Create a tensor from a Dataframe (gota)

This howto explains how to create a tensor from a dataframe using gota The goal is to read a csv file and create a *tensor.Dense with shape (2,2).

Create the dataframe from a csv file

Consider a csv file with the following content:

sepal_length,sepal_width,petal_length,petal_width,species
5.1         ,3.5        ,1.4         ,0.2        ,setosa
4.9         ,3.0        ,1.4         ,0.2        ,setosa
4.7         ,3.2        ,1.3         ,0.2        ,setosa
4.6         ,3.1        ,1.5         ,0.2        ,setosa
5.0         ,3.6        ,1.4         ,0.2        ,setosa
...

This is extract from the Iris flower data set. A copy of the dataset can be found here

We want to create a tensor with all values but the species.

Create the dataframe with gota.

gota’s dataframe package has a function ReadCSV that takes an io.Reader as argument.

f, err := os.Open("iris.csv")
if err != nil {
    log.Fatal(err)
}
defer f.Close()
df := dataframe.ReadCSV(f)

df is a DataFrame that contains all the data present in the file.

gota uses te first line of the CSV to reference the columns in the dataframe

Let’s remove the species column:

xDF := df.Drop("species")

Convert the dataframe into a matrix

To make things easier, we will convert our dataframe into a Matrix as defined by gonum (see the matrix godoc). matrix is an interface. gota’s dataframe does not fulfill the Matrix interface. As described into gota’s documentation, we create a wrapper around DataFrame to fulfil the Matrix interface.

type matrix struct {
	dataframe.DataFrame
}

func (m matrix) At(i, j int) float64 {
	return m.Elem(i, j).Float()
}

func (m matrix) T() mat.Matrix {
	return mat.Transpose{Matrix: m}
}

Create the tensor

Now we can create a *Dense tensor thanks to the function tensor.FromMat64 by wrapping the dataframe into the matrix structure.

xT := tensor.FromMat64(mat.DenseCopyOf(&matrix{xDF}))