50 lines
1.2 KiB
Python
50 lines
1.2 KiB
Python
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import pandas as pd
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import numpy as np
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from tensorflow import keras
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import matplotlib.pyplot as plt
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from sklearn.metrics import accuracy_score, f1_score
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learned_model = 'model'
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model = keras.models.load_model(learned_model)
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train=pd.read_csv('train.csv', header=None, skiprows=1)
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indexNames = train[train[1] ==2].index
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train.drop(indexNames, inplace=True)
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cols=[0,2,3]
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X=train[cols].to_numpy()
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y=train[1].to_numpy()
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X=np.asarray(X).astype('float32')
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test=pd.read_csv('test.csv', header=None, skiprows=1)
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cols=[0,2,3]
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indexNames = test[test[1] ==2].index
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test.drop(indexNames, inplace=True)
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X_test=test[cols].to_numpy()
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y_test=test[1].to_numpy()
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X_test=np.asarray(X_test).astype('float32')
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predictions = model.predict(X_test)
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acc = accuracy_score(y_test, predictions)
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print('Accuracy: ', acc)
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f1=f1_score(y_test, predictions)
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print('F1: ', f1)
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with open('evaluation.txt', 'a') as f:
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f.write(str(acc) + "\n")
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with open('evaluation.txt', 'r') as f:
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lines = f.readlines()
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fig = plt.figure(figsize=(5,5))
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chart = fig.add_subplot()
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chart.set_ylabel("Accuracy")
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chart.set_xlabel("Build")
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x = np.arange(0, len(lines), 1)
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y = [float(x) for x in lines]
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plt.plot(x, y, "go")
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plt.savefig("evaluation.png")
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