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Using PyTorch and a history of average temperatures by month, use a deep neural network to predict temperatures
# Toy example of using a deep neural network to predict average temperature
# by month. Note that this is not any better than just taking the average
# of the dataset; it's just meant as an example of a regression analysis using
# neural networks.
import logging
import datetime
import pandas as pd
import torch
import torch.nn as nn
from torch.autograd import Variable
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
log = logging.getLogger(__name__)
BATCH_SIZE = 12
HIDDEN_SIZE = 512
NUM_LAYERS = 2
NUM_EPOCHS = 100
LEARNING_RATE = 0.005
DROPOUT = 0.2
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size, n_layers=1, dropout=DROPOUT):
super(RNN, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.n_layers = n_layers
self.encoder = nn.Embedding(input_size, hidden_size)
self.m = nn.Sequential(nn.ReLU(),
nn.Dropout(p=0.2),
nn.ReLU())
self.decoder = nn.Linear(hidden_size, output_size)
def forward(self, inp, hidden):
inp = self.encoder(inp)
# output = BATCH_SIZE, SEQLEN, HIDDEN_SIZE
# ex. torch.Size([30, 50, 512])
output = self.m(inp)
# Now BATCHSIZE * SEQLEN, HIDDEN_SIZE
# torch.Size([1500, 512])
output = output.contiguous().view(-1, hidden.size(2))
# Should now be BATCH_SIZE * SEQLEN, VOCAB_SIZE
# torch.Size([1500, 154])
logits = self.decoder(output)
return logits, hidden
def init_hidden(self):
# The hidden state will use BATCH_SIZE in the 1st position even if we hand data as batch_first
return Variable(torch.zeros(self.n_layers, BATCH_SIZE, self.hidden_size).cuda())
def main():
print_every = 10
# Data from https://www.kaggle.com/berkeleyearth/climate-change-earth-surface-temperature-data
# licened under https://creativecommons.org/licenses/by-nc-sa/4.0/
df = pd.read_csv('GlobalLandTemperaturesByState.csv')
df = df.loc[df.State == 'Massachusetts']
df = df.dropna()
df['dt'] = pd.to_datetime(df.dt)
df = df.loc[df.dt >= datetime.datetime(1800, 1, 1)] # Dates before this are too variable
df = df.loc[df.dt < datetime.datetime(2012, 1, 1)] # 2013 has incomplete data
df['month'] = df.dt.dt.month
df['year'] = df.dt.dt.year
rnn = RNN(12, HIDDEN_SIZE, 1, n_layers=NUM_LAYERS)
rnn.cuda()
optimizer = torch.optim.Adam(rnn.parameters(), lr=LEARNING_RATE)
criterion = torch.nn.MSELoss(size_average=False)
criterion.cuda()
loss_avg = 0
total_count = 0
for epoch in range(0, NUM_EPOCHS + 1):
df = df.sample(frac=1) # Shuffle
# For each year in the sequence, create a batch of length 12 (each month)
for year in range(df.year.min(), df.year.max()):
data = df.loc[df.year == year]
inp = Variable(torch.LongTensor([int(i - 1) for i in data.month.values]), requires_grad=False).cuda()
targets = Variable(torch.FloatTensor([float(f) for f in data.AverageTemperature.values]), requires_grad=False).cuda()
hidden = rnn.init_hidden()
rnn.train()
rnn.zero_grad()
output, _ = rnn(inp, hidden)
loss = criterion(output, targets)
loss_avg += loss.data[0] # [ BATCHSIZE x SEQLEN ]
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_count += 1
if epoch % print_every == 0:
log.info("epoch=%d, %d%% loss=%.4f", epoch, epoch / NUM_EPOCHS * 100, loss_avg / total_count)
torch.save(rnn, "weather-predictor")
# I'm an American
df['f'] = df.AverageTemperature.apply(lambda x: (9.0 / 5.0 * x) + 32)
temps = []
# Prediction stage: predict by month
for i in range(0, 12):
rnn.eval()
inp = Variable(torch.LongTensor([[i]]), volatile=True).cuda()
hidden = rnn.init_hidden()
logits, hidden = rnn(inp, hidden)
pred = logits[-1, :].data[0]
temp = (9.0 / 5.0 * pred) + 32
temps.append(temp)
print("Average temp in month ", i + 1, int(temp))
# Compare against the most recent year for which we have data excluded from our training set
year = df.loc[df.year == 2012]
p = pd.DataFrame([year.f, temps], index=[year.month])
p.plot()
plt.show()
if __name__ == '__main__':
main()
@lizadaly
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lizadaly commented Apr 27, 2017

Outputs:

image

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