diff --git a/gext/fitting.py b/gext/fitting.py
index 4d058aaa349e2e3d764bafc4b9c64b435afcdbd3..94f4e871c9fd221fdbae3009c1accb3b5562200e 100644
--- a/gext/fitting.py
+++ b/gext/fitting.py
@@ -16,8 +16,9 @@ class AbstractFitting(abc.ABC):
"""Base method for setting options."""
@abc.abstractmethod
- def compute(self, vectors: List[np.ndarray], target:np.ndarray):
+ def fit(self, vectors: List[np.ndarray], target:np.ndarray):
"""Base method for computing new fitting coefficients."""
+ return np.zeros(0)
def linear_combination(self, vectors: List[np.ndarray],
coefficients: np. ndarray) -> np.ndarray:
@@ -53,7 +54,7 @@ class LeastSquare(AbstractFitting):
or self.options["regularization"] > 100:
raise ValueError("Unsupported value for regularization")
- def compute(self, vectors: List[np.ndarray], target: np.ndarray):
+ def fit(self, vectors: List[np.ndarray], target: np.ndarray):
"""Given a set of vectors and a target return the fitting
coefficients."""
matrix = np.vstack(vectors).T
@@ -67,5 +68,6 @@ class QuasiTimeReversible(AbstractFitting):
def set_options(self, **kwargs):
"""Set options for quasi time reversible fitting"""
- def compute(self, vectors: List[np.ndarray], target: np.ndarray):
+ def fit(self, vectors: List[np.ndarray], target: np.ndarray):
"""Time reversible least square minimization fitting."""
+ return np.zeros(0)
diff --git a/gext/main.py b/gext/main.py
index 1c9e692e22035a252444827029898617eb22232f..e87e37ef8e123c7eb25516a4f2c46afb6fa5ddb0 100644
--- a/gext/main.py
+++ b/gext/main.py
@@ -61,7 +61,7 @@ class Extrapolator:
if not option in self.options:
self.options[option] = default_value
- if self.options["nsteps"] <= 1 or self.options["nsteps"] >= 100:
+ if self.options["nsteps"] < 1 or self.options["nsteps"] >= 100:
raise ValueError("Unsupported nsteps")
if self.options["descriptor"] == "distance":
@@ -101,25 +101,35 @@ class Extrapolator:
def guess_coefficients(self, coords: np.ndarray, overlap = None) -> np.ndarray:
"""Get a new coefficient matrix to be used as a guess."""
+ # check if we have enough data points to perform an extrapolation
+ count = self.descriptors.count
if self.options["allow_partially_filled"]:
- n = min(self.options["nsteps"], self.descriptors.count)
+ if count == 0:
+ raise ValueError("Not enough data loaded in the extrapolator")
+ n = min(self.options["nsteps"], count)
else:
n = self.options["nsteps"]
+ if count < n:
+ raise ValueError("Not enough data loaded in the extrapolator")
+ # use the descriptors to find the fitting coefficients
prev_descriptors = self.descriptors.get(n)
descriptor = self._compute_descriptor(coords)
- fit_coefficients = self.fitting_calculator.compute(prev_descriptors, descriptor)
+ fit_coefficients = self._fit(prev_descriptors, descriptor)
+ # use the fitting coefficients and the previous gammas to
+ # extrapolate a new gamma
gammas = self.gammas.get(n)
gamma = self.fitting_calculator.linear_combination(gammas, fit_coefficients)
- fit_descriptor = self.fitting_calculator.linear_combination(
- prev_descriptors, fit_coefficients)
-
if self.options["verbose"]:
+ fit_descriptor = self.fitting_calculator.linear_combination(
+ prev_descriptors, fit_coefficients)
print("error on descriptor:", \
np.linalg.norm(fit_descriptor - descriptor, ord=np.inf))
+ # if the overlap is not given, use the coefficients to fit
+ # a new overlap
if overlap is None:
overlaps = self.overlaps.get(n)
overlap = self.fitting_calculator.linear_combination(overlaps, fit_coefficients)
@@ -127,10 +137,9 @@ class Extrapolator:
else:
inverse_sqrt_overlap = self._inverse_sqrt_overlap(overlap)
+ # use the overlap and gamma to find a new set of coefficients
c_guess = self._grassmann_exp(gamma)
- c_guess = inverse_sqrt_overlap @ c_guess
-
- return c_guess
+ return inverse_sqrt_overlap @ c_guess
def _get_tangent(self) -> np.ndarray:
"""Get the tangent point."""
@@ -176,3 +185,8 @@ class Extrapolator:
def _compute_descriptor(self, coords) -> np.ndarray:
"""Given a set of coordinates compute the corresponding descriptor."""
return self.descriptor_calculator.compute(coords)
+
+ def _fit(self, prev_descriptors, descriptor) -> np.ndarray:
+ """Fit the current descriptor using previous descriptors and
+ the specified fitting scheme."""
+ return self.fitting_calculator.fit(prev_descriptors, descriptor)
diff --git a/tests/test_descriptor_fitting.py b/tests/test_descriptor_fitting.py
index cc8c42be6ea39e764fd41a17308e238b26fbc8d9..94e1ca9dca1f47abbcf044176fcd8321d007b3ae 100644
--- a/tests/test_descriptor_fitting.py
+++ b/tests/test_descriptor_fitting.py
@@ -39,7 +39,7 @@ def test_descriptor_fitting(datafile):
for start in range(0, 9):
vectors = descriptors[start:-1]
- fit_coefficients = fitting_calculator.compute(vectors, target)
+ fit_coefficients = fitting_calculator.fit(vectors, target)
fitted_target = fitting_calculator.linear_combination(vectors, fit_coefficients)
errors.append(np.linalg.norm(target - fitted_target, ord=np.inf))
@@ -49,7 +49,7 @@ def test_descriptor_fitting(datafile):
# used for the fitting
vectors = descriptors[:-1]
vectors[0] = target
- fit_coefficients = fitting_calculator.compute(vectors, target)
+ fit_coefficients = fitting_calculator.fit(vectors, target)
fitted_target = fitting_calculator.linear_combination(vectors, fit_coefficients)
assert np.linalg.norm(target - fitted_target, ord=np.inf) < SMALL
diff --git a/tests/test_extrapolation.py b/tests/test_extrapolation.py
index eed97dcd10f31c7bfaa8735cf1752e40c8ed1d44..a727c4c106d120b347da0a9e546350d0877c92be 100644
--- a/tests/test_extrapolation.py
+++ b/tests/test_extrapolation.py
@@ -106,3 +106,37 @@ def test_coefficient_extrapolation(datafile):
assert np.linalg.norm(guessed_density - density, ord=np.inf) < THRESHOLD
assert np.linalg.norm(guessed_density - density, ord=np.inf) \
/np.linalg.norm(density, ord=np.inf) < THRESHOLD
+
+@pytest.mark.parametrize("datafile", ["urea.json", "glucose.json"])
+def test_errors(datafile):
+
+ # load test data from json file
+ data = utils.load_json(f"tests/{datafile}")
+ nelectrons = data["nelectrons"]
+ natoms = data["trajectory"].shape[1]
+ nbasis = data["overlaps"].shape[1]
+ nframes = data["trajectory"].shape[0]
+
+ # amount of data we want to use for fitting
+ n = 9
+ assert n < nframes
+
+ # initialize an extrapolator
+ extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n)
+
+ with pytest.raises(ValueError):
+ extrapolator.guess(data["trajectory"][0])
+
+ # initialize a new extrapolator
+ extrapolator = gext.Extrapolator(nelectrons, nbasis, natoms, nsteps=n,
+ allow_partially_filled=False)
+
+ # load data in the extrapolator up to index m - 1
+ m = 4
+ for (coords, coeff, overlap) in zip(data["trajectory"][:m],
+ data["coefficients"][:m], data["overlaps"][:m]):
+ extrapolator.load_data(coords, coeff, overlap)
+
+ # check an extrapolation at index m
+ with pytest.raises(ValueError):
+ extrapolator.guess(data["trajectory"][m])