Save imports

audio_toolset/audio_augmentation.py

@@ -1,4 +1,9 @@
+try:
     import librosa
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `librosa` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install librosa`.')
+
 import numpy as np
 
 

audio_toolset/audio_io.py

@@ -1,5 +1,14 @@
+try:
     import librosa
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `librosa` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install librosa`.')
+try:
     from scipy.signal import butter, lfilter
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `scikit` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install scikit-learn`.')
+
 
 import numpy as np
 

evaluation/classification.py

@@ -1,13 +1,21 @@
+try:
     import matplotlib.pyplot as plt
-from sklearn.metrics import roc_curve, auc
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `matplotlib` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install matplotlib`.')
+try:
+    from sklearn.metrics import roc_curve, auc, recall_score
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `sklearn` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install scikit-learn`.')
 
 
 class ROCEvaluation(object):
 
     linewidth = 2
 
-    def __init__(self, plot_roc=False):
-        self.plot_roc = plot_roc
+    def __init__(self, plot=False):
+        self.plot = plot
         self.epoch = 0
 
     def __call__(self, prediction, label):
@@ -15,7 +23,7 @@ class ROCEvaluation(object):
         # Compute ROC curve and ROC area
         fpr, tpr, _ = roc_curve(prediction, label)
         roc_auc = auc(fpr, tpr)
-        if self.plot_roc:
+        if self.plot:
             _ = plt.gcf()
             plt.plot(fpr, tpr, color='darkorange', lw=self.linewidth, label=f'ROC curve (area = {roc_auc})')
             self._prepare_fig()
@@ -32,3 +40,32 @@ class ROCEvaluation(object):
         fig.legend(loc="lower right")
 
         return fig
+
+
+class UAREvaluation(object):
+
+    def __init__(self, labels: list, plot=False):
+        self.labels = labels
+        self.plot_roc = plot
+        self.epoch = 0
+
+    def __call__(self, prediction, label):
+
+        # Compute uar score - UnweightedAverageRecal
+
+        uar_score = recall_score(label, prediction, labels=self.labels, average='macro',
+                                 sample_weight=None, zero_division='warn')
+        return uar_score
+
+    def _prepare_fig(self):
+        raise NotImplementedError # TODO Implement a nice visualization
+        fig = plt.gcf()
+        ax = plt.gca()
+        plt.plot([0, 1], [0, 1], color='navy', lw=self.linewidth, linestyle='--')
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.xlabel('False Positive Rate')
+        plt.ylabel('True Positive Rate')
+        fig.legend(loc="lower right")
+
+        return fig

visualization/tools.py

@@ -1,5 +1,10 @@
-from pathlib import Path
+try:
     import matplotlib.pyplot as plt
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `matplotlib` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install matplotlib`.')
+
+from pathlib import Path
 
 
 class Plotter(object):