Latest n diagonal in heatmap() #372
Replies: 8 comments
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Yes, you can use the import chainladder as cl
cl.Development(n_periods=3).fit_transform(cl.load_sample('raa')).link_ratio.heatmap() |
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Ahh, similar solution to what I came up with. raa = cl.load_sample("raa")
cl.Development().fit_transform(raa[raa.valuation.year >= raa.valuation_date.year-3]).link_ratio.heatmap() I think I am getting better at this. Only problem is now we can't see the older link ratios.😅 |
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Cool. By your approach you don't even need the raa = cl.load_sample('raa')
raa.link_ratio[raa.link_ratio.valuation.year >= raa.valuation_date.year-3].heatmap() |
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Yes, even better! |
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Should this be reclassifed discussion forum Q&A? |
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Yes, unless you think it's useful to add an argument to only heatmap the last n diagonals? It's a bit better as we can see the older diagonals that are not heat-mapped. But it will mean that it's another piece of code that we have to maintain. What do you think? |
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I fear n_diagonals will be short lived. Eventually someone will want the same but with drop_hi and drop_low. I dont want to create more args for those use cases. Maybe it's better to create a Boolean arg in heatmap that allows the dropped link ratios from the Development estimator to be visible or not. |
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That's actually a great idea. I like that. With this we should be able to see if the remaining LDFs have any patterns. |
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Is there an easy way to
heatmap()
only the latest n diagonal? I supposed we can add in another parameter, I proposelast_n_diagonal
, default toNone
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