Commit 5af8294b by Roman Sarrazin-Gendron

### Final paper supplementary version

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 import pickle import numpy as np from matplotlib import pyplot as plt from pylab import rcParams top = [] max5 = [] ... ... @@ -14,7 +13,7 @@ actual_module = [] moip_motifs = [] to_sort = [] b = pickle.load(open("../output/full_shuffled.cPickle","rb")) b = pickle.load(open("../output/dishuffled.cPickle","rb")) predictions_shuffled = [] accuracy_shuffled = [] for motif in b: ... ... @@ -37,20 +36,17 @@ for motif in b: a = pickle.load(open("../output/3dmotif_newalign_cv.cPickle","rb")) for motif in a: motif_scores = [] for sequence in a[motif]: for sequence in motif: #print(sequence) seq_scores = [] for result in sequence[:1]: score = result[0] seq_scores.append(score) actual_module.append(score) if result[2]>-1000: if result[0]>=0.5: bayespairing_prediction.append(result[2]) positives.append(result[2]) else: bayespairing_prediction.append(result[2]) negatives.append(result[2]) if result[2]>-100: bayespairing_prediction.append(result[2]) positives.append(result[2]) elif result[2]<0: bayespairing_prediction.append(0.00001) if len(seq_scores) == 0: ... ... @@ -114,7 +110,7 @@ for treshold in tresholds: #plt.plot(np.linspace(0,1,10),np.linspace(0,1,10)) #plt.xticks(range(0,200)[::5],[round(x,1) for x in tresholds][::5]) #plt.show() rcParams['figure.figsize'] = 10,10 plt.plot(FDR) plt.title("FDR - Rna3dmotif") plt.xlabel("threshold") ... ... @@ -122,5 +118,15 @@ plt.ylabel("FDR") plt.xticks(range(0,200)[::8],[int(x) for x in tresholds][::8]) plt.show() #print(FDR) for ind,iFDR in enumerate(FDR): if iFDR<0.1: print("TRESHOLD FOR FDR UNDER 0.1:",tresholds[ind]) break for ind, iFDR in enumerate(FDR): if iFDR < 0.01: print("TRESHOLD FOR FDR UNDER 0.01:",tresholds[ind]) break #print(sorted(negatives)) #print(sorted(positives)) \ No newline at end of file
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