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[FIX] Apply Ruff fix to tutorial and user guide. #1300

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[FIX] Apply Ruff fix to tutorial and user guide. #1300

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c56e176
[FIX] Apply Ruff fix to tutorial and user guide.
genedan Sep 5, 2026
e034d23
[FIX] Apply Ruff fix to tutorial and user guide.
genedan Sep 5, 2026
ebaa08e
Merge remote-tracking branch 'origin/gd_ruff_fix_user_guide' into gd_…
genedan Sep 5, 2026
8d2f02e
Merge branch 'gd_ruff_fix_adjustments' into gd_ruff_fix_user_guide
genedan Sep 5, 2026
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993 changes: 499 additions & 494 deletions docs/getting_started/tutorials/stochastic-tutorial.ipynb
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5 changes: 2 additions & 3 deletions docs/getting_started/tutorials/tail-tutorial.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,6 @@
"import pandas as pd\n",
"import numpy as np\n",
"import chainladder as cl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"print(\"pandas: \" + pd.__version__)\n",
"print(\"numpy: \" + np.__version__)\n",
Expand Down Expand Up @@ -1229,14 +1228,14 @@
"try:\n",
" cl.TailCurve().fit(cl.Development().fit(quarterly))\n",
" print(\"This passes.\")\n",
"except:\n",
"except Exception:\n",
" print(\"This fails because we did not transform the triangle\")\n",
"\n",
"print(\"\\nSecond attempt:\")\n",
"try:\n",
" cl.TailCurve().fit(cl.Development().fit_transform(quarterly))\n",
" print(\"This passes because we transformed the triangle\")\n",
"except:\n",
"except Exception:\n",
" print(\"This fails.\")"
],
"outputs": [
Expand Down
76 changes: 47 additions & 29 deletions docs/user_guide/adjustments.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@
"import chainladder as cl\n",
"import pandas as pd\n",
"\n",
"raa = cl.load_sample('raa')\n",
"raa = cl.load_sample(\"raa\")\n",
"cl.BootstrapODPSample(n_sims=500).fit_transform(raa)"
]
},
Expand Down Expand Up @@ -160,7 +160,7 @@
}
],
"source": [
"cl.BootstrapODPSample(n_sims=100, drop=[('1982', 12)]).fit_transform(raa)"
"cl.BootstrapODPSample(n_sims=100, drop=[(\"1982\", 12)]).fit_transform(raa)"
]
},
{
Expand All @@ -187,8 +187,8 @@
"\n",
":::{grid-item-card}\n",
":columns: 4\n",
":link: ../gallery/plot_bootstrap_comparison\n",
":link-type: doc\n",
":link: ../gallery/plot_bootstrap_comparison\n",
":link-type: doc\n",
"**[BootstrapODPSample Variability]**\n",
"```{image} ../images/plot_bootstrap_comparison.png\n",
"---\n",
Expand All @@ -199,7 +199,7 @@
"{bdg-warning}`medium`\n",
"\n",
":::\n",
"::::\n",
"::::\n",
"{cite}`shapland2016`"
]
},
Expand Down Expand Up @@ -364,14 +364,17 @@
}
],
"source": [
"triangle = cl.load_sample('berqsherm').loc['MedMal']\n",
"triangle = cl.load_sample(\"berqsherm\").loc[\"MedMal\"]\n",
"berq = cl.BerquistSherman(\n",
" paid_amount='Paid', incurred_amount='Incurred',\n",
" reported_count='Reported', closed_count='Closed',\n",
" trend=0.15).fit(triangle)\n",
" paid_amount=\"Paid\",\n",
" incurred_amount=\"Incurred\",\n",
" reported_count=\"Reported\",\n",
" closed_count=\"Closed\",\n",
" trend=0.15,\n",
").fit(triangle)\n",
"\n",
"# Only Reported triangle is left unadjusted\n",
"(triangle / berq.adjusted_triangle_)['Reported']"
"(triangle / berq.adjusted_triangle_)[\"Reported\"]"
]
},
{
Expand Down Expand Up @@ -517,7 +520,7 @@
}
],
"source": [
"(triangle / berq.adjusted_triangle_)['Paid']"
"(triangle / berq.adjusted_triangle_)[\"Paid\"]"
]
},
{
Expand All @@ -540,8 +543,8 @@
"\n",
":::{grid-item-card}\n",
":columns: 4\n",
":link: ../gallery/plot_berqsherm_case\n",
":link-type: doc\n",
":link: ../gallery/plot_berqsherm_case\n",
":link-type: doc\n",
"**[BerquistSherman Adjustment]**\n",
"```{image} ../images/plot_berqsherm_case.png\n",
"---\n",
Expand Down Expand Up @@ -649,8 +652,9 @@
],
"source": [
"rate_history = pd.DataFrame({\n",
" 'EffDate': ['2016-07-15', '2017-03-01', '2018-01-01', '2019-10-31'],\n",
" 'RateChange': [0.02, 0.05, -.03, 0.1]})\n",
" \"EffDate\": [\"2016-07-15\", \"2017-03-01\", \"2018-01-01\", \"2019-10-31\"],\n",
" \"RateChange\": [0.02, 0.05, -0.03, 0.1],\n",
"})\n",
"rate_history"
]
},
Expand Down Expand Up @@ -722,10 +726,13 @@
],
"source": [
"data = pd.DataFrame({\n",
" 'Year': [2016, 2017, 2018, 2019, 2020],\n",
" 'EarnedPremium': [10_000]*5})\n",
"prem_tri = cl.Triangle(data, origin='Year', columns='EarnedPremium', cumulative = True)\n",
"prem_tri = cl.ParallelogramOLF(rate_history, change_col='RateChange', date_col='EffDate').fit_transform(prem_tri)\n",
" \"Year\": [2016, 2017, 2018, 2019, 2020],\n",
" \"EarnedPremium\": [10_000] * 5,\n",
"})\n",
"prem_tri = cl.Triangle(data, origin=\"Year\", columns=\"EarnedPremium\", cumulative=True)\n",
"prem_tri = cl.ParallelogramOLF(\n",
" rate_history, change_col=\"RateChange\", date_col=\"EffDate\"\n",
").fit_transform(prem_tri)\n",
"prem_tri.olf_"
]
},
Expand All @@ -747,8 +754,8 @@
"\n",
":::{grid-item-card}\n",
":columns: 4\n",
":link: ../gallery/plot_capecod_onlevel\n",
":link-type: doc\n",
":link: ../gallery/plot_capecod_onlevel\n",
":link-type: doc\n",
"**[CapeCod Onleveling]**\n",
"```{image} ../images/plot_capecod_onlevel.png\n",
"---\n",
Expand Down Expand Up @@ -793,16 +800,27 @@
}
],
"source": [
"ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n",
"ppauto_prem = cl.load_sample('clrd').groupby('LOB').sum() \\\n",
" .loc['ppauto']['EarnedPremDIR'].latest_diagonal\n",
"ppauto_loss = cl.load_sample(\"clrd\").groupby(\"LOB\").sum().loc[\"ppauto\", \"CumPaidLoss\"]\n",
"ppauto_prem = (\n",
" cl\n",
" .load_sample(\"clrd\")\n",
" .groupby(\"LOB\")\n",
" .sum()\n",
" .loc[\"ppauto\"][\"EarnedPremDIR\"]\n",
" .latest_diagonal\n",
")\n",
"\n",
"# Simple trend\n",
"a = cl.CapeCod(trend=0.05).fit(ppauto_loss, sample_weight=ppauto_prem).ultimate_.sum()\n",
"\n",
"# Equivalent using a Trend Estimator. This allows us to convert to more complex trends\n",
"b = cl.CapeCod().fit(cl.Trend(.05).fit_transform(ppauto_loss), sample_weight=ppauto_prem).ultimate_.sum()\n",
"a == b\n"
"b = (\n",
" cl\n",
" .CapeCod()\n",
" .fit(cl.Trend(0.05).fit_transform(ppauto_loss), sample_weight=ppauto_prem)\n",
" .ultimate_.sum()\n",
")\n",
"a == b"
]
},
{
Expand Down Expand Up @@ -998,10 +1016,10 @@
}
],
"source": [
"ppauto_loss = cl.load_sample('clrd').groupby('LOB').sum().loc['ppauto', 'CumPaidLoss']\n",
"ppauto_loss = cl.load_sample(\"clrd\").groupby(\"LOB\").sum().loc[\"ppauto\", \"CumPaidLoss\"]\n",
"cl.Trend(\n",
" trends=[.05, .03],\n",
" dates=[('1997-12-31', '1995-01-01'),('1995-01-01', '1992-07-01')]\n",
" trends=[0.05, 0.03],\n",
" dates=[(\"1997-12-31\", \"1995-01-01\"), (\"1995-01-01\", \"1992-07-01\")],\n",
").fit(ppauto_loss).trend_.round(2)"
]
}
Expand Down
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