Replaced visx by library version.

fdc/visualize.py

@@ -1,154 +1,156 @@
 import seaborn as sns
 import matplotlib.pyplot as plt
+import numpy as np
 
 def plotCluster(data, clusterName="cluster", xName="FDC_1", yName="FDC_2", stroke=20):
-    colors_set = [
-        'lightcoral', 'cornflowerblue', 'orange','mediumorchid', 'lightseagreen'
-        , 'olive', 'chocolate', 'steelblue', 'paleturquoise',  'lightgreen'
-        , 'burlywood', 'lightsteelblue']
+  colors_set = [
+    'lightcoral', 'cornflowerblue', 'orange','mediumorchid', 'lightseagreen'
+    , 'olive', 'chocolate', 'steelblue', 'paleturquoise',  'lightgreen'
+    , 'burlywood', 'lightsteelblue']
 
-    customPalette_set = sns.set_palette(sns.color_palette(colors_set))
+  customPalette_set = sns.set_palette(sns.color_palette(colors_set))
 
-    sns.lmplot(
-        x=xName
-        , y=yName
-        , data=data
-        , fit_reg=False
-        , legend=True
-        , hue=clusterName
-        , scatter_kws={"s": stroke}
-        , palette=customPalette_set
-        )
-    plt.show()
+  sns.lmplot(
+    x=xName
+    , y=yName
+    , data=data
+    , fit_reg=False
+    , legend=True
+    , hue=clusterName
+    , scatter_kws={"s": stroke}
+    , palette=customPalette_set
+    )
+  plt.show()
 
 
 
 def plotMapping(data, xName="UMAP_0", yName="UMAP_1"):
-    colors_set1 = [
-        "lightcoral", "lightseagreen", "mediumorchid", "orange", "burlywood"
-        , "cornflowerblue", "plum", "yellowgreen"]
+  colors_set1 = [
+    "lightcoral", "lightseagreen", "mediumorchid", "orange", "burlywood"
+    , "cornflowerblue", "plum", "yellowgreen"]
 
-    customPalette_set1 = sns.set_palette(sns.color_palette(colors_set1))
+  customPalette_set1 = sns.set_palette(sns.color_palette(colors_set1))
 
-    sns.lmplot(x=xName
-        , y=yName
-        , data=data
-        , fit_reg=False
-        , legend=False
-        , scatter_kws={"s": 3}
-        , palette=customPalette_set1)
-    plt.show()
+  sns.lmplot(x=xName
+    , y=yName
+    , data=data
+    , fit_reg=False
+    , legend=False
+    , scatter_kws={"s": 3}
+    , palette=customPalette_set1)
+  plt.show()
 
 
 
-def vizx(feature_list, cluster_df_list, main_data, umap_data, cont_features):
-    vizlimit = 15
-    plt.rcParams["figure.figsize"] = (12, 6)
-    
-    col = sns.color_palette("Set2")
+def vizx(feature_list, cluster_df_list, main_data, umap_data, cont_features, rev_dict, xName="FDC_1", yName="FDC_2"):
+  vizlimit = 15
+  plt.rcParams["figure.figsize"] = (12, 6)
+  
+  col = sns.color_palette("Set2")
+  
+  rows = 3
+  columns = 3
+  
+  for feature in feature_list:
+    print('Feature name:', feature.upper())
+    print('\n')
+  
+    if len(main_data[feature].value_counts()) <= vizlimit:
+      for cluster_counter, cluster in enumerate(cluster_df_list):
+        print('Cluster '+ str(cluster_counter + 1) + ' frequency distribution')
+        if feature in list(rev_dict.keys()):
+          feat_keys=rev_dict[feature]
+          r = dict(zip(feat_keys.values(), feat_keys.keys()))
+          print(cluster.replace({feature:r})[feature].value_counts())
+        else:
+          print(cluster[feature].value_counts())
+        print('\n')
     
-    rows = 3
-    columns = 3
+      print('\n')
+      print('\n')
     
-    for feature in feature_list:
-        print('Feature name:', feature.upper())
-        print('\n')
+      cluster_bar = []
+      for cluster in cluster_df_list:
+        if feature in list(rev_dict.keys()):
+          y = np.array(cluster.replace({feature:r})[feature].value_counts())
+          x = np.array(cluster.replace({feature:r})[feature].value_counts().index)
+          cluster_bar.append([x,y])
+        else:
+          y = np.array(cluster[feature].value_counts().sort_index())
+          x = np.array(cluster[feature].value_counts().sort_index().index)
+          cluster_bar.append([x,y])
+          
+      cluster_bar = np.array(cluster_bar)
     
-        if len(main_data[feature].value_counts()) <= vizlimit:
-            for cluster_counter, cluster in enumerate(cluster_df_list):
-                print('Cluster '+ str(cluster_counter + 1) + ' frequency distribution')
-                if feature in list(rev_dict.keys()):
-                    feat_keys=rev_dict[feature]
-                    r = dict(zip(feat_keys.values(), feat_keys.keys()))
-                    print(cluster.replace({feature:r})[feature].value_counts())
-                else:
-                    print(cluster[feature].value_counts())
-                print('\n')
-        
-            print('\n')
-            print('\n')
-        
-            cluster_bar = []
-            for cluster in cluster_df_list:
-                if feature in list(rev_dict.keys()):
-                    y = np.array(cluster.replace({feature:r})[feature].value_counts())
-                    x = np.array(cluster.replace({feature:r})[feature].value_counts().index)
-                    cluster_bar.append([x,y])
-                else:
-                    y = np.array(cluster[feature].value_counts().sort_index())
-                    x = np.array(cluster[feature].value_counts().sort_index().index)
-                    cluster_bar.append([x,y])
-                
-            cluster_bar = np.array(cluster_bar)
-        
-            figx, ax = plt.subplots(rows, columns)
-            figx.set_size_inches(10.5, 28.5)
-            cluster_in_subplot_axis_dict = np.array([[0,0],[0,1],[0,2],[1,0],[1,1],[1,2],[2,0],[1,1],[2,2]])
-            c = 0
-            for i in range(rows):
-                for j in range(columns):
-                    ax[i,j].bar(cluster_bar[c,0], cluster_bar[c,1], color=col)
-                    ax[i,j].tick_params(axis='x', which='major', labelsize=8, rotation=90)
-                    ax[i,j].set_title('Cluster: ' + str(c + 1))
-                    if c > len(cluster_df_list):
-                        break
-                    else:
-                        c += 1
-            
-        means = []
-        sds = []
-        cluster_labels = []
-        for cluster_counter, cluster in enumerate(cluster_df_list):
-            if feature in cont_features:
-                print('Cluster '+ str(cluster_counter + 1) + ' summary statistics')
-                print('\n')
-                cm = cluster[feature].mean()
-                cs = cluster[feature].std()
-                print('feature mean:', cm)
-                print('feature standard deviation:', cs)
-                print('feature median:', cluster[feature].median())
-                print('\n')
-                means.append(cm)
-                sds.append(cs)
-                cluster_labels.append('C' + str(cluster_counter + 1))
-            
-        means = np.array(means)
-        sds = np.array(sds)
-        cluster_labels = np.array(cluster_labels)
-        
-        print('\n')  
-        
-        print('Distribution of feature across clusters')
-        if feature in cont_features:   
-            fig, ax7 = plt.subplots()
-            ax7.bar(cluster_labels, means, yerr=sds, color=sns.color_palette("Set3"))
-            ax7.tick_params(axis='both', which='major', labelsize=10)
-            plt.xlabel(feature, fontsize=15)
-            plt.show()
-        
+      figx, ax = plt.subplots(rows, columns)
+      figx.set_size_inches(10.5, 28.5)
+      cluster_in_subplot_axis_dict = np.array(
+        [[0,0],[0,1],[0,2],[1,0],[1,1],[1,2],[2,0],[1,1],[2,2]])
+      c = 0
+      for i in range(rows):
+        for j in range(columns):
+          if c >= len(cluster_df_list):
+            break
+          ax[i,j].bar(cluster_bar[c,0], cluster_bar[c,1], color=col)
+          ax[i,j].tick_params(axis='x', which='major', labelsize=8, rotation=90)
+          ax[i,j].set_title('Cluster: ' + str(c + 1))
+          c += 1
+      
+    means = []
+    sds = []
+    cluster_labels = []
+    for cluster_counter, cluster in enumerate(cluster_df_list):
+      if feature in cont_features:
+        print('Cluster '+ str(cluster_counter + 1) + ' summary statistics')
         print('\n')
+        cm = cluster[feature].mean()
+        cs = cluster[feature].std()
+        print('feature mean:', cm)
+        print('feature standard deviation:', cs)
+        print('feature median:', cluster[feature].median())
         print('\n')
+        means.append(cm)
+        sds.append(cs)
+        cluster_labels.append('C' + str(cluster_counter + 1))
         
-        colors_set = ['lightgray', 'lightcoral', 'cornflowerblue', 'orange', 'mediumorchid'
-            , 'lightseagreen', 'olive', 'chocolate', 'steelblue', 'paleturquoise', 'lightgreen'
-            , 'burlywood','lightsteelblue']
-        customPalette_set = sns.set_palette(sns.color_palette(colors_set))
-        
-        if feature not in cont_features:
-            print('Feature distribution in UMAP embedding')
-            if feature in list(rev_dict.keys()):
-                umap_data[feature] = np.array(main_data.replace({feature:r})[feature])
-            else:
-                umap_data[feature] = np.array(main_data[feature])
-            sns.lmplot(x="FDC_1", y="FDC_2",
-                data=umap_data, 
-                fit_reg=False, 
-                legend=True,
-                hue=feature, # color by cluster
-                scatter_kws={"s": 20},
-                palette=customPalette_set) # specify the point size
-            plt.show()
-        
-        print('\n')
-        print('\n')
+    means = np.array(means)
+    sds = np.array(sds)
+    cluster_labels = np.array(cluster_labels)
+    
+    print('\n')  
+    
+    print('Distribution of feature across clusters')
+    if feature in cont_features:   
+      fig, ax7 = plt.subplots()
+      ax7.bar(cluster_labels, means, yerr=sds, color=sns.color_palette("Set3"))
+      ax7.tick_params(axis='both', which='major', labelsize=10)
+      plt.xlabel(feature, fontsize=15)
+      plt.show()
+    
+    print('\n')
+    print('\n')
+    
+    customPalette_set = sns.set_palette(sns.color_palette(
+      [ 'lightgray', 'lightcoral', 'cornflowerblue', 'orange', 'mediumorchid'
+      , 'lightseagreen', 'olive', 'chocolate', 'steelblue', 'paleturquoise'
+      , 'lightgreen', 'burlywood','lightsteelblue'
+      ]))
+    
+    if feature not in cont_features:
+      print('Feature distribution in UMAP embedding')
+      if feature in list(rev_dict.keys()):
+        umap_data[feature] = np.array(main_data.replace({feature:r})[feature])
+      else:
+        umap_data[feature] = np.array(main_data[feature])
+      sns.lmplot(x=xName, y=yName,
+        data=umap_data, 
+        fit_reg=False, 
+        legend=True,
+        hue=feature, # color by cluster
+        scatter_kws={"s": 20},
+        palette=customPalette_set) # specify the point size
+      plt.show()
+    
+    print('\n')
+    print('\n')