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[FIX] Apply Ruff fix to docs/gallery. #1292

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165f6b2
[FIX] Apply Ruff fix to docs/gallery.
genedan Sep 4, 2026
7bd6da7
Merge branch 'gd_ruff_fix' into gd_ruff_fix_plots
genedan Sep 5, 2026
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26 changes: 14 additions & 12 deletions docs/gallery/plot_advanced_triangle.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -44,23 +44,23 @@
},
"outputs": [],
"source": [
"clrd = cl.load_sample('clrd')\n",
"clrd = clrd[clrd['LOB']=='comauto']\n",
"clrd = cl.load_sample(\"clrd\")\n",
"clrd = clrd[clrd[\"LOB\"] == \"comauto\"]\n",
"\n",
"# Create a loss ratio virtual column\n",
"clrd['LossRatio'] = lambda clrd: clrd['IncurLoss'] / clrd['EarnedPremDIR']\n",
"clrd[\"LossRatio\"] = lambda clrd: clrd[\"IncurLoss\"] / clrd[\"EarnedPremDIR\"]\n",
"\n",
"# Identify the largest companies (by premium) for 1997\n",
"top_10 = clrd['EarnedPremDIR'].groupby('GRNAME').sum().latest_diagonal\n",
"top_10 = top_10.loc[..., '1997', :].to_frame().nlargest(10)\n",
"top_10 = clrd[\"EarnedPremDIR\"].groupby(\"GRNAME\").sum().latest_diagonal\n",
"top_10 = top_10.loc[..., \"1997\", :].to_frame().nlargest(10)\n",
"\n",
"# Group any companies together that are not in the top 10\n",
"clrd = clrd.groupby(clrd.index['GRNAME'].map(\n",
" lambda x: x if x in top_10.index else 'Remainder')).sum()\n",
"clrd = clrd.groupby(\n",
" clrd.index[\"GRNAME\"].map(lambda x: x if x in top_10.index else \"Remainder\")\n",
").sum()\n",
"\n",
"# Sort by company volume, but keep Remainder as last entry\n",
"clrd = clrd.loc[top_10.index.to_list() + ['Remainder']].iloc[::-1]\n",
"\n"
"clrd = clrd.loc[top_10.index.to_list() + [\"Remainder\"]].iloc[::-1]"
]
},
{
Expand Down Expand Up @@ -90,12 +90,14 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"\n",
"ax = clrd.latest_diagonal.sum('origin')['LossRatio'].plot(\n",
" kind='barh', title='Loss Ratio');"
"ax = clrd.latest_diagonal.sum(\"origin\")[\"LossRatio\"].plot(\n",
" kind=\"barh\", title=\"Loss Ratio\"\n",
");"
]
},
{
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33 changes: 18 additions & 15 deletions docs/gallery/plot_ave_analysis.ipynb
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Expand Up @@ -43,8 +43,8 @@
},
"outputs": [],
"source": [
"tri_1997 = cl.load_sample('clrd')\n",
"tri_1997 = tri_1997.groupby('LOB').sum().loc['medmal']['CumPaidLoss']"
"tri_1997 = cl.load_sample(\"clrd\")\n",
"tri_1997 = tri_1997.groupby(\"LOB\").sum().loc[\"medmal\"][\"CumPaidLoss\"]"
]
},
{
Expand All @@ -62,12 +62,12 @@
"outputs": [],
"source": [
"# Create a triangle as of the previous valuation and build IBNR model\n",
"tri_1996 = tri_1997[tri_1997.valuation < '1997']\n",
"tri_1996 = tri_1997[tri_1997.valuation < \"1997\"]\n",
"model_1996 = cl.Chainladder().fit(cl.TailCurve().fit_transform(tri_1996))\n",
"\n",
"# Slice the expected losses from the 1997 calendar period of the model\n",
"ave = model_1996.full_triangle_.dev_to_val()\n",
"ave = ave[ave.valuation==tri_1997.valuation_date].rename('columns', 'Expected')"
"ave = ave[ave.valuation == tri_1997.valuation_date].rename(\"columns\", \"Expected\")"
]
},
{
Expand All @@ -85,7 +85,7 @@
"outputs": [],
"source": [
"# Slice the actual losses from the 1997 calendar period for prior AYs\n",
"ave['Actual'] = tri_1997.latest_diagonal[tri_1997.origin < '1997']\n",
"ave[\"Actual\"] = tri_1997.latest_diagonal[tri_1997.origin < \"1997\"]\n",
"df = ave.to_frame().T.iloc[::-1]"
]
},
Expand Down Expand Up @@ -116,23 +116,26 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"# Plotting\n",
"fig, ax = plt.subplots()\n",
"ax.grid(axis='x')\n",
"ax.grid(axis=\"x\")\n",
"plt.hlines(\n",
" y=df.index.astype(str), \n",
" xmin=df['Actual'], \n",
" xmax=df['Expected'],\n",
" color='grey', alpha=0.4)\n",
"plt.scatter(df['Actual'], df.index.astype(str), alpha=1, label='Actual')\n",
"plt.scatter(df['Expected'], df.index.astype(str), alpha=0.8 , label='Expected')\n",
" y=df.index.astype(str),\n",
" xmin=df[\"Actual\"],\n",
" xmax=df[\"Expected\"],\n",
" color=\"grey\",\n",
" alpha=0.4,\n",
")\n",
"plt.scatter(df[\"Actual\"], df.index.astype(str), alpha=1, label=\"Actual\")\n",
"plt.scatter(df[\"Expected\"], df.index.astype(str), alpha=0.8, label=\"Expected\")\n",
"plt.legend()\n",
"plt.title(\"Actual vs Expected results in 1997\")\n",
"plt.xlabel('Difference')\n",
"plt.ylabel('Origin');"
"plt.xlabel(\"Difference\")\n",
"plt.ylabel(\"Origin\");"
]
},
{
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46 changes: 26 additions & 20 deletions docs/gallery/plot_benktander.ipynb
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Expand Up @@ -46,21 +46,25 @@
"outputs": [],
"source": [
"# Load Data\n",
"clrd = cl.load_sample('clrd').groupby('LOB').sum()\n",
"X = clrd.loc['medmal', 'CumPaidLoss']\n",
"sample_weight = clrd.loc['medmal', 'EarnedPremDIR'].latest_diagonal\n",
"clrd = cl.load_sample(\"clrd\").groupby(\"LOB\").sum()\n",
"X = clrd.loc[\"medmal\", \"CumPaidLoss\"]\n",
"sample_weight = clrd.loc[\"medmal\", \"EarnedPremDIR\"].latest_diagonal\n",
"\n",
"# Specify Model\n",
"grid = cl.GridSearch(\n",
" estimator=cl.Pipeline(steps=[\n",
" ('dev', cl.Development()),\n",
" ('tail', cl.TailCurve()),\n",
" ('model', cl.Benktander())]), \n",
" param_grid = dict(\n",
" model__n_iters=list(range(1, 100, 2)),\n",
" model__apriori=[0.50, 0.75, 1.00]), \n",
" scoring={'IBNR': lambda x: x.named_steps.model.ibnr_.sum()},\n",
" n_jobs=-1)"
" estimator=cl.Pipeline(\n",
" steps=[\n",
" (\"dev\", cl.Development()),\n",
" (\"tail\", cl.TailCurve()),\n",
" (\"model\", cl.Benktander()),\n",
" ]\n",
" ),\n",
" param_grid=dict(\n",
" model__n_iters=list(range(1, 100, 2)), model__apriori=[0.50, 0.75, 1.00]\n",
" ),\n",
" scoring={\"IBNR\": lambda x: x.named_steps.model.ibnr_.sum()},\n",
" n_jobs=-1,\n",
")"
]
},
{
Expand Down Expand Up @@ -120,7 +124,8 @@
],
"source": [
"from sklearn import set_config\n",
"set_config(display='diagram')\n",
"\n",
"set_config(display=\"diagram\")\n",
"grid"
]
},
Expand Down Expand Up @@ -225,9 +230,8 @@
"\n",
"# Analyze results\n",
"output = grid.results_.pivot(\n",
" index='model__n_iters', \n",
" columns='model__apriori', \n",
" values='IBNR') \n",
" index=\"model__n_iters\", columns=\"model__apriori\", values=\"IBNR\"\n",
")\n",
"output.head()"
]
},
Expand Down Expand Up @@ -266,13 +270,15 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"ax = (output / 1e6).plot(\n",
" ylabel='IBNR (Millions)',\n",
" xlabel='Number of Iterations',\n",
" title='Benktander convergence to Chainladder');"
" ylabel=\"IBNR (Millions)\",\n",
" xlabel=\"Number of Iterations\",\n",
" title=\"Benktander convergence to Chainladder\",\n",
");"
]
},
{
Expand Down
30 changes: 18 additions & 12 deletions docs/gallery/plot_bondy_sensitivity.ipynb
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Expand Up @@ -47,19 +47,19 @@
"outputs": [],
"source": [
"# Fit basic development to a triangle\n",
"tri = cl.load_sample('tail_sample')['paid']\n",
"dev = cl.Development(average='simple').fit_transform(tri)\n",
"tri = cl.load_sample(\"tail_sample\")[\"paid\"]\n",
"dev = cl.Development(average=\"simple\").fit_transform(tri)\n",
"\n",
"# Return both the tail factor and the Bondy exponent in the scoring function\n",
"scoring = {\n",
" 'tail_factor': lambda x: x.tail_.values[0,0],\n",
" 'bondy_exponent': lambda x : x.b_.values[0,0]}\n",
" \"tail_factor\": lambda x: x.tail_.values[0, 0],\n",
" \"bondy_exponent\": lambda x: x.b_.values[0, 0],\n",
"}\n",
"\n",
"# Vary the 'earliest_age' assumption in GridSearch\n",
"param_grid=dict(earliest_age=list(range(12, 120, 12)))\n",
"param_grid = dict(earliest_age=list(range(12, 120, 12)))\n",
"grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)\n",
"results = grid.fit(dev).results_\n",
"\n"
"results = grid.fit(dev).results_"
]
},
{
Expand Down Expand Up @@ -89,13 +89,19 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"ax = results.plot(x='earliest_age', y='bondy_exponent',\n",
" title='Bondy Assumption Sensitivity', marker='o')\n",
"results.plot(x='earliest_age', y='tail_factor', grid=True,\n",
" secondary_y=True, ax=ax, marker='o');"
"ax = results.plot(\n",
" x=\"earliest_age\",\n",
" y=\"bondy_exponent\",\n",
" title=\"Bondy Assumption Sensitivity\",\n",
" marker=\"o\",\n",
")\n",
"results.plot(\n",
" x=\"earliest_age\", y=\"tail_factor\", grid=True, secondary_y=True, ax=ax, marker=\"o\"\n",
");"
]
},
{
Expand Down
36 changes: 24 additions & 12 deletions docs/gallery/plot_bootstrap.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -42,10 +42,8 @@
},
"outputs": [],
"source": [
"import chainladder as cl\n",
"\n",
"# Grab a Triangle\n",
"tri = cl.load_sample('genins')\n",
"tri = cl.load_sample(\"genins\")\n",
"\n",
"# Generate bootstrap samples\n",
"sims = cl.BootstrapODPSample(random_state=42).fit_transform(tri)\n",
Expand All @@ -57,7 +55,7 @@
"plot2 = (sims.sum() / 1000).T / 1e6\n",
"plot3a = sim_ldf.T\n",
"plot3b = cl.Development().fit(tri).ldf_.drop_duplicates().T\n",
"plot4 = sim_ldf.T.loc['12-24']"
"plot4 = sim_ldf.T.loc[\"12-24\"]"
]
},
{
Expand Down Expand Up @@ -87,27 +85,41 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"# Plot the Data\n",
"fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))\n",
"fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10, 10))\n",
"\n",
"# Plot 1\n",
"plot1.plot(ax=ax00, title='Raw Data', xlabel='Development', ylabel='Incurred (Millions)')\n",
"plot1.plot(\n",
" ax=ax00, title=\"Raw Data\", xlabel=\"Development\", ylabel=\"Incurred (Millions)\"\n",
")\n",
"\n",
"# Plot 2\n",
"plot2.plot(ax=ax01, title='Mean Simulation (Millions)', xlabel='Development')\n",
"plot2.plot(ax=ax01, title=\"Mean Simulation (Millions)\", xlabel=\"Development\")\n",
"\n",
"# Plot 3\n",
"plot3a.plot(legend=False, color='lightgray', ax=ax10, \n",
" title='Simulated LDF', xlabel='Development', ylabel='LDF')\n",
"plot3a.plot(\n",
" legend=False,\n",
" color=\"lightgray\",\n",
" ax=ax10,\n",
" title=\"Simulated LDF\",\n",
" xlabel=\"Development\",\n",
" ylabel=\"LDF\",\n",
")\n",
"plot3b.plot(legend=False, ax=ax10, grid=True)\n",
"\n",
"# Plot 4\n",
"plot4.plot(\n",
" kind='hist', bins=50, alpha=0.5, ax=ax11,\n",
" title='Age 12-24 LDF Distribution', xlabel='LDF');"
" kind=\"hist\",\n",
" bins=50,\n",
" alpha=0.5,\n",
" ax=ax11,\n",
" title=\"Age 12-24 LDF Distribution\",\n",
" xlabel=\"LDF\",\n",
");"
]
},
{
Expand Down
35 changes: 23 additions & 12 deletions docs/gallery/plot_bootstrap_comparison.ipynb
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Expand Up @@ -42,23 +42,29 @@
"outputs": [],
"source": [
"# Load triangle\n",
"triangle = cl.load_sample('genins')\n",
"triangle = cl.load_sample(\"genins\")\n",
"\n",
"# Use bootstrap sampler to get resampled triangles\n",
"s1 = cl.BootstrapODPSample(\n",
" n_sims=5000, random_state=42).fit(triangle).resampled_triangles_\n",
"s1 = (\n",
" cl\n",
" .BootstrapODPSample(n_sims=5000, random_state=42)\n",
" .fit(triangle)\n",
" .resampled_triangles_\n",
")\n",
"\n",
"## Alternatively use fit_transform() to access resampled triangles dropping\n",
"# Alternatively use fit_transform() to access resampled triangles dropping\n",
"# outlier link-ratios from resampler\n",
"s2 = cl.BootstrapODPSample(\n",
" drop_high=[True] * 5+ [False] * 4, \n",
" drop_high=[True] * 5 + [False] * 4,\n",
" drop_low=[True] * 5 + [False] * 4,\n",
" n_sims=5000, random_state=42).fit_transform(triangle)\n",
" n_sims=5000,\n",
" random_state=42,\n",
").fit_transform(triangle)\n",
"\n",
"# Summarize results of first model\n",
"results = cl.Chainladder().fit(s1).ibnr_.sum('origin').rename('columns', ['Original'])\n",
"results = cl.Chainladder().fit(s1).ibnr_.sum(\"origin\").rename(\"columns\", [\"Original\"])\n",
"# Add another column to triangle with second set of results.\n",
"results['Dropped'] = cl.Chainladder().fit(s2).ibnr_.sum('origin')"
"results[\"Dropped\"] = cl.Chainladder().fit(s2).ibnr_.sum(\"origin\")"
]
},
{
Expand Down Expand Up @@ -88,13 +94,18 @@
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"\n",
"plt.style.use(\"ggplot\")\n",
"%config InlineBackend.figure_format = 'retina'\n",
"# Plot both IBNR distributions\n",
"ax = results.to_frame().plot(\n",
" kind='hist', bins=50, alpha=0.5, \n",
" grid=True, xlabel='Ultimate',\n",
" title='Reserve Variability')"
" kind=\"hist\",\n",
" bins=50,\n",
" alpha=0.5,\n",
" grid=True,\n",
" xlabel=\"Ultimate\",\n",
" title=\"Reserve Variability\",\n",
")"
]
}
],
Expand Down
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