set()):
     if not x.select_dtypes(exclude=np.number).empty:
         raise RuntimeError("X includes non numeric columns")
     if not set(np.unique(y)) == {0, 1}:
-        raise RuntimeError(f"y should only contain 0s and 1s, currently y contains {set(np.unique(y))}.")
+        raise RuntimeError(
+            f"y should only contain 0s and 1s, currently y contains {set(np.unique(y))}."
+        )
 
     # if no subsets are provided all uncertainties with non dtype object
     # are in the same subset, the name of this is r, for rotation
@@ -1026,7 +1028,9 @@ def __init__(
         for column in x_nominal.columns.values:
             if np.unique(x[column]).shape == (1,):
                 x = x.drop(column, axis=1)
-                _logger.info(f"{column} dropped from analysis " "because only a single category")
+                _logger.info(
+                    f"column {column} dropped from analysis because it has only one category"
+                )
 
         x_nominal = x.select_dtypes(exclude=np.number)
         self.x_nominal = x_nominal.values
@@ -1100,7 +1104,7 @@ def find_box(self):
             box._frozen = True
 
         if self.yi_remaining.shape[0] == 0:
-            _logger.info("no data remaining")
+            _logger.info("no data remaining, exiting")
             return
 
         # log how much data and how many coi are remaining
@@ -1119,7 +1123,7 @@ def find_box(self):
         box = self._paste(box)
         _logger.debug("pasting completed")
 
-        message = "mean: {0}, mass: {1}, coverage: {2}, " "density: {3} restricted_dimensions: {4}"
+        message = "mean: {0}, mass: {1}, coverage: {2}, density: {3} restricted_dimensions: {4}"
         message = message.format(box.mean, box.mass, box.coverage, box.density, box.res_dim)
 
         if (self.threshold_type == ABOVE) & (box.mean >= self.threshold):
@@ -1133,7 +1137,8 @@ def find_box(self):
         else:
             # make a dump box
             _logger.info(
-                f"box does not meet threshold criteria, value is {box.mean}, returning dump box"
+                f"box mean ({box.mean}) does not meet threshold criteria ({self.threshold_type} {self.threshold}),"
+                f"returning dump box"
             )
             box = PrimBox(self, self.box_init, self.yi_remaining[:])
             self._boxes.append(box)