def get_data_accel_v2(model_id, driver_id, repeat, test=False): seed = random.Random(x=driver_id + model_id) da = DataAccess() if test: 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 else: 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 = [ util.get_list_string(util.get_accelerations_v2(ride)) for ride in set1 ] set2 = [ util.get_list_string(util.get_accelerations_v2(ride)) for ride in set2 ] vectorizer = CountVectorizer(min_df=1) 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 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 write_ride_segments(self, driver_id, ride_id, lengths, times, angles, version=1): filename = '%s/%s_%s.csv' % (settings.SEGMENTS_FOLDER[version], driver_id, ride_id) f = open(filename, 'w') f.write('%s\n' % util.get_list_string(lengths)) f.write('%s\n' % util.get_list_string(times)) f.write('%s' % util.get_list_string(angles)) f.close()
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 get_data_segment_angles(model_id, driver_id, repeat, test=False, segment_version=1, extra=((1, 1), 2)): seed = random.Random(x=driver_id + model_id) da = DataAccess() ngram_range, min_df = extra if test: set1 = list(da.get_rides_segments(driver_id, version=segment_version)) set2 = list( da.get_random_rides(settings.BIG_CHUNK_TEST * repeat, driver_id, segments=True, version=segment_version, seed=seed)) else: driver_train, driver_test = da.get_rides_split(driver_id, settings.BIG_CHUNK, segments=True, version=segment_version) other_train = list( da.get_random_rides(settings.BIG_CHUNK * repeat, driver_id, segments=True, version=segment_version, seed=seed)) other_test = list( da.get_random_rides(settings.SMALL_CHUNK, driver_id, segments=True, version=segment_version)) set1 = driver_train + other_train set2 = driver_test + other_test # create features for each (segment, angle, segment) tuple set1 = [[ '%s_%s_%s' % (d[0][i - 1], d[1][i - 1], d[0][i]) for i in xrange(1, len(d[0])) ] for d in set1] set2 = [[ '%s_%s_%s' % (d[0][i - 1], d[1][i - 1], d[0][i]) for i in xrange(1, len(d[0])) ] for d in set2] set1 = [util.get_list_string(d) for d in set1] set2 = [util.get_list_string(d) for d in set2] vectorizer = CountVectorizer(min_df=min_df, ngram_range=ngram_range) set1 = vectorizer.fit_transform(set1) set2 = vectorizer.transform(set2) return set1, set2
def get_data_segment_lengths(model_id, driver_id, repeat, test=False, segment_version=1, extra=((1, 8), 1)): seed = random.Random(x=driver_id + model_id) da = DataAccess() ngram_range, min_df = extra if test: set1 = list(da.get_rides_segments(driver_id, version=segment_version)) set2 = list( da.get_random_rides(settings.BIG_CHUNK_TEST * repeat, driver_id, segments=True, version=segment_version, seed=seed)) else: driver_train, driver_test = da.get_rides_split(driver_id, settings.BIG_CHUNK, segments=True, version=segment_version) other_train = list( da.get_random_rides(settings.BIG_CHUNK * repeat, driver_id, segments=True, version=segment_version, seed=seed)) other_test = list( da.get_random_rides(settings.SMALL_CHUNK, driver_id, segments=True, version=segment_version)) set1 = driver_train + other_train set2 = driver_test + other_test # keep only lengths set1 = [d[0] for d in set1] set2 = [d[0] for d in set2] # convert to text set1 = [util.get_list_string(d) for d in set1] set2 = [util.get_list_string(d) for d in set2] vectorizer = CountVectorizer(min_df=min_df, ngram_range=ngram_range) set1 = vectorizer.fit_transform(set1) set2 = vectorizer.transform(set2) return set1, set2
def get_data_segment_lengths(model_id, driver_id, repeat, test=False, segment_version=1, extra=((1,8),1)): seed = random.Random(x=driver_id+model_id) da = DataAccess() ngram_range, min_df = extra if test: set1 = list(da.get_rides_segments(driver_id, version=segment_version)) set2 = list(da.get_random_rides( settings.BIG_CHUNK_TEST * repeat, driver_id, segments=True, version=segment_version, seed=seed )) else: driver_train, driver_test = da.get_rides_split( driver_id, settings.BIG_CHUNK, segments=True, version=segment_version ) other_train = list(da.get_random_rides( settings.BIG_CHUNK * repeat, driver_id, segments=True, version=segment_version, seed=seed )) other_test = list(da.get_random_rides( settings.SMALL_CHUNK, driver_id, segments=True, version=segment_version )) set1 = driver_train + other_train set2 = driver_test + other_test # keep only lengths set1 = [d[0] for d in set1] set2 = [d[0] for d in set2] # convert to text set1 = [util.get_list_string(d) for d in set1] set2 = [util.get_list_string(d) for d in set2] vectorizer = CountVectorizer(min_df=min_df, ngram_range=ngram_range) set1 = vectorizer.fit_transform(set1) set2 = vectorizer.transform(set2) return set1, set2
def get_data_segment_angles_v2(model_id, driver_id, repeat, test=False, segment_version=1, extra=((1,3),1)): seed = random.Random(x=driver_id+model_id) da = DataAccess() ngram_range, min_df = extra if test: set1 = list(da.get_rides_segments(driver_id, version=segment_version)) set2 = list(da.get_random_rides( settings.BIG_CHUNK_TEST * repeat, driver_id, segments=True, version=segment_version, seed=seed )) else: driver_train, driver_test = da.get_rides_split( driver_id, settings.BIG_CHUNK, segments=True, version=segment_version ) other_train = list(da.get_random_rides( settings.BIG_CHUNK * repeat, driver_id, segments=True, version=segment_version, seed=seed )) other_test = list(da.get_random_rides( settings.SMALL_CHUNK, driver_id, segments=True, version=segment_version )) set1 = driver_train + other_train set2 = driver_test + other_test # create features for each (segment, angle, segment) tuple set1 = [['%s_%s' % (d[0][i-1], d[1][i-1]) for i in xrange(1, len(d[0]))] for d in set1] set2 = [['%s_%s' % (d[0][i-1], d[1][i-1]) for i in xrange(1, len(d[0]))] for d in set2] set1 = [util.get_list_string(d) for d in set1] set2 = [util.get_list_string(d) for d in set2] vectorizer = CountVectorizer(min_df=min_df, ngram_range=ngram_range) 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 get_data_accel_v2(model_id, driver_id, repeat, test=False): seed = random.Random(x=driver_id+model_id) da = DataAccess() if test: 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 else: 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 = [util.get_list_string(util.get_accelerations_v2(ride)) for ride in set1] set2 = [util.get_list_string(util.get_accelerations_v2(ride)) for ride in set2] vectorizer = CountVectorizer(min_df=1) set1 = vectorizer.fit_transform(set1) set2 = vectorizer.transform(set2) return set1, set2
def process(ride, digitize): g_forces = util.get_g_forces(ride) if digitize: g_forces = np.digitize(g_forces, range(0, 800, digitize)) return util.get_list_string(g_forces)