Re-implemented the code to do correlation matrix

npf/statistics.py

@@ -107,6 +107,26 @@ def printline(n):
                         print("  %s : %.02f " % (vs, tot / n))
                 print("")
 
+            print('')
+
+            ys = np.ndarray(shape = (len(X), len(dataset)))
+
+            for i,d in enumerate(dataset):
+                ys[:,i] = d[2]
+            import pandas as pd
+            df = pd.DataFrame(np.concatenate((X,ys),axis=1),columns=list(vars_values.keys()) + [d[0] if d[0] else "y" for d in dataset])
+            print("Correlation matrix:")
+            corr = df.corr()
+            corr.style.background_gradient(cmap='viridis')
+            print(corr)
+            corr
+            import seaborn as sn
+            import matplotlib.pyplot as plt
+            sn.heatmap(corr, annot=True)
+            f = npf.build_filename(test, build, filename if not filename is True else None, {}, 'pdf', result_type, show_serie=False, suffix="correlation")
+            plt.savefig(f)
+            print(f"Graph of correlation matrix saved to {f}")
+
     @classmethod
     def buildDataset(cls, all_results: Dataset, test: Test) -> List[tuple]:
         #map of every <variable name, format>

setup.py

@@ -30,8 +30,9 @@
     'importlib_metadata',
     'npf-web-extension >= 0.6.4',
     'jinja2',
-    'spellwise'
-        ]
+    'spellwise',
+    'seaborn'
+    ]
 
 setuptools.setup(
     name="npf",