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_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 run_single_validation(self, grid_size, validation_date, time_unit):
data = ProcessData(self.dataset_info['name'], self.dataset_info['path'])
df = data.get_formated_df()
self.dataset_info['dict'] = data.dataset_dict #update dataset dictionary on experiment instance
df_filtered = ProcessData.filter_by_field(df, self.custom_filter['field'], self.custom_filter['value'])
validation = ValidateModel(df_filtered, self.dataset_info['dict'], time_unit, None)
prediction_results = validation.inner_loop_validation(self.model, grid_size, self.train_dates, datetime.strptime(validation_date,'%Y-%m-%d'), self.metrics)
return prediction_results
def run_ncv_experiment(self, time_unit, grid_size, region):
""" Run nested-cross validation
:region: An instance of :class: open_cp.data.RectangularRegion,
if 'None', the region will dfined based on training points
:return: An array with prediction results
"""
self.check_exp_params()
data = ProcessData(self.dataset_info['name'], self.dataset_info['path'])
df = data.get_formated_df()
self.dataset_info['dict'] = data.dataset_dict #update dataset dictionary on experiment instance
dates_interval = {'initial': self.train_dates['initial'], 'final': self.validation_dates['final']}
df_train_validation = ProcessData.select_data(df, self.dataset_info['dict'], self.custom_filter, dates_interval)
validation = ValidateModel(df_train_validation, self.dataset_info['dict'], time_unit, region)
prediction_results = validation.walk_fwd_chain(self.model, grid_size, self.train_dates, self.validation_dates, self.metrics)
return prediction_results
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})
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)