color scatter + model eval

data_viz.py

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import random
 import pandas as pd
-
+import matplotlib.colors as mcolors
 
 from mass_prediction import compute_frag_mz_ration
 
@@ -124,22 +124,37 @@ def histo_abs_error(dataframe, display=False, save=False, path=None):
     ## combine these different collections into a list
     data_to_plot = [points]
 
+
     # Create a figure instance
     fig, ax = plt.subplots()
 
     # Create the boxplot
     ax.set_xlabel('abs error')
     ax.violinplot(data_to_plot, vert=False, side='high', showmedians=True, quantiles=[0.95])
+    ax.set_xlim(0,175)
     if display :
         plt.show()
 
     if save :
         plt.savefig(path)
 
-def scatter_rt(dataframe, display=False, save=False, path=None):
-    fig, ax = plt.subplots()
 
-    ax.scatter(dataframe['true rt'], dataframe['rt pred'], s=.1)
+def random_color_deterministic(df):
+
+    def rd10(str):
+        color = list(mcolors.CSS4_COLORS)
+        random.seed(str)
+        return color[random.randint(0,147)]
+
+    df['color']=df['seq'].map(rd10)
+
+def scatter_rt(dataframe, display=False, save=False, path=None, color = False):
+    fig, ax = plt.subplots()
+    if color :
+        random_color_deterministic(dataframe)
+        ax.scatter(dataframe['true rt'], dataframe['rt pred'], s=.1, color = dataframe['color'])
+    else :
+        ax.scatter(dataframe['true rt'], dataframe['rt pred'], s=.1)
     ax.set_xlabel('true RT')
     ax.set_ylabel('pred RT')
     x = np.array([min(dataframe['true rt']), max(dataframe['true rt'])])
@@ -153,6 +168,8 @@ def scatter_rt(dataframe, display=False, save=False, path=None):
         plt.savefig(path)
 
 
+
+
 def histo_abs_error_by_length(dataframe, display=False, save=False, path=None):
     data_to_plot =[]
     max_length = max(dataframe['length'])
@@ -160,6 +177,8 @@ def histo_abs_error_by_length(dataframe, display=False, save=False, path=None):
     for l in range(min_length, max_length):
         data_to_plot.append(dataframe['abs_error'].where(dataframe['length']==l))
 
+    # data_to_plot.append()
+
 
     fig, ax = plt.subplots()
 
@@ -234,9 +253,23 @@ def add_length(dataframe):
     dataframe['length']=dataframe['seq'].map(fonc)
 
 
-df = pd.read_csv('output/out_common_isa_no_tape.csv')
+df = pd.read_csv('output/output_common_data_ISA.csv')
+add_length(df)
+df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
+histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_ISA_ISA.png')
+scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_ISA_ISA.png', color=True)
+histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_ISA_ISA.png')
+
+df = pd.read_csv('output/out_prosit_common.csv')
+add_length(df)
+df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
+histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_prosit_prosit.png')
+scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_prosit_prosit.png', color=True)
+histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_prosit_prosit.png')
+
+df = pd.read_csv('output/out_common_transfer.csv')
 add_length(df)
 df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
-# histo_abs_error(df, display=False, save=True, path='temp.png')
-scatter_rt(df, display=False, save=True, path='RT_pred_ISA_ISA.png')
-# histo_length_by_error(df, 10, save=True, path='temp.png')
+histo_abs_error(df, display=False, save=True, path='fig/custom model res/histo_prosit_ISA.png')
+scatter_rt(df, display=False, save=True, path='fig/custom model res/RT_pred_prosit_ISA.png', color=True)
+histo_length_by_error(df, bins=10, display=False, save=True, path='fig/custom model res/histo_length_prosit_ISA.png')
\ No newline at end of file

main_custom.py

@@ -108,6 +108,7 @@ def eval(model, data_val, epoch, criterion_rt, criterion_intensity, metric_rt, m
     losses_int = 0.
     dist_rt_acc = 0.
     dist_int_acc = 0.
+    model.eval()
     for param in model.parameters():
         param.requires_grad = False
     if forward == 'both':
@@ -273,6 +274,7 @@ def get_n_params(model):
 
 def save_pred(model, data_val, forward, output_path):
     data_frame = pd.DataFrame()
+    model.eval()
     for param in model.parameters():
         param.requires_grad = False
     if forward == 'both':