# CyclicalFeatures#

The `CyclicalFeatures()` creates 2 new features from numerical variables that better capture the cyclical nature of the original variable. `CyclicalFeatures()` return 2 new features per variable, according to:

• var_sin = sin(variable * (2. * pi / max_value))

• var_cos = cos(variable * (2. * pi / max_value))

where max_value is the maximum value in the variable, and pi is 3.14…

There are some features that are cyclic by nature. For example the hours of a day or the months in a year. In these cases, the higher values of the variable are closer to the lower values. For example, December (12) is closer to January (1) than to June (6). By applying a cyclical transformations, that is, with the sine and cosine transformations of the original variables, we can capture the cyclic nature and obtain a better representation of the proximity between values.

## Examples#

In the code example below, we show how to obtain cyclical features from days and months in a toy dataframe.

We first create a toy dataframe with the variables “days” and “months”:

```import pandas as pd
from feature_engine.creation import CyclicalFeatures

df = pd.DataFrame({
'day': [6, 7, 5, 3, 1, 2, 4],
'months': [3, 7, 9, 12, 4, 6, 12],
})
```

Now we set up the transformer to find the maximum value of each variable automatically:

```cyclical = CyclicalFeatures(variables=None, drop_original=False)

X = cyclical.fit_transform(df)
```

The maximum values used for the transformation are stored in the attribute `max_values_`:

```print(cyclical.max_values_)
```
```{'day': 7, 'months': 12}
```

Let’s have a look at the transformed dataframe:

```print(X.head())
```

We can see that the new variables were added at the right of our dataframe.

```   day  months       day_sin   day_cos    months_sin    months_cos
0    6       3 -7.818315e-01  0.623490  1.000000e+00  6.123234e-17
1    7       7 -2.449294e-16  1.000000 -5.000000e-01 -8.660254e-01
2    5       9 -9.749279e-01 -0.222521 -1.000000e+00 -1.836970e-16
3    3      12  4.338837e-01 -0.900969 -2.449294e-16  1.000000e+00
4    1       4  7.818315e-01  0.623490  8.660254e-01 -5.000000e-01
```

We set the paramter `drop_original` to False, which means that we keep the original variables. If we want them dropped, we can set the parameter to True.

Finally, we can obtain the names of the variables in the transformed dataset as follows:

```cyclical.get_feature_names_out()
```

Which will return the name of all the variables in the final output, original and and new:

```['day', 'months', 'day_sin', 'day_cos', 'months_sin', 'months_cos']
```

Or we can return the names of the new features only, as follows:

```cyclical.get_feature_names_out(["months", "day"])
```

which will return the names of all the new features:

```['months_sin', 'months_cos', 'day_sin', 'day_cos']
```

Or, if we are interested in the new features derived of a particular variable, we can obtain their names as follows:

```cyclical.get_feature_names_out(["day"])
```

which will return the names of all the new features derived from `day`:

```['day_sin', 'day_cos']
```

## Motivation#

Let’s discuss more the logic behind using the sine and cosine to transform cyclical or periodic variables like months of the year, or days of the week.

We mentioned that with cyclical or periodic features, values that are very different in absolute magnitude are actually close. For example, January is close to December, even though their absolute magnitude suggests otherwise.

We can use periodic functions like sine and cosine, to transform cyclical features and help machine learning models pick up their intrinsic nature.

Let’s create a toy dataframe and explain this in more detail:

```import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame([i for i in range(24)], columns=['hour'])
```

Our dataframe looks like this:

```df.head()

hour
0     0
1     1
2     2
3     3
4     4
```

Let’s now create the sine and cosine features to understand more their nature:

```cyclical = CyclicalFeatures(variables=None)

df = cyclical.fit_transform(df)

```
```   hour  hour_sin  hour_cos
0     0  0.000000  1.000000
1     1  0.269797  0.962917
2     2  0.519584  0.854419
3     3  0.730836  0.682553
4     4  0.887885  0.460065
```

Let’s now plot the hour variable against its sine transformation. We add perpendicular lines to flag the hours 0 and 22.

```plt.scatter(df["hour"], df["hour_sin"])

# Axis labels
plt.ylabel('Sine of hour')
plt.xlabel('Hour')
plt.title('Sine transformation')

plt.vlines(x=0, ymin=-1, ymax=0, color='g', linestyles='dashed')
plt.vlines(x=22, ymin=-1, ymax=-0.25, color='g', linestyles='dashed')
```

After the transformation, we see that the new values for the hors 0 and 22 are actually closer to each other (follow the dashed lines). The problem with trigonometric transformations, is that, because they are periodic, 2 different observations can also return similar values after the transformation. Let’s explore that:

```plt.scatter(df["hour"], df["hour_sin"])

# Axis labels
plt.ylabel('Sine of hour')
plt.xlabel('Hour')
plt.title('Sine transformation')

plt.hlines(y=0, xmin=0, xmax=11.5, color='r', linestyles='dashed')

plt.vlines(x=0, ymin=-1, ymax=0, color='g', linestyles='dashed')
plt.vlines(x=11.5, ymin=-1, ymax=0, color='g', linestyles='dashed')
```

In the plot below, we see that the hours 0 and 11.5 obtain very similar values after the sine transformation. So how can we differentiate them? We need to use the 2 transformations together, sine and cosine, to fully code the information of the hour. Adding the cosine function, which is out-of-phase with the sine function, breaks the symmetry and gives each hour a unique codification. Let’s explore that:

```plt.scatter(df["hour"], df["hour_sin"])
plt.scatter(df["hour"], df["hour_cos"])

# Axis labels
plt.ylabel('Sine and cosine of hour')
plt.xlabel('Hour')
plt.title('Sine and Cosine transformation')

plt.hlines(y=0, xmin=0, xmax=11.5, color='r', linestyles='dashed')

plt.vlines(x=0, ymin=-1, ymax=1, color='g', linestyles='dashed')
plt.vlines(x=11.5, ymin=-1, ymax=1, color='g', linestyles='dashed')
```

The hour 0, after the transformation, takes the values of sine 0 and cosine 1, which makes it different from the hour 11.5, which takes values of sine 0 and cosine -1. In other words, with the 2 functions together, we are able to distinguish all observations within our original variable. An intuitive way to show the new representation is to plot the sine vs the cosine transformation of the hour. It will show as a 24 hour clock, and now, the distance between two points corresponds to the difference in time as we would expect from a 24-hour cycle.

```fig, ax = plt.subplots(figsize=(7, 5))
sp = ax.scatter(df["hour_sin"], df["hour_cos"], c=df["hour"])
ax.set(
xlabel="sin(hour)",
ylabel="cos(hour)",
)
_ = fig.colorbar(sp)
``` We hope that cleared things up a bit.

### More details#

You can find more details on to use the `CyclicalFeatures()` in the following Jupyter notebooks.

All notebooks can be found in a dedicated repository.