61 lines
1.4 KiB
Python
61 lines
1.4 KiB
Python
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
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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model = keras.models.load_model('wine_model.h5')
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print('evaluating')
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data =pd.read_csv('test.csv')
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#########################################
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y = data['quality']
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x = data.drop('quality', axis=1)
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citricacid = x['fixed acidity'] * x['citric acid']
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citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
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density_acidity = x['fixed acidity'] * x['density']
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density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
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x = data.join(citric_acidity).join(density_acidity)
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print(y)
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bins = (2, 5, 8)
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gnames = ['bad', 'nice']
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y = pd.cut(y, bins = bins, labels = gnames)
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enc = LabelEncoder()
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yenc = enc.fit_transform(y)
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scale = StandardScaler()
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scaled_x = scale.fit_transform(x)
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##################################
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y_pred = model.predict(scaled_x)
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y_pred = np.around(y_pred, decimals=0)
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results = accuracy_score(yenc,y_pred)
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with open('results.txt', 'a+', encoding="UTF-8") as f:
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f.write(str(results) +"\n")
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with open('results.txt', 'r', encoding="UTF-8") as f:
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lines = f.readlines()
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fig = plt.figure(figsize=(10,10))
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chart = fig.add_subplot()
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chart.set_ylabel("Accuracy")
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chart.set_xlabel("Number of 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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print(y)
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plt.plot(x,y,"ro")
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plt.savefig("evaluation.png") |