def run_benchmark(config, classifiers, classifiers_gridparameters):
""" Runs the benchmark code, see voya_config_example for argument explanation
"""
default_config = {
"data_file": None, # input data
"test_df": None, # instead of data_file, give split data
"train_df": None,
"out_path": None,
"num_folds": 5,
"test_size": 0.2,
"num_cores": 1,
"pu_learning": False,
"pu_rand_samp_frac": False,
"verbosity": 0,
"random_forest_tree_plot": False,
"auc_folds": 1,
'u_to_p_ratio': False,
'ranking_Frac': None,
'include_neg_inTrain': True,
'grisearch_metric': 'AUC',
}
default_config.update(config)
config = default_config
set_verbosity_level(config["verbosity"])
voya_logger.info("Starting Benchmark")
out_path = config['out_path']
if out_path is not None:
if not os.path.isdir(out_path):
os.makedirs(out_path)
# If we are given the test / train sets explicitly
test_df = config["test_df"]
train_df = config["train_df"]
if test_df is not None and train_df is not None:
y_test, X_test = datasetup.split_df_labels_features(test_df)
y_train, X_train = datasetup.split_df_labels_features(train_df)
elif config["data_file"] is not None: # or load all the data and auto split
voya_logger.info('loading data from: {}'.format(config['data_file']))
try:
df = datasetup.load_data(config['data_file'])
except IOError: # file doesnt exist, try seeing is its a df instead
df = config['data_file']
voya_logger.info("Input data labels \n{}".format(df.label.value_counts()))
try:
datasetup.scale_dataframe_features(df)
except TypeError: # Got a string as the DF (after IOError)
raise VoyaConfigError('data_file is not a valid path to a file or a Pandas DF, got {}'.format(df))
if config["pu_learning"]: # input of positive, negative and unlabeled labels (1, -1, 0)
voya_logger.info("PU Learning Mode On")
if config["u_to_p_ratio"]:
df = datasetup.downsample_pu_df(df, config["u_to_p_ratio"])
df_test, df_train = datasetup.split_test_train_df_pu(df, config['test_size'],)
assert set(df_test['label'].unique()) == set((1, 0))
y_test, X_test = datasetup.split_df_labels_features(df_test)
y_train, X_train = datasetup.split_df_labels_features(df_train)
else: # input of positive and negative (i.e 1, 0)
X, y = datasetup.split_df_labels_features(df)
X_train, y_train, X_test, y_test = datasetup.get_stratifed_data(y, X, config['test_size'])
else:
raise ValueError("You must give either `test_df` and `train_df` OR `data_file` in config")
results_table_rows = {} # each row is a dict with column_name: value
for clf_name, clf_notoptimized in classifiers.iteritems():
voya_logger.info("Running {}".format(clf_name))
clf_results = {'clf_name': clf_name}
param_grid = classifiers_gridparameters[clf_name]
if param_grid is None:
voya_logger.info('Skipping grid search for {}'.format(clf_name))
voya_logger.debug("clf_notoptimized {}".format(clf_notoptimized))
clf_fitted = clf_notoptimized.fit(X_train, y_train)
else:
voya_logger.info('Performing grid search for {}'.format(clf_name))
skf = sklearn.cross_validation.StratifiedKFold(y_train, n_folds=config['num_folds'])
ranking = voya_plotter.PrInRanking(config['ranking_Frac'], config['desired_retention'])
if (config['gridsearch_metric'] == 'PosRate'):
clf = GridSearchCV(estimator=clf_notoptimized, param_grid=param_grid, cv=skf, scoring=ranking.pr_in_ranking,
n_jobs=config['num_cores'])
elif (config['gridsearch_metric'] == 'Frac'):
clf = GridSearchCV(estimator=clf_notoptimized, param_grid=param_grid, cv=skf, scoring=ranking.frac_to_Xpercent,
n_jobs=config['num_cores'])
else:
clf = GridSearchCV(estimator=clf_notoptimized, param_grid=param_grid, cv=skf, scoring='roc_auc',
n_jobs=config['num_cores'])
clf_fitted = clf.fit(X_train, y_train).best_estimator_
clf_optimal_parameters = clf.best_params_
clf_results['clf_optimal_parameters'] = clf_optimal_parameters
voya_logger.info('Optimal parameters are {}'.format(clf_optimal_parameters))
voya_logger.debug('X = {}'.format(clf_fitted))
y_pred = clf_fitted.predict_proba(X_test)[:, 1]
y_pred_label = clf_fitted.predict(X_test)
clf_results.update({
'y_pred': y_pred,
'y_pred_label': y_pred_label,
'clf': clf_fitted,
'clf_notoptimized': clf_notoptimized,
'X_train': X_train,
'y_train': y_train,
'X_test': X_test,
'y_test': y_test,
'param_grid': param_grid,
})
voya_logger.info("Benchmarking {}".format(clf_name))
benchmarks.all_benchmarks(clf_results, out_path, config["auc_folds"], # TODO (ryan) split this up now into benchmarks and plots?
config['ranking_Frac'])
if out_path is not None: # TODO (ryan) non conforming plots, move to benchmarks
if config["random_forest_tree_plot"] and isinstance(clf_fitted, sklearn.ensemble.RandomForestClassifier):
voya_logger.debug('Generating random forrest plot')
# TODO (ryan) weve hardcoded '2' where the feature start several times, export to var?
feature_names = [colname.replace('url/tech/', '').replace('cid/tech/', '') for colname in
df.columns[2:]]
voya_plotter.plot_trees(clf_results['clf'], feature_names)
results_table_rows[clf_name] = clf_results
voya_logger.info("\n#######\nResults\n#######")
num_positives_y_train = y_train.sum()
voya_logger.info("Training: positives = {}, negatives/unlabelled={}".format(num_positives_y_train,
len(y_train) - num_positives_y_train))
num_positives_y_test = y_test.sum()
voya_logger.info(
"Testing: positives = {}, negatives={}".format(num_positives_y_test, len(y_test) - num_positives_y_test))
results_table = benchmarks.results_dict_to_data_frame(results_table_rows)
voya_logger.info('\n{}'.format(results_table))
return results_table_rows
def run_search_benchmark(config, classifiers, classifiers_gridparameters):
""" This works like run_bench except it calls runbench multiple times varying the fraction of unlabelled in the
sample.
Currently PU learning only, varies the fraction of unlabelled in the classifier as a function of positive
Search parameters are set in the config dictionary. See the code for required config (in addition to that in runbench).
:param config:
:param classifiers:
:param classifiers_gridparameters:
:return:
"""
default_config = {
"data_file": None, # input data
"test_df": None, # instead of data_file, give split data
"train_df": None,
"out_path": None,
"num_folds": 5,
"test_size": 0.2,
"num_cores": 3,
"pu_learning": False,
"pu_rand_samp_frac": False,
"verbosity": 0,
"random_forest_tree_plot": False,
"auc_folds": 1,
'u_to_p_ratio': False,
'voya_mode': 'pusearch',
'search_results_file': '', # csv file that records the results of each run
'soft_search_run': True, # if True builds on the previous results, if false overwrites the results file
'search_range': (0.5, 1, 2), # range of values to run over
'runs_per_search': 3, # number of times to run the search per parameter per classifier
'search_live_plot': False,
'constant_test_train': True, # otherwise will resplit every run_per_search
'test_neg_to_pos_ratio': None,
'includes_neg_inTrain': False,
}
default_config.update(config)
config = default_config
out_path = config['out_path']
if out_path is not None:
if not os.path.isdir(out_path):
os.makedirs(out_path)
if config['constant_test_train']: # Split test / train so we have a constant testing set
try:
df = datasetup.load_data(config['data_file'])
except IOError: # file doesnt exist, try seeing is its a df instead
df = config['data_file']
df_test, df_train = datasetup.split_test_train_df_pu(df, config['test_size'],
test_neg_to_pos_ratio=config['test_neg_to_pos_ratio'],
includes_neg_inTrain=config['includes_neg_inTrain'])
config["test_df"] = df_test
config["train_df"] = df_train
config["data_file"] = None
if not config['runs_per_search'] == 1: # no point doing more if we have a constant test/train
voya_logger.warning('Setting runs_per_search to 1 as constant_test_train is True, change auc_folds instead')
config['runs_per_search'] = 1
save_file = config['search_results_file']
search_range = config['search_range']
voya_logger.info('Starting search benchmark')
if not os.path.exists(save_file) or not config['soft_search_run']:
with open(save_file, 'wb') as f:
if config['constant_test_train']:
f.write('gamma, folds, clf, auc, auc_std, auc_stderr, ranking_Frac, local_auc, local_auc_std, local_auc_stderr, local_pr, frac_to_desRet, frac_to_desRet_stderr\n')
else:
f.write('gamma, clf, auc, local_pr, frac_to_desRet\n')
fig = None
for gamma_num, gamma in enumerate(search_range): # gamma is a single value in the search range
voya_logger.info('Running classifiers for gamma={} ({}/{})'.format(gamma, gamma_num + 1, len(search_range)))
config.update({"u_to_p_ratio": gamma})
if config['constant_test_train']:
config["train_df"] = datasetup.downsample_pu_df(df_train, config["u_to_p_ratio"])
results_dict = run_benchmark(config, classifiers, classifiers_gridparameters)
# Output
csv_output = []
for clf_name in classifiers.keys():
if config['auc_folds']>1:
csv_row = (gamma, results_dict[clf_name]['auc_folds'], clf_name, results_dict[clf_name]['auc_score'],
results_dict[clf_name]['auc_std'], results_dict[clf_name]['auc_std_err'], results_dict[clf_name]['ranking_Frac'],
results_dict[clf_name]['local_auc_score'], results_dict[clf_name]['local_auc_std'],
results_dict[clf_name]['local_auc_std_err'], results_dict[clf_name]['local_pr'],
results_dict[clf_name]['frac_to_ret'], results_dict[clf_name]['frac_to_ret_stderr'])
else:
csv_row = (gamma, clf_name, results_dict[clf_name]['auc_score'], results_dict[clf_name]['local_pr'], results_dict[clf_name]['frac_to_ret'])
csv_output.append(csv_row)
with open(save_file, 'ab') as f:
csv_f = csv.writer(f)
csv_f.writerows(csv_output)
if config['search_live_plot']:
plt.clf()
fig = voya_plotter.pu_search_result(save_file, fig)
plt.draw()
if out_path is not None:
voya_logger.info('Generating prVSranking all methods plot')
voya_plotter.prVSranking_methodComparison(results_dict)
plt.savefig(os.path.join(out_path, 'prVsRankComparison__Gamma__{}.png'.format(gamma)), bbox_inches='tight')
