<jupyter_start><jupyter_text>__Fasttext Results__<jupyter_code>field = 'text'
classifier_name = 'dt'
wv_model_name = 'ft'

out_colname = 'feat_concat_ft_'+field+'_'+classifier_name
in_colname = 'feat_concat_ft_'+field

classifier_func = lambda: DecisionTreeClassifier(max_depth=None, criterion='entropy', min_samples_leaf=10)
trees, df_comparisons = cross_validate_concat(dfs_val_comparisons, df_comparisons, classifier_func, out_colname, in_colname, n_val_splits)
print_prec_recall(out_colname)
field = 'text'
classifier_name = 'rf'
wv_model_name = 'ft'

out_colname = 'feat_concat_ft_'+field+'_'+classifier_name
in_colname = 'feat_concat_ft_'+field

classifier_func = lambda: RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None)
trees, df_comparisons = cross_validate_concat(dfs_val_comparisons, df_comparisons, classifier_func, out_colname, in_colname, n_val_splits)
print_prec_recall(out_colname)
field = 'text'
classifier_name = 'svm'
wv_model_name = 'ft'

out_colname = 'feat_concat_ft_'+field+'_'+classifier_name
in_colname = 'feat_concat_ft_'+field

classifier_func = lambda: SVC(C=1000000, kernel='rbf', gamma=1e-5)
svms, df_comparisons = cross_validate_concat(dfs_val_comparisons, df_comparisons, classifier_func, out_colname, in_colname, n_val_splits)
print_prec_recall(out_colname)<jupyter_output>Warning! number of positive predictions: 0
╒═════════════════════════╤══════════╤══════╤════════╤══════════╤══════╤═══════╤══════╤══════╕
│ METHOD                  │      ACC │   F1 │   PREC │   RECALL │   TP │    TN │   FP │   FN │
╞═════════════════════════╪══════════╪══════╪════════╪══════════╪══════╪═══════╪══════╪══════╡
│ feat_concat_ft_text_svm │ 0.889785 │    0 │      0 │        0 │    0 │ 15000 │    0 │ 1858 │
╘═════════════════════════╧══════════╧══════╧════════╧══════════╧══════╧═══════╧══════╧══════╛<jupyter_text>__Word2VEc results__<jupyter_code>field = 'text'
classifier_name = 'rf'
wv_model_name = 'wv'

out_colname = 'feat_concat_ft_'+field+'_'+classifier_name
in_colname = 'feat_concat_ft_'+field

classifier_func = lambda: RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=None)
trees, df_comparisons = cross_validate_concat(dfs_val_comparisons, df_comparisons, classifier_func, out_colname, in_colname, n_val_splits)
print_prec_recall(out_colname)
field = 'text'
classifier_name = 'svm'
wv_model_name = 'wv'

out_colname = 'feat_concat_ft_'+field+'_'+classifier_name
in_colname = 'feat_concat_ft_'+field

classifier_func = lambda: SVC(C=1000000, kernel='rbf', gamma=1e-5)
svms, df_comparisons = cross_validate_concat(dfs_val_comparisons, df_comparisons, classifier_func, out_colname, in_colname, n_val_splits)
print_prec_recall(out_colname)<jupyter_output>Warning! number of positive predictions: 0
╒═════════════════════════╤══════════╤══════╤════════╤══════════╤══════╤═══════╤══════╤══════╕
│ METHOD                  │      ACC │   F1 │   PREC │   RECALL │   TP │    TN │   FP │   FN │
╞═════════════════════════╪══════════╪══════╪════════╪══════════╪══════╪═══════╪══════╪══════╡
│ feat_concat_ft_text_svm │ 0.889785 │    0 │      0 │        0 │    0 │ 15000 │    0 │ 1858 │
╘═════════════════════════╧══════════╧══════╧════════╧══════════╧══════╧═══════╧══════╧══════╛
{'fraction_non_alphanumeric': 0.25892857142857145, 'fraction_numerical': 0.02247536945812808, 'mean_word_length': 7.595238095238095, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '411481', 'license': 'MIT', 'n_tokens_mistral': 1566, 'n_tokens_neox': 1316, 'n_words': 213, 'path': 'v06_supervised_we_concat.ipynb', 'repo_name': 'donanimhaber/newsmatching', 'size': '3248'}