def run_test(self):
driver_source = Driver.DriverSource()
main_driver = driver_source.get_driver(1081)
for n in range(5):
trip = main_driver.get_data_random_trip()
plotTripData(trip.tang_accel_mag, trip.speed)
def run_one(self):
"""
Use drive id: ####
get all his matching trips (~60). Split 40/20 train/test
Get 60 random driver, and for each select a random trip
and gain split 40/20
for each trip, extract:
mean_speed_to_stop; mean_speed_from_stop, mean_accel_inout_bends, mean_trip_speed
Run SVM with RBF kernal and use 4 fold cross val to obtain gamma and C.
See what we get with model in Test. Exceed 90 - go for it with add drivers
:return:
"""
# minimum number of trips to process
MIN_MATCH = 100
# get a particular driver, obtain his/her trips then find the ones that match
driver_source = Driver.DriverSource()
main_driver = driver_source.get_driver(2053)
trip_list = main_driver.get_all_trips()
trips_to_process = main_driver.get_matching_trips()
# fill up the trip collection with other trips, using random trips from
# other drivers if necessary
record_count = len(trips_to_process)
print 'Matching Records:', record_count
if record_count < MIN_MATCH:
trip_ids_now = [trip.Id for trip in trips_to_process]
other_trips = [trip for trip in trip_list if trip.Id not in trip_ids_now]
extra_trips = rnd.sample(other_trips, MIN_MATCH - record_count)
trips_to_process.extend(extra_trips)
record_count = len(trips_to_process)
# get randoom other driver but make sure it is not this one
rand_drivers = driver_source.get_rand_drivers(1)
while rand_drivers[0].id() == main_driver.id():
rand_drivers = driver_source.get_rand_drivers(1)
# add in all the trips for this random driver
rand_trips = rand_drivers[0].get_all_trips()
trips_to_process.extend( rnd.sample(rand_trips,record_count) )
# classify the trips (set Y)
targ = np.full((len(trips_to_process),1),False,dtype=bool)
targ[0:record_count]=True
target = targ.flatten()
# create a vector to hold features that will be calculated
trips_features=np.zeros(shape=[len(trips_to_process),11], dtype=float)
# calc and add in the features...
for idx, trip in enumerate(trips_to_process):
mean_speed, mean_accel, mean_decel = trip.mean_speed_accel_decel()
trips_features[idx,0] = mean_speed
trips_features[idx,1] = mean_accel
trips_features[idx,2] = mean_decel
std_speed, std_accel, st_decel = trip.std_speed_accel_decel()
trips_features[idx,3] = std_speed
trips_features[idx,4] = std_accel
trips_features[idx,5] = st_decel
m_ta_l, s_ta_l = trip.mean_std_tang_accel_left()
trips_features[idx,6] = m_ta_l
trips_features[idx,7] = s_ta_l
acpm = trip.accel_change_per_meter()
trips_features[idx,8] = acpm
m_ac, s_ac = trip.mean_std_accel_changes()
trips_features[idx,9] = m_ac
trips_features[idx,10] = s_ac
# set up our split for train cross validation and test
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
trips_features, target, test_size=0.4)
# set up our grid to find optimal parameters
param_grid = [{'learning_rate': [0.01, 0.1, 1], 'n_estimators': [100,200], 'max_depth': [1,2,3]}]
top_score = 0.0
best_params = None
scores = ['precision', 'recall']
for score in scores:
clf = GridSearchCV(GradientBoostingClassifier(), param_grid, scoring=score)
clf.fit(X_train, y_train)
print("Best parameters set found on training set:")
print()
print(clf.best_estimator_)
print()
for params, mean_score, scores in clf.grid_scores_:
if mean_score > top_score:
top_score = mean_score
best_params = params
print("%0.3f (+/-%0.03f) for %r" % (mean_score, scores.std() / 2, params))
print()
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
y_true, y_pred = y_test, clf.predict(X_test)
print(classification_report(y_true, y_pred))
print()
print
print best_params
def run_process(driver_list, fname):
FEATURE_COUNT = 20
RAND_DRIVER_COUNT = 5
driver_source = Driver.DriverSource()
for the_driver in driver_list:
main_driver = driver_source.get_driver(the_driver)
trip_list = main_driver.get_trips()
record_count = len(trip_list)
# set up the feature and target arrays and set the first 200 targets to True
trips_features=np.zeros(shape=[2 * record_count, FEATURE_COUNT], dtype=float)
targ = np.zeros(shape=[2 * record_count,1],dtype=int)
targ[0:record_count]=1
target = targ.flatten()
# fill out the feature array for the main driver...
for idx, trip in enumerate(trip_list):
p_accel = trip.percentiles_accel()
p_decel = trip.percentiles_decel()
p_speed = trip.percentiles_speed()
p_delta = trip.percentiles_delta_accel()
p_tang = trip.percentiles_tang_accel()
trips_features[idx,0] = p_accel[0]
trips_features[idx,1] = p_accel[1]
trips_features[idx,2] = p_accel[2]
trips_features[idx,3] = p_accel[3]
trips_features[idx,4] = p_decel[0]
trips_features[idx,5] = p_decel[1]
trips_features[idx,6] = p_decel[2]
trips_features[idx,7] = p_decel[3]
trips_features[idx,8] = p_speed[0]
trips_features[idx,9] = p_speed[1]
trips_features[idx,10] = p_speed[2]
trips_features[idx,11] = p_speed[3]
trips_features[idx,12] = p_delta[0]
trips_features[idx,13] = p_delta[1]
trips_features[idx,14] = p_delta[2]
trips_features[idx,15] = p_delta[3]
trips_features[idx,16] = p_tang[0]
trips_features[idx,17] = p_tang[1]
trips_features[idx,18] = p_tang[2]
trips_features[idx,19] = p_tang[3]
# now get bunch random drivers...
rand_drivers = driver_source.get_rand_drivers(RAND_DRIVER_COUNT)
while main_driver in rand_drivers:
rand_drivers = driver_source.get_rand_drivers(RAND_DRIVER_COUNT)
# for each of the rand drivers, complete the feature array
# and train the model on the features.
prob_array = np.zeros(shape=[record_count, RAND_DRIVER_COUNT], dtype=float)
col_idx = -1
for drv_rand in rand_drivers:
for i, trip in enumerate(trip_list):
p_accel = trip.percentiles_accel()
p_decel = trip.percentiles_decel()
p_speed = trip.percentiles_speed()
p_delta = trip.percentiles_delta_accel()
p_tang = trip.percentiles_tang_accel()
idx = record_count + i
trips_features[idx,0] = p_accel[0]
trips_features[idx,1] = p_accel[1]
trips_features[idx,2] = p_accel[2]
trips_features[idx,3] = p_accel[3]
trips_features[idx,4] = p_decel[0]
trips_features[idx,5] = p_decel[1]
trips_features[idx,6] = p_decel[2]
trips_features[idx,7] = p_decel[3]
trips_features[idx,8] = p_speed[0]
trips_features[idx,9] = p_speed[1]
trips_features[idx,10] = p_speed[2]
trips_features[idx,11] = p_speed[3]
trips_features[idx,12] = p_delta[0]
trips_features[idx,13] = p_delta[1]
trips_features[idx,14] = p_delta[2]
trips_features[idx,15] = p_delta[3]
trips_features[idx,16] = p_tang[0]
trips_features[idx,17] = p_tang[1]
trips_features[idx,18] = p_tang[2]
trips_features[idx,19] = p_tang[3]
learning_rate = 0.1
n_estimators = 200
max_depth = 2
scaler = preprocessing.StandardScaler().fit(trips_features)
X_train_scaled = scaler.transform(trips_features)
#clf = RandomForestClassifier()
clf = SVC(C=1, probability=True, kernel='rbf',gamma=0.001)
clf.fit(X_train_scaled, target)
X_pred_scaled = scaler.transform(trips_features[0:record_count,:])
probs = clf.predict_proba(X_pred_scaled)
class_label_index = 0
if clf.classes_[1] == 1:
class_label_index = 1
col_idx += 1
prob_array[:,col_idx][:] = probs[:,class_label_index]
trip_probabilities = np.mean(prob_array,axis=1)
print str(int(main_driver.id())) + ' - output to file....'
with open(fname,'a') as writer:
for trip, prob in zip(trip_list, trip_probabilities):
writer.write( str(int(main_driver.id())) + '_' + str(int(trip.Id)) + ', ' + str(prob) + '\n')
