Columns and DataType Not Explicitly Set on line 219 of utils.py

[CodeSmileBot]

Hello!

I found an AI-Specific Code smell in your project.
The smell is called: Columns and DataType Not Explicitly Set

You can find more information about it in this paper: https://dl.acm.org/doi/abs/10.1145/3522664.3528620.

According to the paper, the smell is described as follows:

Problem	If the columns are not selected explicitly, it is not easy for developers to know what to expect in the downstream data schema. If the datatype is not set explicitly, it may silently continue the next step even though the input is unexpected, which may cause errors later. The same applies to other data importing scenarios.
Solution	It is recommended to set the columns and DataType explicitly in data processing.
Impact	Readability

Example:

### Pandas Column Selection
import pandas as pd
df = pd.read_csv('data.csv')
+ df = df[['col1', 'col2', 'col3']]

### Pandas Set DataType
import pandas as pd
- df = pd.read_csv('data.csv')
+ df = pd.read_csv('data.csv', dtype={'col1': 'str', 'col2': 'int', 'col3': 'float'})

You can find the code related to this smell in this link:

ml-on-gcp/tutorials/tensorflow/mlflow_gcp/trainer/utils.py

Lines 209 to 229 in 90153a9

	eval_file_path))
	training_file_path, eval_file_path = download(DATA_DIR)
	# This census data uses the value '?' for missing entries. We use
	# na_values to
	# find ? and set it to NaN.
	# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv
	# .html
	train_df = pd.read_csv(training_file_path, names=_CSV_COLUMNS,
	na_values='?')
	eval_df = pd.read_csv(eval_file_path, names=_CSV_COLUMNS, na_values='?')
	train_df = preprocess(train_df)
	eval_df = preprocess(eval_df)
	# Split train and eval data with labels. The pop method copies and removes
	# the label column from the dataframe.
	train_x, train_y = train_df, train_df.pop(_LABEL_COLUMN)
	eval_x, eval_y = eval_df, eval_df.pop(_LABEL_COLUMN)
	# Join train_x and eval_x to normalize on overall means and standard

.

I also found instances of this smell in other files, such as:

File: https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/example_zoo/tensorflow/models/ncf_main/official/datasets/movielens.py#L243-L253 Line: 248
File: https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/example_zoo/tensorflow/models/ncf_main/official/datasets/movielens.py#L250-L260 Line: 255
File: https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/example_zoo/tensorflow/models/ncf_main/official/recommendation/data_preprocessing.py#L99-L109 Line: 104
File: https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/gce/burst-training/census-analysis.py#L58-L68 Line: 63
File: https://github.com/GoogleCloudPlatform/ml-on-gcp/blob/master/gce/burst-training/census-analysis.py#L63-L73 Line: 68
.

I hope this information is helpful!