Deep-Learning-with-PyTorch

88 CHAPTER 4 Real-world data representation using tensors
4.4.1 Adding a time dimension
In the source data, each row is a separate hour of data (figure 4.5 shows a transposed
version of this to better fit on the printed page). We want to change the row-per-hour
organization so that we have one axis that increases at a rate of one day per index increment,
and another axis that represents the hour of the day (independent of the date).
The third axis will be our different columns of data (weather, temperature, and so on).
Let’s load the data (code/p1ch4/4_time_series_bikes.ipynb).
Listing 4.4
code/p1ch4/4_time_series_bikes.ipynb
# In[2]:
bikes_numpy = np.loadtxt(
"../data/p1ch4/bike-sharing-dataset/hour-fixed.csv",
dtype=np.float32,
delimiter=",",
skiprows=1,
converters={1: lambda x: float(x[8:10])})
bikes = torch.from_numpy(bikes_numpy)
bikes
# Out[2]:
tensor([[1.0000e+00, 1.0000e+00,
[2.0000e+00, 1.0000e+00,
...,
[1.7378e+04, 3.1000e+01,
[1.7379e+04, 3.1000e+01,
..., 1.3000e+01, 1.6000e+01],
..., 3.2000e+01, 4.0000e+01],
..., 4.8000e+01, 6.1000e+01],
..., 3.7000e+01, 4.9000e+01]])
For every hour, the dataset reports the following variables:
Converts date strings to
numbers corresponding to the
day of the month in column 1
• Index of record: instant
• Day of month: day
• Season: season (1: spring, 2: summer, 3: fall, 4: winter)
• Year: yr (0: 2011, 1: 2012)
• Month: mnth (1 to 12)
• Hour: hr (0 to 23)
• Holiday status: holiday
• Day of the week: weekday
• Working day status: workingday
• Weather situation: weathersit (1: clear, 2:mist, 3: light rain/snow, 4: heavy
rain/snow)
• Temperature in °C: temp
• Perceived temperature in °C: atemp
• Humidity: hum
• Wind speed: windspeed
• Number of casual users: casual
• Number of registered users: registered
• Count of rental bikes: cnt