Plot attribute correlation

src/processing.py

@@ -1,15 +1,14 @@
 from numpy import mean, arange
 from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve
-from sklearn.model_selection import cross_val_predict
 from sklearn.naive_bayes import GaussianNB
 from sklearn.neural_network import MLPClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.preprocessing import scale
 from sklearn.svm import LinearSVC
 from sklearn.tree import DecisionTreeClassifier
-from seaborn import set_theme, set_style, heatmap, FacetGrid
+from seaborn import set_theme, set_style, heatmap, countplot
 from matplotlib.pyplot import *
-from pandas import DataFrame
+from pandas import DataFrame, cut
 
 from sys import argv
 
@@ -92,11 +91,28 @@ def plot_confusion_matrix(results):
     fig.savefig(f"docs/assets/{fig_title.replace(' ', '_').lower()}.png")
 
 
-# TODO Add cross_val_score
+def plot_attributes_correlation(data, target):
-def plot_all_figures(results):
+    transformed_data = transform_dataframe(data, target)
+    fig, axes = subplots(nrows=5, ncols=1, figsize=(8, 6))
+    for i in range(len(axes)):
+        countplot(
+            ax=axes[i],
+            x=transformed_data.columns[i],
+            data=transformed_data,
+            hue="Severity",
+        )
+        axes[i].set_title(transformed_data.columns[i])
+    fig_title = "Attribute's correlation"
+    suptitle(fig_title)
+    show()
+    fig.savefig(f"docs/assets/{fig_title.replace(' ', '_').lower()}.png")
+
+
+def plot_all_figures(results, data, target):
     set_theme()
-    plot_roc_auc_curve(results=results)
+    # plot_roc_auc_curve(results=results)
-    plot_confusion_matrix(results=results)
+    # plot_confusion_matrix(results=results)
+    plot_attributes_correlation(data=data, target=target)
 
 
 def create_result_dataframes():
@@ -127,6 +143,13 @@ def rename_model(model):
     return mapping[model]
 
 
+def transform_dataframe(data, target):
+    joined_df = data.join(target)
+    binned_df = joined_df.copy()
+    binned_df["Age"] = cut(x=joined_df["Age"], bins=[15, 30, 45, 60, 75])
+    return binned_df
+
+
 def usage():
     print("Usage: " + argv[0] + "<preprocessing action>")
     print("preprocessing actions:")
@@ -149,7 +172,7 @@ def main():
             individual_result.append(model_results)
         )
     indexed_results = complete_results.set_index("model")
-    plot_all_figures(results=indexed_results)
+    plot_all_figures(results=indexed_results, data=data, target=target)
 
 
 if __name__ == "__main__":