def test_make_counting_grid(self):
""" Test counting grid for a base "well-known" scenario """
## Get grid prediction, to use size and region params
infile = open(
'/Users/anamaria/Desktop/dev/security_project/aggressive_behavior_model/pkl/experiment_seppexp_10_2_siedco_prediction.pkl',
'rb')
loaded_siedco = pickle.load(infile)
infile.close()
grid = loaded_siedco['prediction'].values[0]
## Select points to represent on counting matrix
df = pd.read_csv(
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
data = ProcessData(
"SIEDCO",
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
df_input = data.add_timestamp(df)
timed_pts, _ = ProcessData.get_time_space_points(
df_input, data.dataset_dict)
counting_matrix = prediction_metrics.make_counting_grid(
grid, timed_pts)
self.assertEqual(counting_matrix.xoffset, 958645.8182116301)
self.assertEqual(counting_matrix.yoffset, 904338.0678953262)
self.assertEqual(counting_matrix.xsize, 150)
self.assertEqual(counting_matrix.ysize, 150)
self.assertEqual(counting_matrix._matrix.shape, (816, 343))
self.assertEqual(counting_matrix._matrix.max(), 357)
self.assertEqual(counting_matrix._matrix.min(), 0)
def test_hit_rate_1(self):
""" Test hit_rate=1 if all real events falls on hotspots """
df = pd.read_csv(
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
data = ProcessData(
"SIEDCO",
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
df_input = data.add_timestamp(df)
date = '2018-01-01'
dataset_dict = data.dataset_dict
df_filtered = ProcessData.filter_by_date(df_input, dataset_dict, date,
date)
timed_pts, region = ProcessData.get_time_space_points(
df_filtered, data.dataset_dict)
counting_kernel = open_cp.naive.CountingGridKernel(grid_width=150,
region=region)
counting_kernel.data = timed_pts
grid_prediction = counting_kernel.predict()
coverages = [2, 4, 6, 8, 10]
hit_rates_default = prediction_metrics.measure_hit_rates(
grid_prediction, timed_pts, coverages, 'default')
hit_rates_ground_truth = prediction_metrics.measure_hit_rates(
grid_prediction, timed_pts, coverages, 'ground_truth_coverage')
self.assertEqual(hit_rates_default, {
2: 1.0,
4: 1.0,
6: 1.0,
8: 1.0,
10: 1.0
})
self.assertEqual(hit_rates_ground_truth, {0.46187915216703573: 1.0})
def test_mse_1(self):
""" Test mse=0 if both matrices (prediction and ground truth) are equal """
df = pd.read_csv(
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
data = ProcessData(
"SIEDCO",
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
df_input = data.add_timestamp(df)
timed_pts, region = ProcessData.get_time_space_points(
df_input, data.dataset_dict)
counting_kernel = open_cp.naive.CountingGridKernel(grid_width=150,
region=region)
counting_kernel.data = timed_pts
grid_prediction = counting_kernel.predict()
mse = prediction_metrics.mse(grid_prediction, timed_pts)
self.assertEqual(mse, 0)
def test_hit_rate_2(self):
""" Test hit_rate=0 if no events falls on hotspots """
df = pd.read_csv(
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
data = ProcessData(
"SIEDCO",
"/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_09062020.csv"
)
df_input = data.add_timestamp(df)
date = '2018-01-01'
dataset_dict = data.dataset_dict
df_input = ProcessData.filter_by_date(df_input, dataset_dict, date,
date)
df_1 = ProcessData.filter_by_field(df_input, 'LOCALIDAD', 'SUBA')
df_2 = ProcessData.filter_by_field(df_input, 'LOCALIDAD', 'BOSA')
timed_pts, region = ProcessData.get_time_space_points(
df_1, data.dataset_dict)
counting_kernel = open_cp.naive.CountingGridKernel(grid_width=150,
region=region)
counting_kernel.data = timed_pts
grid_prediction = counting_kernel.predict()
coverages = [2, 4, 6, 8, 10]
eval_pts, _ = ProcessData.get_time_space_points(
df_2, data.dataset_dict)
hit_rates_default = prediction_metrics.measure_hit_rates(
grid_prediction, eval_pts, coverages, 'default')
hit_rates_ground_truth = prediction_metrics.measure_hit_rates(
grid_prediction, eval_pts, coverages, 'ground_truth_coverage')
self.assertEqual(hit_rates_default, {
2: 0.0,
4: 0.0,
6: 0.0,
8: 0.0,
10: 0.0
})
self.assertEqual(hit_rates_ground_truth, {0.6632653061224489: 0.0})
class TestCase(unittest.TestCase):
def setUp(self):
dataset_name = 'SIEDCO'
head_path = '/Users/anamaria/Desktop/dev/security_project/datasets/'
file = 'deduplicate_siedco_10032020.csv'
dataset_path = head_path + file
self.my_data = ProcessData(dataset_name, dataset_path)
def test_set_up(self):
self.assertEqual(self.my_data.dataset_name, 'SIEDCO')
self.assertEqual(
self.my_data.dataset_path,
'/Users/anamaria/Desktop/dev/security_project/datasets/deduplicate_siedco_10032020.csv'
)
self.assertEqual(self.my_data.dataset_dict, siedco_dict)
def test_filter_by_date_case1(self):
#case 1: date on interval, siedco
df = pd.read_csv(self.my_data.dataset_path)
df = self.my_data.add_timestamp(df)
initial_date = '2018-01-01'
final_date = '2018-01-01'
dataset_dict = self.my_data.dataset_dict
df_filtered = ProcessData.filter_by_date(df, dataset_dict,
initial_date, final_date)
df_expected = df.loc[df['FECHA_HECHO'] == "2018-01-01"]
self.assertEqual(len(df_filtered), len(df_expected))
def test_filter_by_date_case2(self):
#case 2: initial date out of available data, siedco
df = pd.read_csv(self.my_data.dataset_path)
df = self.my_data.add_timestamp(df)
initial_date = '2021-01-01'
final_date = '2021-01-02'
dataset_dict = self.my_data.dataset_dict
self.assertWarns(
UserWarning, lambda: ProcessData.filter_by_date(
df, dataset_dict, initial_date, final_date))
def test_filter_by_date_case3(self):
#case 3: date on interval, nuse sample
dataset = {'name': 'NUSE', 'path': ''}
self.my_data.dataset_name = 'NUSE'
self.my_data.dataset_dict = self.my_data.set_dictionary()
head_path = '/Users/anamaria/Desktop/dev/security_project/datasets/'
file = 'verify_enrich_nuse_29112019.csv'
df = pd.read_csv(head_path + file)
df = self.my_data.add_timestamp(df)
initial_date = '2018-01-01'
final_date = '2018-01-01'
dataset_dict = self.my_data.dataset_dict
df_filtered = ProcessData.filter_by_date(df, dataset_dict,
initial_date, final_date)
df_expected = df.loc[df['FECHA'] == "2018-01-01"]
self.assertEqual(len(df_filtered), len(df_expected))
def test_filter_by_date_case4(self):
#case 4: date on interval, nuse full data
dataset = {'name': 'NUSE', 'path': ''}
self.my_data.dataset_name = 'NUSE'
self.my_data.dataset_dict = self.my_data.set_dictionary()
head_path = '/Users/anamaria/Desktop/dev/security_project/datasets/'
file = 'verify_enrich_nuse_29112019.csv'
df = pd.read_csv(head_path + file)
df = self.my_data.add_timestamp(df)
initial_date = '2018-01-01'
final_date = '2018-01-01'
dataset_dict = self.my_data.dataset_dict
df_filtered = ProcessData.filter_by_date(df, dataset_dict,
initial_date, final_date)
df_expected = df.loc[df['FECHA'] == "2018-01-01"]
self.assertEqual(len(df_filtered), len(df_expected))
def test_filter_by_date_case5(self):
#case 5: date on interval, rnmc
dataset = {'name': 'RNMC', 'path': ''}
self.my_data.dataset_name = 'RNMC'
self.my_data.dataset_dict = self.my_data.set_dictionary()
head_path = '/Users/anamaria/Desktop/dev/security_project/datasets/'
file = '06. verify_enrich_rnmc_12022020.csv'
df = pd.read_csv(head_path + file)
df = self.my_data.add_timestamp(df)
initial_date = '2018-01-01'
final_date = '2018-01-01'
dataset_dict = self.my_data.dataset_dict
df_filtered = ProcessData.filter_by_date(df, dataset_dict,
initial_date, final_date)
df_expected = df.loc[df['FECHA'] == "2018-01-01"]
self.assertEqual(len(df_filtered), len(df_expected))
def test_filter_by_field_case1(self):
#case 1: filter successful
df = pd.read_csv(self.my_data.dataset_path)
df_filtered = ProcessData.filter_by_field(df, 'LOCALIDAD', 'BOSA')
self.assertEqual(df_filtered.LOCALIDAD.unique()[0], 'BOSA')
def test_filter_by_field_case2(self):
#case 2: filter successful, without field value
df = pd.read_csv(self.my_data.dataset_path)
df_filtered = ProcessData.filter_by_field(df, '', '')
assertion_proxy = df_filtered.equals(df)
self.assertEqual(assertion_proxy, True)
def test_filter_by_field_case3(self):
#case 3: error, field doesn't exist
df = pd.read_csv(self.my_data.dataset_path)
self.assertRaises(
ValueError,
lambda: ProcessData.filter_by_field(df, 'nombre', 'Pedro'))
def test_filter_by_field_case4(self):
#case 4: error, value doesn't exist
df = pd.read_csv(self.my_data.dataset_path)
self.assertRaises(
ValueError,
lambda: ProcessData.filter_by_field(df, 'LOCALIDAD', 'NORMANDIA'))
def test_normalize_matrix(self):
matrix = np.arange(1, 5)
matrix = matrix.reshape(2, 2)
matrix_expected = np.array([[0.25, 0.5], [0.75, 1]])
matrix_normalized = ProcessData.normalize_matrix(matrix)
self.assertTrue((matrix_normalized == matrix_expected).all())