104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
import sacred
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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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import pandas as pd
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import numpy as np
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import sys
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import os
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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from sklearn.preprocessing import StandardScaler
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scaler = StandardScaler()
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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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from sacred.observers import MongoObserver
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from sklearn.metrics import accuracy_score
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from sacred.observers.file_storage import file_storage_option
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from sacred.observers.mongo import mongo_db_option
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ex = Experiment("s478815",save_git_info=False)
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ex.observers.append(FileStorageObserver('experiment'))
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#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password@localhost:27017', db_name='sacred'))
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@ex.config
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def my_config():
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epochs = 1000
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# Model
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(1,1)
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def forward(self, x):
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y_predicted = torch.sigmoid(self.linear(x))
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return y_predicted
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data = pd.read_csv('data.csv')
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data.dropna()
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training_data = data.sample(frac=0.9, random_state=25)
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testing_data = data.drop(training_data.index)
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print(f"No. of training examples: {training_data.shape[0]}")
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print(f"No. of testing examples: {testing_data.shape[0]}")
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training_data = training_data[['sqft_living', 'price']]
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testing_data = testing_data[['sqft_living', 'price']]
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training_data[['price']] = training_data[['price']] / 10000000
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training_data[['sqft_living']] = training_data[['sqft_living']] / 10000
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testing_data[['price']] = testing_data[['price']] / 10000000
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testing_data[['sqft_living']] = testing_data[['sqft_living']] / 10000
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# Tensory
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X_training = training_data[['sqft_living']].to_numpy()
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X_testing = testing_data[['sqft_living']].to_numpy()
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y_training = training_data[['price']].to_numpy()
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y_testing = testing_data[['price']].to_numpy()
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torch.from_file
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X_training = torch.from_numpy(X_training.astype(np.float32))
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X_testing = torch.from_numpy(X_testing.astype(np.float32))
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y_training = torch.from_numpy(y_training.astype(np.float32))
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y_testing = torch.from_numpy(y_testing.astype(np.float32))
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model = Model()
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criterion = nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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@ex.automain
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def my_main(epochs, _run):
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# Trening
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#num_epochs = EPOCHS
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for epochs in range(epochs):
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y_predicted = model(X_training)
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loss = criterion(y_predicted,y_training)
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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if (epochs%100==0):
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print(f'epoch:{epochs+1},loss = {loss.item():.4f}')
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with torch.no_grad():
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y_predicted = model(X_testing)
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y_predicted_cls = y_predicted.round()
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acc = y_predicted_cls.eq(y_testing).sum()/float(y_testing.shape[0])
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print(f'{acc:.4f}')
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result = open("output",'w+')
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result.write(f'{y_predicted}')
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torch.save(model, "modelP.pkl")
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_run.info['accuracy'] = accuracy_score(y_testing, np.argmax(y_predicted, axis=1))
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ex.run() |