clease - npm Package Compare versions

Comparing version 0.11.6 to 1.0.0

clease.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: clease
-Version: 0.11.6
+Version: 1.0.0
 Summary: CLuster Expansion in Atomistic Simulation Environment
@@ -12,3 +12,3 @@ Home-page: https://gitlab.com/computationalmaterials/clease/
 Keywords: Cluster Expansion,Monte Carlo,Computational materials,Materials research
-Classifier: Development Status :: 4 - Beta
+Classifier: Development Status :: 5 - Production/Stable
 Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
@@ -15,0 +15,0 @@ Classifier: Programming Language :: Python :: 3.7

clease.egg-info/requires.txt

@@ -16,21 +16,21 @@ ase<3.23,>=3.20
 [all]
+clease-gui
+clang-format>=14.0.3
 ipython
-mock
+pytest
+pyclean>=2.0.0
+pytest-mock
+pylint
+black>=22.1.0
+sphinx
 pre-commit
-pytest-cov
 cython
-clease-gui
-clang-format>=14.0.3
+tox>=3.24.0
 pip
-black>=22.1.0
-tox>=3.24.0
-pytest-benchmark[histogram]>=3.4.1
-pylint
+twine
+mock
 sphinx_rtd_theme
 build
-twine
-pytest
-sphinx
-pytest-mock
-pyclean>=2.0.0
+pytest-benchmark[histogram]>=3.4.1
+pytest-cov
 
@@ -37,0 +37,0 @@ [dev]

clease.egg-info/SOURCES.txt

@@ -11,3 +11,2 @@ LICENSE.md
 clease/cluster_coverage.py
-clease/concentration.py
 clease/convexhull.py
@@ -26,7 +25,4 @@ clease/corr_func.py
 clease/mp_logger.py
-clease/new_struct.py
 clease/plot_post_process.py
-clease/regression_old.py
 clease/sparsifier.py
-clease/structure_generator.py
 clease/structure_mapper.py
@@ -33,0 +29,0 @@ clease/svd.py

clease/__init__.py

 # pylint: disable=undefined-variable
 import logging
-from deprecated import deprecated
 from .version import *
@@ -5,0 +4,0 @@

clease/_version.txt

@@ -1,1 +0,1 @@
-0.11.6
+1.0.0

clease/cluster/cluster_manager.py

@@ -314,3 +314,3 @@ from typing import Sequence, Set, Dict, List, Iterator, Tuple
             )
-            logger.info("Trivial supercell with repetition: (%d, %d, %d)", nx, ny, nz)
+            logger.debug("Trivial supercell with repetition: (%d, %d, %d)", nx, ny, nz)
         else:
@@ -323,3 +323,3 @@ # Choose the generalized pathway.
             )
-            logger.info("Non-trivial supercell, will wrap using cartesian coordinates")
+            logger.debug("Non-trivial supercell, will wrap using cartesian coordinates")
 
@@ -326,0 +326,0 @@ lut = self.create_four_vector_lut(template)

clease/evaluate.py

@@ -8,4 +8,5 @@ """Module that fits ECIs to energy data."""
 import multiprocessing as mp
-from typing import Optional, Dict, List
+from typing import Dict, List
 
+from deprecated import deprecated
 import numpy as np
@@ -23,3 +24,3 @@ from ase.db import connect
 
-__all__ = ("Evaluate",)
+__all__ = ("Evaluate", "supports_alpha_cv")
 
@@ -116,3 +117,2 @@ # Initialize a module-wide logger
         self.scheme = None
-        self.scheme_string = None
         self.nsplits = nsplits
@@ -159,3 +159,2 @@ self.num_repetitions = num_repetitions
         self.eci = None
-        self._alpha = None
         self.e_pred_loo = None
@@ -173,2 +172,17 @@ self.parallel = parallel
     @property
+    def scoring_scheme(self) -> str:
+        return self._scoring_scheme
+
+    @scoring_scheme.setter
+    def scoring_scheme(self, value: str) -> None:
+        if not isinstance(value, str):
+            raise TypeError(f"Scoring scheme should be string, got {value!r}")
+        value = value.lower()
+        allowed_schemas = {"loocv", "loocv_fast", "k-fold"}
+        if value not in allowed_schemas:
+            schemas_s = ", ".join(sorted(allowed_schemas))
+            raise ValueError(f"Unknown scoring scheme: {value}. Allowed schemas: {schemas_s}")
+        self._scoring_scheme = value
+
+    @property
     def concentrations(self):
@@ -188,3 +202,3 @@ """
 
-        allowed_fitting_schemes = ["ridge", "tikhonov", "lasso", "l1", "l2", "ols"]
+        allowed_fitting_schemes = ["ridge", "tikhonov", "lasso", "l1", "l2", "ols", "linear"]
         if isinstance(fitting_scheme, LinearRegression):
@@ -194,3 +208,2 @@ self.scheme = fitting_scheme
             fitting_scheme = fitting_scheme.lower()
-            self.scheme_string = fitting_scheme
             if fitting_scheme not in allowed_fitting_schemes:
@@ -206,5 +219,7 @@ raise ValueError(f"Fitting scheme has to be one of {allowed_fitting_schemes}")
                 self.scheme = Lasso(alpha=alpha)
-            else:
+            elif fitting_scheme in ["ols", "linear"]:
                 # Perform ordinary least squares
                 self.scheme = LinearRegression()
+            else:
+                raise ValueError(f"Unknown fitting scheme: {fitting_scheme}")
         else:
@@ -217,9 +232,4 @@ raise ValueError(
 
-        # If the fitting scheme is changed, the ECIs computed are no
-        # longer consistent with the scheme
-        # By setting it to None, a new calculation is performed
-        # when the ECIs are requested
-        # Also reset the "last alpha"
+        # Unset the ECI, so a new fit is required.
         self.eci = None
-        self._alpha = None
 
@@ -281,60 +291,47 @@ N = len(self.e_dft)
         """Check whether we need to calculate the ECI values."""
-        return self.eci is None or self._alpha != self.scheme.alpha
+        return self.eci is None
 
     def fit(self) -> None:
-        """Determine the ECI for a given alpha.
+        """Determine the ECI with the given regressor.
 
-        This method also saves the last value of alpha used and
-        the corresponding ECIs (self.eci) such that ECIs are not calculated
-        repeated if alpha value is unchanged.
+        This will always calculate a new fit.
         """
-        if self.fit_required():
-            self._do_fit()
+        self.eci = self.scheme.fit(self.cf_matrix, self.e_dft)
         assert self.eci is not None
 
-    def _do_fit(self) -> None:
-        """Fit using the current scheme, and remember the alpha"""
-        self.eci = self.scheme.fit(self.cf_matrix, self.e_dft)
-        # Remember the latest alpha
-        self._alpha = self.scheme.alpha
-
-    def get_eci(self, allow_fit: bool = True) -> np.ndarray:
+    def get_eci(self) -> np.ndarray:
         """Determine and return ECIs for a given alpha.
+        Raises a ValueError if no fit has been performed yet.
 
-        Args:
-            allow_fit (bool, optional): Is a new fit allowed to be run before returning the ECI's?
-                If no ECI's are present yet, and no re-fitting was allowed, then a ValueError
-                is raised.
-                Defaults to True.
-
         Returns:
             np.ndarray: A 1D array of floats with all ECI values.
         """
-        if allow_fit:
-            self.fit()
         if self.eci is None:
             # getting ECI's was not allowed to fit, and we havn't run a fit yet.
-            raise ValueError("ECI's has not been fit yet.")
+            raise ValueError("ECI's has not been fit yet. Call the Evaluate.fit method first.")
 
         return self.eci
 
-    def get_eci_dict(self, allow_fit: bool = True):
+    def get_eci_dict(self, cutoff_tol: float = 1e-14) -> Dict[str, float]:
+        """Determine cluster names and their corresponding ECI value and return
+        them in a dictionary format.
+
+        Args:
+            cutoff_tol (float, optional): Cutoff value below which the absolute ECI
+                value is considered to be 0. Defaults to 1e-14.
+
+        Returns:
+            Dict[str, float]: Dictionary with the CF names and the corresponding
+                ECI value.
         """
-        Determine cluster names and their corresponding ECI value and return
-        them in a dictionary format."""
-        self.get_eci(allow_fit=allow_fit)
+        eci = self.get_eci()
 
         # sanity check
-        if len(self.cf_names) != len(self.eci):
+        if len(self.cf_names) != len(eci):
             raise ValueError("lengths of cf_names and ECIs are not same")
 
-        i_nonzeros = np.nonzero(self.eci)[0]
-        pairs = []
-        for i, cname in enumerate(self.cf_names):
-            if i not in i_nonzeros:
-                continue
-            pairs.append((cname, self.eci[i]))
+        all_nonzero = np.abs(eci) > cutoff_tol
+        # Only keep the all non-zero values.
+        return {cf: val for cf, val, nonzero in zip(self.cf_names, eci, all_nonzero) if nonzero}
 
-        return dict(pairs)
-
     def load_eci_dict(self, eci_dict: Dict[str, float]) -> None:
@@ -365,3 +362,3 @@ """Load the ECI's from a dictionary. Any ECI's which are missing
 
-    def save_eci(self, fname="eci.json"):
+    def save_eci(self, fname="eci.json", **kwargs):
         """
@@ -375,7 +372,10 @@ Save a dictionary of cluster names and their corresponding ECI value
             json filename. If no extension if given, .json is added
+        kwargs:
+            Extra keywords are passed on to the :meth:`~get_eci_dict` method.
         """
         full_fname = add_file_extension(fname, ".json")
         with open(full_fname, "w") as outfile:
-            json.dump(self.get_eci_dict(), outfile, indent=2, separators=(",", ": "))
+            json.dump(self.get_eci_dict(**kwargs), outfile, indent=2, separators=(",", ": "))
 
+    @deprecated(reason="Use the clease.plot_post_process module instead.", version="0.11.7")
     def plot_fit(self, interactive=False, savefig=False, fname=None, show_hull=True):
@@ -419,9 +419,6 @@ """Plot calculated (DFT) and predicted energies for a given alpha.
         cv_name = "LOOCV"
-        if self.scoring_scheme == "loocv":
-            cv = self.loocv() * 1000.0
-        elif self.scoring_scheme == "loocv_fast":
-            cv = self.loocv_fast() * 1000.0
-        elif self.scoring_scheme == "k-fold":
-            cv = self.k_fold_cv() * 1000.0
+        cv = self.get_cv_score() * 1000.0
+        if self.scoring_scheme == "k-fold":
             cv_name = f"{self.nsplits}-fold"
+
         t = np.arange(rmin - 10, rmax + 10, 1)
@@ -629,18 +626,9 @@ fig = plt.figure()
 
-        if fitting_schemes is None:
-            if self.scheme_string is None:
-                raise ValueError("No fitting scheme supplied!")
-            if self.scheme_string in ["lasso", "l1"]:
-                from clease.regression import Lasso
+        if not supports_alpha_cv(self.scheme):
+            raise ValueError(f"Scheme must support scalar alpha, got {self.scheme!r}")
 
-                fitting_schemes = Lasso.get_instance_array(
-                    alpha_min, alpha_max, num_alpha=num_alpha, scale=scale
-                )
-            elif self.scheme_string in ["ridge", "l2", "tikhonov"]:
-                from clease.regression import Tikhonov
+        fitting_schemes = self.scheme.get_instance_array(
+            alpha_min, alpha_max, num_alpha=num_alpha, scale=scale
+        )
 
-                fitting_schemes = Tikhonov.get_instance_array(
-                    alpha_min, alpha_max, num_alpha=num_alpha, scale=scale
-                )
-
         for scheme in fitting_schemes:
@@ -691,9 +679,5 @@ if not isinstance(scheme, LinearRegression):
                 self.set_fitting_scheme(fitting_scheme=scheme)
-                if self.scoring_scheme == "loocv":
-                    cv[i] = self.loocv()
-                elif self.scoring_scheme == "loocv_fast":
-                    cv[i] = self.loocv_fast()
-                elif self.scoring_scheme == "k-fold":
-                    cv[i] = self.k_fold_cv()
-                num_eci = len(np.nonzero(self.get_eci(allow_fit=True))[0])
+                cv[i] = self.get_cv_score()
+                self.fit()
+                num_eci = len(np.nonzero(self.get_eci())[0])
                 alpha = scheme.get_scalar_parameter()
@@ -722,9 +706,5 @@ alphas.append(alpha)
             self.scheme.alpha = alpha
-            if self.scoring_scheme == "loocv":
-                cv = self.loocv()
-            elif self.scoring_scheme == "loocv_fast":
-                cv = self.loocv_fast()
-            elif self.scoring_scheme == "k-fold":
-                cv = self.k_fold_cv()
-            num_eci = len(np.nonzero(self.get_eci(allow_fit=True))[0])
+            cv = self.get_cv_score()
+            self.fit()
+            num_eci = len(np.nonzero(self.get_eci())[0])
             self._cv_scores.append({"alpha": alpha, "cv": cv})
@@ -740,2 +720,3 @@ logger.info(f"{alpha:.10f}\t {num_eci}\t {cv:.10f}")
 
+    @deprecated(reason="Use the alpha_CV method instead.", version="0.11.7")
     def plot_CV(
@@ -840,2 +821,3 @@ self,
 
+    @deprecated(reason="Logfile is being removed.", version="0.11.7")
     def _get_alphas_cv_from_file(self, logfile):
@@ -893,2 +875,3 @@ alphas = []
 
+    @deprecated(reason="Use the clease.plot_post_process module instead.", version="0.11.7")
     def plot_ECI(self, ignore_sizes=(0,), interactive=True):
@@ -1133,3 +1116,3 @@ """Plot the all the ECI.
 
-            pred = self.eci.dot(cf)
+            pred = self.get_eci().dot(cf)
 
@@ -1172,7 +1155,9 @@ if row.get("final_struct_id", -1) != -1:
         if self.scoring_scheme == "loocv":
-            cv = self.loocv() * 1000.0
+            cv = self.loocv()
         elif self.scoring_scheme == "loocv_fast":
-            cv = self.loocv_fast() * 1000.0
+            cv = self.loocv_fast()
         elif self.scoring_scheme == "k-fold":
-            cv = self.k_fold_cv() * 1000.0
+            cv = self.k_fold_cv()
+        else:
+            raise ValueError(f"Unknown scoring scheme: {self.schoring_scheme}")
         return cv
@@ -1186,30 +1171,5 @@
         """
-        if self.eci is None:
-            raise ValueError("ECIs have not been fitted yet.")
-        return self.cf_matrix.dot(self.eci)
+        eci = self.get_eci()
+        return self.cf_matrix.dot(eci)
 
-    def subtract_predict_dft(self) -> np.ndarray:
-        """
-        Subtract CE predicted energy from DFT energy
-
-        :return: Energy difference between DFT and CE model
-            (i.e., E_DFT - E_CE) in the original unit (typically eV)
-        """
-        e_pred = self.get_energy_predict()
-        delta_e = self.e_dft - e_pred
-        return delta_e
-
-    def subtract_predict_dft_loo(self) -> Optional[np.ndarray]:
-        """
-        Subtract CE predicted energy from DFT energy in the leave-one-out
-        scheme.
-
-        :return: Energy difference between DFT and CE model
-            (i.e., E_DFT - E_CE_LOO) in the original unit (typically eV)
-        """
-        if self.e_pred_loo is None:
-            return None
-        delta_e_loo = self.e_dft - self.e_pred_loo
-        return delta_e_loo
-
     def get_eci_by_size(self) -> Dict[str, Dict[str, list]]:
@@ -1275,4 +1235,12 @@ """
         cv = evaluator.k_fold_cv()
-    num_eci = len(np.nonzero(evaluator.get_eci(allow_fit=True))[0])
+    evaluator.fit()
+    num_eci = len(np.nonzero(evaluator.get_eci())[0])
     logger.info("%.10f\t %d\t %.10f", alpha, num_eci, cv)
     return cv
+
+
+def supports_alpha_cv(scheme: LinearRegression) -> bool:
+    """Determine whether a regression scheme supports alpha CV"""
+    if scheme.is_scalar() and hasattr(scheme, "alpha"):
+        return True
+    return False

clease/montecarlo/montecarlo.py

@@ -79,2 +79,3 @@ """Monte Carlo method for ase."""
         self.reset_averagers()
+        self._reset_internal_counters()
 
@@ -183,3 +184,2 @@ def update_current_energy(self) -> None:
                 ms_per_step = 1000.0 * self.status_every_sec / (self.current_step - prev)
-                accept_rate = self.num_accepted / self.current_step
                 logger.info(
@@ -190,3 +190,3 @@ "%d of %d steps. %.2f ms per step. Acceptance rate: %.2f",
                     ms_per_step,
-                    accept_rate,
+                    self.current_accept_rate,
                 )
@@ -261,2 +261,10 @@ prev = self.current_step
 
+    @property
+    def current_accept_rate(self) -> float:
+        """Return the current accept rate as a value between 0 and 1."""
+        if self.current_step == 0:
+            # No steps have been taken yet.
+            return 0.0
+        return self.num_accepted / self.current_step
+
     def get_thermodynamic_quantities(self):
@@ -271,2 +279,3 @@ """Compute thermodynamic quantities."""
         quantities["temperature"] = self.temperature
+        quantities["accept_rate"] = self.current_accept_rate
         at_count = self.count_atoms()
@@ -273,0 +282,0 @@ for key, value in at_count.items():

clease/plot_post_process.py

@@ -6,2 +6,3 @@ from typing import List, Tuple
 from clease import Evaluate, ConvexHull
+from clease.evaluate import supports_alpha_cv
 
@@ -28,6 +29,6 @@
         plot_args = {}
-    X = evaluate.get_energy_predict()
-    Y = evaluate.e_dft
-    xlabel = plot_args.get("xlabel", "E_CE (eV/atom)")
-    ylabel = plot_args.get("ylabel", "E_DFT (eV/atom)")
+    X = evaluate.e_dft
+    Y = evaluate.get_energy_predict()
+    xlabel = plot_args.get("xlabel", "E_DFT (eV/atom)")
+    ylabel = plot_args.get("ylabel", "E_CE (eV/atom)")
     title = plot_args.get("title", f"Fit using {len(evaluate.e_dft)} data points.")
@@ -51,3 +52,3 @@
 
-    cv = evaluate.get_cv_score()
+    cv = evaluate.get_cv_score() * 1000
     rmse = evaluate.rmse() * 1000
@@ -92,3 +93,5 @@
 
-def plot_fit_residual(evaluate: Evaluate, plot_args: dict = None) -> Figure:
+def plot_fit_residual(
+    evaluate: Evaluate, plot_args: dict = None, interactive: bool = False
+) -> Figure:
     """
@@ -112,3 +115,4 @@ Figure object subtracted (DFT) and predicted energies.
     X = evaluate.e_dft
-    Y = evaluate.subtract_predict_dft()
+    Y = evaluate.get_energy_predict() - X  # eV/atom
+    Y *= 1000  # meV/atom
     xlabel = plot_args.get("xlabel", "#OCC")
@@ -120,6 +124,7 @@ ylabel = plot_args.get("ylabel", r"$E_{DFT} - E_{pred}$ (meV/atom)")
     fig, ax = plt.subplots(ncols=2, sharey=True, gridspec_kw=gridspec_kw)
+    ax[0].axhline(0, ls="--")
+    plot_line = ax[0].plot(X, Y, "v", mfc="none")[0]
+    ax[0].set_xlabel(r"$E_{DFT}$ (eV/atom)")
+    ax[0].set_ylabel(ylabel)
     ax[0].set_title(title)
-    ax[0].set_ylabel(ylabel)
-    ax[0].axhline(0, ls="--")
-    ax[0].plot(X, Y, "v", mfc="none")
 
@@ -133,2 +138,18 @@ hist, bin_edges = np.histogram(Y, bins=30)
 
+    if interactive:
+        # pylint: disable=import-outside-toplevel
+        from clease.interactive_plot import ShowStructureOnClick, AnnotatedAx
+
+        annotations = _make_annotations_plot_fit(evaluate)
+        # Construct the annotated axis objects.
+        annotated_ax = AnnotatedAx(
+            ax[0],
+            [plot_line],
+            annotations,
+            structure_names=[evaluate.names],
+        )
+
+        # Attach interactivity to the fig object.
+        ShowStructureOnClick(fig, annotated_ax, evaluate.settings.db_name)
+
     return fig
@@ -202,2 +223,4 @@
     """
+    if not supports_alpha_cv(evaluate.scheme):
+        raise ValueError(f"Scheme {evaluate.scheme!r} doesn't support alpha CV.")
     if plot_args is None:
@@ -204,0 +227,0 @@ plot_args = {}

clease/regression/constrained_ridge.py

@@ -58,2 +58,6 @@ from typing import Tuple
         """
+        if len(self.alpha) != X.shape[1]:
+            msg = "Alpha has wrong number of dimensions. It should be equal to the number "
+            msg += f"of features {X.shape[1]}, but got {len(self.alpha)}."
+            raise ValueError(msg)
         if self._constraint is None:
@@ -60,0 +64,0 @@ return X.T.dot(X) + self.alpha, X.T.dot(y)

clease/regression/physical_ridge.py

@@ -10,3 +10,3 @@ from typing import List, Sequence, Dict, Union, Callable, Tuple
 from clease.data_normalizer import DataNormalizer
-from clease.tools import split_dataset
+from clease.tools import split_dataset, get_size_from_cf_name
 from .regression import LinearRegression
@@ -69,2 +69,5 @@ from .constrained_ridge import ConstrainedRidge
         is recommended to put normalize=False for such cases.
+
+    :param cf_names: List of strings, used to initialize the size and diameters
+        which will be used.
     """
@@ -79,2 +82,3 @@
         normalize: bool = True,
+        cf_names: List[str] = None,
     ) -> None:
@@ -91,2 +95,5 @@ super().__init__()
         self._constraint = None
+        if cf_names is not None:
+            self.diameters_from_names(cf_names)
+            self.sizes_from_names(cf_names)
 
@@ -144,3 +151,3 @@ def add_constraint(self, A: np.ndarray, c: np.ndarray) -> None:
         """
-        self.sizes = [int(n[1]) for n in names]
+        self.sizes = [get_size_from_cf_name(n) for n in names]
 
@@ -158,3 +165,4 @@ def diameters_from_names(self, names: List[str]) -> None:
         for n in names:
-            if n[1] == "0" or n[1] == "1":
+            size = get_size_from_cf_name(n)
+            if size in {0, 1}:
                 diameters.append(0.0)
@@ -161,0 +169,0 @@ else:

clease/settings/settings.py

@@ -11,3 +11,2 @@ """Definition of ClusterExpansionSettings Class.
 
-from deprecated import deprecated
 import numpy as np
@@ -62,6 +61,2 @@ from ase import Atoms
 
-        max_cluster_size (int | None, optional): Deprecated. Specifies the maximum cluster
-            body size. Defaults to None. A DeprecationWarning will be raised,
-            if this value is not None.
-
         max_cluster_dia (Sequence[float], optional): A list of int or float containing the
@@ -107,5 +102,2 @@ maximum diameter of clusters (in Å). Defaults to ``(5., 5., 5.)``, i.e.
         db_name: str = "clease.db",
-        # max_cluster_size is only here for deprecation purposes
-        # if it is not None, the user has manually specified a value
-        max_cluster_size=None,
         max_cluster_dia: Sequence[float] = (5.0, 5.0, 5.0),
@@ -150,5 +142,3 @@ include_background_atoms: bool = False,
 
-        self.max_cluster_dia = _format_max_cluster_dia(
-            max_cluster_dia, max_cluster_size=max_cluster_size
-        )
+        self.max_cluster_dia = np.array(max_cluster_dia)
 
@@ -691,3 +681,4 @@ self.basis_func_type = basis_func_type
             # For compatibility, since it's no longer in KWARG_KEYS
-            kwargs["max_cluster_size"] = dct.pop("max_cluster_size")
+            # Avoid crashing.
+            dct.pop("max_cluster_size")
 
@@ -753,50 +744,2 @@ settings = cls(*args, **kwargs)
 
-def _format_max_cluster_dia(max_cluster_dia, max_cluster_size=None):
-    """Formatter of max_cluster_dia."""
-    if max_cluster_size is None and not isinstance(max_cluster_dia, (int, float)):
-        # Assume max_cluster_dia is sequence[float], and user didn't specify any
-        # (now deprecated) max_cluster sizes.
-        return np.array(max_cluster_dia)
-    return _old_format_max_cluster_dia(max_cluster_dia, max_cluster_size)
-
-
-def _old_format_max_cluster_dia(max_cluster_dia, max_cluster_size):
-
-    # User specified an old version of MCS and MCD
-    dep_msg = f"""
-    max_cluser_size should no longer be specfied explicitly,
-    and max_cluster_dia should no longer be an int or float.
-
-    Specify cluster sizes with max_cluster_dia as an array-like instead.
-    Got max_cluster_size '{max_cluster_size}' and max_cluster_dia '{max_cluster_dia}'.
-    Try instead to use max_cluster_dia as an array, e.g. max_cluster_dia=[5., 5., 5.]
-    for 2-, 3- and 4-body clusters of cutoff 5 Å.
-    """
-
-    @deprecated(version="0.10.6", reason=dep_msg)
-    def _formatter():
-        # max_cluster_dia is list or array
-        if isinstance(max_cluster_dia, (list, np.ndarray, tuple)):
-            # Length should be either max_cluster_size+1 or max_cluster_size-1
-            mcd = np.array(max_cluster_dia, dtype=float)
-            if len(max_cluster_dia) == max_cluster_size + 1:
-                # Remove the first two entries, assume they are 0- and 1-body diameters
-                mcd = mcd[2:]
-            elif len(max_cluster_dia) == max_cluster_size - 1:
-                # Assume max_cluster_dia contains 2+ body clusters
-                pass
-            else:
-                raise ValueError("Invalid length for max_cluster_dia.")
-        elif isinstance(max_cluster_dia, (int, float)):
-            if max_cluster_size is None:
-                raise ValueError("Received no max_cluster_size, but a float for max_cluster_dia")
-            mcd = np.ones(max_cluster_size - 1, dtype=float) * max_cluster_dia
-        # Case for *None* or something else
-        else:
-            raise TypeError(f"max_cluster_dia is of wrong type, got: {type(max_cluster_dia)}")
-        return mcd.round(decimals=3)
-
-    return _formatter()
-
-
 def _get_prim_cell_id_from_connection(prim_cell: Atoms, connection: Database) -> int:
@@ -803,0 +746,0 @@ """Retrieve the primitive cell ID from a database connection"""

doc/source/aucu_evaluate.rst

@@ -54,2 +54,4 @@ .. testsetup::
   >>> from clease import Evaluate
+  >>> import clease.plot_post_process as pp
+  >>> import matplotlib.pyplot as plt
   >>>
@@ -64,7 +66,10 @@ >>> eva = Evaluate(settings=settings, scoring_scheme='k-fold', nsplits=10)
   >>> eva.set_fitting_scheme(fitting_scheme='l1', alpha=alpha)
-  >>> eva.plot_fit(interactive=False)
+  >>> eva.fit()  # Run the fit with these settings.
   >>>
+  >>> fig = pp.plot_fit(eva)
+  >>> plt.show()
+  >>>
   >>> # plot ECI values
-  >>> eva.plot_ECI()
-  >>>
+  >>> fig = pp.plot_eci(eva)
+  >>> plt.show()
   >>> # save a dictionary containing cluster names and their ECIs
@@ -71,0 +76,0 @@ >>> eva.save_eci(fname='eci_l1')

doc/source/releasenotes.rst

@@ -7,2 +7,24 @@ .. _releasenotes:
 
+1.0.0
+======
+* 21 June 2022 - CLEASE is no longer considered beta.
+* :class:`~clease.evaluate.Evaluate` can now properly support fitting with custom LinearRegression
+  schemes, even if they don't support alpha cross-validation.
+* :class:`~clease.evaluate.Evaluate` now required explicit calls to
+  :py:meth:`~clease.evaluate.Evaluate.fit`. Calls to :py:meth:`~clease.evaluate.Evaluate.get_eci`
+  and :py:meth:`~clease.evaluate.Evaluate.get_eci_dict` can no longer implicitly do fitting.
+  This un-does a change introduced in version 0.11.6.
+* Added the :py:attr:`~clease.montecarlo.montecarlo.Montecarlo.current_accept_rate` property,
+  and export the current accept rate in the thermodynamic quantities dictionary under the
+  ``accept_rate`` key.
+* Removed a series of deprecated things:
+
+  * Removed the ``clease.concentration`` module.
+  * Removed the ``clease.new_struct`` module.
+  * Removed old regression imports. Regression classes must now be imported from the
+    ``clease.regression`` module.
+  * Removed the ``clease.structure_generator`` module.
+  * Removed the ``max_cluster_size`` settings argument.
+
+
 0.11.6
@@ -9,0 +31,0 @@ =======

PKG-INFO

 Metadata-Version: 2.1
 Name: clease
-Version: 0.11.6
+Version: 1.0.0
 Summary: CLuster Expansion in Atomistic Simulation Environment
@@ -12,3 +12,3 @@ Home-page: https://gitlab.com/computationalmaterials/clease/
 Keywords: Cluster Expansion,Monte Carlo,Computational materials,Materials research
-Classifier: Development Status :: 4 - Beta
+Classifier: Development Status :: 5 - Production/Stable
 Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
@@ -15,0 +15,0 @@ Classifier: Programming Language :: Python :: 3.7

setup.cfg

@@ -20,3 +20,3 @@ [metadata]
 classifiers = 
-	Development Status :: 4 - Beta
+	Development Status :: 5 - Production/Stable
 	License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
@@ -23,0 +23,0 @@ Programming Language :: Python :: 3.7

clease/concentration.py

-from deprecated import deprecated
-from . import settings
-
-__all__ = ("Concentration",)
-
-
-@deprecated(version="0.10.0", reason="Use Concentration class from clease.settings instead")
-class Concentration(settings.Concentration):
-    """Concentration class was moved to clease.settings"""

clease/new_struct.py

-"""Deprecated import of new_struct.
-Module has been moved to clease.structgen, so
-use clease.structgen.new_struct instead """
-
-from warnings import warn
-from deprecated import deprecated
-import numpy as np
-from clease.structgen import new_struct
-
-MSG = "Import {} from clease.structgen.new_struct instead"
-DEP_VERSION = "0.10.2"  # Deprecation version
-
-MODULE_DEP_MSG = f"""
-The clease.new_struct module has been moved as of version {DEP_VERSION}.
-Please use the clease.structgen.new_struct module instead"""
-
-# Print the message on import
-warn(MODULE_DEP_MSG, np.VisibleDeprecationWarning)
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("NewStructures"))
-class NewStructures(new_struct.NewStructures):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("MaxAttemptReachedError"))
-class MaxAttemptReachedError(new_struct.MaxAttemptReachedError):
-    pass

clease/regression_old.py

-"""Module for defining deprecated regression class imports"""
-# pylint: skip-file
-from deprecated import deprecated
-from clease import regression
-
-__all__ = (
-    "LinearRegression",
-    "Tikhonov",
-    "Lasso",
-    "BayesianCompressiveSensing",
-    "ConstrainedRidge",
-    "GAFit",
-    "GeneralizedRidgeRegression",
-    "PhysicalRidge",
-    "SequentialClusterRidge",
-)
-
-MSG = "Import {} from clease.regression instead"
-DEP_VERSION = "0.10.0"  # Deprecation version
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("LinearRegression"))
-class LinearRegression(regression.LinearRegression):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("Tikhonov"))
-class Tikhonov(regression.Tikhonov):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("Lasso"))
-class Lasso(regression.Lasso):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("BayesianCompressiveSensing"))
-class BayesianCompressiveSensing(regression.BayesianCompressiveSensing):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("ConstrainedRidge"))
-class ConstrainedRidge(regression.ConstrainedRidge):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("GAFit"))
-class GAFit(regression.GAFit):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("GeneralizedRidgeRegression"))
-class GeneralizedRidgeRegression(regression.GeneralizedRidgeRegression):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("PhysicalRidge"))
-class PhysicalRidge(regression.PhysicalRidge):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("SequentialClusterRidge"))
-class SequentialClusterRidge(regression.SequentialClusterRidge):
-    pass

clease/structure_generator.py

-"""Deprecated import of structure_generator.
-Module has been moved to clease.structgen, so
-use clease.structgen.structure_generator instead"""
-
-from warnings import warn
-from deprecated import deprecated
-import numpy as np
-from clease.structgen import structure_generator
-
-MSG = "Import {} from clease.structgen.structure_generator instead"
-DEP_VERSION = "0.10.2"  # Deprecation version
-
-MODULE_DEP_MSG = f"""
-The clease.new_struct module has been moved as of version {DEP_VERSION}.
-Please use the clease.structgen.structure_generator module instead"""
-
-# Print the message on import
-warn(MODULE_DEP_MSG, np.VisibleDeprecationWarning)
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("StructureGenerator"))
-class StructureGenerator(structure_generator.StructureGenerator):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("GSStructure"))
-class GSStructure(structure_generator.GSStructure):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("MetropolisTrajectory"))
-class MetropolisTrajectory(structure_generator.MetropolisTrajectory):
-    pass
-
-
-@deprecated(version=DEP_VERSION, reason=MSG.format("ProbeStructure"))
-class ProbeStructure(structure_generator.ProbeStructure):
-    pass

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Worsened metrics

Total package byte prevSize

1587639

-0.31%

Number of package files

197

-1.99%

Lines of code

16627

-0.82%