| |
| import os |
| import numpy as np |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| import pdb |
| from sklearn.model_selection import train_test_split |
| import json |
|
|
| |
| df = pd.read_csv('data.csv') |
| l = df.columns |
|
|
| |
| df[l[1]] = df[l[1]].dropna() |
|
|
| |
| df[l[1]] = df[l[1]].fillna('Nan') |
| df[l[3]] = df[l[3]].fillna('Nan2') |
| df[l[4]] = df[l[4]].fillna('Nan3') |
| df[l[6]] = df[l[6]].fillna('Nan4') |
| df[l[7]] = df[l[7]].fillna('Nan5') |
|
|
| df[l[1]] = df[l[1]].str.split('/') |
| df[l[3]] = df[l[3]].str.split(',') |
| df[l[4]] = df[l[4]].str.split('/') |
| df[l[7]] = df[l[7]].str.split('/') |
|
|
| unique_words_1 = list(set(word for row in df[l[1]] for word in row)) |
| unique_words_3 = list(set(word for row in df[l[3]] for word in row)) |
| unique_words_4 = list(set(word for row in df[l[4]] for word in row)) |
| unique_words_7 = list(set(word for row in df[l[7]] for word in row)) |
|
|
| def create_ordered_list(words, unique_words): |
| ordered_list = [1 if word in words else 0 for word in unique_words] |
| return ordered_list |
|
|
|
|
| df['ordered_list_1'] = df[l[1]].apply(lambda x: create_ordered_list(x, unique_words_1)) |
| df['ordered_list_3'] = df[l[3]].apply(lambda x: create_ordered_list(x, unique_words_3)) |
| df['ordered_list_4'] = df[l[4]].apply(lambda x: create_ordered_list(x, unique_words_4)) |
| df['ordered_list_7'] = df[l[7]].apply(lambda x: create_ordered_list(x, unique_words_7)) |
|
|
| df.to_csv('new_data.csv', index=False) |
|
|
| l = df.columns |
|
|
| |
| df = df[[l[0], l[8], l[9], l[10], l[6], l[11]]] |
|
|
| |
| X_train, X_val, y_train, y_val = train_test_split( |
| df[l[0]], df.loc[:, df.columns != l[0]], test_size=0.1, random_state=42) |
|
|
| print(X_train.shape, y_train.shape, X_val.shape, y_val.shape) |
|
|
| |
| os.makedirs('data', exist_ok=True) |
| y_train.to_csv('data/data_ytrain.csv', index=False) |
| y_val.to_csv('data/data_yval.csv', index=False) |
|
|
| with open('data/data_Xtrain.json', 'w') as file: |
| print(len(X_train.tolist())) |
| json.dump(X_train.tolist(), file) |
| |
| with open('data/data_Xval.json', 'w') as file: |
| json.dump(X_val.tolist(), file) |
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