version 1.2.0

bibmon/_generic_model.py

@@ -6,6 +6,7 @@
 import seaborn as sns
 import sklearn.model_selection
 import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
 
 from abc import ABC, abstractmethod
 
@@ -53,6 +54,11 @@ class GenericModel (ABC):
             Square Prediction Error. One-dimensional vectors containing
             the SPE values calculated at each time step in 
             training and testing.  
+
+        SPE_contrib_train, SPE_contrib_test: pd.DataFrame
+            Contributions to SPE. Bidimensional matrices containing
+            the SPE contributions of each variable at each time step in
+            training and testing. Valid for reconstruction models.
 
         * Training parameters:
         ----------
@@ -117,11 +123,13 @@ class GenericModel (ABC):
         map_from_X (@abstractmethod)
         set_hyperparameters
         load_model
+        compute_SPE_contributions
         pre_train
         train
         pre_test
         test
         plot_SPE
+        plot_SPE_contributions
         fit
         predict       
     """
@@ -210,6 +218,30 @@ def load_model (self, limSPE, SPE_mean, count_window_size,
 
     ###########################################################################
 
+    def compute_SPE_contributions (self, pred, X):
+        """
+        Calculation of SPE contributions for diagnosis based on partial
+        decomposition analysis. Valid for reconstruction models,
+        in which self.has_Y = False.
+
+        Parâmetros:
+        ----------
+        pred, X: numpy.array
+            Data windows to compute contributions. 
+
+        Retornos:
+        ----------                
+        SPE_contributions: numpy.array
+           Contributions to the SPE.
+        """       
+
+        X = np.array(X)
+        X_pred = np.array(pred)
+
+        return np.absolute(X*(X-X_pred))
+
+    ###########################################################################
+
     def pre_train (self, X_train, Y_train = None, 
                    f_pp = ['remove_empty_variables',
                            'ffill_nan',
@@ -352,6 +384,12 @@ def train (self, lim_conf = 0.99, delete_training_data = False):
         self.limSPE = iSPE[int(lim_conf*self.X_train.shape[0])]
 
         self.limSPE_train = copy.deepcopy(self.limSPE)
+
+        # calculating contributions
+        if not self.has_Y:
+            self.SPE_contrib_train = self.compute_SPE_contributions(
+                                        self.X_train_pred.values,
+                                        self.X_train.values)            
 
         # denormalizing
 
@@ -372,7 +410,12 @@ def train (self, lim_conf = 0.99, delete_training_data = False):
         self.index_train = self.X_train.index
 
         self.SPE_train = pd.Series(self.SPE_train, index = self.index_train)
-
+
+        if not self.has_Y:
+            self.SPE_contrib_train = pd.DataFrame(self.SPE_contrib_train,
+                                        index = self.index_train,
+                                        columns = self.tags_X)
+
         # deleting training data, if applicable
         if delete_training_data:
             del self.X_train, self.X_train_orig, \
@@ -381,7 +424,8 @@ def train (self, lim_conf = 0.99, delete_training_data = False):
                 del self.Y_train, self.Y_train_orig, \
                     self.Y_train_pred, self.Y_train_pred_orig
             else:
-                del self.X_train_pred, self.X_train_pred_orig    
+                del self.X_train_pred, self.X_train_pred_orig, \
+                    self.SPE_contrib_train    
 
     ###########################################################################
 
@@ -619,7 +663,7 @@ def test (self, redefine_limit = False, delete_testing_data = False):
 
         self.test_time = end_test - start_test
 
-        # storing results and calculating SPE
+        # storing results and calculating SPE and SPE_contrib
 
         if self.has_Y:
             self.Y_test_pred = pd.DataFrame(test_pred,
@@ -636,7 +680,10 @@ def test (self, redefine_limit = False, delete_testing_data = False):
             self.SPE_test = np.sum((self.X_test.values-
                                     self.X_test_pred.values)**2,
                                     axis=1)   
-
+            self.SPE_contrib_test = self.compute_SPE_contributions(
+                                             self.X_test_pred.values, 
+                                             self.X_test.values)
+
         # redefining the limit, for the validation case
 
         if redefine_limit:
@@ -681,6 +728,11 @@ def test (self, redefine_limit = False, delete_testing_data = False):
         self.SPE_test = pd.Series(self.SPE_test, 
                                 index = self.X_test.index)
 
+        if not self.has_Y:
+            self.SPE_contrib_test = pd.DataFrame(self.SPE_contrib_test,
+                                        index = self.X_test.index,
+                                        columns = self.tags_test_X)
+
         self.alarms = {}
 
         self.alarmOutlier = pd.Series(self.alarmOutlier,
@@ -696,12 +748,14 @@ def test (self, redefine_limit = False, delete_testing_data = False):
         # deleting testing data, if applicable
         if delete_testing_data:
             del self.X_test, self.X_test_orig, \
-                self.SPE_test, self.alarms, self.alarmOutlier
+                self.SPE_test, \
+                self.alarms, self.alarmOutlier
             if self.has_Y:
                 del self.Y_test, self.Y_test_orig, \
                     self.Y_test_pred, self.Y_test_pred_orig
             else:
-                del self.X_test_pred, self.X_test_pred_orig
+                del self.X_test_pred, self.X_test_pred_orig, \
+                    self.SPE_contrib_test,
             if hasattr(self, 'alarmCount'): del self.alarmCount
 
     ###########################################################################
@@ -757,7 +811,55 @@ def plot_SPE (self, ax = None, train_or_test = 'train', logy = True,
 
         if legends:
             ax.legend(fontsize=12);
-
+
+    ###########################################################################
+
+    def plot_SPE_contributions(self, ax = None, train_or_test = 'test'):
+        """
+        Plotting the temporal evolution of SPE contributions on a heatmap.
+
+        Parameters:
+        ----------
+        ax: matplotlib.axes._subplots.AxesSubplot
+            Axis where the graph will be plotted.
+        train_or_test: string
+            Indicates whether to plot the 'train' or 'test' graph.
+        """
+
+        if self.has_Y:
+            print('This is not a reconstruction model!')
+            return
+
+        if ax is None:
+            fig, ax = plt.subplots(figsize=(20, 8))
+
+        if train_or_test == 'train':
+            SPE_contrib = self.SPE_contrib_train
+        elif train_or_test == 'test':
+            SPE_contrib = self.SPE_contrib_test
+
+        # Plot heatmap
+        sns.heatmap(SPE_contrib, ax=ax, 
+                    yticklabels=int(SPE_contrib.shape[0] / 10),
+                    cmap="Blues")
+
+        yticks = ax.get_yticks()
+        if hasattr(SPE_contrib, 'index') \
+            and hasattr(SPE_contrib.index, 'to_list'):
+            # Convert tick positions to datetime using the DataFrame's index
+            datetime_labels = [SPE_contrib.index[int(tick)] for 
+                               tick in yticks if 0 <= int(tick) < 
+                               len(SPE_contrib.index)]
+        else:
+            # Fallback: Use integer positions as labels
+            datetime_labels = yticks
+
+        ax.set_yticks(yticks)
+        ax.set_yticklabels([d.strftime('%Y-%m-%d %H:%M:%S') 
+                            for d in datetime_labels], rotation=0)
+
+        ax.figure.autofmt_xdate()
+
     ###########################################################################
 
     def plot_predictions (self, ax = None, train_or_test = 'train', 

requirements.txt

@@ -1,5 +1,5 @@
-numpy>=2.1.0
-scipy>=1.14.1
+numpy>=1.26
+scipy>=1.10
 scikit-learn>=1.5.1
 pandas>=2.2.2
 matplotlib>=3.9.0

setup.py

@@ -29,7 +29,7 @@
     name='bibmon',
     description='Library with routines for data-driven process monitoring.',
     license='Apache 2.0',
-    version='1.1.6',
+    version='1.2.0',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/petrobras/bibmon',
@@ -38,6 +38,6 @@
     package_data={
         'bibmon': ['real_process_data/*.csv','tennessee_eastman/*.dat'],
     },
-    python_requires=">=3.12",
+    python_requires=">=3.10",
     install_requires=install_requires
-)
+)