Transform dataset using trained model¶

First, initialize a client with your server credentials and store them in the variable dai.

In [1]:

import driverlessai

dai = driverlessai.Client(address='http://localhost:12345', username='py', password='py')

import driverlessai dai = driverlessai.Client(address='http://localhost:12345', username='py', password='py')

Next, get the experiment that you want to use to transform the dataset.

In [2]:

experiments = dai.experiments.list()
ex = experiments[0]

experiments = dai.experiments.list() ex = experiments[0]

Then get the dataset that you want to transform.

In [3]:

datasets = dai.datasets.list()
datasets

datasets = dai.datasets.list() datasets

Out[3]:

    | Type    | Key                                  | Name
----+---------+--------------------------------------+--------------------------
  0 | Dataset | 089a771c-9aef-11ed-a571-18c04d3b1abd | CreditCard_Cat-train.csv
  1 | Dataset | 0897f88e-9aef-11ed-a571-18c04d3b1abd | CreditCard_Cat-test.csv

In [4]:

ds = datasets[1]

ds = datasets[1]

Transform the dataset.

In [5]:

transformed_data = ex.transform(ds)

transformed_data = ex.transform(ds)

Complete

Finally, download the transformed dataset.

In [6]:

transformed_data.download(dst_dir="./", dst_file="transformed_data.csv")

transformed_data.download(dst_dir="./", dst_file="transformed_data.csv")

Downloaded 'transformed_data.csv'

Out[6]:

'transformed_data.csv'