new metrics

data_viz.py

 import matplotlib.pyplot as plt
 import numpy as np
 import random
+
+from sympy.utilities.misc import replace
+
 from mass_prediction import compute_frag_mz_ration
 
 seq = 'YEEEFLR'
@@ -110,4 +113,89 @@ def frag_spectra_comparison(int_1, seq_1, int_2, seq_2=None):
     ax.spines[["left", "top", "right"]].set_visible(False)
 
     ax.margins(y=0.1)
-    plt.show()
\ No newline at end of file
+    plt.show()
+
+
+def histo_abs_error(dataframe, display=False, save=False, path=None):
+    points = dataframe['abs_error']
+
+    ## combine these different collections into a list
+    data_to_plot = [points]
+
+    # Create a figure instance
+    fig = plt.figure()
+
+    # Create an axes instance
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    # Create the boxplot
+    bp = ax.violinplot(data_to_plot, vert=False)
+    if display :
+        plt.show()
+
+    if save :
+        plt.savefig(path)
+
+
+
+def histo_abs_error_by_length(dataframe, display=False, save=False, path=None):
+    data_to_plot =[]
+    max_length = max(dataframe['length'])
+    min_length = min(dataframe['length'])
+    for l in range(min_length, max_length):
+        data_to_plot.append(dataframe['abs_error'].where(dataframe['length']==l))
+
+
+    # Create a figure instance
+    fig = plt.figure()
+
+    # Create an axes instance
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    # Create the boxplot
+    bp = ax.violinplot(data_to_plot, vert=True)
+    if display:
+        plt.show()
+
+    if save:
+        plt.savefig(path)
+
+def histo_length_by_error(dataframe, bins, display=False, save=False, path=None):
+    data_to_plot = []
+
+    max_error = max(dataframe['abs_error'])
+    inter = np.linspace(0, max_error, num=bins)
+    for i in range(bins):
+        data_to_plot.append(dataframe['length'].where(inter[i] < dataframe['abs_error'] < inter[i+1]))
+
+    # Create a figure instance
+    fig = plt.figure()
+
+    # Create an axes instance
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    # Create the boxplot
+    bp = ax.violinplot(data_to_plot, vert=False)
+    if display:
+        plt.show()
+
+    if save:
+        plt.savefig(path)
+
+def compare_error(df1, df2, display=False, save=False, path=None):
+    size = len(df2)
+    ind = np.random.choice(range(size), size=10, replace=False)
+    seq1 = df1['seq'][ind]
+    seq2 = df2['seq'][ind]
+    data_1 = df1['abs_error'][ind]
+    data_2 = df2['abs_error'][ind]
+
+    fig, ax = plt.subplots(figsize=(2, 1))
+    ax[0, 0].bar(seq1, data_1, width=0.8)
+    ax[1, 0].bar(seq2, data_2, width=0.8)
+
+    if display:
+        plt.show()
+
+    if save:
+        plt.savefig(path)

main_custom.py

@@ -183,6 +183,7 @@ def run(epochs, eval_inter, save_inter, model, data_train, data_val, data_test,
                  wandb=wandb)
         if e % save_inter == 0:
             save(model, 'model_common_' + str(e) + '.pt')
+    save_pred(model, data_val, forward, 'output/out.csv')
 
 
 def main(args):