<jupyter_start><jupyter_text>Predition result of TCM taiwan<jupyter_code>TCM_Taiwan_probs = pd.read_csv('DB_result/TCM_Taiwan_prob.csv', index_col=0)
TCM_Taiwan_probs['Prob'] = 1-TCM_Taiwan_probs['Prob']
TCM_Taiwan_probs
result = []
for CID in set(TCM_Taiwan_probs['ID']):
    row = [CID]
    topprob = 0.0
    toptarget = ''
    for PID in ['NR1C1', 'NR1C2', 'NR1C3', 'NR1H2', 'NR2B1']:
        prob = TCM_Taiwan_probs.loc[(TCM_Taiwan_probs['ID']==CID)&(TCM_Taiwan_probs['PID']==PID), 'Prob'].values[0]
        if prob>=topprob:
            topprob = prob
            toptarget = PID
        row.append(prob)
    row.append(max(row[1:]))
    row.append(toptarget)
    len(set(TCM_Taiwan_probs['ID'])), len(result), row
    result.append(row)
result = pd.DataFrame(result, columns=['CID', 'NR1C1', 'NR1C2', 'NR1C3', 'NR1H2', 'NR2B1', 'MaxProb', 'TopTarget'])
result
result.to_csv('../2021_Result/MMP/Predition.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17777777777777778, 'fraction_numerical': 0.031746031746031744, 'mean_word_length': 4.664670658682635, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '388756', 'license': 'MIT', 'n_tokens_mistral': 433, 'n_tokens_neox': 422, 'n_words': 56, 'path': 'Notebooks/6. Testing in external databases_result.ipynb', 'repo_name': 'ddhmed/pisnrs', 'size': '945'}