chainladder - Property and Casualty Loss Reserving in Python

Last update: Dec 07, 2022

Overview

chainladder (python)

chainladder - Property and Casualty Loss Reserving in Python

This package gets inspiration from the popular R ChainLadder package.

This package strives to be minimalistic in needing its own API. Think in pandas for data manipulation and scikit-learn for model construction. An actuary already versed in these tools will pick up this package with ease. Save your mental energy for actuarial work.

Documentation

Please visit the Documentation page for examples, how-tos, and source code documentation.

Available Estimators

chainladder has an ever growing list of estimators that work seemlessly together:

Loss Development	Tails Factors	IBNR Models	Adjustments	Workflow
Development	TailCurve	Chainladder	BootstrapODPSample	VotingChainladder
DevelopmentConstant	TailConstant	MackChainladder	BerquistSherman	Pipeline
MunichAdjustment	TailBondy	BornhuettterFerguson	ParallelogramOLF	GridSearch
ClarkLDF	TailClark	Benktander	Trend
IncrementalAdditive		CapeCod
CaseOutstanding
TweedieGLM
DevelopmentML
BarnettZehnwirth

Getting Started Tutorials

Tutorial notebooks are available for download here.

Installation

To install using pip: pip install chainladder

To instal using conda: conda install -c conda-forge chainladder

Alternatively for pre-release functionality, install directly from github: pip install git+https://github.com/casact/chainladder-python/

Note: This package requires Python>=3.5 pandas 0.23.0 and later, sparse 0.9 and later, scikit-learn 0.23.0 and later.

Questions or Ideas?

Join in on the github discussions. Your question is more likely to get answered here than on Stack Overflow. We're always happy to answer any usage questions or hear ideas on how to make chainladder better.

Want to contribute?

Check out our contributing guidelines.

Comments

Independence tests for Chain Ladder

It is important that some critical assumptions around chain ladder are tested before applying the method. Thomas Mack suggested some tests, for example in "Measuring the variability of Chain Ladder reserve estimates", 1997. Below are implementations for tests of correlation among development factors and for the impact of calendar years. I don't feel confident enough to modify the package code directly but hopefully this can be a useful template.

def developFactorsCorrelation(df):
    # Mack (1997) test for Correlations between Subsequent Development Factors
    # results should be between -.67x and +.67x stdError otherwise too much correlation
    m1=df.rank() # rank the development factors by column
    m2=df.to_numpy(copy=True) #does the same but ignoring the anti-diagonal
    np.fill_diagonal(np.fliplr(m2),np.nan)
    m2=pd.DataFrame(np.roll(m2,1),columns=m1.columns, index=m1.index).iloc[:,1:] #leave out the first column
    m2=m2.rank()
    numerator=((m1-m2) **2).sum(axis=0)
    SpearmanFactor=pd.DataFrame(range(1,len(m1.columns)+1),index=m1.columns, columns=['colNo'])
    I = SpearmanFactor['colNo'].max()+1
    SpearmanFactor['divisor'] = (I-SpearmanFactor['colNo'])**3 - I +SpearmanFactor['colNo'] #(I-k)^3-I+k
    SpearmanFactor['value']= 1-6*numerator.T/SpearmanFactor['divisor']
    SpearmanFactor['weighted'] = SpearmanFactor['value'] * (I-SpearmanFactor['colNo']-1) / (SpearmanFactor[1:-1]['colNo']-1).sum() #weight sum excludes 1 and I
    SpearmanCorr=SpearmanFactor['weighted'].iloc[1:-1].sum() # exlcuding 1st and last elements as not significant
    SpearmanCorrVar = 2/((I-2)*(I-3))
    return SpearmanCorr,SpearmanCorrVar

from scipy.stats import binom
def calendarCorrelation(df, pCritical=.1):
    # Mack (1997) test for calendar year effect
    # A calendar period has impact across developments if the probability of the number of small (or large)
    # development factors in that period occurring randomly is less than pCritical
    # df should have period as the row index, on the assumption that the first anti-diagonal is in relation to the same period (development=0) 
    m1=df.rank() # rank the development factors by column
    med=m1.median(axis=0) # find the median value for each column
    m1large=m1.apply(lambda r: r>med, axis=1) # sets to True those elements in each column which are large (above the median rank)
    m1small=m1.apply(lambda r: r<med, axis=1)
    m2large=m1large.to_numpy(copy=True)
    m2small=m1small.to_numpy(copy=True)
    S=[np.diag(m2small[:,::-1],k).sum() for k in range(min(m2small.shape),-1,-1)] # number of large elements in anti-diagonal (calendar year)
    L=[np.diag(m2large[:,::-1],k).sum() for k in range(min(m2large.shape),-1,-1)] # number of large elements in anti-diagonal (calendar year)
    probs=[binom.pmf(S[i], S[i]+L[i], 0.5)+binom.pmf(L[i], S[i]+L[i], 0.5) for i in range(len(S))] # probability of NOT having too many large or small items in anti-diagonal (calendar year)
    return pd.Series([p<pCritical for p in probs[1:]], index=df.index)

And using the example in the paper

MackEx='''1,2,3,4,5,6,7,8,9
1.6,1.32,1.08,1.15,1.2,1.1,1.033,1,1.01
40.4,1.26,1.98,1.29,1.13,.99,1.043,1.03,
2.6,1.54,1.16,1.16,1.19,1.03,1.026,,
2,1.36,1.35,1.1,1.11,1.04,,,
8.8,1.66,1.4,1.17,1.01,,,,
4.3,1.82,1.11,1.23,,,,,
7.2,2.72,1.12,,,,,,
5.1,1.89,,,,,,,
1.7,,,,,,,,
'''
df=pd.read_csv(StringIO(MackEx), header=0)
df.index = df.index+1 #reindex rows from 1
dfCorr,dfCorrVar = developFactorsCorrelation(df)
print('Development factors correlation is {:.2%}'.format(dfCorr))
print('Factor independence if correlation is in range [{:.2%} to {:.2%}]'.format(-.67*np.sqrt(dfCorrVar),  .67*np.sqrt(dfCorrVar)))
print('Dependence on calendar year:')
print(calendarCorrelation(df))

Result is

Development factors correlation is 6.96%
Factor independence if correlation is in range [-12.66% to 12.66%]
Dependence on calendar year:
1    False
2    False
3    False
4    False
5    False
6    False
7    False
8    False
9    False
dtype: bool

Enhancement

opened by gig67 26

Requirement - Dropping Link Ratios conditionally (based on any input value)

Hi! I hope that you all are fine.

There are several arguments for dropping/discarding problematic link ratios as well as excluding whole valuation periods or highs and lows. Any combination of the ‘drop’ arguments is permissible.

I would like to ask you that is there any provision/function (in chainladder-python library) to drop link ratios conditionally (based on any input value), please guide me in this regards. Please go through the example below (which we need):

Example: This is sample link ratio:

### Source - Link Ratio (before applying any dropping condition) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | | 2017Q1 | 1.7566 | 1.1415 | 1.0917 | 1.0091 | 1.0062 | 1.0007 | 1.0014| | 2017Q2 | 1.9576 | 1.2615 | 1.0640 | 0.8965 | 1.0008 | 1.0029 | | 2017Q3 | 2.4820 | 0.9930 | 1.0588 | 1.0491 | 1.0121 | | 2017Q4 | 1.8434 | 1.0787 | 1.0773 | 0.9654 | | 2018Q1 | 1.4951 | 0.9120 | 1.0428 | | 2018Q2 | 2.3292 | 1.1350 | | 2018Q3 | 1.1851 |

Link Ratio-Cuttoff (input value): 0.9999

Our requirement is that: If any value of above 'Source - Link Ratio' is getting less than 'Link Ratio-Cuttoff (input value)', the particular value will be dropped as mentioned in the below 'Target - Link Ratio'.

### Target - Link Ratio (After applying condition based on the above input value) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | | 2017Q1 | 1.7566 | 1.1415 | 1.0917 | 1.0091 | 1.0062 | 1.0007 | 1.0014 | | 2017Q2 | 1.9576 | 1.2615 | 1.0640 | | 1.0008 | 1.0029 | | 2017Q3 | 2.4820 | | 1.0588 | 1.0491| 1.0121 | | 2017Q4 | 1.8434 | 1.0787 | 1.0773 | | | 2018Q1 | 1.4951 | | 1.0428 | | 2018Q2 | 2.3292 | 1.1350 | | 2018Q3 | 1.1851 |

I have made many tries to explore particular subjective dropping/omitting link-ratio feature in the provided chainladder-python documentation. However, I was unable to find out this. You are kindly requested to guide me in this regard, if feature is already available.

If it is not available and if it is possible, please provide the following conditional dropping link ratio feature at your earliest.

Best Regards, Muhammad Shamsher Khan
Enhancement

opened by muhammadshamsherkhan2002 24

Having some trouble with origin periods

Trying to bring in premium data for BF. For some reason the origin type gets set to Q-JUN (loss had was Q-DEC no problem).

prem = [1000,1200,1300,1400,1500,1600,1700,1800]
dates = ["2015-07-01","2015-10-01","2016-01-01","2016-04-01","2016-07-01","2016-10-01","2017-01-01","2017-04-01"]
premium_df = pd.DataFrame(data={"Premium":prem,"Date":dates})
premium_df["val"] = "2017-12-31"
premium_tri = cl.Triangle(
    data=premium_df,
    origin="Date",
    development="val",
    columns="Premium",
    cumulative=False,
)
premium_tri.origin

Bug

opened by henrydingliu 16

Unpredictable behaviour of drop_high

drop_high does some weird stuff sometimes

prism_df = pd.read_csv(
    "https://raw.githubusercontent.com/casact/chainladder-python/master/chainladder/utils/data/prism.csv"
)
prism_df["AccidentDate"] = pd.to_datetime(prism_df["AccidentDate"])
prism_df["PaymentDate"] = pd.to_datetime(prism_df["PaymentDate"])
prism_df.loc[(prism_df["AccidentDate"].dt.year > 2008) & (prism_df["PaymentDate"].dt.year > 2012),"Incurred"] = 0
prism = cl.Triangle(
    data=prism_df,
    origin="AccidentDate",
    development="PaymentDate",
    columns="Incurred",
    cumulative=False,
)
prism = prism.grain("OYDY").incr_to_cum()
prism_dev = cl.Development(drop_high = 1, drop_low = 1).fit_transform(prism)
print(prism.age_to_age)
print(prism_dev.age_to_age)

drop_high seems to not work on 12-24, 24-36, 36-48, and 48-60,

Bug

opened by henrydingliu 15

Drop High/Drop Low are not working correctly
I get a different answer depending on whether I fit just the work comp LOB vs all LOBs and then slice work comp. These approaches should yield the same answer, but they don't:

import chainladder as cl clrd = cl.load_sample('clrd').groupby('LOB')['CumPaidLoss'].sum() # Passes assert cl.Development(n_periods=5).fit(clrd).ldf_.loc['wkcomp'] == cl.Development(n_periods=5).fit(clrd.loc['wkcomp']).ldf_ # Fails assert cl.Development(drop_low=2).fit(clrd).ldf_.loc['wkcomp'] == cl.Development(drop_low=2).fit(clrd.loc['wkcomp']).ldf_

I don't think this ever worked. The reason is that we only have one 2D (origin x development) matrix of the entries to be dropped. This matrix is shared across all triangles. When dropping using n_periods, drop_valutation, drop_origin, etc, this is fine. But dropping based on link-ratio values it is not.
Bug
opened by jbogaardt 14
Issue on page /tutorials/data-tutorial.html

error at the bottom of the page

plt.plot( (model_cl.ultimate_["Paid"]).to_frame().index.astype("string"), (model_cl.ultimate_["Paid"] / model_cl.ultimate_["reportedCount"]).to_frame(), ) plt.xticks(np.arange(0, 10 * 12, 12), np.arange(2008, 2018, 1))
Documentation

opened by henrydingliu 14
Expected Loss Method bug

using the example provided in https://chainladder-python.readthedocs.io/en/latest/tutorials/deterministic-tutorial.html#expected-loss-method

EL_model.full_triangle_ shows negatives in the next diagonal. The bottom triangle should be filled in by allocating the selected IBNR using conditional distributions from the selected dev pattern, rather than doing implied LDF calcs.

Might also be an issue with Benktander. Will investigate and report back.

opened by henrydingliu 13

Address origin_as_datetime bug

Using try/except to check if origin_as_datetime can be converted

I am not sure if I am preserving the *args or **kargs correctly. Can you take a look?

The following test code works:

import chainladder as cl
raa = cl.load_sample("raa")
cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=False)
cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=True) # no warning thrown
cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=False)
cl.Chainladder().fit(raa).cdf_.to_frame(origin_as_datetime=True) # warning thrown

Bug

opened by kennethshsu 13

Bug in to_frame.origin_as_datetime argument

Will try to create a new ticket so we can better keep track for patch notes going forward.

Is there a standardized warning message to use? I was thinking Deprecation warning: in an upcoming version of the package, origin_as_datetime will be defaulted to True in to_frame(...), use origin_as_datetime=False to preserve current setting.
Bug

opened by kennethshsu 13
full_triangle_ for BF method

BF method doesn't seem to be creating the runoff triangle correctly. In my data, accident months can have volatile reported losses as of the latest valuation date. Using an expected IBNR method can therefore produce volatile LDF's (implied, using ultimate_ / latest.diagonal ). In my particular case, when reported losses are an outlier on the low end, the implied LDF is very high. When I extract the first diagonal from the runoff triangle and divide that into my ultimate*, the resulting LDF's are much more homogeneous than they were the previous months. Assuming my development pattern is relatively benign, there shouldn't be this much smoothing (development) in a one month projection. For example, if the percent of ultimate for month n is estimated to be 90% and for n+1 it's 95%, the LDF shouldn't drop from 3.0 to 1.2, it should drop to 1.6 (only 3.3% of ultimate has been reported for the month in questions as of month n). The projected ultimate is only 13.3% and an expectation of 5% being reported next month would yield an expected reported loss of 8.3% and an implied ldf of 1.6.

Use case here is daily interpolation between month start and month end (ie, subsequent) LDF's. It's better to interpolate to the expected month-end ldf than the subsequent accident month's ldf as this could produce large swings in your projected ultimate losses during the month.

*First diagonal from runoff triangle: bf_table2 = bf_table.full_triangle_.dev_to_val() bf_table3 = bf_table2[bf_table2.valuation==(triangle.valuation_date + relativedelta(months=+1))] next_mo_ldf = bf_table.ultimate_ / bf_table3

for relativedelta, use "from dateutil.relativedelta import *"

opened by mlbugs 13
Requirement - Provision required for Inputting whole Link Ratio
I hope that you are fine and perfect.

It is good to see that your team provided the many features to accommodate the actuarial team fulfillment in python-chainladder library.

We keep on exploring features from your provided documentation as and when needed and fortunately, most of the things are available in the API.

Nowadays, We are exploring the subjective feature in the API. However, We are unable to find it so far. Our requirement is (provision for inputting whole modified link ratio for re-processing and generation subsequent methods ldf, cdf and so on...), please go through the example below:

-Primarily, we will process triangle, link ratio, ldf, cdf and so on as usual based on the input raw data while using python-chainladder library methods:

for example:

-In 1st attempt

actuarial_df = pd.read_csv("c:/actuarial-data/actuarialData.csv")

triangle = cl.Triangle(actuarial_df,origin="origin",development="development",columns="values",cumulative=True)

triangle.link_ratio

cl.Development(average="volume").fit(triangle).ldf_

and so on

-In 2nd attempt or during re-processing (after necessary modification in the link ratio)

input link ratio (like a triangle) linkRatio_df = pd.read_csv("c:/actuarial-data/linkRatioData.csv")

Method required for taking complete link ratio as input link_ratio = cl.Triangle(linkRatio_df,origin="origin",development="development")

and ldf, cdf and so on...

You are kindly requested to guide me in this regard, if feature is already available. If it is not and if it is possible, please provide the following requirement in your API. We will be very grateful to you.

Best Regards, Muhammad Shamsher Khan
opened by muhammadshamsherkhan2002 11

[BUG]

Describe the bug applying a cl estimator to another triangle doesn't always work

To Reproduce

import chainladder as cl
clrd_total = cl.load_sample('clrd').sum()[["CumPaidLoss"]]
clrd = cl.load_sample('clrd').groupby("LOB").sum()[["CumPaidLoss"]]
est = cl.Chainladder().fit(clrd_total)
try:
    est_other = est.predict(clrd[clrd["LOB"]=="comauto"])
except:
    print("this doesn't work")
est = cl.Chainladder().fit(clrd[clrd["LOB"]=="comauto"])
est_other = est.predict(clrd_total)
print(est_other.ultimate_)
print("but this works")

Expected behavior the code inside the try should work

Desktop (please complete the following information):

Numpy Version 1.21.6
Pandas Version 1.4.4
Chainladder Version 0.8.14

Bug

opened by henrydingliu 1

[BUG] changing grain with an off-cycle close.
Describe the bug The Triangle class supports trailing quarter, semesters and years. However, moving from a triangle grain that is trailing to more aggregate grain produces inaccurate results.

To Reproduce Steps to reproduce the behavior. Code should be self-contained and runnable against publicly available data. For example:

import chainladder as cl prism = cl.load_sample('prism')['Paid'].sum() prism = prism[prism.valuation<prism.valuation_date].incr_to_cum() # (limit data to November) prism = prism.grain('OQDQ', trailing=True) # OK primt(prism.origin_close) # OK - latest origin is complete quarter cut-off at November prism.grain('OYDY') # Not OK.

Expected behavior prism.grain('OYDY') should inherit the trailing property and produce ages 12, 24, 36, etc.
Bug
opened by jbogaardt 0
[BUG] origin_as_datetime does nothing
Describe the bug origin_as_datetime does nothing

To Reproduce This assertion should not fail

import chainladder as cl assert cl.load_sample('raa').to_frame(origin_as_datetime=True).index == pd.DatetimeIndex
Bug
opened by jbogaardt 0
[BUG] Zero Loss values in Expected Loss Methods.
Describe the bug I am still seeing zeros in CapeCod IBNR when I shouldn't. This code is against the unreleased master branch.

To Reproduce Even though our predicted loss triangle is zero for all entries, we should have some IBNR from the non-zero exposure

import chainladder as cl wc = cl.load_sample('clrd').groupby('LOB')[['CumPaidLoss', 'EarnedPremDIR']].sum().loc['wkcomp'] model = cl.CapeCod().fit(wc['CumPaidLoss'], sample_weight=wc['EarnedPremDIR'].latest_diagonal) model.predict( wc['CumPaidLoss']*0, sample_weight=wc['EarnedPremDIR'].latest_diagonal ).ibnr_

Manually pushing the CapeCod.apriori_ into the BornheutterFerguson doesn't resolve the issue:

cl.BornhuetterFerguson().fit(wc['CumPaidLoss']*0, sample_weight=model.apriori_ * wc['EarnedPremDIR'].latest_diagonal).ibnr_

Expected behavior I expect there to be non-zero IBNR for this workflow.
Bug
opened by jbogaardt 0

[BUG] CapeCod predict at different index grain

Describe the bug The chainladder estimators are supposed to broadcast LDFs and Aprioris down do a lower grain. For example, one might fit countrywide patterns, but predict at a state level. For BornheutterFerguson and Chainladder this works.

import chainladder as cl
prism = cl.load_sample('prism')[['reportedCount', 'Paid']]

bf_pipe = cl.Pipeline(
    [('dev', cl.Development()),
     ('model', cl.BornhuetterFerguson())]
)
# Fit at Line level
bf_pipe.fit(
    X=prism.groupby('Line')['Paid'].sum(), 
    sample_weight=prism.groupby('Line')['reportedCount'].sum().sum('development'))

# Predict at claim level
bf_pipe.predict(
    X=prism['Paid'], 
    sample_weight=prism['reportedCount'].sum('development')
)

To Reproduce The same code errors out when using the CapeCod estimator:

import chainladder as cl
prism = cl.load_sample('prism')[['reportedCount', 'Paid']]
# CapeCod Fails
cc_pipe = cl.Pipeline(
    [('dev', cl.Development()),
     ('model', cl.CapeCod())]
)
# Fit at Line level
cc_pipe.fit(
    X=prism.groupby('Line')['Paid'].sum(), 
    sample_weight=prism.groupby('Line')['reportedCount'].sum().sum('development'))
# Predict at claim level
cc_pipe.predict(
    X=prism['Paid'], 
    sample_weight=prism['reportedCount'].sum('development'))

Expected behavior I expect the CapeCod code not to crash.

Bug

opened by jbogaardt 3

[BUG] Chainladder doesn't coerce valuation triangle correctly

Describe the bug See offending code below.

To Reproduce Steps to reproduce the behavior. Code should be self-contained and runnable against publicly available data. For example:

import chainladder as cl
raa = cl.load_sample('raa')
model = cl.Chainladder().fit(raa)
# works 
assert model.predict(raa.latest_diagonal.val_to_dev()).ultimate_ == model.ultimate_
# doesn't work
assert model.predict(raa.latest_diagonal).ultimate_ == model.ultimate_

Bug

opened by jbogaardt 1

Releases(v0.8.14)

v0.8.14(Nov 25, 2022)
New Contributors

A big thank you to our new contributors this release

@henrydingliu made their first contribution in https://github.com/casact/chainladder-python/pull/349

@wleescor made their first contribution in https://github.com/casact/chainladder-python/pull/384

Bug Fixes

Updating drop high logic by @henrydingliu in https://github.com/casact/chainladder-python/pull/349

Adding std_residuals_ to the transformer by @henrydingliu in https://github.com/casact/chainladder-python/pull/352

add semi-annual key by @wleescor in https://github.com/casact/chainladder-python/pull/384

addressed #321 by @kennethshsu in https://github.com/casact/chainladder-python/pull/368

Adding 4D support to drop_hi/lo by @henrydingliu in https://github.com/casact/chainladder-python/pull/370

Corrected number of days in a year to 365.25 by @kennethshsu in https://github.com/casact/chainladder-python/pull/371

#294 and #313 are fixed here by @kennethshsu in https://github.com/casact/chainladder-python/pull/373

adding 4D support for drop_above/below by @henrydingliu in https://github.com/casact/chainladder-python/pull/375

QoL improvement by @henrydingliu in https://github.com/casact/chainladder-python/pull/381

#330 Fix for full triangle per suggestion by @henrydingliu

#367 Triangle instatiation issues @jbogaardt

Documentation

Documentation by @kennethshsu in https://github.com/casact/chainladder-python/pull/311

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/335

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/336

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/337

addressed #319 and #333 by @kennethshsu in https://github.com/casact/chainladder-python/pull/338

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/339

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/350

Fixed paths by @kennethshsu in https://github.com/casact/chainladder-python/pull/351

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/355

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/358

Annual meeting prep by @kennethshsu in https://github.com/casact/chainladder-python/pull/360

Found some typos by @kennethshsu in https://github.com/casact/chainladder-python/pull/361

Corrected some more typos by @kennethshsu in https://github.com/casact/chainladder-python/pull/363

Fixed readthedocs build #374 @jbogaardt

Enhancements

Mack correlation test enhancements by @genedan in https://github.com/casact/chainladder-python/pull/342

Refine Mack DevelopmentCorrelation class by @genedan in https://github.com/casact/chainladder-python/pull/344

Adding Friedland & Mack 97 data sets by @genedan in https://github.com/casact/chainladder-python/pull/347

Add Friedland WC Self-Insurer data set. by @genedan in https://github.com/casact/chainladder-python/pull/348

Add Friedland gl and xyz disposal rate data sets. by @genedan in https://github.com/casact/chainladder-python/pull/353

Add Friedland US Industry Auto - case outstanding technique data set. by @genedan in https://github.com/casact/chainladder-python/pull/357

Add Friedland XYZ Insurer - case outstanding technique data set. by @genedan in https://github.com/casact/chainladder-python/pull/359

Add 3 datasets by @genedan in https://github.com/casact/chainladder-python/pull/362

Add 3 datasets: by @genedan in https://github.com/casact/chainladder-python/pull/366

Full Changelog: https://github.com/casact/chainladder-python/compare/v0.8.13...v0.8.14
Source code(tar.gz)
Source code(zip)
v0.8.13(Jun 27, 2022)
The majority of this release was written by @kennethshsu and contains a first-time contribution from @genedan. Thank you!

Bug Fixes:

#270 Fixed an issue affecting triangle instantiation

#251 Fixed a bug in predict method that failed to honor development ages appropriatly

#274 Addressed a pandas deprecation notice in the heatmap method.

#258 Addressed silent error when predicting on a Triangle that is larger than the underlying model.

#288 Addressed Bug in to_frame method

#283 Addressed Bug in the calculation of LDFs in CaseOutstanding

Enhancements

#293 Expanded functionality drop_high and drop_low arguments of Development estimator.

#298, #299, #304, #305, #308 Documentation clean-up.

Source code(tar.gz)
Source code(zip)
v0.8.12(Mar 8, 2022)
Bug Fixes:

#254 Fixed an undesired mutation when using cl.concat

#257 to_frame bug fix on empty triangles

#248 to_frame bug and deprecation notice in origin_as_datetime argument.

#250 Bug fix in triangle initialization

#258 Addressed silent error when predicting on a Triangle that is larger than the underlying model.

#261 Addressed a pandas>1.4.0 bug

Enhancements

Introduced ExpectedLoss method

#242 Added threshold based dropping of link ratios to Development estimator

#158 Triangles can now be instantiated with only one data point.

#250 python 3.10 support

#260 fillna method added to Triangle class that supports filling with constants or other Triangles.

Support for 'H' or 'S' when using half year grain

Source code(tar.gz)
Source code(zip)
v0.8.11-beta(Mar 7, 2022)

Source code(tar.gz)
Source code(zip)
v0.8.11-alpha(Mar 7, 2022)
Bug Fixes:

#239 val_to_dev bug fixed

#254 Fixed an undesired mutation when using cl.concat

#257 to_frame bug fix on empty triangles

#248 to_frame bug and deprecation notice in origin_as_datetime argument.

#250 Bug fix in triangle initialization

#258 Addressed silent error when predicting on a Triangle that is larger than the underlying model.

#261 Addressed a pandas>1.4.0 bug

Enhancements

Introduced ExpectedLoss method

#242 Added threshold based dropping of link ratios to Development estimator

#158 Triangles can now be instantiated with only one data point.

#250 python 3.10 support

#260 fillna method added to Triangle class that supports filling with constants or other Triangles.

Source code(tar.gz)
Source code(zip)
v0.8.10(Dec 4, 2021)
Enhancements

#225 - Added support for mid-year triangles

Remove xlcompose as a dependency

#233 and #219 - Added more explicit warning when is_cumulative property of triangle is not set.

#192 Expanded drop_high and drop_low functionality to include integers and variable length lists. PR courtesy of @kennethshsu.

Bug fixes

#234 Coerce MackChainladder to zero variance when inbr_ and cdf_ are 0.

Fix bug in BarnettZehnwirth.transform that was previously not applying the log-transform correctly

Source code(tar.gz)
Source code(zip)
v0.8.9(Oct 24, 2021)
Enhancements

#198 Added projection_period to all Tail estimators. This allows for analyzing run-off beyond a one year time horizon.

#214 A refreshed docs theme using jupyter-book!

#200 Added more flexibility to TailCurve.fit_period to allow boolean lists - similar to Development.drop_high

@kennethshsu further improved tutorials

Bug Fixes

#210 Fixed regression in triangle instantiation where grain=='S' is being interpreted as seconds and not semesters.

#213 Fixed an unintended Triangle mutation when reassigning columns on a sparse backend.

#221 Fixed origin/development broadcasting issue that was causing silent bugs in calculations on malformed triangles.

Source code(tar.gz)
Source code(zip)
v0.8.8(Sep 13, 2021)
Enhancements

#140 Improved test coverage

#126 Relaxed BootstrapODPSample column restriction

#207 Improved tutorials

Bug Fixes

#204 Fixed regression in grain

#203 Fixed regression in slice

Fixed regression in Benktander index broadcasting

#205 Fixed CI/CD build

Source code(tar.gz)
Source code(zip)
v0.8.7(Aug 29, 2021)
Minor release to fix some chainladder==0.8.6 regressions.

Bug Fixes

Fixed #190 - 0 values getting into heatmap

Fixed #191 regression that didn't support earlier versions of pandas

Fixed #197 Index broadcasting edge case

Fixed valuation_date regression when instantiating Triangles as vectors

Enhancements

#99 Added Semester as an additional grain

Added parallel support to GridSearch using n_jobs argument

More tutorial improvements from @kennethshsu

Source code(tar.gz)
Source code(zip)
v0.8.6(Aug 18, 2021)
Ehancements

@kennethshsu improved heatmap shading

Support for numpy reductions, e.g. np.sum(cl.load_sample('raa'))

@kennethshsu further improved tutorials

Bug fixes

#186 Fix bug that disallowed instantiating a full triangles

#180 Fix bug that mutated original DataFrame when instantiating Triangle

#182 Eliminate new deprecations warnings from pandas>=1.3

#183 Better alignment with pandas on index broadcasting

#179 Fixed nan befavior on val_to_dev

implement TriangleGroupby.__getitem__ to support column selection on groupby operations to align with pandas, e.g. cl.load_sample('clrd').groupby('LOB')['CumPaidLoss']

Source code(tar.gz)
Source code(zip)
v0.8.5(Jul 11, 2021)
Enhancements

#154 - Added groupby hyperparameter to several more estimators including the widely used Development estimator. This allows fitting development patterns at a higher grain than the Triangle all within the estiamtor or Pipeline.

Improved index broadcasting for sparse arrays. Prior to 0.8.5, this code would inappropriately consume too much memory

prism = cl.load_sample('prism') prism / prism.groupby('Line').sum()

Arithmetic label matching improved for all axes to align more with pandas

Added model_diagnostics utility function to be used on fitted estimators.

Initial support for dask arrays. Current support is basic, but will eventually allow for distributed computations on massive triangles.

added numpy array protocol support to the Triangle class. Now numpy functions can be called on Triangles. For example:

np.sin(cl.load_sample('raa'))

#169 - Made Triangle tutorial more beginner friendly - courtesy of @kennethhsu

Bug fixes

Better Estimator pickling support when callables are included in estimators.

Minor bug fix in grain estimator.

Source code(tar.gz)
Source code(zip)
v0.8.4(May 9, 2021)
Enhancements

#153 - Introduced CapeCod groupby parameter that allows for apriroi computation at user-specified grain for granular triangles

#154 - Introduced groupby support in key development estimators

Bug fixes

#152 - CapeCod apriori is not supporting nans effectively

#157 - cum_to_incr() not working as expected on full_triangle_

#155 - full_triangle_ cdf broadcasting bug

#156 - Unable to inspect sigma_ or std_err_ properties after tail fit

Source code(tar.gz)
Source code(zip)
v0.8.3(Apr 25, 2021)
Enhancements

#135 - Added .at and .iat slicing and value assignment

Bug Fixes

#144 - Eliminated error when trying to assign a column from a different array backend.

#134 - Clearer error handling when attempting to instantiate a triangle with ages instead of date-likes

#143 - Reworked full_triangle_ run-off for expected loss methods.

Source code(tar.gz)
Source code(zip)
v0.8.2(Mar 27, 2021)
Enhancements

#131 - Added a BarnettZenwirth development estimator

#117 - VotingChainladder enhancements to allow for more flexibility in defining weights (courtesy of @cbalona)

Bug Fixes

#130 - Fixed bug in triangle aggregation

#134 - Fixed a bug in triangle broadcasting

#137 - Fixed valuation_date bug occuring when partial year Triangle is instantiated as a vector.

#138 - Introduced fix for incompatibility with sparse>=0.12.0

#122 - Implemented nightly continuous integration to identify new bugs associated with verion bumps of dependencies.

Source code(tar.gz)
Source code(zip)
v0.8.1(Mar 1, 2021)
Enhancements

Included a truncation_age in the TailClark estimator to replicate examples from the paper

#129 Included new development estimators TweedieGLM and DevelopmentML to take advantage of a broader set of (sklearn-compliant) regression frameworks.

Included a helper Transformer PatsyFormula to make working with ML algorithms easier.

Bug fixes

#128 Made IncrementalAdditive method comply with the rest of the package API

Source code(tar.gz)
Source code(zip)
v0.8.0(Feb 16, 2021)
Enhancements

#112 Advanced groupby support.

#113 Added reg_threshold argument to TailCurve for finer control of fit. Huge thanks to @brian-13 for the contribution.

#115 Introducing VotingChainladder workflow estimator to allow for averaging multiple IBNR estimators Huge thanks to @cbalona for the contribution.

Introduced CaseOutstanding development estimator

#124 Standardized CapCod functionality to that of Benktander and BornhuetterFerguson

Bug Fixes

#83 Fixed grain issues when using the trailing argument

#119 Fixed pickle compatibility issue introduced in v0.7.12

#120 Fixed Trend estimator API

#121 Fixed numpy==1.20.1 compatibility issue

#123 Fixed BerquistSherman estimator

#125 Fixed various issues with compatibility of estimators in a Pipeline

Deprecations

#118 Deprecation warnings on xlcompose. Will be removed as a depdendency in v0.9.0

Source code(tar.gz)
Source code(zip)
v0.7.12(Jan 20, 2021)

No code changes from 0.7.11. Bump release to fix conda packaging.
Source code(tar.gz)
Source code(zip)
v0.7.11a(Jan 19, 2021)
Enhancements

#110 Added virtual_column functionality

Bug fixes

Minor bug fix on sort_index not accepting kwargs

Minor bug fix in DevelopmentConstant when using a callable

Misc

Bringing release up to parity with docs.

Source code(tar.gz)
Source code(zip)
v0.7.11(Jan 19, 2021)
Enhancements

#110 Added virtual_column functionality

Bug fixes

Minor bug fix on sort_index not accepting kwargs

Minor bug fix in DevelopmentConstant when using a callable

Misc

Bringing release up to parity with docs.

Source code(tar.gz)
Source code(zip)
v0.7.10(Jan 16, 2021)
Bug fixes

#108 - sample_weight error handling on predict - thank you @cbalona

#107 - Latest diagonal of empty triangle now resolves

#106 - Improved loc consistency with pandas

#105 - Addressed broadcasting error during triangle arithmetic

#103 - Fixed Index alignment with triangle arithmetic consistent with pd.Series

Source code(tar.gz)
Source code(zip)
v0.7.9(Nov 6, 2020)
Bug fixes

#101 Bug where LDF labels were not aligned with underlying LDF array

Enhancements

#66 Allow for onleveling with new ParallelogramOLF transformer

#98 Allow for more complex trends in estimators with Trend transformer Refer to this example on how to apply the new estimators.

Source code(tar.gz)
Source code(zip)
v0.7.8(Oct 23, 2020)
Bug Fixes

Resolved #87 val_to_dev with malformed triangle

Enhancements

Major overhaul of Triangle internals for better code clarity and more efficiency

Made sparse operations more efficient for larger triangles

to_frame now works on Triangles that are 3D or 4D. For example clrd.to_frame()

Advanced groupby operations supported. For (trivial) example:

clrd = cl.load_sample('clrd') # Split companies with names less than 15 characters vs those above: clrd.groupby(clrd.index['GRNAME'].str.len()<15).sum()
Source code(tar.gz)
Source code(zip)
v0.7.7(Sep 13, 2020)
Enhancements

#97, loc and iloc now support Ellipsis

Development can now take a float value for averaging. When float value is used, it corresponds to weight exponent (delta in Barnett/Zenwirth). Only special cases had previously existed - {"regression": 0.0, "volume": 1.0, "simple": 2.0}

Major improvements in slicing performance.

Bug fixes

#96, Fix for TailBase transform

#94, n_periods with asymmetric triangles fixed

Source code(tar.gz)
Source code(zip)
v0.7.6(Aug 27, 2020)
Enhancements

Four Dimensional slicing is now supported.

clrd = cl.load_sample('clrd') clrd.iloc[[0,10, 3], 1:8, :5, :] clrd.loc[:'Aegis Grp', 'CumPaidLoss':, '1990':'1994', :48]

#92 to_frame() now takes optional origin_as_datetime argument for better compatibility with various plotting libraries (Thank you @johalnes )

tri.to_frame(origin_as_datetime=True)

Bug Fixes

Patches to the interaction between sparse and numpy arrays to accomodate more scenarios.

Patches to multi-index broadcasting

Improved performance of latest_diagonal for sparse backends

#91 Bug fix to MackChainladder which errored on asymmetric triangles (Thank you @johalnes for reporting)

Source code(tar.gz)
Source code(zip)
v0.7.5(Aug 15, 2020)
Enhancements

Enabled multi-index broadcasting.

clrd = cl.load_sample('clrd') clrd / clrd.groupby('LOB').sum() # LOB alignment works now instead of throwing error

Added sparse representation of triangles which substantially increases the size limit of in-memory triangles. Check out the new Large Datasets tutorial for details

Bug fixes

Fixed cupy backend which had previously been neglected

Fixed xlcompose issue where Period fails when included as column header

Source code(tar.gz)
Source code(zip)
v0.7.4(Jul 26, 2020)
Tiny release.

Bug Fixes:

Fixed a bug where Triangle did not support full accident dates at creation

Fixed an inappropriate index mutation in Triangle index

Enhancements

Added head and tail methods to Triangle

Prepped Triangle class to support sparse backend

Added prism sample dataset for sparse demonstrations and unit tests

Source code(tar.gz)
Source code(zip)
v0.7.3(Jul 12, 2020)
Enhancements

Improved performance of valuation axis

Improved performance of groupby

Added sort_index method to Triangle consistent with pandas

Allow for fit_predict to be called on a Pipeline estimator

Bug fixes

Fixed issue with Bootstrap process variance where it was being applied more than once

Fixed but where Triangle.index did not ingest numeric columns appropriately.

Source code(tar.gz)
Source code(zip)
v0.7.2(Jul 2, 2020)
Bug fixes:

Index slicing not compatible with pandas #84 fixed

arithmetic fail #68 - Substantial reworking of how arithmetic works.

JSON IO on sub-triangles now works

predict and fit_predict methods added to all IBNR models and now function as expected

Enhancements:

Allow DevelopmentConstant to take on more than one set of patterns by passing in a callable

MunichAdjustmentAllow ` does not work when P/I or I/P ratios cannot be calculated. You can now optionally back-fill zero values with expectaton from simple chainladder so that Munich can be performed on sparser triangles.

Refactors:

Performance optimized several triangle functions including slicing and val_to_dev

Reduced footprint of ldf_, sigma, and std_err_ triangles

Standardized IBNR model methods

Changed cdf_, full_triangle_, full_expectation_, ibnr_ to function-based properties instead of in-memory objects to reduce memory footprint

Source code(tar.gz)
Source code(zip)
v0.7.1(Jun 22, 2020)
Enhancements

Added heatmap method to Triangle - allows for conditionally formatting a 2D triangle. Useful for detecting link_ratio outliers

Introduced BerquistSherman estimator

Better error messaging when triangle columns are non-numeric

Broadened the functionality of Triangle.trend

Allow for nested estimators in to_json. Required addition for the new BerquistSherman method

Docs, docs, and more docs.

Bug Fixes

Mixed an inappropriate mutation in MunichAdjustment.transform

Triangle column slicing now supports pd.Index objects instead of just lists

Other

Moved BootstrapODPSample to workflow section as it is not a development estimator.

Source code(tar.gz)
Source code(zip)
v0.7.0(Jun 2, 2020)
Bug fixes

TailBondy now works with multiple (4D) triangles

TailBondy computes correctly when earliest_age is selected

Sub-triangles now honor index and column slicing of the parent.

fit_transform for all tail estimators now correctly propagate all estimator attributes

Bondy decay now uses the generalized Bondy formula instead of exponential decay

Enhancements

Every tail estimator now has a tail_ attribute representing the point estimate of the tail

Every tail estimator how has an attachment_age parameter to allow for attachment before the end of the triangle

TailCurve now has slope_ and intercept_ attributes for a diagnostics of the estimator.

TailBondy now has earliest_ldf_ attributes to allow for diagnostics of the estimator.

Substantial improvement to the documents on Tails.

Introduced the deterministic components of ClarkLDF and TailClark estimators to allow for growth curve selection of development patterns.

Source code(tar.gz)
Source code(zip)

chainladder - Property and Casualty Loss Reserving in Python

Related tags

Overview

chainladder (python)

chainladder - Property and Casualty Loss Reserving in Python

Documentation

Available Estimators

Getting Started Tutorials

Installation

Questions or Ideas?

Want to contribute?

Comments

Releases(v0.8.14)

v0.8.14(Nov 25, 2022)

New Contributors

Bug Fixes

Documentation

Enhancements

v0.8.13(Jun 27, 2022)

Bug Fixes:

Enhancements

v0.8.12(Mar 8, 2022)

Bug Fixes:

Enhancements

v0.8.11-beta(Mar 7, 2022)

v0.8.11-alpha(Mar 7, 2022)

Bug Fixes:

Enhancements

v0.8.10(Dec 4, 2021)

Enhancements

Bug fixes

v0.8.9(Oct 24, 2021)

v0.8.8(Sep 13, 2021)

Enhancements

Bug Fixes

v0.8.7(Aug 29, 2021)

Bug Fixes

Enhancements

v0.8.6(Aug 18, 2021)

Ehancements

Bug fixes

v0.8.5(Jul 11, 2021)

Enhancements

Bug fixes

v0.8.4(May 9, 2021)

Enhancements

Bug fixes

v0.8.3(Apr 25, 2021)

Enhancements

Bug Fixes

v0.8.2(Mar 27, 2021)

Enhancements

Bug Fixes

v0.8.1(Mar 1, 2021)

Enhancements

Bug fixes

v0.8.0(Feb 16, 2021)

Enhancements

Bug Fixes

Deprecations

v0.7.12(Jan 20, 2021)

v0.7.11a(Jan 19, 2021)

Enhancements

Bug fixes

Misc

v0.7.11(Jan 19, 2021)

Enhancements

Bug fixes

Misc

v0.7.10(Jan 16, 2021)

Bug fixes

v0.7.9(Nov 6, 2020)

Bug fixes

Enhancements

v0.7.8(Oct 23, 2020)

Bug Fixes

Enhancements

v0.7.7(Sep 13, 2020)

Enhancements

Bug fixes