def main():
"""
Get data from db and save it as csv
"""
# Helpers
bq = bqhandler.BQHandler()
io = IO(gs_bucket=options.gs_bucket)
viz = Viz(io)
# Configuration
starttime, endtime = io.get_dates(options)
logging.info('Using dataset {} and time range {} - {}'.format(
options.feature_dataset, starttime.strftime('%Y-%m-%d'),
endtime.strftime('%Y-%m-%d')))
all_param_names = options.label_params + options.feature_params + options.meta_params
aggs = io.get_aggs_from_param_names(options.feature_params)
# Initialise classifier
if hasattr(options, 'classifier_file'):
classifier = io.load_scikit_model(options.classifier_file)
else:
if options.classifier == 'svc':
params = {
'kernel': options.kernel,
'gamma': options.gamma,
'C': options.penalty,
'probability': options.probability
}
classifier = SVC(**params)
elif options.classifier == 'rfc':
classifier = RandomForestClassifier(n_jobs=-1)
else:
raise (
'Model not specificied or wrong. Add "classifier: bgm" to config file.'
)
# Initialise regression model
if options.regression == 'rfr':
model = RandomForestRegressor(
n_estimators=options.n_estimators,
n_jobs=-1,
min_samples_leaf=options.min_samples_leaf,
min_samples_split=options.min_samples_split,
max_features=options.max_features,
max_depth=options.max_depth,
bootstrap=options.bootstrap)
regressor = _trans.Regressor(model=model)
else:
raise (
'Model not specificied or wrong. Add "classifier: bgm" to config file.'
)
# Initialise transformer
transformer = _trans.Selector(classifier=classifier)
# Initialise pipeline
pipe = Pipeline([('selector', transformer), ('regression', regressor)])
sum_columns = ['delay']
if options.reason_code_table is not None:
sum_columns = ['count']
# Pick only selected month
where = {}
if options.pick_month is not None:
where = {'EXTRACT(MONTH from time)': options.pick_month}
logging.info('Reading data...')
bq.set_params(loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.feature_table,
parameters=all_param_names,
reason_code_table=options.reason_code_table,
where=where)
data = bq.get_rows(starttime, endtime)
data = io.filter_train_type(labels_df=data,
train_types=options.train_types,
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=sum_columns,
aggs=aggs)
data.sort_values(by=['time', 'trainstation'], inplace=True)
logging.info('Processing {} rows...'.format(len(data)))
# Filter only timesteps with large distribution in the whole network
if options.filter_delay_limit is not None:
data = io.filter_delay_with_limit(data, options.filter_delay_limit)
# Binary class
logging.info('Adding binary class to the dataset with limit {}...'.format(
options.delay_limit))
data['class'] = data['delay'].map(lambda x: 1
if x > options.delay_limit else -1)
# Separate train and validation sets
data_train, data_test = train_test_split(data, test_size=0.3)
# Balance
if options.balance:
logging.info('Balancing training data...')
count = data_train.groupby('class').size().min()
# SVC can't handle more than 50 000 samples
if options.classifier == 'svc': count = min(count, 50000)
data_train = pd.concat([
data_train.loc[data_train['class'] == -1].sample(n=count),
data_train.loc[data_train['class'] == 1].sample(n=count)
])
logging.info('Train data:')
io.log_class_dist(data_train.loc[:, 'class'].values, labels=[-1, 1])
logging.info('Test data:')
io.log_class_dist(data_test.loc[:, 'class'].values, labels=[-1, 1])
# Adding month
if options.month:
logging.info('Adding month to the datasets...')
data_train['month'] = data_train.loc[:, 'time'].map(lambda x: x.month)
data_test['month'] = data_test.loc[:, 'time'].map(lambda x: x.month)
options.feature_params.append('month')
data_train.set_index('time', inplace=True)
y_train = data_train.loc[:, ['delay', 'class']].astype(np.int32).values
y_test = data_test.loc[:, ['delay', 'class']].astype(np.int32).values
X_train = data_train.loc[:,
options.feature_params].astype(np.float32).values
X_test = data_test.loc[:, options.feature_params].astype(np.float32).values
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# io.log_class_dist(y_train[:,1], [-1,1])
# If asked, save used train and test splits into big query
if options.save_data:
tname = options.model + '_' + options.feature_dataset + '_' + options.config_name + '_train'
bq.nparray_to_table([X_train, y_train],
[options.feature_params, ['delay', 'class']],
options.project, options.feature_dataset, tname)
tname = options.model + '_' + options.feature_dataset + '_' + options.config_name + '_test'
bq.nparray_to_table([X_test, y_test],
[options.feature_params, ['delay', 'class']],
options.project, options.feature_dataset, tname)
if options.normalize:
logging.info('Normalizing data...')
if hasattr(options, 'xscaler_file'):
scaler = io.load_scikit_model(options.xscaler_file)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
else:
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
if options.cv:
logging.info('Doing random search for hyper parameters...')
raise ("No param_grid set for given model ({})".format(
options.regression))
random_search = RandomizedSearchCV(model,
param_distributions=param_grid,
n_iter=int(options.n_iter_search),
n_jobs=-1)
random_search.fit(X_train, y_train)
logging.info("RandomizedSearchCV done.")
fname = options.output_path + '/random_search_cv_results.txt'
report_cv_results(random_search.cv_results_, fname)
io._upload_to_bucket(filename=fname, ext_filename=fname)
sys.exit()
else:
logging.info('Training...')
transformer.set_y(y_train[:, 0])
regressor.set_classifier(transformer)
pipe.fit(X_train, y_train[:, 1])
# Metrics
y_pred_proba = pipe.steps[0][1].predict_proba(X_test)
y_pred = pipe.steps[0][1].predict(X_test, type='int')
io.save_scikit_model(pipe,
filename=options.save_file,
ext_filename=options.save_file)
# Classification performance
y_test_class = y_test[:, 1]
acc = accuracy_score(y_test_class, y_pred)
precision = precision_score(y_test_class, y_pred, average='micro')
recall = recall_score(y_test_class, y_pred, average='micro')
f1 = f1_score(y_test_class, y_pred, average='micro')
logging.info('Classification accuracy: {}'.format(acc))
logging.info('Classification precision: {}'.format(precision))
logging.info('Classification recall: {}'.format(recall))
logging.info('Classification F1 score: {}'.format(f1))
io.log_class_dist(y_pred, [-1, 1])
# Confusion matrices
fname = '{}/confusion_matrix_validation.png'.format(options.output_path)
viz.plot_confusion_matrix(y_test_class,
y_pred,
np.arange(2),
filename=fname)
fname = '{}/confusion_matrix_validation_normalised.png'.format(
options.output_path)
viz.plot_confusion_matrix(y_test_class,
y_pred,
np.arange(2),
True,
filename=fname)
# Precision-recall curve
fname = '{}/precision-recall-curve.png'.format(options.output_path)
viz.prec_rec_curve(y_test_class, y_pred_proba, filename=fname)
# ROC
fname = '{}/roc.png'.format(options.output_path)
viz.plot_binary_roc(y_test_class, y_pred_proba, filename=fname)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(scaler, filename=fname, ext_filename=fname)
if options.regression == 'rfr':
fname = options.output_path + '/rfc_feature_importance.png'
viz.rfc_feature_importance(pipe.steps[1][1].get_feature_importances(),
fname,
feature_names=options.feature_params)
# Regression performance
#y_pred = pipe.steps[1][1].predict(X_test)
y_test_reg = y_test[:, 0]
pipe.set_params(selector__full=True)
y_pred = pipe.predict(X_test, full=True)
rmse = np.sqrt(mean_squared_error(y_test_reg, y_pred))
mae = mean_absolute_error(y_test_reg, y_pred)
r2 = r2_score(y_test_reg, y_pred)
logging.info('Regression RMSE: {}'.format(rmse))
logging.info('Regression MAE: {}'.format(mae))
logging.info('Regression R2 score: {}'.format(r2))
error_data = {
'acc': [acc],
'precision': [precision],
'recall': [recall],
'f1': [f1],
'rmse': [rmse],
'mae': [mae],
'r2': [r2]
}
fname = '{}/training_time_classification_validation_errors.csv'.format(
options.output_path)
io.write_csv(error_data, filename=fname, ext_filename=fname)
############################################################################
# EVALUATE
############################################################################
if options.evaluate:
logging.info('Loading test data...')
test_data = bq.get_rows(dt.datetime.strptime('2010-01-01', "%Y-%m-%d"),
dt.datetime.strptime('2019-01-01', "%Y-%m-%d"),
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.test_table,
parameters=all_param_names)
test_data = io.filter_train_type(labels_df=test_data,
train_types=['K', 'L'],
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=['delay'],
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
test_data.sort_values(by=['time', 'trainstation'], inplace=True)
test_data.set_index('time', inplace=True)
logging.info('Test data contain {} rows...'.format(len(test_data)))
logging.info(
'Adding binary class to the test dataset with limit {}...'.format(
options.delay_limit))
#logging.info('Adding binary class to the dataset with limit {}...'.format(limit))
#data['class'] = data['count'].map(lambda x: 1 if x > options.delay_count_limit else -1)
test_data['class'] = test_data['delay'].map(
lambda x: 1 if x > options.delay_limit else -1)
io.log_class_dist(test_data.loc[:, 'class'].values, labels=[-1, 1])
if options.month:
logging.info('Adding month to the test dataset...')
test_data['month'] = test_data.index.map(lambda x: x.month)
times = [('2014-01-01', '2014-02-01'), ('2016-06-01', '2016-07-01'),
('2017-02-01', '2017-03-01'), ('2011-02-01', '2017-03-01')]
for start, end in times:
try:
y_pred_proba, y_pred, y = predict_timerange(
test_data, options.feature_params, pipe.steps[0][1],
scaler, start, end)
perf_metrics(y_pred_proba, y_pred, y, start, end, viz, io)
except EmptyDataError:
logging.info('No data for {} - {}'.format(start, end))
def main():
"""
Get data from db and save it as csv
"""
# Helpers
bq = bqhandler.BQHandler()
io = IO(gs_bucket=options.gs_bucket)
viz = Viz(io)
predictor = Predictor(io, ModelLoader(io), options)
### OPTIONS ################################################################
# Configuration
starttime, endtime = io.get_dates(options)
logging.info('Using dataset {} and time range {} - {}'.format(options.feature_dataset,
starttime.strftime('%Y-%m-%d'),
endtime.strftime('%Y-%m-%d')))
all_param_names = options.label_params + options.feature_params + options.meta_params
aggs = io.get_aggs_from_param_names(options.feature_params)
### MODELS #################################################################
# Initialise classifier
if hasattr(options, 'classifier_file'):
classifier = io.load_scikit_model(options.classifier_file)
else:
if options.classifier == 'svc':
params = {'kernel': options.kernel, 'gamma': options.gamma, 'C': options.penalty, 'probability': options.probability}
#classifier = SVC(**params)
classifier = SVCClassifier(params, limit=options.class_limit)
elif options.classifier == 'graphsvc':
classifier = GraphSVCClassifier()
graph_data = pd.read_csv(options.graph_data, names=['date', 'start_hour', 'src', 'dst', 'type', 'sum_delay','sum_ahead','add_delay','add_ahead','train_count'])
classifier.fetch_connections(graph_data)
elif options.classifier == 'gaussiannb':
classifier = GaussianNBClassifier()
elif options.classifier == 'lstm':
num_of_features = len(options.feature_params)
if options.month: num_of_features += 1
class_weight=None
if hasattr(options, 'class_weight'):
class_weight=eval(options.class_weight)
params = {'length': options.time_steps, 'batch_size': options.batch_size, 'epochs': options.epochs, 'num_of_features': num_of_features, 'log_dir': options.log_dir, 'class_weight':class_weight}
classifier = LSTMClassifier(**params)
else:
raise('Model not specificied or wrong. Add "classifier: bgm" to config file.')
# Initialise regression model
if options.regression == 'rfr':
regressor = RandomForestRegressor(n_estimators=options.n_estimators,
n_jobs=-1,
min_samples_leaf=options.min_samples_leaf,
min_samples_split=options.min_samples_split,
max_features=options.max_features,
max_depth=options.max_depth,
bootstrap=options.bootstrap
)
#regressor = _trans.Regressor(model=model)
else:
raise('Model not specificied or wrong. Add "classifier: bgm" to config file.')
# Initialise transformer
#transformer = _trans.Selector(classifier=classifier)
# Initialise pipeline
#pipe = Pipeline(
# [('selector', transformer),
# ('regression', regressor)]
#)
### DATA ###################################################################
sum_columns = ['delay']
if 'train_count' in options.meta_params:
sum_columns.append('train_count')
# Pick only selected month
where = {}
if options.pick_month is not None:
where = {'EXTRACT(MONTH from time)': options.pick_month}
logging.info('Reading data...')
bq.set_params(loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.feature_table,
parameters=all_param_names,
locations=options.locations,
only_winters=options.only_winters,
reason_code_table=options.reason_code_table,
where=where)
data = bq.get_rows(starttime,
endtime)
data = io.filter_train_type(labels_df=data,
train_types=options.train_types,
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=sum_columns,
aggs=aggs)
data['delay'] = data.loc[:, 'delay'].replace(-99, np.nan)
data.sort_values(by=['trainstation', 'time'], inplace=True)
logging.info('Processing {} rows...'.format(len(data)))
# Filter only timesteps with large distribution in the whole network
if options.filter_delay_limit is not None:
data = io.filter_delay_with_limit(data, options.filter_delay_limit)
# Binary class
logging.info('Adding binary class to the dataset with limit {}...'.format(options.delay_limit))
def set_class(x):
if x > options.delay_limit:
return binary_labels[1]
elif x < options.delay_limit:
return binary_labels[0]
return np.nan
data['class'] = data['delay'].map(lambda x: set_class(x))
# Separate train and validation sets
data_train, data_test = train_test_split(data, test_size=0.3, shuffle=False)
# Balance
if options.balance:
logging.info('Balancing training data...')
count = data_train.groupby('class').size().min()
# SVC can't handle more than 50 000 samples
if options.classifier == 'svc': count = min(count, 50000)
data_train = pd.concat([data_train.loc[data_train['class'] == 0].sample(n=count),
data_train.loc[data_train['class'] == 1].sample(n=count)])
logging.info('Train data:')
io.log_class_dist(data_train.loc[:, 'class'].values, labels=binary_labels)
logging.info('Test data:')
io.log_class_dist(data_test.loc[:, 'class'].values, labels=binary_labels)
# Adding month
if options.month:
logging.info('Adding month to the datasets...')
data_train['month'] = data_train.loc[:,'time'].map(lambda x: x.month)
data_test['month'] = data_test.loc[:,'time'].map(lambda x: x.month)
options.feature_params.append('month')
#data_train.set_index('time', inplace=True)
#y_train_class = data_train.loc[:,['class']].astype(np.int32).values.ravel()
#y_train_delay = data_train.loc[:,['delay']].astype(np.int32).values.ravel()
y_train_class = data_train.loc[:,['class']].values.ravel()
y_train_delay = data_train.loc[:,['delay']].values.ravel()
#y_test_class = data_test.loc[:,['class']].astype(np.int32).values.ravel()
#y_test_delay = data_test.loc[:,['delay']].astype(np.int32).values.ravel()
y_test_class = data_test.loc[:,['class']].values.ravel()
y_test_delay = data_test.loc[:,['delay']].values.ravel()
X_train = data_train.loc[:,options.feature_params].astype(np.float32).values
X_test = data_test.loc[:,options.feature_params].astype(np.float32).values
if options.smote:
logging.info('Smoting...')
sm = SMOTE()
X_train_class, y_class = sm.fit_resample(X_train, y_train_class)
io.log_class_dist(y_class, labels=binary_labels)
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# io.log_class_dist(y_train[:,1], [-1,1])
# If asked, save used train and test splits into big query
if options.save_data:
tname = options.model+'_'+options.feature_dataset+'_'+options.config_name+'_train'
columns = [options.feature_params, ['delay'], ['class']]
bq.nparray_to_table([X_train, y_train_class, y_train_delay],
columns,
options.project,
options.feature_dataset,
tname
)
tname = options.model+'_'+options.feature_dataset+'_'+options.config_name+'_test'
bq.nparray_to_table([X_test, y_test_class, y_test_delay],
columns,
options.project,
options.feature_dataset,
tname
)
if options.normalize:
logging.info('Normalizing data...')
#scale=(0,1)
if hasattr(options, 'xscaler_file'):
xscaler = io.load_scikit_model(options.xscaler_file)
X_train = xscaler.transform(X_train)
X_test = xscaler.transform(X_test)
else:
xscaler = MinMaxScaler(feature_range=(-1,1))
#xscaler = StandardScaler()
X_train = xscaler.fit_transform(X_train)
X_test = xscaler.transform(X_test)
fname = options.save_path+'/xscaler.pkl'
io.save_scikit_model(xscaler, fname, fname)
if hasattr(options, 'yscaler_file'):
yscaler = io.load_scikit_model(options.yscaler_file)
y_train_delay = yscaler.transform(y_train_delay)
y_test_delay = yscaler.transform(y_test_delay)
else:
#yscaler = MinMaxScaler(feature_range=(0,1))
yscaler=StandardScaler()
y_train_delay = yscaler.fit_transform(y_train_delay.reshape(-1,1)).ravel()
y_test_delay = yscaler.transform(y_test_delay.reshape(-1,1)).ravel()
fname = options.save_path+'/yscaler.pkl'
io.save_scikit_model(yscaler, fname, fname)
data_train.loc[:,options.feature_params].to_csv('data/x_train.csv', index=False)
data_test.loc[:,options.feature_params].to_csv('data/x_test.csv', index=False)
data_train.loc[:,['class']].fillna(-99).astype(np.int).to_csv('data/y_train.csv', index=False)
data_test.loc[:,['class']].fillna(-99).astype(np.int).to_csv('data/y_test.csv', index=False)
sys.exit()
### TRAIN ##################################################################
if options.cv:
logging.info('Doing random search for hyper parameters...')
raise("No param_grid set for given model ({})".format(options.regression))
random_search = RandomizedSearchCV(model,
param_distributions=param_grid,
n_iter=int(options.n_iter_search),
n_jobs=-1)
random_search.fit(X_train, y_train)
logging.info("RandomizedSearchCV done.")
fname = options.output_path+'/random_search_cv_results.txt'
report_cv_results(random_search.cv_results_, fname)
io._upload_to_bucket(filename=fname, ext_filename=fname)
sys.exit()
else:
logging.info('Training classifier...')
if options.classifier == 'graphsvc':
classifier.fit(X_train, y_train_class, stations=data_train.loc[:, 'trainstation'].values)
else:
history = classifier.fit(X_train, y_train_class, X_test, y_test_class)
# Save classifier
if options.classifier == 'lstm':
history_fname = options.save_path+'/history.pkl'
fname = options.save_path+'/classifier.h5'
io.save_keras_model(fname, history_fname, classifier, history.history)
else:
fname = options.save_path+'/classifier.pkl'
io.save_scikit_model(classifier, filename=fname, ext_filename=fname)
# Drop data with no delay information
X_train = X_train[~np.isnan(y_train_delay)]
y_train_delay = y_train_delay[~np.isnan(y_train_delay)]
y_train_class = y_train_class[~np.isnan(y_train_class)]
y_pred_train_bin = classifier.predict(X_train, type='bool')
# debug
#y_pred_train_bin
#indices = np.random.choice(np.arange(y_pred_train_bin.size),
# replace=False,
# size=int(y_pred_train_bin.size * 0.2))
#y_pred_train_bin[indices] = True
#print('y_pred_train_bin: {}'.format(y_pred_train_bin.shape))
#print('y_train_delay: {}'.format(y_train_delay.shape))
#print('y_train_class: {}'.format(y_train_class.shape))
# Pick only severe values
#y_train_delay_ = y_train_delay[(len(y_train_class)-len(y_pred_train_bin)):]
#X_train_ = X_train[(len(y_train_class)-len(y_pred_train_bin)):]
y_train_delay_ = y_train_delay[(len(y_train_delay)-len(y_pred_train_bin)):]
X_train_ = X_train[(len(y_train_delay)-len(y_pred_train_bin)):]
#print('y_train_delay_: {}'.format(y_train_delay_.shape))
y_train_severe = y_train_delay_[y_pred_train_bin]
X_train_severe = X_train_[y_pred_train_bin]
logging.info('Training regressor...')
regressor.fit(X_train_severe, y_train_severe)
# Save regressor
io.save_scikit_model(regressor, filename=options.save_file, ext_filename=options.save_file)
# Learning history
# fname = options.output_path+'/learning_over_time.png'
# viz.plot_nn_perf(history.history, metrics={'Error': {'mean_squared_error': 'MSE',
# 'mean_absolute_error': 'MAE'}},
# filename=fname)
### RESULTS FOR VALIDATION SET #############################################
# Drop data with missing delay
X_test = X_test[~np.isnan(y_test_class)]
y_test_class = y_test_class[~np.isnan(y_test_class)]
data_test = data_test[~np.isnan(data_test.delay)]
# Metrics
#y_pred_proba = classifier.predict_proba(X_test)
y_pred = classifier.predict(X_test)
y_pred_proba = classifier.y_pred_proba
#y_test_delay = y_test_delay[~np.isnan(y_test_delay)]
# Classification performance
# LSTM don't have first timesteps
y_test_class = y_test_class[(len(X_test)-len(y_pred)):]
acc = accuracy_score(y_test_class, y_pred)
precision = precision_score(y_test_class, y_pred, average='micro')
recall = recall_score(y_test_class, y_pred, average='micro')
f1 = f1_score(y_test_class, y_pred, average='micro')
logging.info('Classification accuracy: {}'.format(acc))
logging.info('Classification precision: {}'.format(precision))
logging.info('Classification recall: {}'.format(recall))
logging.info('Classification F1 score: {}'.format(f1))
io.log_class_dist(y_pred, binary_labels)
# Confusion matrices
fname = '{}/confusion_matrix_validation.png'.format(options.output_path)
viz.plot_confusion_matrix(y_test_class, y_pred, np.arange(2), filename=fname)
fname = '{}/confusion_matrix_validation_normalised.png'.format(options.output_path)
viz.plot_confusion_matrix(y_test_class, y_pred, np.arange(2), True, filename=fname)
# Precision-recall curve
fname = '{}/precision-recall-curve_validation.png'.format(options.output_path)
viz.prec_rec_curve(y_test_class, y_pred_proba, filename=fname)
# ROC
fname = '{}/roc_validation.png'.format(options.output_path)
viz.plot_binary_roc(y_test_class, y_pred_proba, filename=fname)
if options.regression == 'rfr':
fname = options.output_path+'/rfc_feature_importance.png'
viz.rfc_feature_importance(regressor.feature_importances_, fname, feature_names=options.feature_params)
# Regression performance
y_pred_reg, y_test_reg = predictor.pred(data=data_test)
#y_test_reg = y_test[(len(y_test)-len(y_pred)):,0]
rmse = np.sqrt(mean_squared_error(y_test_reg, y_pred))
mae = mean_absolute_error(y_test_reg, y_pred)
r2 = r2_score(y_test_reg, y_pred)
logging.info('Regression RMSE: {}'.format(rmse))
logging.info('Regression MAE: {}'.format(mae))
logging.info('Regression R2 score: {}'.format(r2))
error_data = {'acc': [acc],
'precision': [precision],
'recall': [recall],
'f1': [f1],
'rmse': [rmse],
'mae': [mae],
'r2': [r2]}
fname = '{}/training_time_classification_validation_errors.csv'.format(options.output_path)
io.write_csv(error_data, filename=fname, ext_filename=fname)
############################################################################
# EVALUATE
############################################################################
if options.evaluate:
logging.info('Loading test data...')
test_data = bq.get_rows(dt.datetime.strptime('2010-01-01', "%Y-%m-%d"),
dt.datetime.strptime('2019-01-01', "%Y-%m-%d"),
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.test_table,
reason_code_table=options.reason_code_table,
locations=options.locations,
parameters=all_param_names)
test_data = io.filter_train_type(labels_df=test_data,
train_types=['K','L'],
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=['delay'],
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
#test_data.sort_values(by=['time', 'trainstation'], inplace=True)
# Filter only timesteps with large distribution in the whole network
if options.filter_delay_limit is not None:
test_data = io.filter_delay_with_limit(test_data, options.filter_delay_limit)
test_data.set_index('time', inplace=True)
logging.info('Test data contain {} rows...'.format(len(test_data)))
logging.info('Adding binary class to the test dataset with limit {}...'.format(options.delay_limit))
test_data['class'] = test_data['delay'].map(lambda x: binary_labels[1] if x > options.delay_limit else binary_labels[0])
io.log_class_dist(test_data.loc[:, 'class'].values, labels=binary_labels)
if options.month:
logging.info('Adding month to the test dataset...')
test_data['month'] = test_data.index.map(lambda x: x.month)
times = [('2014-01-01', '2014-02-01'), ('2016-06-01', '2016-07-01'), ('2017-02-01', '2017-03-01'), ('2011-02-01', '2011-03-01')]
for start, end in times:
try:
y_pred_proba, y_pred, y = predict_timerange(test_data, options.feature_params, classifier, xscaler, start, end)
perf_metrics(y_pred_proba, y_pred, y, start, end, viz, io)
except EmptyDataError:
logging.info('No data for {} - {}'.format(start, end))
def main():
"""
Get data from db and save it as csv
"""
bq = BQHandler()
io = IO(gs_bucket=options.gs_bucket)
viz = Viz(io)
starttime, endtime = io.get_dates(options)
logging.info('Using dataset {} and time range {} - {}'.format(
options.feature_dataset, starttime.strftime('%Y-%m-%d'),
endtime.strftime('%Y-%m-%d')))
all_param_names = options.label_params + options.feature_params + options.meta_params
aggs = io.get_aggs_from_param_names(options.feature_params)
if options.model == 'bgm':
model = BayesianGaussianMixture(
weight_concentration_prior_type="dirichlet_process",
n_components=options.n_components)
elif options.model == 'gaussiannb':
model = GaussianNB()
elif options.model == 'rfc':
model = RandomForestClassifier(n_jobs=-1)
elif options.model == 'svc':
params = {'kernel': 'rbf', 'gamma': 0.5, 'C': 1, 'probability': True}
model = SVC(**params)
else:
raise (
'Model not specificied or wrong. Add for example "model: bgm" to config file.'
)
if options.pca:
ipca = IncrementalPCA(n_components=options.pca_components,
whiten=options.whiten,
copy=False)
sum_columns = ['delay']
if options.reason_code_table is not None:
sum_columns = ['count']
logging.info('Reading data...')
data = bq.get_rows(starttime,
endtime,
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.feature_table,
parameters=all_param_names,
reason_code_table=options.reason_code_table,
only_winters=options.only_winters)
data = io.filter_train_type(labels_df=data,
train_types=options.train_types,
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=sum_columns,
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
data.sort_values(by=['time', 'trainstation'], inplace=True)
data.set_index('time', inplace=True)
logging.info('Data contain {} rows...'.format(len(data)))
logging.info('Adding binary class to the dataset with limit {}...'.format(
options.delay_limit))
#logging.info('Adding binary class to the dataset with limit {}...'.format(limit))
#data['class'] = data['count'].map(lambda x: 1 if x > options.delay_count_limit else -1)
data['class'] = data['delay'].map(lambda x: 1
if x > options.delay_limit else -1)
io.log_class_dist(data.loc[:, 'class'].values, labels=[-1, 1])
if options.balance:
logging.info('Balancing dataset...')
count = data.groupby('class').size().min()
data = pd.concat([
data.loc[data['class'] == -1].sample(n=count),
data.loc[data['class'] == 1].sample(n=count)
])
io.log_class_dist(data.loc[:, 'class'].values, labels=[-1, 1])
if options.month:
logging.info('Adding month to the dataset...')
data['month'] = data.index.map(lambda x: x.month)
options.feature_params.append('month')
target = data.loc[:, 'class'].astype(np.int32).values.ravel()
features = data.loc[:, options.feature_params].astype(np.float32).values
X_train, X_test, y_train, y_test = train_test_split(features,
target,
test_size=0.3)
if options.normalize:
logging.info('Normalizing data...')
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
logging.debug('Features shape after pre-processing: {}'.format(
X_train.shape))
if options.cv:
logging.info('Doing random search for hyper parameters...')
if options.model == 'bgm':
param_grid = {
"n_components": [1, 2, 4, 8, 16],
"covariance_type": ['full', 'tied', 'diag', 'spherical'],
"init_params": ['kmeans', 'random']
}
elif options.model == 'rfc':
raise ("Not implemented. Get back to work!")
elif options.model == 'svc':
features_compinations = [
[
'lat', 'lon', 'pressure', 'max_temperature',
'min_temperature', 'mean_temperature', 'mean_dewpoint',
'mean_humidity', 'mean_winddirection', 'mean_windspeedms',
'max_windgust', 'max_precipitation1h', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'max_temperature', 'min_temperature',
'mean_temperature', 'mean_dewpoint', 'mean_humidity',
'mean_winddirection', 'mean_windspeedms', 'max_windgust',
'max_precipitation1h', 'max_snowdepth', 'max_n', 'min_vis',
'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_windgust',
'max_precipitation1h', 'max_snowdepth', 'max_n', 'min_vis',
'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation3h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'max_n', 'min_vis', 'min_clhb', 'max_precipitation1h'
],
[
'pressure', 'min_temperature', 'mean_dewpoint',
'mean_winddirection', 'mean_windspeedms', 'max_snowdepth',
'min_vis', 'max_precipitation1h'
],
[
'pressure', 'min_temperature', 'mean_winddirection',
'mean_windspeedms', 'max_snowdepth', 'max_precipitation1h'
]
]
param_grid = {
"C": [0.0001, 0.001, 0.01, 0.1, 1],
"kernel": ['rbf', 'poly'],
"degree": [2, 3],
"gamma": [0.5],
"coef0": [0.1],
"probability": [True],
"features": features_compinations
}
from lib.svc import SVCF
model = SVCF(all_features=options.feature_params)
else:
raise ("No param_grid set for given model ({})".format(
options.model))
print(model.get_params().keys())
ftwo_scorer = make_scorer(fbeta_score, beta=2)
scoring = {
'accuracy': 'accuracy',
'precision': 'precision',
'recall': 'recall',
'f1': 'f1',
'f2': ftwo_scorer
}
random_search = RandomizedSearchCV(model,
param_distributions=param_grid,
n_iter=int(options.n_iter_search),
verbose=1,
scoring=scoring,
refit='recall',
n_jobs=-1)
random_search.fit(X_train, y_train)
logging.info("RandomizedSearchCV done.")
scores = ['accuracy', 'precision', 'recall', 'f1', 'f2']
fname = options.output_path + '/random_search_cv_results.txt'
io.report_cv_results(random_search.cv_results_,
scores=scores,
filename=fname,
ext_filename=fname)
model = random_search.best_estimator_
io.save_scikit_model(model,
filename=options.save_file,
ext_filename=options.save_file)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(scaler, filename=fname, ext_filename=fname)
else:
logging.info('Training...')
model.fit(X_train, y_train)
# Save model and xscaler (no reason to save xscaler before the model has fitted as well)
io.save_scikit_model(model,
filename=options.save_file,
ext_filename=options.save_file)
if options.normalize:
fname = options.save_path + '/xscaler.pkl'
io.save_scikit_model(scaler, filename=fname, ext_filename=fname)
# Metrics
y_pred_proba = model.predict_proba(X_test)
y_pred = np.argmax(y_pred_proba, axis=1)
# We want [-1,1] classes as y values are
y_pred[y_pred == 0] = -1
acc = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='binary')
recall = recall_score(y_test, y_pred, average='binary')
f1 = f1_score(y_test, y_pred, average='binary')
logging.info('Accuracy: {}'.format(acc))
logging.info('Precision: {}'.format(precision))
logging.info('Recall: {}'.format(recall))
logging.info('F1 score: {}'.format(f1))
io.log_class_dist(y_pred, labels=[-1, 1])
error_data = {
'acc': [acc],
'precision': [precision],
'recall': [recall],
'f1': [f1]
}
fname = '{}/training_time_validation_errors.csv'.format(
options.output_path)
io.write_csv(error_data, filename=fname, ext_filename=fname)
# Confusion matrices
fname = '{}/confusion_matrix_validation.png'.format(options.output_path)
viz.plot_confusion_matrix(y_test, y_pred, np.arange(2), filename=fname)
fname = '{}/confusion_matrix_validation_normalised.png'.format(
options.output_path)
viz.plot_confusion_matrix(y_test,
y_pred,
np.arange(2),
True,
filename=fname)
# Precision-recall curve
fname = '{}/precision-recall-curve.png'.format(options.output_path)
viz.prec_rec_curve(y_test, y_pred_proba, filename=fname)
# ROC
fname = '{}/roc.png'.format(options.output_path)
viz.plot_binary_roc(y_test, y_pred_proba, filename=fname)
############################################################################
# EVALUATE
############################################################################
if options.evaluate:
logging.info('Loading test data...')
test_data = bq.get_rows(dt.datetime.strptime('2010-01-01', "%Y-%m-%d"),
dt.datetime.strptime('2019-01-01', "%Y-%m-%d"),
loc_col='trainstation',
project=options.project,
dataset=options.feature_dataset,
table=options.test_table,
parameters=all_param_names)
test_data = io.filter_train_type(labels_df=test_data,
train_types=['K', 'L'],
sum_types=True,
train_type_column='train_type',
location_column='trainstation',
time_column='time',
sum_columns=['delay'],
aggs=aggs)
# Sorting is actually not necessary. It's been useful for debugging.
test_data.sort_values(by=['time', 'trainstation'], inplace=True)
test_data.set_index('time', inplace=True)
logging.info('Test data contain {} rows...'.format(len(test_data)))
logging.info(
'Adding binary class to the test dataset with limit {}...'.format(
options.delay_limit))
#logging.info('Adding binary class to the dataset with limit {}...'.format(limit))
#data['class'] = data['count'].map(lambda x: 1 if x > options.delay_count_limit else -1)
test_data['class'] = test_data['delay'].map(
lambda x: 1 if x > options.delay_limit else -1)
io.log_class_dist(test_data.loc[:, 'class'].values, labels=[-1, 1])
if options.month:
logging.info('Adding month to the test dataset...')
test_data['month'] = test_data.index.map(lambda x: x.month)
times = [('2011-02-01', '2011-03-01'), ('2016-06-01', '2016-07-01'),
('2017-02-01', '2017-03-01'), ('2011-02-01', '2017-03-01')]
for start, end in times:
try:
y_pred_proba, y_pred, y = predict_timerange(
test_data, options.feature_params, model, scaler, start,
end)
perf_metrics(y_pred_proba, y_pred, y, start, end, viz, io)
except EmptyDataError:
logging.info('No data for {} - {}'.format(start, end))
