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testmodel.py
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testmodel.py
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import pandas as pd
import math
from auxFonctions import AminoAcid
import fonctionsSupervisedLearning2 as fsl2
import fonctionsSupervisedLearning1 as fsl1
# import thundersvm as tsvm
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from sklearn.model_selection import ParameterGrid
import joblib
import fonctionskernel as fk
# open model
model = joblib.load("data/models/best_svm_model_accuracy2.pkl")
# open data
data = pd.read_csv("data/df.csv")
data = fsl2.convert_df_to_vectors2(data).head(100)
X = data['P_Structure_vector']
# X = np.array(X)
# X = X.reshape(1,-1)
pos = data['Cleavage_Site']
# predict
def main():
# find the cleavage for the whole dataset
predictions = [fsl1.find_cleavage2(x, model) for x in X]
# number of correct predictions
correct = 0
for i in range(len(predictions)):
if pos[i] in predictions[i]:
correct += 1
#average accuracy
accuracy = correct/len(predictions)
#average number of predictions:
avg_pred = sum([len(x) for x in predictions])/len(predictions)
#average distance to the real cleavage site, if pred is not empty
flat_list = [abs(p - pos[i]) for i, x in enumerate(predictions) if x for p in x]
avg_dist = sum(flat_list) / len(flat_list) if flat_list else 0
print("Average accuracy: ", accuracy)
print("Average number of predictions: ", avg_pred)
print("Average distance to the real cleavage site: ", avg_dist)
if __name__ == "__main__":
main()