116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
import numpy as np
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import torch
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from torch import nn
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from torch.autograd import Variable
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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import torch.nn.functional as F
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import pandas as pd
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from sklearn import preprocessing
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import matplotlib.pyplot as plt
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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, 160)
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# self.layer2 = nn.Linear(320, 160)
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self.layer2 = nn.Linear(160, 80)
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self.layer3 = nn.Linear(80, 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 load_dataset_raw():
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""" Load data from .csv file. """
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cars = pd.read_csv('./Car_Prices_Poland_Kaggle.csv', usecols=[1, 4, 5, 6, 10], sep=',')
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return cars
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def remove_rows(dataset):
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# dataset.drop(dataset[dataset['mark'] == 'alfa-romeo'].index, inplace=True)
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# dataset.drop(dataset[dataset['mark'] == 'chevrolet'].index, inplace=True)
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# dataset.drop(dataset[dataset['mark'] == 'mitsubishi'].index, inplace=True)
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# dataset.drop(dataset[dataset['mark'] == 'mini'].index, inplace=True)
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# audi bmw ford opel volkswagen
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new_data = dataset.loc[(dataset['mark'] == 'audi') | (dataset['mark'] == 'bmw') | (dataset['mark'] == 'ford') | (dataset['mark'] == 'opel') | (dataset['mark'] == 'volkswagen')]
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return new_data
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# dataset = dataset.drop(dataset)
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# return dataset
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def prepare_dataset_raw(dataset):
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""" Label make column"""
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le = preprocessing.LabelEncoder()
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mark_column = np.array(dataset[:]['mark'])
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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(['mark'], 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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# Prepare dataset
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print("Loading dataset...")
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dataset = load_dataset_raw()
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print("Dataset loaded")
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print("Preparing dataset...")
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dataset = remove_rows(dataset)
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labels, features = prepare_dataset_raw(dataset)
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print("Dataset prepared")
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plot = plt.hist(labels, bins=[i for i in range(len(set(labels)))], edgecolor="black")
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plt.xticks(np.arange(0, len(set(labels)), 1))
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plt.show()
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features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42,
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shuffle=True)
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# Training
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model = Model(features_train.shape[1])
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optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
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loss_fn = nn.CrossEntropyLoss()
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epochs = 100
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print("Starting model training...")
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x_train, y_train = Variable(torch.from_numpy(features_train)).float(), Variable(torch.from_numpy(labels_train)).long()
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for epoch in range(1, epochs + 1):
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print("Epoch #", epoch)
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y_pred = model(x_train)
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loss = loss_fn(y_pred, y_train)
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print(f"The loss calculated: {loss}")
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# Zero gradients
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optimizer.zero_grad()
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loss.backward() # Gradients
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optimizer.step() # Update
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print("Model training finished")
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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("The accuracy is", accuracy_score(labels_test, np.argmax(pred, axis=1)))
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# Checking for first value
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# print(np.argmax(model(x_test[0]).detach().numpy(), axis=0))
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# print(labels_test[0])
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print("Saving model to file...")
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torch.save(model, "CarPrices_pytorch.pkl")
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print("Model saved with name: CarPrices_pytorch.pkl")
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saved_model = torch.load("CarPrices_pytorch.pkl")
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print(np.argmax(saved_model(x_test[0]).detach().numpy(), axis=0))
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pd_predictions = pd.DataFrame(pred)
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pd_predictions.to_csv("./prediction_results.csv") |