rapids/src/models/select_targets.py

import pandas as pd

from helper import retain_target_column

cleaned_sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])
target_variable_name = snakemake.params["target_variable"]

model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
model_input.dropna(axis ="index", how="any", subset=["target"], inplace=True)

model_input.to_csv(snakemake.output[0], index=False)
Select target columns (no parsing necessary). 2022-04-06 18:16:49 +02:00			`import pandas as pd`

Extract a function to be used elsewhere. 2022-04-08 15:36:32 +02:00			`from helper import retain_target_column`
Select target columns (no parsing necessary). 2022-04-06 18:16:49 +02:00
			`cleaned_sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])`
Extract a function to be used elsewhere. 2022-04-08 15:36:32 +02:00			`target_variable_name = snakemake.params["target_variable"]`
Select target columns (no parsing necessary). 2022-04-06 18:16:49 +02:00
Extract a function to be used elsewhere. 2022-04-08 15:36:32 +02:00			`model_input = retain_target_column(cleaned_sensor_features, target_variable_name)`
Drop NaN targets. This mirrors INNER join in merge_features_and_targets_for_individual_model.py: data = pd.concat([sensor_features, targets[["target"]]], axis=1, join="inner") 2022-04-12 17:01:49 +02:00			`model_input.dropna(axis ="index", how="any", subset=["target"], inplace=True)`
Select target columns (no parsing necessary). 2022-04-06 18:16:49 +02:00
			`model_input.to_csv(snakemake.output[0], index=False)`