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fix: Improved the functionality for dropping missing values. #328

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fix: Improved the functionality for dropping missing values. #328

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49 changes: 38 additions & 11 deletions geochemistrypi/data_mining/cli_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
CLUSTERING_MODELS,
CLUSTERING_MODELS_WITH_MISSING_VALUES,
DECOMPOSITION_MODELS,
DROP_MISSING_VALUE_STRATEGY,
FEATURE_SCALING_STRATEGY,
FEATURE_SELECTION_STRATEGY,
IMPUTING_STRATEGY,
Expand Down Expand Up @@ -281,23 +282,49 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
if is_process_missing_value == 1:
process_missing_value_flag = True
# If the user wants to deal with the missing values, then ask the user which strategy to use.
clear_output()
print("-*-*- Strategy for Missing Values -*-*-")
num2option(MISSING_VALUE_STRATEGY)
print("Notice: Drop the rows with missing values may lead to a significant loss of data if too many features are chosen.")
print("Which strategy do you want to apply?")
missing_value_strategy_num = limit_num_input(MISSING_VALUE_STRATEGY, SECTION[1], num_input)
clear_output()
if missing_value_strategy_num == 1:
# Drop the rows with missing values
data_selected_dropped = data_selected.dropna()
# Reset the index of the data set after dropping the rows with missing values.
data_selected_dropped = data_selected_dropped.reset_index(drop=True)
print("Successfully drop the rows with missing values.")
print("The Selected Data Set After Dropping:")
print(data_selected_dropped)
print("Basic Statistical Information:")
save_data(data_selected_dropped, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
drop_rows_with_missing_value_flag = True
imputed_flag = False
print("-*-*- Drop the rows with Missing Values -*-*-")
num2option(DROP_MISSING_VALUE_STRATEGY)
print("Notice: Drop the rows with missing values may lead to a significant loss of data if too many features are chosen.")
print("Which strategy do you want to apply?")
drop_missing_value_strategy_num = limit_num_input(DROP_MISSING_VALUE_STRATEGY, SECTION[1], num_input)
if drop_missing_value_strategy_num == 1:
# Drop the rows with missing values
data_selected_dropped = data_selected.dropna()
# Reset the index of the data set after dropping the rows with missing values.
data_selected_dropped = data_selected_dropped.reset_index(drop=True)
print("Successfully drop the rows with missing values.")
print("The Selected Data Set After Dropping:")
print(data_selected_dropped)
print("Basic Statistical Information:")
save_data(data_selected_dropped, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
drop_rows_with_missing_value_flag = True
imputed_flag = False
elif drop_missing_value_strategy_num == 2:
show_data_columns(data_selected.columns)
drop_data_selected = create_sub_data_set(data_selected)
for column_name in drop_data_selected.columns:
# Drop the rows with missing values
data_selected_dropped = data_selected.dropna(subset=[column_name])
# Reset the index of the data set after dropping the rows with missing values.
data_selected_dropped = data_selected_dropped.reset_index(drop=True)
print("Successfully drop the rows with missing values.")
print("The Selected Data Set After Dropping:")
print(data_selected_dropped)
print("Basic Statistical Information:")
save_data(data_selected_dropped, "Data Selected Dropped-Imputed", GEOPI_OUTPUT_ARTIFACTS_DATA_PATH, MLFLOW_ARTIFACT_DATA_PATH)
drop_rows_with_missing_value_flag = True
imputed_flag = False
missing_value_flag = check_missing_value(data_selected_dropped)
if missing_value_flag:
process_missing_value_flag = False
elif missing_value_strategy_num == 2:
# Don't drop the rows with missing values but use imputation techniques to deal with the missing values later.
# No need to save the data set here because it will be saved after imputation.
Expand Down
2 changes: 2 additions & 0 deletions geochemistrypi/data_mining/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,3 +105,5 @@
FEATURE_SELECTION_STRATEGY = ["Generic Univariate Select", "Select K Best"]

CALCULATION_METHOD_OPTION = ["Micro", "Macro", "Weighted"]

DROP_MISSING_VALUE_STRATEGY = ["Drop All Rows with Missing Values", "Drop Rows with Missing Values by Specific Columns"]
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