77 lines
1.8 KiB
Python
77 lines
1.8 KiB
Python
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from matplotlib import pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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import pandas as pd
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import numpy as np
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data = pd.read_csv('sberbank_data.csv', index_col='id')
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x = data[['timestamp', 'full_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'material', 'kremlin_km']]
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x = x.replace('NA', 0)
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x.fillna(0, inplace=True)
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col_date = []
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for val in x['timestamp']:
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col_date.append(val.split('-', 1)[0])
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x = x.drop(columns='timestamp')
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x['timestamp'] = col_date
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y = []
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for val in data['price_doc']:
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if val < 1500000:
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y.append('low')
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elif val < 3000000:
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y.append('medium')
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elif val < 5500000:
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y.append('high')
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elif val < 10000000:
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y.append('premium')
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else:
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y.append('oligarch')
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=42)
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min_scores = []
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med_scores = []
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max_scores = []
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def do_test(iters_num):
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global x_train, x_test, y_train, y_test, min_scores, med_scores, max_scores
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print("Testing iterations number "+str(iters_num)+":")
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scores = []
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for i in range(10):
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neuro = MLPClassifier(max_iter=200)
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neuro.fit(x_train, y_train)
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scr = neuro.score(x_test, y_test)
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print("res"+str(i+1)+": "+str(scr))
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scores.append(scr)
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print("Medium result: "+str(np.mean(scores)))
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min_scores.append(np.min(scores))
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med_scores.append(np.mean(scores))
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max_scores.append(np.max(scores))
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def start():
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global min_scores, med_scores, max_scores
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iter_nums = [200, 400, 600, 800, 1000]
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for num in iter_nums:
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do_test(num)
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plt.figure(1, figsize=(16, 9))
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plt.plot(iter_nums, min_scores, c='r')
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plt.plot(iter_nums, med_scores, c='b')
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plt.plot(iter_nums, max_scores, c='b')
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plt.show()
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start()
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