[ENH] - Re-organize metric(s) functionality

specparam/metrics/definitions.py

@@ -0,0 +1,73 @@
+"""Collect together library of available built in metrics."""
+
+from specparam.metrics.metrics import Metric
+from specparam.metrics.error import (compute_mean_abs_error, compute_mean_squared_error,
+                                     compute_root_mean_squared_error, compute_median_abs_error)
+from specparam.metrics.gof import compute_r_squared, compute_adj_r_squared
+
+###################################################################################################
+## ERROR METRICS
+
+error_mae = Metric(
+    category='error',
+    measure='mae',
+    description='Mean absolute error of the model fit to the data.',
+    func=compute_mean_abs_error,
+)
+
+error_mse = Metric(
+    category='error',
+    measure='mse',
+    description='Mean squared error of the model fit to the data.',
+    func=compute_mean_squared_error
+)
+
+error_rmse = Metric(
+    category='error',
+    measure='rmse',
+    description='Root mean squared error of the model fit to the data.',
+    func=compute_root_mean_squared_error,
+)
+
+error_medae = Metric(
+    category='error',
+    measure='medae',
+    description='Median absolute error of the model fit to the data.',
+    func=compute_median_abs_error,
+)
+
+###################################################################################################
+## GOF
+
+gof_rsquared = Metric(
+    category='gof',
+    measure='rsquared',
+    description='R-squared between the model fit and the data.',
+    func=compute_r_squared,
+)
+
+gof_adjrsquared = Metric(
+    category='gof',
+    measure='adjrsquared',
+    description='Adjusted R-squared between the model fit and the data.',
+    func=compute_adj_r_squared,
+    kwargs={'n_params' : lambda data, results: \
+            results.params.periodic.params.size + results.params.aperiodic.params.size},
+)
+
+###################################################################################################
+## COLLECT ALL METRICS TOGETHER
+
+METRICS = {
+
+    # Available error metrics
+    'error_mae' : error_mae,
+    'error_mse' : error_mse,
+    'error_rmse' : error_rmse,
+    'error_medae' : error_medae,
+
+    # Available GOF / r-squared metrics
+    'gof_rsquared' : gof_rsquared,
+    'gof_adjrsquared' : gof_adjrsquared,
+
+}

specparam/metrics/metric.py

@@ -0,0 +1,85 @@
+"""Metric object."""
+
+import numpy as np
+
+###################################################################################################
+###################################################################################################
+
+class Metric():
+    """Define a metric to apply to a power spectrum model.
+
+    Parameters
+    ----------
+    category : str
+        The category of measure, e.g. 'error' or 'gof'.
+    measure : str
+        The specific measure, e.g. 'r_squared'.
+    description : str
+        Description of the metric.
+    func : callable
+        The function that computes the metric.
+    kwargs : dictionary
+        Additional keyword argument to compute the metric.
+        Each key should be the name of the additional argument.
+        Each value should be a lambda function that takes 'data' & 'results'
+        and returns the desired parameter / computed value.
+    """
+
+    def __init__(self, category, measure, description, func, kwargs=None):
+        """Initialize metric."""
+
+        self.category = category
+        self.measure = measure
+        self.description = description
+        self.func = func
+        self.result = np.nan
+        self.kwargs = {} if not kwargs else kwargs
+
+
+    def __repr__(self):
+        """Set string representation of object."""
+
+        return 'Metric: ' + self.label
+
+
+    @property
+    def label(self):
+        """Define label property."""
+
+        return self.category + '_' + self.measure
+
+
+    @property
+    def flabel(self):
+        """Define formatted label property."""
+
+        if self.category == 'error':
+            flabel = '{} ({})'.format(self.category.capitalize(), self.measure.upper())
+        if self.category == 'gof':
+            flabel = '{} ({})'.format(self.category.upper(), self.measure)
+
+        return flabel
+
+
+    def compute_metric(self, data, results):
+        """Compute metric.
+
+        Parameters
+        ----------
+        data : Data
+            Model data.
+        results : Results
+            Model results.
+        """
+
+        kwargs = {}
+        for key, lfunc in self.kwargs.items():
+            kwargs[key] = lfunc(data, results)
+
+        self.result = self.func(data.power_spectrum, results.model.modeled_spectrum, **kwargs)
+
+
+    def reset(self):
+        """Reset metric result."""
+
+        self.result = np.nan

specparam/objs/metrics.py → specparam/metrics/metrics.py

@@ -4,86 +4,11 @@
 
 import numpy as np
 
+from specparam.metrics.metric import Metric
+
 ###################################################################################################
 ###################################################################################################
 
-class Metric():
-    """Define a metric to apply to a power spectrum model.
-
-    Parameters
-    ----------
-    category : str
-        The category of measure, e.g. 'error' or 'gof'.
-    measure : str
-        The specific measure, e.g. 'r_squared'.
-    func : callable
-        The function that computes the metric.
-    kwargs : dictionary
-        Additional keyword argument to compute the metric.
-        Each key should be the name of the additional argument.
-        Each value should be a lambda function that takes 'data' & 'results'
-        and returns the desired parameter / computed value.
-    """
-
-    def __init__(self, category, measure, func, kwargs=None):
-        """Initialize metric."""
-
-        self.category = category
-        self.measure = measure
-        self.func = func
-        self.result = np.nan
-        self.kwargs = {} if not kwargs else kwargs
-
-
-    def __repr__(self):
-        """Set string representation of object."""
-
-        return 'Metric: ' + self.label
-
-
-    @property
-    def label(self):
-        """Define label property."""
-
-        return self.category + '_' + self.measure
-
-
-    @property
-    def flabel(self):
-        """Define formatted label property."""
-
-        if self.category == 'error':
-            flabel = '{} ({})'.format(self.category.capitalize(), self.measure.upper())
-        if self.category == 'gof':
-            flabel = '{} ({})'.format(self.category.upper(), self.measure)
-
-        return flabel
-
-
-    def compute_metric(self, data, results):
-        """Compute metric.
-
-        Parameters
-        ----------
-        data : Data
-            Model data.
-        results : Results
-            Model results.
-        """
-
-        kwargs = {}
-        for key, lfunc in self.kwargs.items():
-            kwargs[key] = lfunc(data, results)
-
-        self.result = self.func(data.power_spectrum, results.model.modeled_spectrum, **kwargs)
-
-
-    def reset(self):
-        """Reset metric result."""
-
-        self.result = np.nan
-
-
 class Metrics():
     """Define a collection of metrics.
 

specparam/models/model.py

@@ -38,6 +38,13 @@ class SpectralModel(BaseModel):
         Which approach to take for fitting the aperiodic component.
     periodic_mode : {'gaussian', 'skewed_gaussian', 'cauchy'}
         Which approach to take for fitting the periodic component.
+    metrics : Metrics or list of Metric or list or str
+        Metrics definition(s) to use to evaluate the model.
+    bands : Bands or dict or int or None, optional
+        Bands object with band definitions, or definition that can be turned into a Bands object.
+    debug : bool, optional, default: False
+        Whether to run in debug mode.
+        If in debug, any errors encountered during fitting will raise an error.
     verbose : bool, optional, default: True
         Verbosity mode. If True, prints out warnings and general status updates.
     **model_kwargs

specparam/objs/results.py

@@ -7,10 +7,10 @@
 
 from specparam.bands.bands import check_bands
 from specparam.modes.modes import Modes
-from specparam.objs.metrics import Metrics
 from specparam.objs.params import ModelParameters
 from specparam.objs.components import ModelComponents
-from specparam.measures.metrics import METRICS
+from specparam.metrics.metrics import Metrics
+from specparam.metrics.definitions import METRICS
 from specparam.utils.checks import check_inds, check_array_dim
 from specparam.modutils.errors import NoModelError
 from specparam.modutils.docs import (copy_doc_func_to_method, docs_get_section,
@@ -37,8 +37,8 @@ class Results():
         Modes object with fit mode definitions.
     metrics : Metrics
         Metrics object with metric definitions.
-    bands : Bands
-        Bands object with band definitions.
+    bands : Bands or dict or int or None
+        Bands object with band definitions, or definition that can be turned into a Bands object.
 
     Attributes
     ----------

specparam/tests/measures/test_metrics.py → specparam/tests/metrics/test_definitions.py

@@ -1,8 +1,8 @@
-"""Test functions for specparam.measures.metrics."""
+"""Test functions for specparam.metrics.definitions."""
 
-from specparam.objs.metrics import Metric
+from specparam.metrics.metrics import Metric
 
-from specparam.measures.metrics import *
+from specparam.metrics.definitions import *
 
 ###################################################################################################
 ###################################################################################################

specparam/tests/measures/test_error.py → specparam/tests/metrics/test_error.py

@@ -1,6 +1,6 @@
-"""Test functions for specparam.measures.error."""
+"""Test functions for specparam.metrics.error."""
 
-from specparam.measures.error import *
+from specparam.metrics.error import *
 
 ###################################################################################################
 ###################################################################################################

specparam/tests/measures/test_gof.py → specparam/tests/metrics/test_gof.py

@@ -1,6 +1,6 @@
-"""Test functions for specparam.measures.gof."""
+"""Test functions for specparam.metrics.gof."""
 
-from specparam.measures.gof import *
+from specparam.metrics.gof import *
 
 ###################################################################################################
 ###################################################################################################

specparam/tests/metrics/test_metric.py

@@ -0,0 +1,30 @@
+"""Tests for specparam.metrics.metric"""
+
+from specparam.metrics.error import compute_mean_abs_error
+from specparam.metrics.gof import compute_adj_r_squared
+
+from specparam.metrics.metric import *
+
+###################################################################################################
+###################################################################################################
+
+def test_metric(tfm):
+
+    metric = Metric('error', 'mae', 'Description.', compute_mean_abs_error)
+    assert isinstance(metric, Metric)
+    assert isinstance(metric.label, str)
+
+    metric.compute_metric(tfm.data, tfm.results)
+    assert isinstance(metric.result, float)
+
+def test_metric_kwargs(tfm):
+
+    metric = Metric('gof', 'ar2', 'Description.', compute_adj_r_squared,
+                    {'n_params' : lambda data, results: \
+                        results.params.periodic.params.size + results.params.aperiodic.params.size})
+
+    assert isinstance(metric, Metric)
+    assert isinstance(metric.label, str)
+
+    metric.compute_metric(tfm.data, tfm.results)
+    assert isinstance(metric.result, float)