<jupyter_start><jupyter_text>Number of Entries for Industries app      167 elec     142 toys      93 jewel     63 cos       55 auto      37<jupyter_code>score_map = dict()
for i in range(len(cols_y)):
    ind_clf.fit(X_train,y_train[:,i])
    ind_name = label_enc_industry.inverse_transform(i)
    y_test_p = y_test[:,i]
    y_pred_p = ind_clf.predict(X_test)
    cm = confusion_matrix(y_test_p, y_pred_p)
    accuracy = accuracy_score(y_test_p,y_pred_p)
    score_map[ind_name] = cm.ravel()+tuple([accuracy])
    
    plt.clf()
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)
    classNames = ['Negative','Positive']
    plt.title('Confusion Matrix - %s'%ind_name)
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    tick_marks = np.arange(len(classNames))
    plt.xticks(tick_marks, classNames, rotation=45)
    plt.yticks(tick_marks, classNames)
    s = [['TN','FP'], ['FN', 'TP']]

    for i in range(2):
        for j in range(2):
            plt.text(j,i, str(s[i][j])+" = "+str(cm[i][j]))
    plt.show()
print score_map<jupyter_output>{'cos': array([156.86956522,   6.86956522,  18.86956522,   4.86956522]), 'elec': array([123.73369565,  15.73369565,  34.73369565,  12.73369565]), 'auto': array([168.91304348,   2.91304348,  14.91304348,   0.91304348]), 'app': array([126.83695652,  14.83695652,  16.83695652,  28.83695652]), 'jewel': array([157.85869565,   6.85869565,  20.85869565,   1.85869565]), 'toys': array([136.8423913,  11.8423913,  18.8423913,  19.8423913])}
{'fraction_non_alphanumeric': 0.16733067729083664, 'fraction_numerical': 0.16865869853917662, 'mean_word_length': 3.8456591639871385, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '185864', 'license': 'MIT', 'n_tokens_mistral': 768, 'n_tokens_neox': 628, 'n_words': 103, 'path': 'model.ipynb', 'repo_name': 'menikhilpandey/AART', 'size': '1506'}