Fix smoother to remove a non-invertible design matrix bug

_delphi_utils_python/delphi_utils/smooth.py

@@ -142,7 +142,9 @@ def __init__(
         if smoother_name == "savgol":
             # The polynomial fitting is done on a past window of size window_length
             # including the current day value.
-            self.coeffs = self.savgol_coeffs(-self.window_length + 1, 0)
+            self.coeffs = self.savgol_coeffs(
+                -self.window_length + 1, 0, self.poly_fit_degree
+            )
         else:
             self.coeffs = None
 
@@ -279,30 +281,35 @@ def left_gauss_linear_smoother(self, signal):
             signal_smoothed[signal_smoothed <= self.minval] = self.minval
         return signal_smoothed
 
-    def savgol_predict(self, signal):
+    def savgol_predict(self, signal, poly_fit_degree, nr):
         """Predict a single value using the savgol method.
 
         Fits a polynomial through the values given by the signal and returns the value
-        of the polynomial at the right-most signal-value. More precisely, fits a polynomial
-        f(t) of degree poly_fit_degree through the points signal[-n], signal[-n+1] ..., signal[-1],
-        and returns the evaluation of the polynomial at the location of signal[0].
+        of the polynomial at the right-most signal-value. More precisely, for a signal of length
+        n, fits a poly_fit_degree polynomial through the points signal[-n+1+nr], signal[-n+2+nr],
+        ..., signal[nr], and returns the evaluation of the polynomial at signal[0]. Hence, if
+        nr=0, then the last value of the signal is smoothed, and if nr=-1, then the value after
+        the last signal value is anticipated.
 
         Parameters
         ----------
         signal: np.ndarray
             A 1D signal to smooth.
+        poly_fit_degree: int
+            The degree of the polynomial fit.
+        nr: int
+            An integer that determines the position of the predicted value relative to the signal.
 
         Returns
         ----------
         predicted_value: float
             The anticipated value that comes after the end of the signal based on a polynomial fit.
         """
-        # Add one 
-        coeffs = self.savgol_coeffs(-len(signal) + 1, 0)
+        coeffs = self.savgol_coeffs(-len(signal) + 1 + nr, nr, poly_fit_degree)
         predicted_value = signal @ coeffs
         return predicted_value
 
-    def savgol_coeffs(self, nl, nr):
+    def savgol_coeffs(self, nl, nr, poly_fit_degree):
         """Solve for the Savitzky-Golay coefficients.
 
         The coefficients c_i give a filter so that
@@ -322,23 +329,20 @@ def savgol_coeffs(self, nl, nr):
             The right window bound for the polynomial fit, inclusive.
         poly_fit_degree: int
             The degree of the polynomial to be fit.
-        gaussian_bandwidth: float or None
-            If float, performs regression with Gaussian weights whose variance is
-            the gaussian_bandwidth. If None, performs unweighted regression.
 
         Returns
         ----------
         coeffs: np.ndarray
-            A vector of coefficients of length nl that determines the savgol
+            A vector of coefficients of length nr - nl + 1 that determines the savgol
             convolution filter.
         """
         if nl >= nr:
             raise ValueError("The left window bound should be less than the right.")
         if nr > 0:
-            raise warnings.warn("The filter is no longer causal.")
+            warnings.warn("The filter is no longer causal.")
 
-        A = np.vstack(  # pylint: disable=invalid-name
-            [np.arange(nl, nr + 1) ** j for j in range(self.poly_fit_degree + 1)]
+        A = np.vstack(
+            [np.arange(nl, nr + 1) ** j for j in range(poly_fit_degree + 1)]
         ).T
 
         if self.gaussian_bandwidth is None:
@@ -389,8 +393,10 @@ def savgol_smoother(self, signal):
                     # At the very edge, the design matrix is often singular, in which case
                     # we just fall back to the raw signal
                     try:
-                        signal_smoothed[ix] = self.savgol_predict(signal[:ix+1])
-                    except np.linalg.LinAlgError:
+                        signal_smoothed[ix] = self.savgol_predict(
+                            signal[: ix + 1], self.poly_fit_degree, 0
+                        )
+                    except np.linalg.LinAlgError:  # for small ix, the design matrix is singular
                         signal_smoothed[ix] = signal[ix]
             return signal_smoothed
         elif self.boundary_method == "identity":
@@ -403,19 +409,13 @@ def savgol_smoother(self, signal):
     def savgol_impute(self, signal):
         """Impute the nan values in signal using savgol.
 
-        This method fills the nan values in the signal with an M-degree polynomial fit
+        This method fills the nan values in the signal with a quadratic polynomial fit
         on a rolling window of the immediate past up to window_length data points.
 
-        In the case of a single data point in the past, the single data point is
-        continued. In the case of no data points in the past (i.e. the signal starts
-        with nan), an error is raised.
+        A number of boundary cases are handled involving nan filling close to the boundary.
 
         Note that in the case of many adjacent nans, the method will use previously
-        imputed values to do the fitting for later values. E.g. for
-        >>> x = np.array([1.0, 2.0, np.nan, 1.0, np.nan])
-        the last np.nan will be fit on np.array([1.0, 2.0, *, 1.0]), where * is the
-        result of imputing based on np.array([1.0, 2.0]) (depends on the savgol
-        settings).
+        imputed values to do the fitting for later values.
 
         Parameters
         ----------
@@ -428,20 +428,27 @@ def savgol_impute(self, signal):
             An imputed 1D signal.
         """
         signal_imputed = np.copy(signal)
-        for ix in np.where(np.isnan(signal))[0]:
+        for ix in np.where(np.isnan(signal_imputed))[0]:
             # Boundary cases
             if ix < self.window_length:
-                # A nan following a single value should just be extended
+                # At the boundary, a single value should just be extended
                 if ix == 1:
-                    signal_imputed[ix] = signal_imputed[0]
-                # Otherwise, use savgol fitting
+                    signal_imputed[ix] = signal_imputed[ix - 1]
+                # Reduce the polynomial degree if needed
+                elif ix == 2:
+                    signal_imputed[ix] = self.savgol_predict(
+                        signal_imputed[:ix], 1, -1
+                    )
+                # Otherwise, use savgol fitting on the largest window prior
                 else:
-                    coeffs = self.savgol_coeffs(-ix, -1)
-                    signal_imputed[ix] = signal_imputed[:ix] @ coeffs
-            # Use a polynomial fit on the past window length to impute
+                    signal_imputed[ix] = self.savgol_predict(
+                        signal_imputed[:ix], self.poly_fit_degree, -1
+                    )
+            # Away from the boundary, use savgol fitting on a fixed window
             else:
-                coeffs = self.savgol_coeffs(-self.window_length, -1)
-                signal_imputed[ix] = (
-                    signal_imputed[ix - self.window_length : ix] @ coeffs
+                signal_imputed[ix] = self.savgol_predict(
+                    signal_imputed[ix - self.window_length : ix],
+                    self.poly_fit_degree,
+                    -1,
                 )
         return signal_imputed

_delphi_utils_python/tests/test_smooth.py

@@ -10,11 +10,24 @@
 
 
 class TestSmoothers:
+    def test_bad_inputs(self):
+        with pytest.raises(ValueError):
+            Smoother(smoother_name="hamburger")
+        with pytest.raises(ValueError):
+            Smoother(impute_method="hamburger")
+        with pytest.raises(ValueError):
+            Smoother(boundary_method="hamburger")
+
     def test_identity_smoother(self):
         signal = np.arange(30) + np.random.rand(30)
         assert np.allclose(signal, Smoother(smoother_name="identity").smooth(signal))
 
     def test_moving_average_smoother(self):
+        # Test non-integer window-length
+        with pytest.raises(ValueError):
+            signal = np.array([1, 1, 1])
+            Smoother(smoother_name="window_average", window_length=5.5).smooth(signal)
+
         # The raw and smoothed lengths should match
         signal = np.ones(30)
         smoother = Smoother(smoother_name="moving_average")
@@ -109,10 +122,13 @@ def test_causal_savgol_smoother(self):
             smoother_name="savgol", window_length=window_length, poly_fit_degree=1,
         )
         smoothed_signal2 = smoother.smooth(signal)
-
         assert np.allclose(smoothed_signal1, smoothed_signal2)
 
     def test_impute(self):
+        # test front nan error
+        with pytest.raises(ValueError):
+            Smoother().impute(signal=np.array([np.nan, 1, 1]))
+
         # test the nan imputer
         signal = np.array([i if i % 3 else np.nan for i in range(1, 40)])
         assert np.allclose(Smoother(impute_method=None).impute(signal), signal, equal_nan=True)
@@ -180,6 +196,14 @@ def test_impute(self):
         smoothed_signal = smoother.savgol_impute(signal)
         assert np.allclose(smoothed_signal, signal)
 
+        # test that we don't hit a matrix inversion error when there are
+        # nans on less than window_length away from the boundary
+        signal = np.hstack([[1], np.nan*np.ones(12), np.arange(5)])
+        smoother = Smoother(smoother_name="savgol", poly_fit_degree=2,
+                            boundary_method="identity", window_length=10)
+        smoothed_signal = smoother.impute(signal)
+        assert np.allclose(smoothed_signal, np.hstack([[1], np.ones(12), np.arange(5)]))
+
     def test_pandas_series_input(self):
         # The savgol method should match the linear regression method on the first
         # window_length-many values of the signal, if the savgol_weighting is set to true,