When I fit a pipe using automatminer to a dataset like below, how do I extract the data (i.e the original data frame that has been augmented with additional features) from the newly learned pipe? Thank you!!
Sincerely,
tom
from matminer.datasets.convenience_loaders import load_castelli_perovskites
user inputs
target = ‘gap gllbsc’
RS = 29
timelimitmins = 120
print('timelimitmins = ', timelimitmins)
model_type = ‘regression’
scoring = ‘r2’
df_init = load_castelli_perovskites()
from automatminer.pipeline import MatPipe
Fit a pipeline to training data to predict band gap
You can actually use the output of pipe.digest to check out all the available attribute names.
To get the df with features used for fitting, you can use pipe.post_fit_df, it should contain all the features
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On Friday, December 14, 2018 at 8:29:00 AM UTC-8, thomas heiman wrote:
Hi,
When I fit a pipe using automatminer to a dataset like below, how do I extract the data (i.e the original data frame that has been augmented with additional features) from the newly learned pipe? Thank you!!
Sincerely,
tom
from matminer.datasets.convenience_loaders import load_castelli_perovskites
user inputs
target = ‘gap gllbsc’
RS = 29
timelimitmins = 120
print('timelimitmins = ', timelimitmins)
model_type = ‘regression’
scoring = ‘r2’
df_init = load_castelli_perovskites()
from automatminer.pipeline import MatPipe
Fit a pipeline to training data to predict band gap