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| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| class WaterPotabilityDataLoader: | |
| def __init__(self, file_csv, test_size=0.1, random_state=4): | |
| self.file_csv = file_csv | |
| self.test_size = test_size | |
| self.random_state = random_state | |
| self.df_csv = None | |
| self.df_train = None | |
| self.df_test = None | |
| self.X_train = None | |
| self.Y_train = None | |
| self.X_test = None | |
| self.Y_test = None | |
| def read_csv_file(self): | |
| self.df_csv = pd.read_csv(self.file_csv) | |
| return | |
| def split_data(self): | |
| self.df_train, self.df_test = train_test_split( | |
| self.df_csv, test_size=self.test_size, random_state=self.random_state | |
| ) | |
| return | |
| def get_data_from_data_frame(self, which_set="train"): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| which_set : str | |
| a string indicating for which set the data arrays should be returned | |
| ------- | |
| Returns | |
| ------- | |
| (X_arr, Y_arr) : tuple | |
| a tuple of numpy arrays of features and labels for the appropriate set | |
| """ | |
| if which_set == "train": | |
| data_frame = self.df_train | |
| else: | |
| data_frame = self.df_test | |
| arr = data_frame.to_numpy() | |
| X_arr, Y_arr = arr[:, :-1], arr[:, -1:].reshape(-1) | |
| return X_arr, Y_arr | |
| def get_dict_nan_counts_per_col(data_frame): | |
| """ | |
| --------- | |
| Arguments | |
| --------- | |
| data_frame : pd.DataFrame | |
| a pandas dataframe of some dataset | |
| ------- | |
| Returns | |
| ------- | |
| dict_nan_counts_per_col : dict | |
| a dictionary of NaN counts per column | |
| """ | |
| dict_nan_counts_per_col = data_frame.isna().sum().to_dict() | |
| dict_nan_counts_per_col = dict( | |
| sorted(dict_nan_counts_per_col.items(), key=lambda kv: kv[1], reverse=True) | |
| ) | |
| return dict_nan_counts_per_col | |