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How to use report_test_name method in Testify
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run_magellan.py
Source:run_magellan.py 
1import pandas as pd2import numpy as np3np.random.seed(42)4import random5random.seed(42)6import os7import time8import glob9import py_entitymatching as em10from sklearn.linear_model import LogisticRegression11from sklearn.naive_bayes import GaussianNB12from sklearn.ensemble import RandomForestClassifier13from sklearn.svm import LinearSVC14from sklearn.tree import DecisionTreeClassifier15from sklearn.model_selection import RandomizedSearchCV16from sklearn.model_selection import PredefinedSplit17from sklearn.metrics import classification_report18import xgboost as xgb19classifiers = {'NaiveBayes': {'clf':GaussianNB(),20 'params':{}},21 'XGBoost': {'clf':xgb.XGBClassifier(random_state=42, n_jobs=4),22 'params':{"learning_rate": [0.1, 0.01, 0.001],23 "gamma" : [0.01, 0.1, 0.3, 0.5, 1, 1.5, 2],24 "max_depth": [2, 4, 7, 10],25 "colsample_bytree": [0.3, 0.6, 0.8, 1.0],26 "subsample": [0.2, 0.4, 0.5, 0.6, 0.7],27 "reg_alpha": [0, 0.5, 1],28 "reg_lambda": [1, 1.5, 2, 3, 4.5],29 "min_child_weight": [1, 3, 5, 7],30 "n_estimators": [100]}},31 'RandomForest': {'clf':RandomForestClassifier(random_state=42, n_jobs=4),32 'params':{'n_estimators': [100],33 'max_features': ['sqrt', 'log2', None],34 'max_depth': [2,4,7,10],35 'min_samples_split': [2, 5, 10, 20],36 'min_samples_leaf': [1, 2, 4, 8],37 'class_weight':[None, 'balanced_subsample']38 }},39 'DecisionTree': {'clf':DecisionTreeClassifier(random_state=42),40 'params':{'max_features': ['sqrt', 'log2', None],41 'max_depth': [2,4,7,10],42 'min_samples_split': [2, 5, 10, 20],43 'min_samples_leaf': [1, 2, 4, 8],44 'class_weight':[None, 'balanced']45 }},46 'LinearSVC': {'clf':LinearSVC(random_state=42, dual=False),47 'params':{'C': [0.0001 ,0.001, 0.01, 0.1, 1, 10, 100, 1000],48 'class_weight':[None, 'balanced']}},49 'LogisticRegression': {'clf':LogisticRegression(random_state=42, solver='liblinear'),50 'params':{'C': [0.0001 ,0.001, 0.01, 0.1, 1, 10, 100, 1000],51 'class_weight':[None, 'balanced']}},52 }53def run_magellan(train_set, valid_set, test_set, feature_combinations, classifiers, experiment_name,54 write_test_set_for_inspection=False):55 train_path = os.path.dirname(train_set)56 train_file = os.path.basename(train_set)57 test_path = os.path.dirname(test_set)58 test_file = os.path.basename(test_set)59 report_train_name = train_file.replace('.csv', '')60 report_test_name = test_file.replace('.csv', '')61 train_set_left = train_file.replace('pairs', 'left')62 train_set_right = train_file.replace('pairs', 'right')63 test_set_left = test_file.replace('pairs', 'left')64 test_set_right = test_file.replace('pairs', 'right')65 os.makedirs(os.path.dirname('../../../reports/magellan/{}/'.format(experiment_name)),66 exist_ok=True)67 try:68 os.remove('../../../reports/magellan/{}/{}_{}.csv'.format(experiment_name, report_train_name, report_test_name))69 except OSError:70 pass71 with open('../../../reports/magellan/{}/{}_{}.csv'.format(experiment_name, report_train_name, report_test_name),72 "w") as f:73 f.write(74 'feature#####model#####mean_train_score#####std_train_score#####mean_valid_score#####std_valid_score#####precision_test#####recall_test#####f1_test#####best_params#####train_time#####prediction_time#####feature_importance#####experiment_name#####train_set#####test_set\n')75 for run in range(1, 4):76 for feature_combination in feature_combinations:77 A_t = em.read_csv_metadata(train_path + '/' + train_set_left, key='mag_id')78 B_t = em.read_csv_metadata(train_path + '/' + train_set_right, key='mag_id')79 # Load the pre-labeled data80 S_t = em.read_csv_metadata(train_set,81 key='_id',82 ltable=A_t, rtable=B_t,83 fk_ltable='ltable_mag_id', fk_rtable='rtable_mag_id')84 A_gs = em.read_csv_metadata(test_path + '/' + test_set_left, key='mag_id')85 B_gs = em.read_csv_metadata(test_path + '/' + test_set_right, key='mag_id')86 # Load the pre-labeled data87 S_gs = em.read_csv_metadata(test_set,88 key='_id',89 ltable=A_gs, rtable=B_gs,90 fk_ltable='ltable_mag_id', fk_rtable='rtable_mag_id')91 A_t.fillna('', inplace=True)92 A_gs.fillna('', inplace=True)93 B_t.fillna('', inplace=True)94 B_gs.fillna('', inplace=True)95 S_t.fillna('', inplace=True)96 S_gs.fillna('', inplace=True)97 ## DIRTY FIX, CLEAN UP!98 if 'name' in A_t.columns:99 A_t["price"] = A_t["price"].replace(r'^\s*$', np.nan, regex=True)100 A_t["price"] = A_t["price"].astype('float64')101 A_gs["price"] = A_gs["price"].replace(r'^\s*$', np.nan, regex=True)102 A_gs["price"] = A_gs["price"].astype('float64')103 B_t["price"] = B_t["price"].replace(r'^\s*$', np.nan, regex=True)104 B_t["price"] = B_t["price"].astype('float64')105 B_gs["price"] = B_gs["price"].replace(r'^\s*$', np.nan, regex=True)106 B_gs["price"] = B_gs["price"].astype('float64')107 S_t["ltable_price"] = S_t["ltable_price"].replace(r'^\s*$', np.nan, regex=True)108 S_t["ltable_price"] = S_t["ltable_price"].astype('float64')109 S_t["rtable_price"] = S_t["rtable_price"].replace(r'^\s*$', np.nan, regex=True)110 S_t["rtable_price"] = S_t["rtable_price"].astype('float64')111 S_gs["ltable_price"] = S_gs["ltable_price"].replace(r'^\s*$', np.nan, regex=True)112 S_gs["ltable_price"] = S_gs["ltable_price"].astype('float64')113 S_gs["rtable_price"] = S_gs["rtable_price"].replace(r'^\s*$', np.nan, regex=True)114 S_gs["rtable_price"] = S_gs["rtable_price"].astype('float64')115 atypes1 = em.get_attr_types(A_t)116 atypes2 = em.get_attr_types(B_t)117 match_c = em.get_attr_corres(A_t, B_t)118 match_c['corres'] = []119 # select attributes to compare120 for feature in feature_combination:121 match_c['corres'].append((feature, feature))122 tok = em.get_tokenizers_for_matching()123 sim = em.get_sim_funs_for_matching()124 F_t = em.get_features(A_t, B_t, atypes1, atypes2, match_c, tok, sim)125 H_t = em.extract_feature_vecs(S_t,126 feature_table=F_t,127 attrs_after=['label', 'pair_id'],128 show_progress=False)129 H_gs = em.extract_feature_vecs(S_gs,130 feature_table=F_t,131 attrs_after='label',132 show_progress=False)133 H_t = H_t.fillna(-1)134 H_gs = H_gs.fillna(-1)135 validation_ids_df = pd.read_csv(valid_set)136 val_df = H_t[H_t['pair_id'].isin(validation_ids_df['pair_id'].values)]137 train_only_df = H_t[~H_t['pair_id'].isin(validation_ids_df['pair_id'].values)]138 train_only_df = train_only_df.drop(columns='pair_id')139 val_df = val_df.drop(columns='pair_id')140 train_only_df = train_only_df.sample(frac=1, random_state=42)141 pos_neg = H_t['label'].value_counts()142 pos_neg = round(pos_neg[0] / pos_neg[1])143 train_ind = []144 val_ind = []145 for i in range(len(train_only_df) - 1):146 train_ind.append(-1)147 for i in range(len(val_df) - 1):148 val_ind.append(0)149 ps = PredefinedSplit(test_fold=np.concatenate((train_ind, val_ind)))150 train_df = pd.concat([train_only_df, val_df])151 for k, v in classifiers.items():152 classifier = v['clf']153 if 'random_state' in classifier.get_params().keys():154 classifier = classifier.set_params(**{'random_state': run})155 # add pos_neg ratio to XGBoost params156 if k == 'XGBoost':157 v['params']['scale_pos_weight']: [1, pos_neg]158 model = RandomizedSearchCV(cv=ps, estimator=classifier, param_distributions=v['params'],159 random_state=42, n_jobs=4, scoring='f1', n_iter=500, pre_dispatch=8,160 return_train_score=True)161 feats_train = train_df.drop(['_id', 'ltable_mag_id', 'rtable_mag_id', 'label'], axis=1)162 labels_train = train_df['label']163 feats_gs = H_gs.drop(['_id', 'ltable_mag_id', 'rtable_mag_id', 'label'], axis=1)164 labels_gs = H_gs['label']165 try:166 model.fit(feats_train, labels_train)167 except ValueError:168 set_trace()169 parameters = model.best_params_170 score_names = ['mean_train_score', 'std_train_score', 'mean_test_score', 'std_test_score']171 scores = {}172 score_string = ''173 for name in score_names:174 scores[name] = model.cv_results_[name][model.best_index_]175 score_string = score_string + name + ': ' + str(scores[name]) + ' '176 feature_names = list(feats_train.columns)177 if k == 'LogisticRegression' or k == 'LinearSVC':178 most_important_features = model.best_estimator_.coef_179 word_importance = zip(feature_names, most_important_features[0].tolist())180 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)181 if k == 'RandomForest' or k == 'DecisionTree':182 most_important_features = model.best_estimator_.feature_importances_183 word_importance = zip(feature_names, most_important_features.tolist())184 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)185 if k == 'NaiveBayes':186 word_importance = ''187 if k == 'XGBoost':188 most_important_features = model.best_estimator_.feature_importances_189 word_importance = zip(feature_names, most_important_features.tolist())190 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)191 if k == 'LogisticRegression':192 learner = LogisticRegression(random_state=run, solver='liblinear', **parameters)193 elif k == 'NaiveBayes':194 learner = GaussianNB()195 elif k == 'DecisionTree':196 learner = DecisionTreeClassifier(random_state=run, **parameters)197 elif k == 'LinearSVC':198 learner = LinearSVC(random_state=run, dual=False, **parameters)199 elif k == 'RandomForest':200 learner = RandomForestClassifier(random_state=run, n_jobs=4, **parameters)201 elif k == 'XGBoost':202 learner = xgb.XGBClassifier(random_state=run, n_jobs=4, **parameters)203 else:204 print('Learner is not a valid option')205 break206 model = learner207 feats_train = train_only_df.sample(frac=1, random_state=42)208 feats_train = train_only_df.drop(['_id', 'ltable_mag_id', 'rtable_mag_id', 'label'], axis=1)209 labels_train = train_only_df['label']210 start = time.time()211 model.fit(feats_train, labels_train)212 end = time.time()213 train_time = end - start214 start = time.time()215 preds_gs = model.predict(feats_gs)216 end = time.time()217 pred_time = end - start218 gs_report = classification_report(labels_gs, preds_gs, output_dict=True)219 feature_report = '+'.join(feature_combination)220 if write_test_set_for_inspection:221 out_path = '../../../data/processed/wdc-lspc/inspection/{}/magellan/'.format(experiment_name)222 os.makedirs(os.path.dirname(out_path), exist_ok=True)223 file_name = '_'.join([os.path.basename(train_set), os.path.basename(test_set), k, feature_report])224 file_name = file_name.replace('.csv', '')225 file_name += f'_{run}.pkl.gz'226 test_inspection_df = S_gs.copy()227 if k == 'LinearSVC':228 proba_gs = model.decision_function(feats_gs).tolist()229 else:230 proba_gs = model.predict_proba(feats_gs).tolist()231 test_inspection_df['pred'] = preds_gs232 test_inspection_df['Class Prob'] = proba_gs233 test_inspection_df.to_pickle(out_path + file_name, compression='gzip')234 with open('../../../reports/magellan/{}/{}_{}.csv'.format(experiment_name, report_train_name,235 report_test_name), "a") as f:236 f.write(feature_report + '#####' + k + '#####' + str(237 scores['mean_train_score']) + '#####' + str(scores['std_train_score'])238 + '#####' + str(scores['mean_test_score']) + '#####' + str(239 scores['std_test_score']) + '#####' + str(gs_report['1']['precision']) + '#####' + str(240 gs_report['1']['recall']) + '#####' + str(gs_report['1']['f1-score'])241 + '#####' + str(parameters) + '#####' + str(train_time) + '#####' + str(pred_time)242 + '#####' + str(word_importance[243 0:100]) + '#####' + experiment_name + '#####' + report_train_name + '#####' + report_test_name + '\n')244if __name__ == '__main__':245 # learning-curve experiment246 feature_combinations = [['title'], ['title', 'description'], ['title', 'description', 'brand'],247 ['title', 'description', 'brand', 'specTableContent']]248 experiment_name = 'learning-curve'249 for file in glob.glob('../../../data/processed/wdc-lspc/magellan/learning-curve/formatted/*'):250 if 'train_' in file and 'pairs' in file and 'metadata' not in file:251 valid = file.replace('train_', 'valid_')252 test_cat = '_'.join(os.path.basename(file).split('_')[:2])253 test = '../../../data/processed/wdc-lspc/magellan/learning-curve/formatted/{}_gs_magellan_pairs_formatted.csv'.format(254 test_cat)255 run_magellan(file, valid, test, feature_combinations, classifiers, experiment_name,...
run_wordcooc.py
Source:run_wordcooc.py
1import pandas as pd2import numpy as np3np.random.seed(42)4import random5random.seed(42)6import scipy7import os8import time9import glob10import json11from sklearn.linear_model import LogisticRegression12from sklearn.naive_bayes import BernoulliNB13from sklearn.ensemble import RandomForestClassifier14from sklearn.svm import LinearSVC15from sklearn.tree import DecisionTreeClassifier16from sklearn.model_selection import RandomizedSearchCV17from sklearn.model_selection import PredefinedSplit18from sklearn.metrics import classification_report19import xgboost as xgb20classifiers = {'NaiveBayes': {'clf':BernoulliNB(),21 'params':{}},22 'XGBoost': {'clf':xgb.XGBClassifier(random_state=42, n_jobs=4),23 'params':{"learning_rate": [0.1, 0.01, 0.001],24 "gamma" : [0.01, 0.1, 0.3, 0.5, 1, 1.5, 2],25 "max_depth": [2, 4, 7, 10],26 "colsample_bytree": [0.3, 0.6, 0.8, 1.0],27 "subsample": [0.2, 0.4, 0.5, 0.6, 0.7],28 "reg_alpha": [0, 0.5, 1],29 "reg_lambda": [1, 1.5, 2, 3, 4.5],30 "min_child_weight": [1, 3, 5, 7],31 "n_estimators": [100]}},32 'RandomForest': {'clf':RandomForestClassifier(random_state=42, n_jobs=4),33 'params':{'n_estimators': [100],34 'max_features': ['sqrt', 'log2', None],35 'max_depth': [2,4,7,10],36 'min_samples_split': [2, 5, 10, 20],37 'min_samples_leaf': [1, 2, 4, 8],38 'class_weight':[None, 'balanced_subsample']39 }},40 'DecisionTree': {'clf':DecisionTreeClassifier(random_state=42),41 'params':{'max_features': ['sqrt', 'log2', None],42 'max_depth': [2,4,7,10],43 'min_samples_split': [2, 5, 10, 20],44 'min_samples_leaf': [1, 2, 4, 8],45 'class_weight':[None, 'balanced']46 }},47 'LinearSVC': {'clf':LinearSVC(random_state=42, dual=False),48 'params':{'C': [0.0001 ,0.001, 0.01, 0.1, 1, 10, 100, 1000],49 'class_weight':[None, 'balanced']}},50 'LogisticRegression': {'clf':LogisticRegression(random_state=42, solver='liblinear'),51 'params':{'C': [0.0001 ,0.001, 0.01, 0.1, 1, 10, 100, 1000],52 'class_weight':[None, 'balanced']}},53 }54def run_wordcooc(train_set, valid_set, test_set, feature_combinations, classifiers, experiment_name,55 write_test_set_for_inspection=False):56 train_path = os.path.dirname(train_set)57 train_file = os.path.basename(train_set)58 test_path = os.path.dirname(test_set)59 test_file = os.path.basename(test_set)60 report_train_name = train_file.replace('.pkl.gz', '')61 report_test_name = test_file.replace('.pkl.gz', '')62 os.makedirs(os.path.dirname('../../../reports/wordcooc/{}/'.format(experiment_name)),63 exist_ok=True)64 try:65 os.remove('../../../reports/wordcooc/{}/{}_{}.csv'.format(experiment_name, report_train_name, report_test_name))66 except OSError:67 pass68 with open('../../../reports/wordcooc/{}/{}_{}.csv'.format(experiment_name, report_train_name, report_test_name),69 "w") as f:70 f.write(71 'feature#####model#####mean_train_score#####std_train_score#####mean_valid_score#####std_valid_score#####precision_test#####recall_test#####f1_test#####best_params#####train_time#####prediction_time#####feature_importance#####experiment_name#####train_set#####test_set\n')72 for run in range(1, 4):73 for feature_combination in feature_combinations:74 train_original_df = pd.read_pickle(train_set, compression='gzip')75 gs_df = pd.read_pickle(test_set, compression='gzip')76 feature_file_name = train_file.replace('.pkl.gz', '_words.json')77 with open(train_path + '/feature-names/' + feature_file_name) as json_data:78 words = json.load(json_data)79 validation_ids_df = pd.read_pickle(valid_set, compression='gzip')80 val_df = train_original_df[train_original_df['pair_id'].isin(validation_ids_df['pair_id'].values)]81 train_only_df = train_original_df[~train_original_df['pair_id'].isin(validation_ids_df['pair_id'].values)]82 train_only_df = train_only_df.sample(frac=1, random_state=42)83 pos_neg = train_original_df['label'].value_counts()84 pos_neg = round(pos_neg[0] / pos_neg[1])85 train_ind = []86 val_ind = []87 for i in range(len(train_only_df) - 1):88 train_ind.append(-1)89 for i in range(len(val_df) - 1):90 val_ind.append(0)91 ps = PredefinedSplit(test_fold=np.concatenate((train_ind, val_ind)))92 train_df = pd.concat([train_only_df, val_df])93 for k, v in classifiers.items():94 classifier = v['clf']95 if 'random_state' in classifier.get_params().keys():96 classifier = classifier.set_params(**{'random_state': run})97 # add pos_neg ratio to XGBoost params98 if k == 'XGBoost':99 v['params']['scale_pos_weight']: [1, pos_neg]100 model = RandomizedSearchCV(cv=ps, estimator=classifier, param_distributions=v['params'],101 random_state=42, n_jobs=4, scoring='f1', n_iter=500, pre_dispatch=8,102 return_train_score=True)103 feats_train = scipy.sparse.vstack(train_df[feature_combination + '_wordcooc'])104 labels_train = train_df['label']105 feats_gs = scipy.sparse.vstack(gs_df[feature_combination + '_wordcooc'])106 labels_gs = gs_df['label']107 model.fit(feats_train, labels_train)108 parameters = model.best_params_109 score_names = ['mean_train_score', 'std_train_score', 'mean_test_score', 'std_test_score']110 scores = {}111 score_string = ''112 for name in score_names:113 scores[name] = model.cv_results_[name][model.best_index_]114 score_string = score_string + name + ': ' + str(scores[name]) + ' '115 if k == 'LogisticRegression' or k == 'LinearSVC':116 most_important_features = model.best_estimator_.coef_117 word_importance = zip(words[feature_combination], most_important_features[0].tolist())118 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)119 if k == 'RandomForest' or k == 'DecisionTree':120 most_important_features = model.best_estimator_.feature_importances_121 word_importance = zip(words[feature_combination], most_important_features.tolist())122 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)123 if k == 'NaiveBayes':124 word_importance = ''125 if k == 'XGBoost':126 most_important_features = model.best_estimator_.feature_importances_127 word_importance = zip(words[feature_combination], most_important_features.tolist())128 word_importance = sorted(word_importance, key=lambda importance: importance[1], reverse=True)129 if k == 'LogisticRegression':130 learner = LogisticRegression(random_state=run, solver='liblinear', **parameters)131 elif k == 'NaiveBayes':132 learner = BernoulliNB()133 elif k == 'DecisionTree':134 learner = DecisionTreeClassifier(random_state=run, **parameters)135 elif k == 'LinearSVC':136 learner = LinearSVC(random_state=run, dual=False, **parameters)137 elif k == 'RandomForest':138 learner = RandomForestClassifier(random_state=run, n_jobs=4, **parameters)139 elif k == 'XGBoost':140 learner = xgb.XGBClassifier(random_state=run, n_jobs=4, **parameters)141 else:142 print('Learner is not a valid option')143 break144 model = learner145 feats_train = scipy.sparse.vstack(train_only_df[feature_combination + '_wordcooc'])146 labels_train = train_only_df['label']147 start = time.time()148 model.fit(feats_train, labels_train)149 end = time.time()150 train_time = end - start151 start = time.time()152 preds_gs = model.predict(feats_gs)153 end = time.time()154 pred_time = end - start155 gs_report = classification_report(labels_gs, preds_gs, output_dict=True)156 if write_test_set_for_inspection:157 out_path = '../../../data/processed/wdc-lspc/inspection/{}/wordcooc/'.format(experiment_name)158 os.makedirs(os.path.dirname(out_path), exist_ok=True)159 file_name = '_'.join(160 [os.path.basename(train_set), os.path.basename(test_set), k, feature_combination])161 file_name = file_name.replace('.csv', '')162 file_name += f'_{run}.pkl.gz'163 test_inspection_df = gs_df.copy()164 if k == 'LinearSVC':165 proba_gs = model.decision_function(feats_gs).tolist()166 else:167 proba_gs = model.predict_proba(feats_gs).tolist()168 test_inspection_df['pred'] = preds_gs169 test_inspection_df['Class Prob'] = proba_gs170 test_inspection_df.to_pickle(out_path + file_name, compression='gzip')171 with open('../../../reports/wordcooc/{}/{}_{}.csv'.format(experiment_name, report_train_name,172 report_test_name), "a") as f:173 f.write(feature_combination + '#####' + k + '#####' + str(174 scores['mean_train_score']) + '#####' + str(scores['std_train_score'])175 + '#####' + str(scores['mean_test_score']) + '#####' + str(176 scores['std_test_score']) + '#####' + str(gs_report['1']['precision']) + '#####' + str(177 gs_report['1']['recall']) + '#####' + str(gs_report['1']['f1-score'])178 + '#####' + str(parameters) + '#####' + str(train_time) + '#####' + str(pred_time)179 + '#####' + str(word_importance[180 0:100]) + '#####' + experiment_name + '#####' + report_train_name + '#####' + report_test_name + '\n')181if __name__ == '__main__':182 # learning-curve experiment183 feature_combinations = ['title', 'title+description', 'title+description+brand',184 'title+description+brand+specTableContent']185 experiment_name = 'learning-curve'186 for file in glob.glob('../../../data/processed/wdc-lspc/wordcooc/learning-curve/*'):187 if 'train_' in file and '_gs' not in file:188 valid = file.replace('train_', 'valid_')189 test_cat = '_'.join(os.path.basename(file).split('_')[:2])190 test = os.path.basename(file)191 test = test.replace('.pkl.gz', '_{}_gs.pkl.gz'.format(test_cat))192 test = '../../../data/processed/wdc-lspc/wordcooc/learning-curve/{}'.format(test)193 run_wordcooc(file, valid, test, feature_combinations, classifiers, experiment_name,...
test_logger.py
Source:test_logger.py
...30 self.results = []31 self.test_case_classes = set()32 def test_start(self, result):33 self.test_case_classes.add((result['method']['module'], result['method']['class']))34 self.report_test_name(result['method'])35 def test_complete(self, result):36 self.report_test_result(result)37 self.results.append(result)38 if not result['success']:39 self.report_failure(result)40 def fixture_start(self, result):41 self.test_case_classes.add((result['method']['module'], result['method']['class']))42 def class_teardown_complete(self, result):43 if not result['success']:44 self.report_test_name(result['method'])45 self.report_test_result(result)46 self.results.append(result)47 def report(self):48 # All the TestCases have been run - now collate results by status and log them49 results_by_status = collections.defaultdict(list)50 for result in self.results:51 if result['success']:52 results_by_status['successful'].append(result)53 elif result['failure'] or result['error']:54 results_by_status['failed'].append(result)55 elif result['interrupted']:56 results_by_status['interrupted'].append(result)57 else:58 results_by_status['unknown'].append(result)59 if self.options.summary_mode:60 self.report_failures(results_by_status['failed'])61 self.report_stats(len(self.test_case_classes), **results_by_status)62 if len(self.results) == 0:63 return False64 else:65 return (66 (67 len(results_by_status['failed']) +68 len(results_by_status['interrupted']) +69 len(results_by_status['unknown'])70 ) == 071 )72 def report_test_name(self, test_method):73 pass74 def report_test_result(self, result):75 pass76 def report_failures(self, failed_results):77 if failed_results:78 self.heading('FAILURES', 'The following tests are expected to pass.')79 for result in failed_results:80 self.failure(result)81 else:82 # throwing this in so that someone looking at the bottom of the83 # output won't have to scroll up to figure out whether failures84 # were expected or not.85 self.heading('FAILURES', 'None!')86 def report_failure(self, result):87 pass88 def report_stats(self, test_case_count, all_results, failed_results, unknown_results):89 pass90 def _format_test_method_name(self, test_method):91 """Take a test method as input and return a string for output"""92 if test_method['module'] != '__main__':93 return "%s %s.%s" % (test_method['module'], test_method['class'], test_method['name'])94 else:95 return "%s.%s" % (test_method['class'], test_method['name'])96class TextTestLogger(TestLoggerBase):97 def __init__(self, options, stream=sys.stdout):98 super(TextTestLogger, self).__init__(options, stream)99 # Checking for color support isn't as fun as we might hope. We're100 # going to use the command 'tput colors' to get a list of colors101 # supported by the shell. But of course we if this fails terribly,102 # we'll want to just fall back to no colors103 self.use_color = False104 # if TERM is not present in environ, tput prints to stderr105 # if tput's stderr is a pipe, it lies.106 if sys.stdin.isatty() and 'TERM' in os.environ:107 try:108 output = subprocess.check_output(('tput', 'colors'))109 if int(output.strip()) >= 8:110 self.use_color = True111 except Exception as e:112 if self.options.verbosity >= VERBOSITY_VERBOSE:113 self.writeln("Failed to find color support: %r" % e)114 def write(self, message):115 """Write a message to the output stream, no trailing newline"""116 if six.PY2:117 self.stream.write(message.encode('UTF-8') if isinstance(message, six.text_type) else message)118 else:119 self.stream.write(message.decode('UTF-8') if isinstance(message, bytes) else message)120 self.stream.flush()121 def writeln(self, message):122 """Write a message and append a newline"""123 self.write(message)124 self.write('\n')125 BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(30, 38)126 def _colorize(self, message, color=CYAN):127 if not color or not self.use_color:128 return message129 else:130 start_color = '\033[1;%sm' % color131 end_color = '\033[m'132 return start_color + message + end_color133 def test_discovery_failure(self, exc):134 self.writeln(self._colorize("DISCOVERY FAILURE!", self.MAGENTA))135 self.writeln("There was a problem importing one or more tests:")136 self.writeln(str(exc))137 def report_test_name(self, test_method):138 if self.options.verbosity >= VERBOSITY_VERBOSE:139 self.write("%s ... " % self._format_test_method_name(test_method))140 def report_test_result(self, result):141 if self.options.verbosity > VERBOSITY_SILENT:142 if result['success']:143 if result['previous_run']:144 status = "flaky"145 else:146 status = "success"147 elif result['failure']:148 status = "fail"149 elif result['error']:150 status = "error"151 elif result['interrupted']:...
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