def test_nc_parameter(self):
# Parameter values c<=0 were not enabled (gh-2402).
# For negative values c and for c=0 results of rv.cdf(0) below were nan
rv = stats.nct(5, 0)
assert_equal(rv.cdf(0), 0.5)
rv = stats.nct(5, -1)
assert_almost_equal(rv.cdf(0), 0.841344746069, decimal=10)
def test_nc_parameter(self):
# Parameter values c<=0 were not enabled (gh-2402).
# For negative values c and for c=0 results of rv.cdf(0) below were nan
rv = stats.nct(5, 0)
assert_equal(rv.cdf(0), 0.5)
rv = stats.nct(5, -1)
assert_almost_equal(rv.cdf(0), 0.841344746069, decimal=10)
def text_variance_gh_issue_2401():
# Computation of the variance of a non-central t-distribution resulted
# in a TypeError: ufunc 'isinf' not supported for the input types,
# and the inputs could not be safely coerced to any supported types
# according to the casting rule 'safe'
rv = stats.nct(4, 0)
assert_equal(rv.var(), 2.0)
def initialize_nct_distribution(df, nc):
np.seterr(over='ignore', invalid='ignore')
test_value = stats.nct.pdf(x=nc, df=df, nc=nc)
if np.isnan(test_value):
return stats.t(df=df, loc=nc)
else:
return stats.nct(df=df, nc=nc)
def text_variance_gh_issue_2401():
# Computation of the variance of a non-central t-distribution resulted
# in a TypeError: ufunc 'isinf' not supported for the input types,
# and the inputs could not be safely coerced to any supported types
# according to the casting rule 'safe'
rv = stats.nct(4, 0)
assert_equal(rv.var(), 2.0)
def ttest_ind_sample_size(mu1,
mu2,
s1,
s2,
r,
power,
sig_level=0.05,
alternative='two-sided',
pooled=True):
n1 = 2 # initialisation
n2 = n1 * r
sim_power = 0
while sim_power < power:
n1 += 1
n2 = n1 * r
if pooled:
dof = n1 + n2 - 2
pooled_var = (((s1**2) * (n1 - 1)) + ((s2**2) * (n2 - 1))) / dof
std_error = np.sqrt(pooled_var * (1 / n1 + 1 / n2))
else:
var1 = (s1**2) # assuming unbiased sample standard deviation
var2 = (s2**2)
dof = (var1 / n1 + var2 / n2)**2
dof /= ((((var1 / n1)**2) / (n1 - 1)) + (((var2 / n2)**2) /
(n2 - 1)))
std_error = np.sqrt(var1 / n1 + var2 / n2)
ncp = (mu2 - mu1) / std_error
t_null = scs.t(df=dof, loc=0, scale=1)
t_alt = scs.nct(df=dof, nc=ncp)
if alternative == 'smaller':
cv = t_null.ppf(sig_level)
sim_power = t_alt.cdf(cv)
elif alternative == 'larger':
cv = t_null.ppf(1 - sig_level)
sim_power = 1 - t_alt.cdf(cv)
elif alternative == 'two-sided':
cv = [t_null.ppf(sig_level / 2), t_null.ppf(1 - (sig_level / 2))]
sim_power = sum([t_alt.cdf(cv[0]), 1 - t_alt.cdf(cv[1])])
print('ncp: ', ncp)
print('Critical t: ', cv)
print('Actual Power: ', sim_power)
return (np.ceil(n1), np.ceil(n2))
def __init__(self, mu, sg, df, nc, lc, sc):
self.norm = sps.norm(mu, sg)
self.nct = sps.nct(df=df, nc=nc, loc=lc, scale=sc)
modeL = nc * np.sqrt(df / (df + 5 / 2))
modeU = nc * np.sqrt(df / (df + 1))
self.nct.modeEst = sc * (modeL + modeU) / 2 + lc
self.norm.max = self.norm.pdf(mu)
self.nct.max = self.nct.pdf(self.nct.modeEst)
self.max = self.norm.max * self.nct.max
def ttest_paired_sample_size(starting_n,
effect_size,
power=0.8,
sig_level=0.05,
alternative='two-sided'):
n = starting_n # initialisation
sim_power = 0
while sim_power < power:
n += 1
dof = n - 1
ncp = effect_size * np.sqrt(n)
t_null = scs.t(df=dof, loc=0, scale=1)
t_alt = scs.nct(df=dof, nc=ncp)
if alternative == 'smaller':
cv = t_null.ppf(sig_level)
sim_power = t_alt.cdf(cv)
elif alternative == 'larger':
cv = t_null.ppf(1 - sig_level)
sim_power = 1 - t_alt.cdf(cv)
elif alternative == 'two-sided':
cv = [t_null.ppf(sig_level / 2), t_null.ppf(1 - (sig_level / 2))]
sim_power = sum([t_alt.cdf(cv[0]), 1 - t_alt.cdf(cv[1])])
print('ncp: ', ncp)
print('Critical t: ', cv)
print('Actual Power: ', sim_power)
return np.ceil(n)
df, nc = 14, 0.24 mean, var, skew, kurt = nct.stats(df, nc, moments='mvsk') # Display the probability density function (``pdf``): x = np.linspace(nct.ppf(0.01, df, nc), nct.ppf(0.99, df, nc), 100) ax.plot(x, nct.pdf(x, df, nc), 'r-', lw=5, alpha=0.6, label='nct pdf') # Alternatively, the distribution object can be called (as a function) # to fix the shape, location and scale parameters. This returns a "frozen" # RV object holding the given parameters fixed. # Freeze the distribution and display the frozen ``pdf``: rv = nct(df, nc) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = nct.ppf([0.001, 0.5, 0.999], df, nc) np.allclose([0.001, 0.5, 0.999], nct.cdf(vals, df, nc)) # True # Generate random numbers: r = nct.rvs(df, nc, size=1000) # And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
c, d = 10.5, 4.3 mean, var, skew, kurt = burr.stats(c, d, moments='mvsk') x = np.linspace(burr.ppf(0.01, c, d), burr.ppf(0.99, c, d), 100) burr.ppf print(stats.norm.__doc__) alpha, loc, beta = b[0], b[1], b[2] pdf = nct.pdf() data = ss.genlogistic.rvs(alpha, loc=loc, scale=beta, size=5000) myHist = plt.hist(distribution_car_price, 500, normed=True) rv = ss.genlogistic(alpha, loc, beta) x = np.linspace(0, 500000) h = plt.plot(x, rv.pdf(x), lw=2) axes = plt.gca() axes.set_xlim([0, 150000]) plt.show() alpha, loc, beta = b[0], b[1], b[2] data = ss.genlogistic.rvs(alpha, loc=loc, scale=beta, size=10000) myHist = plt.hist(distribution_car_price, 500, normed=True) rv = ss.nct(a[0], a[1], a[2], a[3]) x = np.linspace(0, 500000) h = plt.plot(x, rv.pdf(x), lw=2) axes = plt.gca() axes.set_xlim([0, 150000]) plt.show()
9.1193851632305201,
261.3457987967214)
drivingduration_model_dict['exponweib'] = st.exponweib(2.6443841639764942,
0.89242254172118096,
10.603640861374947,
40.28556311444698)
drivingduration_model_dict['gengamma'] = st.gengamma(4.8743515108339581,
0.61806208678747043,
9.4649293818479716,
5.431576919220225)
drivingduration_model_dict['recipinvgauss'] = st.recipinvgauss(
0.499908918842556, 0.78319699707613699, 28.725450197674746)
drivingduration_model_dict['f'] = st.f(9.8757694313677113, 12.347442183821462,
0.051160749890587665,
73.072591767722287)
carprice_model_dict = ct.OrderedDict()
carprice_model_dict['nct'] = st.nct(7.3139456577106312, 3.7415255108348946,
-46.285705145385577, 7917.0860181436065)
carprice_model_dict['genlogistic'] = st.genlogistic(10.736440967148635,
3735.7049978006107,
10095.421377235754)
carprice_model_dict['gumbel_r'] = st.gumbel_r(26995.077239517472,
10774.370808211244)
carprice_model_dict['f'] = st.f(24168.523476867485, 35.805656864712923,
-21087.314142557225, 51154.0328397044)
carprice_model_dict['johnsonsu'] = st.johnsonsu(-1.7479864366935538,
1.8675670208081987,
14796.793096897647,
14716.575397771712)
q = np.linspace(0, 1, 100) e_param = stats.norminvgauss.fit(euro_log) VaR = stats.norminvgauss(param[0], param[1], param[2], param[3]).ppf(q) e_var = stats.norminvgauss(e_param[0], e_param[1], e_param[2], e_param[3]).ppf(q) d, p = stats.kstest(logreturns[2], cdf = 'norminvgauss', args = (param[0], param[1], param[2], param[3])) ks.append(p) #plt.plot(q, VaR, 'r-', label = 'Dogecoin') #plt.plot(q, e_var, 'b--', label = 'Euro') #plt.legend() ################LITECOIN############### param = [1.8693373494312953, -0.08195764686173776, 0.0017432733966738605, 0.021626187586807188] q = np.linspace(0, 1, 100) e_param = stats.nct.fit(euro_log) VaR = stats.nct(param[0], param[1], param[2], param[3]).ppf(q) e_var = stats.nct(e_param[0], e_param[1], e_param[2], e_param[3]).ppf(q) d, p = stats.kstest(logreturns[3], cdf = 'nct', args = (param[0], param[1], param[2], param[3])) ks.append(p) #plt.plot(q, VaR, 'r-', label = 'Litecoin') #plt.plot(q, e_var, 'b--', label = 'Euro') #plt.legend() ################NEXUS################ param = [2.269163744926829, -0.22915037600364918, 0.014003933425093236, 0.048491139607216696] q = np.linspace(0, 1, 100) e_param = stats.nct.fit(euro_log) VaR = stats.nct(param[0], param[1], param[2], param[3]).ppf(q) e_var = stats.nct(e_param[0], e_param[1], e_param[2], e_param[3]).ppf(q) d, p = stats.kstest(logreturns[4], cdf = 'nct', args = (param[0], param[1], param[2], param[3]))
def all_dists():
# dists param were taken from scipy.stats official
# documentaion examples
# Total - 89
return {
"alpha":
stats.alpha(a=3.57, loc=0.0, scale=1.0),
"anglit":
stats.anglit(loc=0.0, scale=1.0),
"arcsine":
stats.arcsine(loc=0.0, scale=1.0),
"beta":
stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0),
"betaprime":
stats.betaprime(a=5, b=6, loc=0.0, scale=1.0),
"bradford":
stats.bradford(c=0.299, loc=0.0, scale=1.0),
"burr":
stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0),
"cauchy":
stats.cauchy(loc=0.0, scale=1.0),
"chi":
stats.chi(df=78, loc=0.0, scale=1.0),
"chi2":
stats.chi2(df=55, loc=0.0, scale=1.0),
"cosine":
stats.cosine(loc=0.0, scale=1.0),
"dgamma":
stats.dgamma(a=1.1, loc=0.0, scale=1.0),
"dweibull":
stats.dweibull(c=2.07, loc=0.0, scale=1.0),
"erlang":
stats.erlang(a=2, loc=0.0, scale=1.0),
"expon":
stats.expon(loc=0.0, scale=1.0),
"exponnorm":
stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
"exponweib":
stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0),
"exponpow":
stats.exponpow(b=2.7, loc=0.0, scale=1.0),
"f":
stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0),
"fatiguelife":
stats.fatiguelife(c=29, loc=0.0, scale=1.0),
"fisk":
stats.fisk(c=3.09, loc=0.0, scale=1.0),
"foldcauchy":
stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
"foldnorm":
stats.foldnorm(c=1.95, loc=0.0, scale=1.0),
# "frechet_r": stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
# "frechet_l": stats.frechet_l(c=3.63, loc=0.0, scale=1.0),
"genlogistic":
stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
"genpareto":
stats.genpareto(c=0.1, loc=0.0, scale=1.0),
"gennorm":
stats.gennorm(beta=1.3, loc=0.0, scale=1.0),
"genexpon":
stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0),
"genextreme":
stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
"gausshyper":
stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0),
"gamma":
stats.gamma(a=1.99, loc=0.0, scale=1.0),
"gengamma":
stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0),
"genhalflogistic":
stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
"gilbrat":
stats.gilbrat(loc=0.0, scale=1.0),
"gompertz":
stats.gompertz(c=0.947, loc=0.0, scale=1.0),
"gumbel_r":
stats.gumbel_r(loc=0.0, scale=1.0),
"gumbel_l":
stats.gumbel_l(loc=0.0, scale=1.0),
"halfcauchy":
stats.halfcauchy(loc=0.0, scale=1.0),
"halflogistic":
stats.halflogistic(loc=0.0, scale=1.0),
"halfnorm":
stats.halfnorm(loc=0.0, scale=1.0),
"halfgennorm":
stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
"hypsecant":
stats.hypsecant(loc=0.0, scale=1.0),
"invgamma":
stats.invgamma(a=4.07, loc=0.0, scale=1.0),
"invgauss":
stats.invgauss(mu=0.145, loc=0.0, scale=1.0),
"invweibull":
stats.invweibull(c=10.6, loc=0.0, scale=1.0),
"johnsonsb":
stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0),
"johnsonsu":
stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
"ksone":
stats.ksone(n=1e03, loc=0.0, scale=1.0),
"kstwobign":
stats.kstwobign(loc=0.0, scale=1.0),
"laplace":
stats.laplace(loc=0.0, scale=1.0),
"levy":
stats.levy(loc=0.0, scale=1.0),
"levy_l":
stats.levy_l(loc=0.0, scale=1.0),
"levy_stable":
stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
"logistic":
stats.logistic(loc=0.0, scale=1.0),
"loggamma":
stats.loggamma(c=0.414, loc=0.0, scale=1.0),
"loglaplace":
stats.loglaplace(c=3.25, loc=0.0, scale=1.0),
"lognorm":
stats.lognorm(s=0.954, loc=0.0, scale=1.0),
"lomax":
stats.lomax(c=1.88, loc=0.0, scale=1.0),
"maxwell":
stats.maxwell(loc=0.0, scale=1.0),
"mielke":
stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0),
"nakagami":
stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
"ncx2":
stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0),
"ncf":
stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
"nct":
stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0),
"norm":
stats.norm(loc=0.0, scale=1.0),
"pareto":
stats.pareto(b=2.62, loc=0.0, scale=1.0),
"pearson3":
stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
"powerlaw":
stats.powerlaw(a=1.66, loc=0.0, scale=1.0),
"powerlognorm":
stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
"powernorm":
stats.powernorm(c=4.45, loc=0.0, scale=1.0),
"rdist":
stats.rdist(c=0.9, loc=0.0, scale=1.0),
"reciprocal":
stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0),
"rayleigh":
stats.rayleigh(loc=0.0, scale=1.0),
"rice":
stats.rice(b=0.775, loc=0.0, scale=1.0),
"recipinvgauss":
stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
"semicircular":
stats.semicircular(loc=0.0, scale=1.0),
"t":
stats.t(df=2.74, loc=0.0, scale=1.0),
"triang":
stats.triang(c=0.158, loc=0.0, scale=1.0),
"truncexpon":
stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
"truncnorm":
stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0),
"tukeylambda":
stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
"uniform":
stats.uniform(loc=0.0, scale=1.0),
"vonmises":
stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
"vonmises_line":
stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0),
"wald":
stats.wald(loc=0.0, scale=1.0),
"weibull_min":
stats.weibull_min(c=1.79, loc=0.0, scale=1.0),
"weibull_max":
stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
"wrapcauchy":
stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0),
}
dist_continu = [
d for d in dir(stats) if isinstance(getattr(stats, d), stats.rv_continuous)
]
dist_discrete = [
d for d in dir(stats) if isinstance(getattr(stats, d), stats.rv_discrete)
]
print 'number of continuous distributions:', len(dist_continu)
print 'number of discrete distributions: ', len(dist_discrete)
##Distributions can be used in one of two ways, either by passing all distribution
##parameters to each method call or by freezing the parameters for the instance
##of the distribution. As an example, we can get the median of the distribution by using
##the percent point function, ppf, which is the inverse of the cdf:
print stats.nct.ppf(0.5, 10, 2.5)
my_nct = stats.nct(10, 2.5)
print my_nct.ppf(0.5)
##`help(stats.nct)` prints the complete docstring of the distribution. Instead
##we can print just some basic information:
print stats.nct.extradoc #contains the distribution specific docs
print 'number of arguments: %d, shape parameters: %s' % (stats.nct.numargs,
stats.nct.shapes)
print 'bounds of distribution lower: %s, upper: %s' % (stats.nct.a,
stats.nct.b)
##We can list all methods and properties of the distribution with
##`dir(stats.nct)`. Some of the methods are private methods, that are
##not named as such, i.e. no leading underscore, for example veccdf or
def get_H1_statistic_distribution(self):
effect_size,N = self.effect_size,self.N
ncp = self.get_ncp() #effect_size * np.sqrt(N)
return stats.nct(N-1,ncp)
def run(self):
self.collector = []
#main op
for contamination in tqdm.tqdm(self.c_grid):
samps = int(contamination * self.n_samples)
if samps < 2:
continue
#init running metrics
running_metrics = defaultdict(list)
for k in self.k_grid:
clf = LocalOutlierFactor(n_neighbors=k,
contamination=contamination)
clf.fit_predict(self.data)
X_scores = np.log(-clf.negative_outlier_factor_)
t0 = X_scores.argsort() #[::-1]
top_k = t0[-samps:]
min_k = t0[:samps]
x_out = X_scores[top_k]
x_in = X_scores[min_k]
mc_out = np.mean(x_out)
mc_in = np.mean(x_in)
vc_out = np.var(x_out)
vc_in = np.var(x_in)
Tck = (mc_out - mc_in) / np.sqrt(
(self.eps + ((1 / samps) * (vc_out + vc_in))))
running_metrics['tck'].append(Tck)
running_metrics['mck_out'].append(mc_out)
running_metrics['mck_in'].append(mc_in)
running_metrics['vck_in'].append(vc_in)
running_metrics['vck_out'].append(vc_out)
largest_idx = np.array(running_metrics['tck']).argsort()[-1]
mean_mc_out = np.mean(running_metrics['mck_out'])
mean_mc_in = np.mean(running_metrics['mck_in'])
mean_vc_out = np.mean(running_metrics['vck_out'])
mean_vc_in = np.mean(running_metrics['vck_in'])
#ncpc - non-centrality parameter
ncpc = (mean_mc_out - mean_mc_in) / np.sqrt(
(self.eps + ((1 / samps) * (mean_vc_out + mean_vc_in))))
#dfc - degrees of freedom
dfc = (2 * samps) - 2
if dfc <= 0:
continue
Z = nct(dfc, ncpc) #non-central t-distribution
Kopt = self.k_grid[largest_idx]
Topt = running_metrics['tck'][largest_idx]
Z = Z.cdf(Topt)
self.collector.append([Kopt, Topt, Z, contamination])
max_cdf = 0.
self.tuned_params = {}
for v in self.collector:
Kopt, Topt, Z, contamination = v
if Z > max_cdf:
max_cdf = Z
if max_cdf == Z:
self.tuned_params['k'] = Kopt
self.tuned_params['c'] = contamination
print("\nTuned LOF Parameters : {}".format(self.tuned_params))
return
