<jupyter_start><jupyter_text>Play around with the data<jupyter_code># Code from Teams:

import pandas as pd
import pickle as pkl

label, rev_index, features = pkl.load(open('./data/feature_index_all.pkl','rb'))
Lindel_training = pd.read_csv("./data/Lindel_training.txt", sep='\t')
Lindel_test = pd.read_csv("./data/Lindel_test.txt", sep='\t')


# # column descriptions
# Lindel_training.iloc[0] # guide sequences
# Lindel_training.iloc[1:3034] # 3033 binary features [2649 MH binary features + 384 one hot encoded features]
# Lindel_training.iloc[3034:] # 557 observed outcome frequencies
Lindel_training
Lindel_test
print("Number of labels : ", len(label.keys()))
print("Number of rev_index : ", len(rev_index.keys()))
print("Number of features : ", len(features.keys()))
forecast_test = pd.read_csv("/Users/bram/Downloads/Lindel_ForeCasT_combined_test (1).txt", sep='\t')
# /Users/bram/Downloads/Lindel_ForeCasT_combined_test.txt
# forecast_large = pd.read_csv("./data/Lindel_ForeCasT_combined_test (1).txt", sep='\t')
forecast_small = pd.read_csv("./data/Lindel_ForeCasT_combined_test.txt", sep='\t')
forecast_large
forecast_small<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1470335339638865, 'fraction_numerical': 0.02235597592433362, 'mean_word_length': 6.708609271523179, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '405872', 'license': 'MIT', 'n_tokens_mistral': 450, 'n_tokens_neox': 415, 'n_words': 77, 'path': 'data_read.ipynb', 'repo_name': 'brmprnk/lindel423', 'size': '1163'}