99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
#!/usr/bin/python
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import torch
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from torch import nn
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import pandas as pd
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from sklearn import preprocessing
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import numpy as np
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from torch.autograd import Variable
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from sklearn.metrics import accuracy_score, f1_score
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from csv import DictWriter
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import torch.nn.functional as F
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import sys
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import os
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import matplotlib.pyplot as plt
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import json
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class Model(nn.Module):
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def __init__(self, input_dim):
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super(Model, self).__init__()
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self.layer1 = nn.Linear(input_dim, 100)
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self.layer2 = nn.Linear(100, 60)
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self.layer3 = nn.Linear(60, 5)
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def forward(self, x):
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x = F.relu(self.layer1(x))
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x = F.relu(self.layer2(x))
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x = F.softmax(self.layer3(x)) # To check with the loss function
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return x
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def prepare_labels_features(dataset):
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""" Label make column"""
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le = preprocessing.LabelEncoder()
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mark_column = np.array(dataset[:]['0'])
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le.fit(mark_column)
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print(list(le.classes_))
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lab = le.transform(mark_column)
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feat = dataset.drop(['0'], axis=1).to_numpy()
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mm_scaler = preprocessing.MinMaxScaler()
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feat = mm_scaler.fit_transform(feat)
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return lab, feat
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def print_metrics(test_labels, predictions):
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# take column with max predicted score
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f1 = f1_score(labels_test, np.argmax(predictions, axis=1), average='weighted')
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accuracy = accuracy_score(test_labels, np.argmax(predictions, axis=1))
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print(f"The F1_score metric is: {f1}")
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print(f"The accuracy metric is: {accuracy}")
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try:
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build_number = sys.argv[1]
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print(f"Build number: {build_number}")
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field_names = ['BUILD_NUMBER', 'F1', 'ACCURACY']
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dict = {'BUILD_NUMBER': build_number, 'F1': f1, 'ACCURACY': accuracy }
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filename = "./metrics.csv"
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file_exists = os.path.isfile(filename)
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with open(filename, 'a') as metrics_file:
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dictwriter_object = DictWriter(metrics_file, fieldnames=field_names)
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if not file_exists:
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dictwriter_object.writeheader()
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dictwriter_object.writerow(dict)
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metrics_file.close()
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except Exception as e:
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print(e)
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def draw_plot():
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metrics = pd.read_csv('metrics.csv', delimiter=',', header=None)
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build_axis = metrics[0][:]
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plt.xlabel('Build')
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plt.ylabel('Score')
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plt.plot(build_axis, metrics[2][:], label='Accuracy')
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plt.plot(build_axis, metrics[1][:], label='F1 Score')
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plt.legend()
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plt.show()
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plt.savefig('metrics.png')
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model = torch.load("CarPrices_pytorch_model.pkl")
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cars_dev = pd.read_csv('./Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names=[str(i) for i in range(5)])
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cars_dev = cars_dev.loc[(cars_dev['0'] == 'audi') | (cars_dev['0'] == 'bmw') | (cars_dev['0'] == 'ford') | (cars_dev['0'] == 'opel') | (cars_dev['0'] == 'volkswagen')]
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labels_test, features_test = prepare_labels_features(cars_dev)
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x_test = Variable(torch.from_numpy(features_test)).float()
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pred = model(x_test)
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pred = pred.detach().numpy()
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# print_metrics(labels_test, pred)
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# draw_plot()
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