def gradient(window_size, states_path, states_list):
ctx = click.get_current_context()
properties = ctx.meta['properties']
if states_path:
states = ctx.meta['files_manager'].read_csv(states_path)
else:
bins = [-inf] + [float(x) for x in states_list.split(' ')] + [inf]
bins_lows_per_property = bins[:-1]
bins_highs_per_property = bins[1:]
bins_lows = np.tile(bins_lows_per_property, len(properties))
bins_highs = np.tile(bins_highs_per_property, len(properties))
states = DataframesGenerator.generate_states(
np.arange(1,
len(bins_lows) + 1),
np.repeat(properties, len(bins_lows_per_property)),
np.tile(np.arange(len(bins_lows_per_property)), len(properties)),
bins_lows, bins_highs, np.full(len(bins_lows), np.nan))
params_df = DataframesGenerator.generate_temporal_abstraction_params(
properties, ['gradient'] * len(properties),
np.full(len(properties), nan), np.full(len(properties), window_size))
system_flow(ctx.meta['files_manager'],
ctx.meta['preprocessing_params_df'],
params_df,
states,
kfold=ctx.meta['kfold'])
def create_prop_states(prop_id, cutpoints, scores=None):
"""
Creates a states dataframe for a single property, containing the cutpoints and scores given as parameter
:param prop_id: int, the property id
:param cutpoints: List, a list of cutpoints
:param scores: List, a list of scores, one for each cut-point
:return: Dataframe, a states dataframe
"""
nb_states = len(cutpoints) + 1
bin_low = [-inf] + cutpoints
bin_high = cutpoints + [inf]
if scores is None:
scores = np.full(nb_states - 1, np.nan)
states = DataframesGenerator.generate_states(
np.arange(1, nb_states + 1),
np.full(nb_states, prop_id),
np.arange(nb_states),
bin_low,
bin_high,
np.append([nan], scores)
)
return states