def the_normal_cdf(samples_std1, samples_std3, samples_std10):
x_std1, y_std1 = ecdf(samples_std1)
x_std3, y_std3 = ecdf(samples_std3)
x_std10, y_std10 = ecdf(samples_std10)
_ = plt.plot(x_std1, y_std1, marker='.', linestyle='none')
_ = plt.plot(x_std3, y_std3, marker='.', linestyle='none')
_ = plt.plot(x_std10, y_std10, marker='.', linestyle='none')
_ = plt.legend(('std = 1', 'std = 3', 'std = 10'), loc='lower right')
plt.show()
def visualizing_bootstrap_samples():
for _ in range(50):
bs_sample = np.random.choice(rainfall, size=len(rainfall))
x, y = ecdf(bs_sample)
_ = plt.plot(x, y, marker='.', linestyle='none',
color='gray', alpha=0.1)
x, y = ecdf(rainfall)
_ = plt.plot(x, y, marker='.')
plt.margins(0.02)
_ = plt.xlabel('yearly rainfall (mm)')
_ = plt.ylabel('ECDF')
plt.show()
def do_the_data_follow_our_story():
x, y = ecdf(nohitter_times)
np.random.seed(42)
tau = np.mean(nohitter_times)
inter_nohitter_time = np.random.exponential(tau, 100000)
x_theor, y_theor = ecdf(inter_nohitter_time)
plt.plot(x_theor, y_theor)
plt.plot(x, y, marker='.', linestyle='none')
plt.margins(0.02)
plt.xlabel('Games between no-hitters')
plt.ylabel('CDF')
plt.show()
def are_belmont_stakes_normally_distributed(belmont_no_outliers):
mu = np.mean(belmont_no_outliers)
sigma = np.std(belmont_no_outliers)
samples = np.random.normal(mu, sigma, size=10000)
x_theor, y_theor = ecdf(samples)
x, y = ecdf(belmont_no_outliers)
_ = plt.plot(x_theor, y_theor)
_ = plt.plot(x, y, marker='.', linestyle='none')
_ = plt.xlabel('Belmont winning time (sec.)')
_ = plt.ylabel('CDF')
plt.show()
def fitmin(pts, mmefit): x = np.array(pts) ec = util.ecdf(x) xi = ec[:,0] ai = ec[:,1] if mmefit == True: (imu, isig) = Lognormal.mmefit(x) else: (imu, isig) = Lognormal.mlefit(x) sqrt2 = math.sqrt(2) xi2 = xi**2.0 ki = Lognormal.__ki(ai) ivs = [imu, isig] ovs = (ki, xi2, sqrt2) (fvals, infodict, ier, mesg) = opt.fsolve(Lognormal.__solve_fitmin, ivs, ovs, None, 1, 0) f_mu = fvals[0] f_sig = fvals[1] if ier != 1: raise LognormalConvergenceError(mesg, (f_mu, f_sig)) return (f_mu, f_sig)
def fitmin(pts, mmefit):
x = np.array(pts)
ec = util.ecdf(x)
xi = ec[:, 0]
ai = ec[:, 1]
if mmefit == True:
(imu, isig) = Lognormal.mmefit(x)
else:
(imu, isig) = Lognormal.mlefit(x)
sqrt2 = math.sqrt(2)
xi2 = xi**2.0
ki = Lognormal.__ki(ai)
ivs = [imu, isig]
ovs = (ki, xi2, sqrt2)
(fvals, infodict, ier, mesg) = opt.fsolve(Lognormal.__solve_fitmin,
ivs, ovs, None, 1, 0)
f_mu = fvals[0]
f_sig = fvals[1]
if ier != 1:
raise LognormalConvergenceError(mesg, (f_mu, f_sig))
return (f_mu, f_sig)
def sampling_out_of_binomial_distribution():
n_defaults = np.random.binomial(100, 0.05, size=10000)
x, y = ecdf(n_defaults)
_ = plt.plot(x, y, marker='.', linestyle='none')
_ = plt.xlabel('number of defaults out of 100 loans')
_ = plt.ylabel('CDF')
plt.show()
def eda_of_beak_depths():
# Compute ECDFs
x_1975, y_1975 = ecdf(bd_1975)
x_2012, y_2012 = ecdf(bd_2012)
# Plot the ECDFs
_ = plt.plot(x_1975, y_1975, marker='.', linestyle='none')
_ = plt.plot(x_2012, y_2012, marker='.', linestyle='none')
# Set margins
plt.margins(0.02)
# Add axis labels and legend
_ = plt.xlabel('beak depth (mm)')
_ = plt.ylabel('ECDF')
_ = plt.legend(('1975', '2012'), loc='lower right')
# Show the plot
plt.show()
def do_neonicotinoid_insecticides_have_unintended_consequences():
# Compute x,y values for ECDFs
x_control, y_control = ecdf(control)
x_treated, y_treated = ecdf(treated)
# Plot the ECDFs
plt.plot(x_control, y_control, marker='.', linestyle='none')
plt.plot(x_treated, y_treated, marker='.', linestyle='none')
# Set the margins
plt.margins(0.02)
# Add a legend
plt.legend(('control', 'treated'), loc='lower right')
# Label axes and show plot
plt.xlabel('millions of alive sperm per mL')
plt.ylabel('ECDF')
plt.show()
def visualizing_permutation_sampling():
for i in range(50):
perm_sample_1, perm_sample_2 = permutation_sample(
rain_june, rain_november)
x_1, y_1 = ecdf(perm_sample_1)
x_2, y_2 = ecdf(perm_sample_2)
# Plot ECDFs of permutation sample
_ = plt.plot(x_1, y_1, marker='.', linestyle='none',
color='red', alpha=0.02)
_ = plt.plot(x_2, y_2, marker='.', linestyle='none',
color='blue', alpha=0.02)
# Create and plot ECDFs from original data
x_1, y_1 = ecdf(rain_june)
x_2, y_2 = ecdf(rain_november)
_ = plt.plot(x_1, y_1, marker='.', linestyle='none', color='red')
_ = plt.plot(x_2, y_2, marker='.', linestyle='none', color='blue')
plt.margins(0.02)
_ = plt.xlabel('monthly rainfall (mm)')
_ = plt.ylabel('ECDF')
plt.show()
def fitmin(points, **kwargs):
"""
Minimization of the FIT metric using the inverse CDF
Usage: ModLav.fitmin(points, [beta=], [c=], [d=])
Input
------
points: Points to run ML estimation
**kwargs: Initial values for the mle fit. Mostly estimate Initial values from the __initial_values
method.
beta = initial beta value
c = initial c value
d = initial d value
Output
------
Return value: Tuple (beta, c, d)
"""
pts = np.array(points)
c = util.ecdf(pts)
iv = ModLav.__initial_values(pts)
i_beta = iv["beta"]
i_c = iv["c"]
i_d = iv["d"]
tol = 1e-10
if "beta" in kwargs:
i_beta = kwargs["beta"]
if "c" in kwargs:
i_c = kwargs["c"]
if "d" in kwargs:
i_d = kwargs["d"]
if "tol" in kwargs:
tol = kwargs["tol"]
ivs = [math.log(i_beta), math.log(i_c), math.log(i_d)]
oval = (c)
(fvals, infodict, ier, mesg) = opt.fsolve(ModLav.__solve_fitmin, ivs, oval, None, 1, 0, tol,2000)
f_beta = math.exp(fvals[0])
f_c = math.exp(fvals[1])
f_d = math.exp(fvals[2])
if ier != 1:
prms = {"beta": f_beta, "c": f_c, "d": f_d}
raise ModLavConvergenceError(mesg, (f_beta, f_c, f_d))
return (f_beta, f_c, f_d)
def distribution_of_no_hitters_and_cycles():
waiting_times = successive_poisson(764, 715, 100000)
_ = plt.hist(waiting_times, bins=100, density=True, histtype='step')
_ = plt.xlabel('waiting time')
_ = plt.ylabel('probability')
plt.show()
x, y = ecdf(waiting_times)
_ = plt.plot(x, y, marker='.', linestyle='none')
_ = plt.xlabel('waiting time')
_ = plt.ylabel('CDF')
plt.show()
def how_is_this_parameter_optimal():
x, y = ecdf(nohitter_times)
np.random.seed(42)
tau = np.mean(nohitter_times)
inter_nohitter_time = np.random.exponential(tau, 100000)
x_theor, y_theor = ecdf(inter_nohitter_time)
plt.plot(x_theor, y_theor)
plt.plot(x, y, marker='.', linestyle='none')
plt.margins(0.02)
plt.xlabel('Games between no-hitters')
plt.ylabel('CDF')
samples_half = np.random.exponential(tau/2, 10000)
samples_double = np.random.exponential(tau*2, 10000)
x_half, y_half = ecdf(samples_half)
x_double, y_double = ecdf(samples_double)
_ = plt.plot(x_half, y_half)
_ = plt.plot(x_double, y_double)
_ = plt.legend(['theory', 'empirical', 'tau/2',
'tau*2'], loc='lower right')
plt.show()
def will_the_bank_fail():
np.random.seed()
n_defaults = np.empty(1000)
for i in range(1000):
n_defaults[i] = perform_bernoulli_trials(100, 0.05)
x, y = ecdf(n_defaults)
_ = plt.plot(x, y, marker='.', linestyle='none')
_ = plt.xlabel('number of defaults')
_ = plt.ylabel('ECDF')
plt.show()
n_lose_money = np.sum(n_defaults >= 10)
print('Number of 100-loan simulations with 10 or more defaults',
n_lose_money)
print('Probability of losing money =', n_lose_money / len(n_defaults))
def optfit(x, lo, hi, n, **kwargs):
"""
Optimum modlav fit using search for the best xmax.
Input:
x: Set of points
lo: Low xmax value
hi: Hi xmax value [Note lo <= max(x) <= hi]
n: Number of searches.
**kwargs:
mlefit: True - use mlefit, False - use mmefit. True by default
mt: True| false. Use mirror transform. False by default
Output:
Dict: {"fit": (ModLav object, xmax, FIT metric), "ks": (ModLav, xmax, ks)}
"""
pts = util.gen_points(lo, hi, n)
fits_fm = dict()
fits_ks = dict()
rval = dict()
x.sort()
c = util.ecdf(x)
mlefit = True
if "mlefit" in kwargs:
mlefit = kwargs["mlefit"]
vmt = False
if "mt" in kwargs:
vmt = kwargs["mt"]
for xmax in pts:
try:
if mlefit == True:
m = ModLav.fromFit(x, xmax=xmax, fit="mlefit",mt=vmt)
else:
m = ModLav.fromFit(x, xmax=xmax, fit="mmefit",mt=vmt)
except ModLavConvergenceError, mlce:
print mlce
continue
except BaseException, err:
print str(err)
continue
def comparing_percentiles_to_ECDF(versicolor_petal_length):
percentiles = np.array([2.5, 25, 50, 75, 97.5])
ptiles_vers = np.percentile(versicolor_petal_length, percentiles)
print(ptiles_vers)
x_vers, y_vers = ecdf(versicolor_petal_length)
_ = plt.plot(x_vers, y_vers, '.')
_ = plt.xlabel('petal length (cm)')
_ = plt.ylabel('ECDF')
# Overlay percentiles as red diamonds.
_ = plt.plot(ptiles_vers,
percentiles / 100,
marker='D',
color='red',
linestyle='none')
# Show the plot
plt.show()
def ksmetric(self, **kwargs): """ Return the kolmogorov-smirnov metric for lognormal Input: **kwargs: points = [set of points to compute the cdf] -or- cdf = [Already computed cdf] Output: ks metric """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) y = self.cdf(c[:,0]) return util.kstest(c[:,1],c[:,2],y)
def ksmetric(self, **kwargs):
"""
Return the kolmogorov-smirnov metric for lognormal
Input:
**kwargs:
points = [set of points to compute the cdf]
-or-
cdf = [Already computed cdf]
Output:
ks metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
y = self.cdf(c[:, 0])
return util.kstest(c[:, 1], c[:, 2], y)
def fitmetric(self, **kwargs): """ Return the FIT metric for MOVLAV Input: **kwargs: points = [set of points to compute the cdf] -or- cdf = [Already computed cdf] Output: Fit metric """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) xi = c[:,0] x_hat_i = self.cdf_inv(c[:,1]) return util.fitmetric(xi, x_hat_i, c[:,1])
def ksmetric(self, **kwargs):
"""
Return the ks metris for truncated pareto
Input:
**kwargs:
points = [set of points]
-or-
cdf = [Precomputed cdf]
Output:
KS metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
y = self.cdf(c[:, 0])
return util.kstest(c[:, 1], c[:, 2], y)
def ksmetric(self, **kwargs): """ Return the ks metris for truncated pareto Input: **kwargs: points = [set of points] -or- cdf = [Precomputed cdf] Output: KS metric """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) y = self.cdf(c[:,0]) return util.kstest(c[:,1],c[:,2],y)
def difference(self, **kwargs): """ Return the Difference metric for MOVLAV Input: **kwargs: points = [set of points to compute the cdf] -or- cdf = [Already computed cdf] Output: Difference metric. The closer the difference to 0 the more similar the fit. """ c = None if "cdf" in kwargs: c = kwargs["cdf"] else: p = kwargs["points"] p.sort() c = util.ecdf(p, issorted=True) xi = c[:,0] x_hat_i = self.cdf_inv(c[:,1]) return 1 - util.chlebus_divgi_sim_fitmetric(xi, x_hat_i, c[:,1])
def difference(self, **kwargs):
"""
Return the difference metric for lognormal
Input:
**kwargs:
points = [set of points to compute the cdf]
-or-
cdf = [Already computed cdf]
Output:
Difference metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
xi = c[:, 0]
x_hat_i = self.cdf_inv(c[:, 1])
return 1 - util.chlebus_divgi_sim_fitmetric(xi, x_hat_i, c[:, 1])
def fitmetric(self, **kwargs):
"""
Return the FIT metric for truncated pareto
Input:
**kwargs:
points = [set of points]
-or-
cdf = [Precomputed cdf]
Output:
FIT metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
xi = c[:, 0]
x_hat_i = self.cdf_inv(c[:, 1])
return util.fitmetric(xi, x_hat_i, c[:, 1])
def fitmetric(self, **kwargs):
"""
Return the fit metric for lognormal
Input:
**kwargs:
points = [set of points to compute the cdf]
-or-
cdf = [Already computed cdf]
Output:
FIT metric
"""
c = None
if "cdf" in kwargs:
c = kwargs["cdf"]
else:
p = kwargs["points"]
p.sort()
c = util.ecdf(p, issorted=True)
xi = c[:, 0]
x_hat_i = self.cdf_inv(c[:, 1])
return util.fitmetric(xi, x_hat_i, c[:, 1])
data=postos_por_ano,
hue='estado',
ax=ax)
ax.legend(sorted(postos_por_ano.estado.unique().tolist()),
loc='center left',
bbox_to_anchor=(1, 0.5),
prop={'size': 18})
plt.title('Número de postos pesquisados anualmente por Estado', fontsize=22)
plt.show()
fig.savefig('imagem.png') # eps, pdf, pgf, png, ps, raw, rgba, svg, svgz
# Preço médio
df_novo.preco_med_rev.describe()
util.ecdf(df_novo, 'preco_med_rev')
# 2 boxplots com escalas diferentes
fig, ax = plt.subplots(nrows=1,
ncols=2,
figsize=(18, 6.5),
gridspec_kw={
"width_ratios": [5, 1],
"wspace": 0
})
# Eixo para produtos com preço médio similares
sns.boxplot(x="produto",
y="preco_med_rev",
data=df_novo[df_novo.produto != "GLP"],
order=["ETANOL", "GASOLINA", "GNV", "DIESEL", "DIESEL S10"],
def draw_graph(graph, prefix):
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph)
edges, weights = zip(*nx.get_edge_attributes(graph, 'weight').items())
nx.draw(graph,
pos,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_graph.png")
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph)
edges, weights = zip(*nx.get_edge_attributes(graph, 'weight').items())
nx.draw_spring(graph,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_spring_graph.png")
plt.figure(figsize=(12, 8))
plt.hist(weights, bins=200)
plt.xlabel('Weights')
plt.yscale('log')
plt.savefig(prefix + "_weights_hist.png")
plt.figure(figsize=(12, 8))
(x, y) = util.ecdf(weights)
plt.scatter(x=x, y=y)
plt.ylabel('percentage')
plt.xlabel('edge weights')
plt.savefig(prefix + '_weights_cdf.png')
plt.show()
graph_filtered = graph
edge_weights = nx.get_edge_attributes(graph_filtered, 'weight')
#Only keep edges with atleast weight 2
graph_filtered.remove_edges_from(
(e for e, w in edge_weights.items() if w < 2))
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph_filtered)
edges, weights = zip(
*nx.get_edge_attributes(graph_filtered, 'weight').items())
nx.draw(graph_filtered,
pos,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_filtered_w2_graph.png")
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph_filtered)
edges, weights = zip(
*nx.get_edge_attributes(graph_filtered, 'weight').items())
nx.draw_circular(graph_filtered,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_filtered_w2_graph_circular.png")
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph_filtered)
edges, weights = zip(
*nx.get_edge_attributes(graph_filtered, 'weight').items())
nx.draw_spectral(graph_filtered,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_filtered_w2_graph_spectral.png")
plt.figure(figsize=(12, 8))
pos = nx.random_layout(graph_filtered)
edges, weights = zip(
*nx.get_edge_attributes(graph_filtered, 'weight').items())
nx.draw_spring(graph_filtered,
node_color='k',
node_size=5,
edgelist=edges,
edge_color=weights,
width=1.0,
edge_cmap=plt.cm.Blues)
plt.savefig(prefix + "_filtered_w2_graph_spring.png")
def test_return_length_of_x(self):
a = [1, 1, 2, 2, 3, 3, 7, 8, 9, 10]
x, y = util.ecdf(a)
self.assertEqual(len(y), len(a))
def test_return_10_y_values(self):
a = [1, 1, 2, 2, 3, 3, 7, 8, 9, 10]
x, y = util.ecdf(a)
assert_array_equal(
y, np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]))
def test_return_3_y_values(self):
a = [1, 1, 2]
x, y = util.ecdf(a)
expected_y = np.array([0.333, 0.666, 0.999])
for i in range(len(a)):
self.assertAlmostEqual(y[i], expected_y[i], places=2)
def test_return_input_as_x(self):
a = [1, 1, 2, 2, 3, 3, 7, 8, 9, 10]
x, y = util.ecdf(a)
assert_array_equal(x, np.array(a))
