def get_data_heading(model_id,
driver_id,
repeat,
test=False,
moving_average_window=3,
stops=False,
version=1):
seed = random.Random(x=driver_id + model_id)
da = DataAccess()
if test:
raise Exception
driver_train, driver_test = da.get_rides_split(driver_id,
settings.BIG_CHUNK)
other_train = list(
da.get_random_rides(settings.BIG_CHUNK * repeat, driver_id, seed=seed))
other_test = list(da.get_random_rides(settings.SMALL_CHUNK, driver_id))
set1 = driver_train + other_train # used for training
set2 = driver_test + other_test # used for testing
set1 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window, stops=stops, version=version) for ride in set1]
set2 = [util.get_list_string(heading.get_ride_heading(ride, \
moving_average_window=moving_average_window, stops=stops, version=version)) for ride in set2]
set1 = list(itertools.chain(*set1))
set1 = [util.get_list_string(r) for r in set1]
vectorizer = CountVectorizer(min_df=2, ngram_range=(1, 15), max_df=1000000)
set1 = vectorizer.fit_transform(set1)
set2 = vectorizer.transform(set2)
return set1, set2
def get_data_heading(model_id, driver_id, repeat, test=False, moving_average_window=3, stops=False, version=1):
seed = random.Random(x=driver_id+model_id)
da = DataAccess()
if test:
raise Exception
driver_train, driver_test = da.get_rides_split(driver_id, settings.BIG_CHUNK)
other_train = list(da.get_random_rides(settings.BIG_CHUNK * repeat, driver_id, seed=seed))
other_test = list(da.get_random_rides(settings.SMALL_CHUNK, driver_id))
set1 = driver_train + other_train # used for training
set2 = driver_test + other_test # used for testing
set1 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window, stops=stops, version=version) for ride in set1]
set2 = [util.get_list_string(heading.get_ride_heading(ride, \
moving_average_window=moving_average_window, stops=stops, version=version)) for ride in set2]
set1 = list(itertools.chain(*set1))
set1 = [util.get_list_string(r) for r in set1]
vectorizer = CountVectorizer(min_df=2, ngram_range=(1,15), max_df=1000000)
set1 = vectorizer.fit_transform(set1)
set2 = vectorizer.transform(set2)
return set1, set2
def test_model_heading(model_id, driver_id, Model, get_data, repeat):
seed = random.Random(x=driver_id + model_id)
da = DataAccess()
set1 = list(da.get_rides(driver_id)) # first half of the train set
set2 = list(
da.get_random_rides(settings.BIG_CHUNK_TEST * repeat,
driver_id,
seed=seed)) # second half of the train set
moving_average_window = 6 if get_data == get_data_heading_v2 else 3
set1 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window) for ride in set1]
set2 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window) for ride in set2]
set1 = [[util.get_list_string(r) for r in four_pack] for four_pack in set1]
set2 = [[util.get_list_string(r) for r in four_pack] for four_pack in set2]
vectorizer = CountVectorizer(min_df=2, ngram_range=(1, 15), max_df=1000000)
vectorizer.fit([r[0] for r in set1])
rides = [[vectorizer.transform([r])[0] for r in four_pack]
for four_pack in set1]
other_rides = [[vectorizer.transform([r])[0] for r in four_pack]
for four_pack in set2]
other_rides = list(itertools.chain(*other_rides))
rides = np.array(rides)
trainY = [1] * settings.BIG_CHUNK_TEST * 4 * repeat + [
0
] * settings.BIG_CHUNK_TEST * 4 * repeat
kf = KFold(200,
n_folds=settings.FOLDS,
shuffle=True,
random_state=driver_id)
predictions = ['bug'] * 200
for train_fold, test_fold in kf:
trainX = rides[train_fold]
trainX = scipy.sparse.vstack(
list(itertools.chain(*trainX)) * repeat + \
other_rides
)
testX = scipy.sparse.vstack([r[0] for r in rides[test_fold]])
assert (trainX.shape[0] == len(trainY))
assert (testX.shape[0] == settings.SMALL_CHUNK_TEST)
model = Model(trainX, trainY, driver_id)
fold_predictions = model.predict(testX)
for i, v in enumerate(test_fold):
predictions[v] = fold_predictions[i]
predictions = np.array(predictions)
if settings.ENABLE_CACHE:
util.cache_results(Model, get_data, driver_id, True, predictions,
repeat)
return driver_id, predictions
def test_model_heading(model_id, driver_id, Model, get_data, repeat):
seed = random.Random(x=driver_id+model_id)
da = DataAccess()
set1 = list(da.get_rides(driver_id)) # first half of the train set
set2 = list(da.get_random_rides(settings.BIG_CHUNK_TEST * repeat, driver_id, seed=seed)) # second half of the train set
moving_average_window = 6 if get_data == get_data_heading_v2 else 3
set1 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window) for ride in set1]
set2 = [heading.get_ride_heading(ride, variations=True, \
moving_average_window=moving_average_window) for ride in set2]
set1 = [[util.get_list_string(r) for r in four_pack] for four_pack in set1]
set2 = [[util.get_list_string(r) for r in four_pack] for four_pack in set2]
vectorizer = CountVectorizer(min_df=2, ngram_range=(1,15), max_df=1000000)
vectorizer.fit([r[0] for r in set1])
rides = [[vectorizer.transform([r])[0] for r in four_pack] for four_pack in set1]
other_rides = [[vectorizer.transform([r])[0] for r in four_pack] for four_pack in set2]
other_rides = list(itertools.chain(*other_rides))
rides = np.array(rides)
trainY = [1] * settings.BIG_CHUNK_TEST * 4 * repeat + [0] * settings.BIG_CHUNK_TEST * 4 * repeat
kf = KFold(200, n_folds=settings.FOLDS, shuffle=True, random_state=driver_id)
predictions = ['bug'] * 200
for train_fold, test_fold in kf:
trainX = rides[train_fold]
trainX = scipy.sparse.vstack(
list(itertools.chain(*trainX)) * repeat + \
other_rides
)
testX = scipy.sparse.vstack([r[0] for r in rides[test_fold]])
assert(trainX.shape[0] == len(trainY))
assert(testX.shape[0] == settings.SMALL_CHUNK_TEST)
model = Model(trainX, trainY, driver_id)
fold_predictions = model.predict(testX)
for i, v in enumerate(test_fold):
predictions[v] = fold_predictions[i]
predictions = np.array(predictions)
if settings.ENABLE_CACHE:
util.cache_results(Model, get_data, driver_id, True, predictions, repeat)
return driver_id, predictions
