Updating regression requirements

CHANGELOG.rst

@@ -1,11 +1,41 @@
 Changelog
 =========
 
+Version 0.2.6
+-------------
+* Updating regression requirements, teach 
+  `atlas-densities cell-densities fit-average-densities` to have `--min-data-points` (#81)
+
+Version 0.2.5
+-------------
+
+* Fix nans in compute_region_volumes (#80)
+
+Version 0.2.4
+-------------
+
+* Add r_square values to the results of the fitting. (#68)
+* Add check to the region filter in case the input dataset is all equal to 0 for the region of interest. (#74)
+* Fix fitting and optimization steps (#75)
+* Faster _compute_region_cell_counts (#72)
+* Cleanup app tests (#78)
+* Faster compute average intensities (#73)
+* Speedup mtype density creation from probability maps using voxcell's ValueToIndexVoxels (#79)
+
+Version 0.2.3
+-------------
+
+* remove pkg_resources, use importlib (#62)
+* Drop literature values from regions that are not in hierarchy or not in the annotation volume (#61)
+
 Version 0.2.2
+-------------
+
 * add ``atlas-densities combination manipulate``
 
 Version 0.2.1
 -------------
+
 * The following *cell-density* sub-commands can now optionally take a ``--group-ids-config-path``:
    *cell-density*, *glia-cell-densities*, *inhibitory-and-excitatory-neuron-densities*, *fit-average-densities*
 

README.rst

@@ -286,6 +286,7 @@ respective standard deviation and `coefficient of determination`_ (`r_square`).
         --fitted-densities-output-path=data/ccfv2/first_estimates/first_estimates.csv               \
         --fitting-maps-output-path=data/ccfv2/first_estimates/fitting.json
 
+Note: One can use the ``--min-data-points`` to require a minimum of points for the linear regression; the default is 1.
 
 Compute inhibitory/excitatory neuron densities
 ----------------------------------------------

atlas_densities/app/cell_densities.py

@@ -808,6 +808,13 @@ def measurements_to_average_densities(
     help="Path to density groups ids config",
     show_default=True,
 )
+@click.option(
+    "--min-data-points",
+    type=int,
+    default=1,
+    help="minimum number of datapoints required for running the linear regression.",
+    show_default=True,
+)
 @log_args(L)
 def fit_average_densities(
     hierarchy_path,
@@ -820,6 +827,7 @@ def fit_average_densities(
     fitted_densities_output_path,
     fitting_maps_output_path,
     group_ids_config_path,
+    min_data_points,
 ):  # pylint: disable=too-many-arguments, too-many-locals
     """
     Estimate average cell densities of brain regions in `hierarchy_path` for the cell types
@@ -935,6 +943,7 @@ def fit_average_densities(
         cell_density_stddev,
         region_name=region_name,
         group_ids_config=group_ids_config,
+        min_data_points=min_data_points,
     )
 
     # Turn index into column to ease off the save and load operations on csv files

atlas_densities/densities/fitting.py

@@ -367,7 +367,10 @@ def compute_average_intensities(
 
 
 def linear_fitting_xy(
-    xdata: list[float], ydata: list[float], sigma: Union[list[float], FloatArray]
+    xdata: list[float],
+    ydata: list[float],
+    sigma: Union[list[float], FloatArray],
+    min_data_points: int,
 ) -> dict[str, float]:
     """
     Compute the coefficient of the linear least-squares regression of the point cloud
@@ -380,6 +383,10 @@ def linear_fitting_xy(
             These are the standard deviations of the values in `ydata`.
             Zero values are replaced by the least positive value if it exists,
             otherwise by 1.0.
+        min_data_points: minimum number of datapoints required for running
+            the linear regression. If the number of datapoints is less than
+            min_data_points then no fitting is done, and np.nan values are
+            returned.
 
     Returns:
         a dict of the form {"coefficient": <float>,
@@ -389,9 +396,8 @@ def linear_fitting_xy(
     Raises:
        AtlasDensitiesError if some of the `sigma`values are negative.
     """
-
-    if len(xdata) == 0:
-        return {"coefficient": np.nan, "standard_deviation": np.nan}
+    if len(xdata) < min_data_points:
+        return {"coefficient": np.nan, "standard_deviation": np.nan, "r_square": np.nan}
 
     sigma = np.array(sigma)
     if np.any(sigma < 0.0):
@@ -422,6 +428,7 @@ def _optimize_func(x, coefficient):
     )
     ss_reg = np.sum((_optimize_func(xdata, parameters[0][0]) - np.mean(ydata)) ** 2)
     ss_tot = np.sum((np.array(ydata) - _optimize_func(xdata, parameters[0][0])) ** 2) + ss_reg
+    L.debug("Length of xdata = %s.  The ss_reg is %s and ss_tot is %s", xdata, ss_reg, ss_tot)
     # if total sum of square is null, no variance can be explained by the fitting
     return {
         "coefficient": parameters[0][0],
@@ -434,7 +441,10 @@ def _optimize_func(x, coefficient):
 
 
 def compute_fitting_coefficients(
-    groups: dict[str, set[str]], average_intensities: pd.DataFrame, densities: pd.DataFrame
+    groups: dict[str, set[str]],
+    average_intensities: pd.DataFrame,
+    densities: pd.DataFrame,
+    min_data_points: int,
 ) -> FittingData:
     """
     Compute the linear fitting coefficient of the cloud of 2D points (average marker intensity,
@@ -477,7 +487,7 @@ def compute_fitting_coefficients(
         The "standard_deviation" value is the standard deviation of the coefficient value.
         The "r_square" value is the coefficient of determination of the coefficient value.
     """
-
+    # pylint: disable=too-many-locals
     if len(densities.index) != len(average_intensities.index) or np.any(
         densities.index != average_intensities.index
     ):
@@ -545,8 +555,14 @@ def compute_fitting_coefficients(
         L.info("Computing regression coefficients for %d cell types ...", len(cell_types))
         for cell_type in tqdm(cell_types):
             cloud = clouds[group_name][cell_type]
+            L.debug(
+                "The length of training data for %s and %s is %s",
+                group_name,
+                cell_type,
+                cloud["xdata"],
+            )
             result[group_name][cell_type] = linear_fitting_xy(
-                cloud["xdata"], cloud["ydata"], cloud["sigma"]
+                cloud["xdata"], cloud["ydata"], cloud["sigma"], min_data_points
             )
             if np.isnan(result[group_name][cell_type]["coefficient"]):
                 warnings.warn(
@@ -730,6 +746,7 @@ def linear_fitting(  # pylint: disable=too-many-arguments
     cell_density_stddevs: Optional[dict[str, float]] = None,
     region_name: str = "root",
     group_ids_config: dict | None = None,
+    min_data_points: int = 5,
 ) -> pd.DataFrame:
     """
     Estimate the average densities of every region in `region_map` using a linear fitting
@@ -772,6 +789,10 @@ def linear_fitting(  # pylint: disable=too-many-arguments
             standard deviations of average cell densities of the corresponding regions.
         region_name: (str) name of the root region of interest
         group_ids_config: mapping of regions to their constituent ids
+        min_data_points: minimum number of datapoints required for running
+            the linear regression. If the number of datapoints is less than
+            min_data_points then no fitting is done, and np.nan values are
+            returned.
 
     Returns:
         tuple (densities, fitting_coefficients)
@@ -782,6 +803,7 @@ def linear_fitting(  # pylint: disable=too-many-arguments
             fitting_coefficients: dict returned by
                 :fun:`atlas_densities.densities.fitting.compute_fitting_coefficients`.
     """
+    # pylint: disable=too-many-locals
     assert group_ids_config is not None
     L.info("Checking input data frames sanity ...")
     _check_average_densities_sanity(average_densities)
@@ -833,7 +855,10 @@ def linear_fitting(  # pylint: disable=too-many-arguments
 
     L.info("Computing fitting coefficients ...")
     fitting_coefficients = compute_fitting_coefficients(
-        groups, average_intensities, densities.drop(densities.index[indexes])
+        groups,
+        average_intensities,
+        densities.drop(densities.index[indexes]),
+        min_data_points=min_data_points,
     )
     L.info("Fitting unknown average densities ...")
     fit_unknown_densities(groups, average_intensities, densities, fitting_coefficients)

atlas_densities/densities/utils.py

@@ -538,5 +538,4 @@ def compute_region_volumes(
         volume = result.loc[list(id_set), "id_volume"].sum()
         volumes.append(volume)
     result["volume"] = volumes
-
     return result

tests/densities/test_fitting.py

@@ -273,17 +273,24 @@ def test_compute_average_intensities(region_map, hierarchy_info):
 
 
 def test_linear_fitting_xy():
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 1.0, 1.0])
+    min_data_points = 1
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 1.0, 1.0], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert np.allclose(actual["r_square"], 1.0)
     assert not np.isinf(actual["standard_deviation"])
 
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 1.0, 4.0], [1.0, 1.0, 1e-5])
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 1.0, 4.0], [1.0, 1.0, 1e-5], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert not np.isinf(actual["standard_deviation"])
     assert np.allclose(actual["r_square"], 0.89286)
 
-    actual = tested.linear_fitting_xy([0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 0.0, 1.0])
+    actual = tested.linear_fitting_xy(
+        [0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [1.0, 0.0, 1.0], min_data_points=min_data_points
+    )
     assert np.allclose(actual["coefficient"], 2.0)
     assert not np.isinf(actual["standard_deviation"])
     assert np.allclose(actual["r_square"], 1.0)
@@ -319,7 +326,10 @@ def test_compute_fitting_coefficients(hierarchy_info):
     data = get_fitting_input_data_(hierarchy_info)
 
     actual = tested.compute_fitting_coefficients(
-        data["groups"], data["intensities"], data["densities"]
+        data["groups"],
+        data["intensities"],
+        data["densities"],
+        min_data_points=1,
     )
 
     for group_name in ["Whole", "Central lobule"]:
@@ -340,18 +350,33 @@ def test_compute_fitting_coefficients_exceptions(hierarchy_info):
     data["densities"].drop(index=["Central lobule"], inplace=True)
 
     with pytest.raises(AtlasDensitiesError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"])
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
     data = get_fitting_input_data_(hierarchy_info)
     data["densities"].drop(columns=["pv+"], inplace=True)
 
     with pytest.raises(AtlasDensitiesError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"])
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
     data = get_fitting_input_data_(hierarchy_info)
     data["densities"].at["Lobule II", "pv+_standard_deviation"] = np.nan
     with pytest.raises(AssertionError):
-        tested.compute_fitting_coefficients(data["groups"], data["intensities"], data["densities"])
+        tested.compute_fitting_coefficients(
+            data["groups"],
+            data["intensities"],
+            data["densities"],
+            min_data_points=1,
+        )
 
 
 @pytest.fixture
@@ -525,6 +550,7 @@ def test_linear_fitting(group_ids_config):
             data["average_densities"],
             data["homogenous_regions"],
             group_ids_config=group_ids_config,
+            min_data_points=1,
         )
         warnings_ = [w for w in warnings_ if isinstance(w.message, AtlasDensitiesWarning)]
         # Three warnings for recording NaN coefficients, three warnings for using them