def time_lagged_correlation():
"""Runs the time-lagged correlation analysis experiment.
This function alculates the time-lagged correlation between the variables for lags of up to 48 hours and plots the
result as a scatterplot matrix.
"""
var_names = [
"dayOfYear", "minuteOfYear", "minuteOfDay", "dayOfWeek", "isWeekend",
"humidity_sensor", "temperature", "precip_intensity", "cloud_cover",
"p1", "p2", "dew_point", "wind_speed"
]
tau_min = 0
tau_max = 48
dataframe, var_list = generate_dataframe(var_names)
print(f"Variable names: {var_names}")
ci_test = ParCorr(significance='analytic')
pcmci = PCMCI(dataframe=dataframe, cond_ind_test=ci_test, verbosity=1)
correlations = pcmci.get_lagged_dependencies(tau_min=tau_min,
tau_max=tau_max)
lag_func_matrix = tp.plot_lagfuncs(
name="experiments/causal_discovery/results/time_lagged_correlation.png",
val_matrix=correlations,
setup_args={
'var_names': var_names,
'figsize': (50, 25),
'label_fontsize': 12,
'label_space_top': 0.025,
'label_space_left': 0.05,
'lag_units': 'hours',
'x_base': 6,
'y_base': .5
})
print(lag_func_matrix)
def prior_knowledge():
"""Runs the experiment for incorporating prior knowledge into the non-linear causal model using RCOT.
This function creates causal models using the RCOT test for a variety of alphas and small range of random Fourier
transformations (num_f). It further limits the solution space by limiting the selected_links used in the PCMCI
algorithm, which effectively enforced independencies in the result. The results are plotted as network and
timeseries graphs. The maximum lag is 1.
"""
var_names = [
"dayOfYear", "minuteOfYear", "minuteOfDay", "dayOfWeek", "isWeekend",
"humidity_sensor", "temperature", "precip_intensity", "cloud_cover",
"p1", "p2", "dew_point", "wind_speed"
]
tau_min = 0
tau_max = 24
dataframe, var_list = generate_dataframe(var_names,
start_index=0,
end_index=2000)
print(f"Variable names: {var_names}")
num_fs = [2**9, 2**10]
for num_f in num_fs:
ci_test = RCOT(significance='analytic', num_f=num_f)
test_alphas(dataframe,
ci_test, [0.05, 0.1, 0.2],
var_names,
tau_min=tau_min,
tau_max=tau_max,
selected_links=generate_links_from_prior_knowledge(
var_names, tau_min, tau_max))
def rcot_hyperparameter_tuning():
"""Runs the experiment for tuning the hyperparameters of the non-linear causal model using RCOT.
This function creates causal models using the RCOT test for a variety of different alphas and numbers of random
Fourier transformations (num_f). The results are plotted as network and timeseries graphs. The maximum lag is 1.
"""
var_names = [
"dayOfYear", "minuteOfYear", "minuteOfDay", "dayOfWeek", "isWeekend",
"humidity_sensor", "temperature", "precip_intensity", "cloud_cover",
"p1", "p2", "dew_point", "wind_speed"
]
tau_min = 0
tau_max = 1
dataframe, var_list = generate_dataframe(var_names,
start_index=0,
end_index=2000)
print(f"Variable names: {var_names}")
num_fs = [2**n for n in range(1, 14)]
for num_f in num_fs:
ci_test = RCOT(significance='analytic', num_f=num_f)
test_alphas(dataframe,
ci_test, [0.05, 0.1, 0.2],
var_names,
tau_min=tau_min,
tau_max=tau_max)
def linear_causal_model():
"""Runs the linear causal model experiment mentioned in the report.
This function creates causal linear models for a variety of different alphas and plots the results as
network and timeseries graphs. The maximum lag is 24.
"""
var_names = [
"dayOfYear", "minuteOfYear", "minuteOfDay", "dayOfWeek", "isWeekend",
"humidity_sensor", "temperature", "precip_intensity", "cloud_cover",
"p1", "p2", "dew_point", "wind_speed"
]
tau_min = 0
tau_max = 24
dataframe, var_list = generate_dataframe(var_names)
print(f"Variable names: {var_names}")
ci_test = ParCorr(significance='analytic')
alphas = [3**-n for n in range(1, 9)]
test_alphas(dataframe,
ci_test,
alphas,
var_names,
tau_min=tau_min,
tau_max=tau_max)