visualise evaluation metrics

dev/KaairaGupta/evaluation metric/visualise_evaluation_metric.py

@@ -0,0 +1,35 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+def visualise_evaluation_metric(table):
+
+    """
+        This function plot a metric against the values of some parameter with repeated runs to assess variability.
+        Input: Table (numpy array) with columns (x, y1, y2, ..., yk), i.e. multiple y values for each x.
+        Output: Plots showing the average y value vs x with the spread of the y-values represented by min-max and standard-deviation of y.
+    """
+
+    x = table[0]
+
+    _mean = np.empty(len(x))
+    _min = np.empty(len(x))
+    _max = np.empty(len(x))
+    _std = np.empty(len(x))
+
+    table = table.transpose()
+
+    for i in range(len(table)):
+        r = table[i]
+
+        _mean[i] = np.mean(r[1:])
+        _min[i] = np.min(r[1:])
+        _max[i] = np.max(r[1:])
+        _std[i] = np.std(r[1:])
+
+    plt.plot(x, _mean, label='Mean', linewidth=2, color='g')
+    plt.plot(x, _min, label='Min',linewidth=1, color='y')
+    plt.plot(x, _max, label='Max',linewidth=1, color='r')
+    plt.fill_between(x, _mean-(_std/2), _mean+(_std/2), alpha=0.3, label='Standard Deviation')
+
+    plt.legend()
+    plt.show()