128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from sacred.observers import FileStorageObserver, MongoObserver
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from sklearn.preprocessing import LabelEncoder
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import pandas as pd
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from sacred import Experiment
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# Model
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class Model(nn.Module):
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def __init__(self, input_features=2, hidden_layer1=60, hidden_layer2=90, output_features=3):
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super().__init__()
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self.fc1 = nn.Linear(input_features, hidden_layer1)
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self.fc2 = nn.Linear(hidden_layer1, hidden_layer2)
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self.out = nn.Linear(hidden_layer2, output_features)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.out(x)
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return x
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# Sacred
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ex = Experiment()
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ex.observers.append(FileStorageObserver('my_runs'))
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# Parametry treningu -> my_runs/X/config.json
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# Plik z modelem jako artefakt -> my_runs/X/model.pkl
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# Kod źródłowy -> my_runs/_sources/biblioteki_ml_XXXXXXXXXXX.py
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# Wyniki (ostateczny loss) -> my_runs/X/metrics.json
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 100
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@ex.automain
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def train_main(epochs, _run):
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# Parametry z konsoli
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# try:
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# epochs = int(sys.argv[1])
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# except:
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# print('No epoch number passed. Defaulting to 100')
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# epochs = 100
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# Ładowanie danych
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train_set = pd.read_csv('d_train.csv', encoding='latin-1')
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train_set = train_set[['Rating', 'Branch', 'Reviewer_Location']]
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test_set = pd.read_csv('d_test.csv', encoding='latin-1')
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test_set = test_set[['Rating', 'Branch', 'Reviewer_Location']]
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# Mapowanie kolumny 'Reviewer_Location' na cyfry
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le = LabelEncoder()
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le.fit(pd.concat([train_set['Reviewer_Location'], test_set['Reviewer_Location']]))
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train_set['Reviewer_Location'] = le.transform(train_set['Reviewer_Location'])
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test_set['Reviewer_Location'] = le.transform(test_set['Reviewer_Location'])
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# Mapowanie kolumny 'Branch' na inny sposób
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mappings = {
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'Disneyland_California': 0,
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'Disneyland_Paris': 1,
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'Disneyland_HongKong': 2
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}
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train_set['Branch'] = train_set['Branch'].apply(lambda x: mappings[x])
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test_set['Branch'] = test_set['Branch'].apply(lambda x: mappings[x])
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# Zamiana danych na tensory
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X_train = train_set[['Rating', 'Reviewer_Location']].to_numpy()
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X_test = test_set[['Rating', 'Reviewer_Location']].to_numpy()
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y_train = train_set['Branch'].to_numpy()
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y_test = test_set['Branch'].to_numpy()
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X_train = torch.FloatTensor(X_train)
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X_test = torch.FloatTensor(X_test)
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y_train = torch.LongTensor(y_train)
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y_test = torch.LongTensor(y_test)
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# Hiperparametry
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model = Model()
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
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# Trening
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losses = []
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for i in range(epochs):
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y_pred = model.forward(X_train)
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loss = criterion(y_pred, y_train)
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losses.append(loss)
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print(f'epoch: {i:2} loss: {loss.item():10.8f}')
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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_run.log_scalar("training.final_loss", losses[-1].item()) # Ostateczny loss
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# Testy
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preds = []
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with torch.no_grad():
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for val in X_test:
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y_hat = model.forward(val)
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preds.append(y_hat.argmax().item())
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df = pd.DataFrame({'Testing Y': y_test, 'Predicted Y': preds})
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df['Correct'] = [1 if corr == pred else 0 for corr, pred in zip(df['Testing Y'], df['Predicted Y'])]
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print(f"{df['Correct'].sum() / len(df)} percent of predictions correct")
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# Zapis do pliku
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df.to_csv('neural_network_prediction_results.csv', index=False)
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torch.save(model, "model.pkl")
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# Zapis Sacred
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ex.add_artifact("model.pkl")
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ex.add_artifact("neural_network_prediction_results.csv")
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