resolves #98

chainladder/methods/capecod.py

@@ -2,15 +2,16 @@
 # License, v. 2.0. If a copy of the MPL was not distributed with this
 # file, You can obtain one at https://mozilla.org/MPL/2.0/.
 from chainladder.methods import MethodBase
-
+import warnings
 
 class CapeCod(MethodBase):
     """Applies the CapeCod technique to triangle **X**
 
     Parameters
     ----------
     trend : float (default=0.0)
-        The cape cod trend assumption
+        The cape cod trend assumption.  Any Trend transformer on X will override
+        this argument.
     decay : float (defaut=1.0)
         The cape cod decay assumption
 
@@ -65,9 +66,12 @@ def _get_ultimate(self, X, sample_weight):
         cdf = self._align_cdf(ult, sample_weight)
         exposure = sample_weight.values
         reported_exposure = exposure / cdf
-        trend_exponent = len_orig - xp.arange(len_orig) - 1
-        trend_array = (1 + self.trend) ** (trend_exponent)
-        trend_array = trend_array[..., None]
+        if hasattr(X, 'trend_'):
+            if self.trend != 0:
+                warnings.warn("CapeCod Trend assumption is ignored when X has a trend_ property.")
+            trend_array = X.trend_.latest_diagonal.values
+        else:
+            trend_array = (X.trend(self.trend) / X).latest_diagonal.values
         if hasattr(sample_weight, 'olf_'):
             sw_olf_array = sample_weight.olf_.values
         else:
@@ -90,6 +94,7 @@ def _get_ultimate(self, X, sample_weight):
         detrended_ultimate = apriori_.values / trend_array / X_olf_array * sw_olf_array
         detrended_apriori_ = ult.copy()
         detrended_apriori_.values = detrended_ultimate
+
         ult.values = latest + detrended_ultimate * (1 - 1 / cdf) * exposure
 
         ult = self._set_ult_attr(ult)

chainladder/workflow/__init__.py

@@ -2,3 +2,4 @@
 from chainladder.workflow.bootstrap import BootstrapODPSample  # noqa (API import)
 from chainladder.workflow.berqsherm import BerquistSherman  # noqa (API import)
 from chainladder.workflow.parallelogram import ParallelogramOLF # noqa (API import)
+from chainladder.workflow.trend import Trend # noqa (API import)

chainladder/workflow/trend.py

@@ -0,0 +1,80 @@
+# This Source Code Form is subject to the terms of the Mozilla Public
+# License, v. 2.0. If a copy of the MPL was not distributed with this
+# file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+from sklearn.base import BaseEstimator, TransformerMixin
+from chainladder.core.io import EstimatorIO
+import pandas as pd
+
+
+class Trend(BaseEstimator, TransformerMixin, EstimatorIO):
+    """
+    Estimator to create and apply trend factors to a Triangle object.  This
+    is commonly used for estimators like `CapeCod`.
+
+    Parameters
+    ----------
+
+    trend : list-like
+        The list containing the annual trends expressed as a decimal. For example,
+        5% decrease should be stated as -0.05
+    date : str
+        A list-like set of dates at which trends start. If None then the valuation
+        date of the triangle is assumed.
+    axis : str (options: [‘origin’, ‘valuation’])
+        The axis on which to apply the trend
+
+    Attributes
+    ----------
+
+    trend_ :
+        A triangle representation of the trend factors
+    """
+
+    def __init__(self, trend=0.0, end_date=None,  axis='origin'):
+        self.trend = trend if type(trend) is list else [trend]
+        self.end_date = end_date if type(end_date) is list else [end_date]
+        self.axis = axis
+
+    def fit(self, X, y=None, sample_weight=None):
+        """Fit the model with X.
+
+        Parameters
+        ----------
+        X : Triangle-like
+            Data to which the model will be applied.
+        y : Ignored
+        sample_weight : Ignored
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        date = [item if item else X.valuation_date.strftime('%Y-%m-%d') for item in self.end_date]
+        date = pd.to_datetime(date).tolist()
+        self.trend_ = X.copy()
+        for i, trend in enumerate(self.trend):
+            self.trend_ = self.trend_.trend(trend, self.axis, date[i])
+        self.trend_  = self.trend_ / X
+        return self
+
+    def transform(self, X, y=None, sample_weight=None):
+        """ If X and self are of different shapes, align self to X, else
+        return self.
+
+        Parameters
+        ----------
+        X : Triangle
+            The triangle to be transformed
+
+        Returns
+        -------
+            X_new : New triangle with transformed attributes.
+        """
+        X_new = X.copy()
+        triangles = ["trend_"]
+        for item in triangles:
+            setattr(X_new, item, getattr(self, item))
+        X_new._set_slicers()
+        return X_new

docs/auto_examples/index.rst

@@ -515,6 +515,27 @@ Examples
 
    /auto_examples/plot_capecod
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to incorporate on-leveling into the CapeCod estimator.  The on-le...">
+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_capecod_onlevel_thumb.png
+     :alt: Include On-leveling into Cape Cod
+
+     :ref:`sphx_glr_auto_examples_plot_capecod_onlevel.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_capecod_onlevel
+
 .. raw:: html
 
     <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates some of the flexibility of the Excel outputs. It creates an Excel fil...">

docs/auto_examples/plot_capecod_onlevel.ipynb

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Include On-leveling into Cape Cod\n\n\nThis example demonstrates how to incorporate on-leveling into the `CapeCod`\nestimator.  The on-level approach emulates the approach taken by Friedland in\n\"Estimating Unpaid Claims Using Basic Techniques\" Chapter 10.  The `ParallelogramOLF`\nestimator is new in chainladder 0.7.9 as is the `xyz` triangle.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import chainladder as cl\nimport pandas as pd\n\n# Grab a triangle\nxyz = cl.load_sample('xyz')\n\n# Premium on-leveling factors\nrate_history = pd.DataFrame({\n    'date': ['1/1/1999', '1/1/2000', '1/1/2001', '1/1/2002', '1/1/2003',\n             '1/1/2004', '1/1/2005', '1/1/2006', '1/1/2007', '1/1/2008'],\n    'rate_change': [.02, .02, .02, .02, .05, .075, .15, .1, -.2, -.2]\n})\n\n# Loss on-leveling factors\ntort_reform = pd.DataFrame({\n    'date': ['1/1/2006', '1/1/2007'],\n    'rate_change': [-0.1067, -.25]\n})\n\n# In addition to development, include onlevel estimator in pipeline for loss\npipe = cl.Pipeline(steps=[\n    ('olf', cl.ParallelogramOLF(tort_reform, change_col='rate_change', date_col='date', vertical_line=True)),\n    ('dev', cl.Development(n_periods=2)),\n    ('model', cl.CapeCod(trend=0.034))\n])\n\n# Define X\nX = cl.load_sample('xyz')['Incurred']\n\n# Separately apply on-level factors for premium\nsample_weight = cl.ParallelogramOLF(\n    rate_history, change_col='rate_change', date_col='date',\n    vertical_line=True).fit_transform(xyz['Premium'].latest_diagonal)\n\n#  Fit Cod Estimator\npipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_\n\n# Create a Cape Cod pipeline without onleveling\npipe2 = cl.Pipeline(steps=[\n    ('dev', cl.Development(n_periods=2)),\n    ('model', cl.CapeCod(trend=0.034))\n])\n\n\n# Finally fit Cod Estimator without on-leveling\npipe2.fit(X, sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_\n\n# Plot results\ncl.concat((\n    pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),\n    pipe2.named_steps.model.ultimate_.rename('columns', ['Without On-level'])), 1).T.plot(\n    title='Cape Cod sensitivity to on-leveling', grid=True);"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

docs/auto_examples/plot_capecod_onlevel.py

@@ -0,0 +1,63 @@
+"""
+=====================================
+Include On-leveling into Cape Cod
+=====================================
+
+This example demonstrates how to incorporate on-leveling into the `CapeCod`
+estimator.  The on-level approach emulates the approach taken by Friedland in
+"Estimating Unpaid Claims Using Basic Techniques" Chapter 10.  The `ParallelogramOLF`
+estimator is new in chainladder 0.7.9 as is the `xyz` triangle.
+"""
+
+import chainladder as cl
+import pandas as pd
+
+# Grab a triangle
+xyz = cl.load_sample('xyz')
+
+# Premium on-leveling factors
+rate_history = pd.DataFrame({
+    'date': ['1/1/1999', '1/1/2000', '1/1/2001', '1/1/2002', '1/1/2003',
+             '1/1/2004', '1/1/2005', '1/1/2006', '1/1/2007', '1/1/2008'],
+    'rate_change': [.02, .02, .02, .02, .05, .075, .15, .1, -.2, -.2]
+})
+
+# Loss on-leveling factors
+tort_reform = pd.DataFrame({
+    'date': ['1/1/2006', '1/1/2007'],
+    'rate_change': [-0.1067, -.25]
+})
+
+# In addition to development, include onlevel estimator in pipeline for loss
+pipe = cl.Pipeline(steps=[
+    ('olf', cl.ParallelogramOLF(tort_reform, change_col='rate_change', date_col='date', vertical_line=True)),
+    ('dev', cl.Development(n_periods=2)),
+    ('model', cl.CapeCod(trend=0.034))
+])
+
+# Define X
+X = cl.load_sample('xyz')['Incurred']
+
+# Separately apply on-level factors for premium
+sample_weight = cl.ParallelogramOLF(
+    rate_history, change_col='rate_change', date_col='date',
+    vertical_line=True).fit_transform(xyz['Premium'].latest_diagonal)
+
+#  Fit Cod Estimator
+pipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_
+
+# Create a Cape Cod pipeline without onleveling
+pipe2 = cl.Pipeline(steps=[
+    ('dev', cl.Development(n_periods=2)),
+    ('model', cl.CapeCod(trend=0.034))
+])
+
+
+# Finally fit Cod Estimator without on-leveling
+pipe2.fit(X, sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_
+
+# Plot results
+cl.concat((
+    pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),
+    pipe2.named_steps.model.ultimate_.rename('columns', ['Without On-level'])), 1).T.plot(
+    title='Cape Cod sensitivity to on-leveling', grid=True);

docs/auto_examples/plot_capecod_onlevel.py.md5

@@ -0,0 +1 @@
+6c44959848b170466aae812e7d76378e