Sourcery refactored master branch

adspy_shared_utilities.py

@@ -18,10 +18,10 @@ def load_crime_dataset():
 
     crime = pd.read_table('CommViolPredUnnormalizedData.txt', sep=',', na_values='?')
     # remove features with poor coverage or lower relevance, and keep ViolentCrimesPerPop target column
-    columns_to_keep = [5, 6] + list(range(11,26)) + list(range(32, 103)) + [145]  
+    columns_to_keep = [5, 6] + list(range(11,26)) + list(range(32, 103)) + [145]
     crime = crime.ix[:,columns_to_keep].dropna()
 
-    X_crime = crime.ix[:,range(0,88)]
+    X_crime = crime.ix[:, range(88)]
     y_crime = crime['ViolentCrimesPerPop']
 
     return (X_crime, y_crime)
@@ -67,9 +67,11 @@ def plot_labelled_scatter(X, y, class_labels):
     plt.xlim(x_min, x_max)
     plt.ylim(y_min, y_max)
 
-    h = []
-    for c in range(0, num_labels):
-        h.append(mpatches.Patch(color=color_array[c], label=class_labels[c]))
+    h = [
+        mpatches.Patch(color=color_array[c], label=class_labels[c])
+        for c in range(num_labels)
+    ]
+
     plt.legend(handles=h)
 
     plt.show()
@@ -80,8 +82,8 @@ def plot_class_regions_for_classifier_subplot(clf, X, y, X_test, y_test, title,
     numClasses = numpy.amax(y) + 1
     color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
     color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
-    cmap_light = ListedColormap(color_list_light[0:numClasses])
-    cmap_bold  = ListedColormap(color_list_bold[0:numClasses])
+    cmap_light = ListedColormap(color_list_light[:numClasses])
+    cmap_bold = ListedColormap(color_list_bold[:numClasses])
 
     h = 0.03
     k = 0.5
@@ -115,7 +117,7 @@ def plot_class_regions_for_classifier_subplot(clf, X, y, X_test, y_test, title,
 
     if (target_names is not None):
         legend_handles = []
-        for i in range(0, len(target_names)):
+        for i in range(len(target_names)):
             patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
             legend_handles.append(patch)
         subplot.legend(loc=0, handles=legend_handles)
@@ -126,8 +128,8 @@ def plot_class_regions_for_classifier(clf, X, y, X_test=None, y_test=None, title
     numClasses = numpy.amax(y) + 1
     color_list_light = ['#FFFFAA', '#EFEFEF', '#AAFFAA', '#AAAAFF']
     color_list_bold = ['#EEEE00', '#000000', '#00CC00', '#0000CC']
-    cmap_light = ListedColormap(color_list_light[0:numClasses])
-    cmap_bold  = ListedColormap(color_list_bold[0:numClasses])
+    cmap_light = ListedColormap(color_list_light[:numClasses])
+    cmap_bold = ListedColormap(color_list_bold[:numClasses])
 
     h = 0.03
     k = 0.5
@@ -159,7 +161,7 @@ def plot_class_regions_for_classifier(clf, X, y, X_test=None, y_test=None, title
 
     if (target_names is not None):
         legend_handles = []
-        for i in range(0, len(target_names)):
+        for i in range(len(target_names)):
             patch = mpatches.Patch(color=color_list_bold[i], label=target_names[i])
             legend_handles.append(patch)
         plt.legend(loc=0, handles=legend_handles)
@@ -181,10 +183,10 @@ def plot_fruit_knn(X, y, n_neighbors, weights):
 
     # Plot the decision boundary by assigning a color in the color map
     # to each mesh point.
-    
+
     mesh_step_size = .01  # step size in the mesh
     plot_symbol_size = 50
-    
+
     x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
     y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
     xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
@@ -207,10 +209,10 @@ def plot_fruit_knn(X, y, n_neighbors, weights):
     patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
     plt.legend(handles=[patch0, patch1, patch2, patch3])
 
-        
+
     plt.xlabel('height (cm)')
     plt.ylabel('width (cm)')
-    
+
     plt.show()
 
 def plot_two_class_knn(X, y, n_neighbors, weights, X_test, y_test):
@@ -226,10 +228,10 @@ def plot_two_class_knn(X, y, n_neighbors, weights, X_test, y_test):
 
     # Plot the decision boundary by assigning a color in the color map
     # to each mesh point.
-    
+
     mesh_step_size = .01  # step size in the mesh
     plot_symbol_size = 50
-    
+
     x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
     y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
     xx, yy = numpy.meshgrid(numpy.arange(x_min, x_max, mesh_step_size),
@@ -246,11 +248,14 @@ def plot_two_class_knn(X, y, n_neighbors, weights, X_test, y_test):
     plt.xlim(xx.min(), xx.max())
     plt.ylim(yy.min(), yy.max())
 
-    title = "Neighbors = {}".format(n_neighbors)
+    title = f"Neighbors = {n_neighbors}"
     if (X_test is not None):
         train_score = clf.score(X_mat, y_mat)
         test_score  = clf.score(X_test, y_test)
-        title = title + "\nTrain score = {:.2f}, Test score = {:.2f}".format(train_score, test_score)
+        title += "\nTrain score = {:.2f}, Test score = {:.2f}".format(
+            train_score, test_score
+        )
+
 
     patch0 = mpatches.Patch(color='#FFFF00', label='class 0')
     patch1 = mpatches.Patch(color='#000000', label='class 1')

visuals.py

@@ -21,7 +21,10 @@ def pca_results(good_data, pca):
     Visualizes the PCA results
     '''
     # Dimension indexing
-    dimensions = dimensions = ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]
+    dimensions = dimensions = [
+        f'Dimension {i}' for i in range(1, len(pca.components_) + 1)
+    ]
+
     # PCA components
     components = pd.DataFrame(np.round(pca.components_, 4), columns = good_data.keys())
     components.index = dimensions
@@ -97,7 +100,7 @@ def biplot(good_data, reduced_data, pca):
     # scatterplot of the reduced data    
     ax.scatter(x=reduced_data.loc[:, 'Dimension 1'], y=reduced_data.loc[:, 'Dimension 2'], 
         facecolors='b', edgecolors='b', s=70, alpha=0.5)
-    
+
     feature_vectors = pca.components_.T
 
     # we use scaling factors to make the arrows easier to see