<jupyter_start><jupyter_text>LGBM<jupyter_code>lgbm_roc = roc_auc_score(y_test, pred_lgbm)
lgbm_score = accuracy_score(y_test, pred_lgbm)
print("LGBM AUC score:", lgbm_roc)
print("LGBM Accuracy Score:",lgbm_score)
total_score.append(['LGBM',lgbm_roc,lgbm_score])
plt.figure(figsize=(10, 10))
cm = confusion_matrix(y_test, pred_lgbm)
sns.heatmap(
    cm,
    cmap="Blues",
    linecolor="black",
    linewidth=1,
    annot=True,
    fmt="",
    xticklabels=["Covid_Negative", "Covid_Positive"],
    yticklabels=["Covid_Negative", "Covid_Positive"],
)
plt.xlabel("Predicted")
plt.ylabel("Actual")<jupyter_output><empty_output><jupyter_text>Catboost<jupyter_code>cat_roc = roc_auc_score(y_test, pred_cat)
cat_score = accuracy_score(y_test, pred_cat)
print("Catboost AUC score:", cat_roc)
print("Catboost Accuracy Score:",cat_score)
total_score.append(['Catboost',cat_roc,cat_score])
plt.figure(figsize=(10, 10))
cm = confusion_matrix(y_test, pred_cat)
sns.heatmap(
    cm,
    cmap="Blues",
    linecolor="black",
    linewidth=1,
    annot=True,
    fmt="",
    xticklabels=["Covid_Negative", "Covid_Positive"],
    yticklabels=["Covid_Negative", "Covid_Positive"],
)
plt.xlabel("Predicted")
plt.ylabel("Actual")<jupyter_output><empty_output><jupyter_text>Naive Baise<jupyter_code>nb_roc = roc_auc_score(y_test, pred_nb)
nb_score = accuracy_score(y_test, pred_nb)
print("Naive Bayes AUC score:", nb_roc)
print("Naive Bayes Accuracy Score:",nb_score)
total_score.append(['Naive Bayes',nb_roc,nb_score])
plt.figure(figsize=(10, 10))
cm = confusion_matrix(y_test, pred_nb)
sns.heatmap(
    cm,
    cmap="Blues",
    linecolor="black",
    linewidth=1,
    annot=True,
    fmt="",
    xticklabels=["Covid_Negative", "Covid_Positive"],
    yticklabels=["Covid_Negative", "Covid_Positive"],
)
plt.xlabel("Predicted")
plt.ylabel("Actual")<jupyter_output><empty_output><jupyter_text>Random Forest<jupyter_code>rf_roc = roc_auc_score(y_test, pred_rf)
rf_score = accuracy_score(y_test, pred_rf)
print("Random Forest AUC score:", rf_roc)
print("Random Forest Accuracy Score:",rf_score)
total_score.append(['Random Forest',rf_roc,rf_score])
plt.figure(figsize=(10, 10))
cm = confusion_matrix(y_test, pred_rf)
sns.heatmap(
    cm,
    cmap="Blues",
    linecolor="black",
    linewidth=1,
    annot=True,
    fmt="",
    xticklabels=["Covid_Negative", "Covid_Positive"],
    yticklabels=["Covid_Negative", "Covid_Positive"],
)
plt.xlabel("Predicted")
plt.ylabel("Actual")
orchest.output(total_score,name='eval')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15558194774346792, 'fraction_numerical': 0.00791765637371338, 'mean_word_length': 7.451505016722408, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '436144', 'license': 'Apache-2.0', 'n_tokens_mistral': 1060, 'n_tokens_neox': 999, 'n_words': 134, 'path': 'evaluation.ipynb', 'repo_name': 'kingabzpro/Covid19-Vaccine-orchest', 'size': '2526'}