Began work on trendconstant

chainladder/adjustments/trend.py

@@ -57,17 +57,112 @@ def fit(self, X, y=None, sample_weight=None):
         dates = [dates] if type(dates) is not list else dates
         if type(dates[0]) is not tuple:
             raise AttributeError(
-                'Dates must be specified as a tuple of start and end dates')
+                "Dates must be specified as a tuple of start and end dates"
+            )
         self.trend_ = X.copy()
         for i, trend in enumerate(trends):
             self.trend_ = self.trend_.trend(
-                trend, self.axis,
-                start=dates[i][0], end=dates[i][1])
+                trend, self.axis, start=dates[i][0], end=dates[i][1]
+            )
         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
+        """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
+
+
+class TrendConstant(BaseEstimator, TransformerMixin, EstimatorIO):
+    """
+    Estimator to create and apply trend factors to a Triangle object.  Allows
+    for compound trends as well as storage of the trend matrix to be used in
+    other estimators.
+
+    Parameters
+    ----------
+
+    trends: list-like
+        The list containing the annual trends expressed as a decimal. For example,
+        5% decrease should be stated as -0.05
+    effective_date: list of date-likes
+        A list-like of (start, end) dates to correspond to the `trend` list.
+    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, trends=0.0, effective_date=None, axis="origin"):
+        self.trends = trends
+        self.effective_date = effective_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.
+        """
+        if type(self.trends):
+            trend_list = self.trends
+        else:
+            trend_list = [self.trends]
+
+        if self.dates is None:
+            dates = [(None, None)]
+        else:
+            dates = self.dates
+
+        if type(dates) is not list:
+            dates = [dates]
+        else:
+            dates = dates
+
+        if type(dates[0]) is not tuple:
+            raise AttributeError(
+                "Dates must be specified as a tuple of start and end dates"
+            )
+
+        self.trend_ = X.copy()
+        for i, trend in enumerate(trend_list):
+            self.trend_ = self.trend_.trend(
+                trend, self.axis, start=dates[i][0], end=dates[i][1]
+            )
+        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

chainladder/core/triangle.py

@@ -125,7 +125,7 @@ def __init__(
             return
         elif not isinstance(data, pd.DataFrame) and hasattr(data, "__dataframe__"):
             data = self._interchange_dataframe(data)
-        
+
         index, columns, origin, development = self._input_validation(
             data, index, columns, origin, development
         )
@@ -276,7 +276,7 @@ def __init__(
             self.ddims = obj.ddims
             self.values = obj.values
             self.valuation_date = pd.Timestamp(options.ULT_VAL)
-    
+
     @staticmethod
     def _split_ult(data, index, columns, origin, development):
         """Deal with triangles with ultimate values"""
@@ -330,17 +330,25 @@ def origin(self):
         if self.is_pattern and len(self.odims) == 1:
             return pd.Series(["(All)"])
         else:
-            freq = {"Y": "Y" if float('.'.join(pd.__version__.split('.')[:-1])) < 2.2 else "A", 
-                    "S": "2Q", "H": "2Q"}.get(
-                self.origin_grain, self.origin_grain
-            )
+            freq = {
+                "Y": (
+                    "Y"
+                    if float(".".join(pd.__version__.split(".")[:-1])) < 2.2
+                    else "A"
+                ),
+                "S": "2Q",
+                "H": "2Q",
+            }.get(self.origin_grain, self.origin_grain)
             freq = freq if freq == "M" else freq + "-" + self.origin_close
             return pd.DatetimeIndex(self.odims, name="origin").to_period(freq=freq)
 
     @origin.setter
     def origin(self, value):
         self._len_check(self.origin, value)
-        freq = {"Y": "Y" if float('.'.join(pd.__version__.split('.')[:-1])) < 2.2 else "A", "S": "2Q"}.get(self.origin_grain, self.origin_grain)
+        freq = {
+            "Y": "Y" if float(".".join(pd.__version__.split(".")[:-1])) < 2.2 else "A",
+            "S": "2Q",
+        }.get(self.origin_grain, self.origin_grain)
         freq = freq if freq == "M" else freq + "-" + self.origin_close
         value = pd.PeriodIndex(list(value), freq=freq)
         self.odims = value.to_timestamp().values
@@ -693,9 +701,11 @@ def grain(self, grain="", trailing=False, inplace=False):
             obj.origin_close = origin_period_end
             d_start = pd.Period(
                 obj.valuation[0],
-                freq=dgrain_old
-                if dgrain_old == "M"
-                else dgrain_old + obj.origin.freqstr[-4:],
+                freq=(
+                    dgrain_old
+                    if dgrain_old == "M"
+                    else dgrain_old + obj.origin.freqstr[-4:]
+                ),
             ).to_timestamp(how="s")
             if len(obj.ddims) > 1 and obj.origin.to_timestamp(how="s")[0] != d_start:
                 addl_ts = (
@@ -763,6 +773,7 @@ def trend(
         Triangle
             updated with multiplicative trend applied.
         """
+        print("in trend")
         if axis not in ["origin", "valuation", 2, -2]:
             raise ValueError(
                 "Only origin and valuation axes are supported for trending"