Merge branch 'pegasystems:master' into master

pegasystems · Oct 17, 2023 · 5aba71a · 5aba71a
2 parents 1d56061 + 552639b
commit 5aba71a
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diff --git a/examples/datamart/healthcheck.Rmd b/examples/datamart/healthcheck.Rmd
@@ -221,18 +221,27 @@ configurations it seems that the framework
 -   Channels with no positive feedback
 
 ```{r Channel overview}
-channelSummary <- datamart$modeldata[, .(`Responses` = max(ResponseCount), 
-                                         `Positives` = max(Positives), 
-                                         `Base rate` = sprintf("%.2f%%", 100*max(Positives)/max(ResponseCount)),
-                                         `Supported by Configurations` = paste(sort(unique(ConfigurationName)), collapse = ", "),
-                                         N = uniqueN(ConfigurationName)), 
-                                     by=c("Channel","Direction")][order(Channel)]
+
+# take the max counts per model
+channelSummaryMaxByModel <- datamart$modeldata[, .(Responses = max(ResponseCount), 
+                                                   Positives = max(Positives)),
+                                               by=c("Direction", "Channel", "ModelID", "ConfigurationName")]
+# sum up the response counts per configuration and channel
+channelSummaryCountsByConfiguration <- channelSummaryMaxByModel[, .(Responses = sum(Responses), 
+                                                                    Positives = sum(Positives)), 
+                                                                by=c("Direction", "Channel", "ConfigurationName")]
+# multiple model configurations could be driving one channel, take the max
+channelSummary <- channelSummaryCountsByConfiguration[, .(Responses = max(Responses),
+                                                          Positives = max(Positives),
+                                                          `Base rate` = sprintf("%.2f%%", 100*max(Positives)/max(Responses)),
+                                                          `Supported by Configurations` = paste(sort(unique(ConfigurationName)), collapse = ", "),
+                                                          N = uniqueN(ConfigurationName)), by=c("Direction", "Channel")][order(Channel, Direction)]
 
 channelSummary[, 1:(ncol(channelSummary)-1)] %>%
   kbl() %>%
   kable_paper(c("striped", "hover"), full_width = F) %>%
-  column_spec(3, background = ifelse(channelSummary[[3]] > 0 ,"white", "red")) %>%
-  column_spec(4, background = ifelse(channelSummary[[4]] > 0 ,"white", "red")) %>%
+  column_spec(3, background = ifelse(channelSummary$Responses > 0 ,"white", "red")) %>%
+  column_spec(4, background = ifelse(channelSummary$Positives > 0 ,"white", "red")) %>%
   column_spec(6, background = ifelse(channelSummary$N <= 2 ,"white", "orange"))
 ```
 

diff --git a/python/pdstools/plots/plots_plotly.py b/python/pdstools/plots/plots_plotly.py
@@ -33,9 +33,10 @@ def distribution_graph(df, title):
             template="none", title=title, xaxis_title="Range", yaxis_title="Responses"
         )
         fig.update_yaxes(title_text="Propensity", secondary_y=True)
-        fig.layout.yaxis2.tickformat = ",.2%"
+        fig.layout.yaxis2.tickformat = ",.3%"
         fig.layout.yaxis2.zeroline = False
         fig.update_yaxes(showgrid=False)
+        fig.update_xaxes(type='category')
 
         return fig
 
@@ -342,6 +343,7 @@ def PredictorBinning(self, df, facet_val, **kwargs):
         name = df.select(["Name"]).row(0)
         modelid, predictorname = facet_val
         title = f"Model name: {name}<br><sup>Model ID: {modelid}<br>Predictor name: {predictorname}</sup>"
+        df = df.sort("BinIndex")
         fig = self.distribution_graph(df, title)
         return self.post_plot(fig, name="Predictor_binning", **kwargs)