def test_anova(self, do_int):
"""Additional aspects of OnewayAnova
"""
oa = OneWayAnova()
oa_custom = OneWayAnova(space='custom')
ds = datasets['uni4large'].copy()
if do_int:
ds.samples = (ds.samples * 1000).astype(np.int)
ds_samples_orig = ds.samples.copy() # to verify that nothing was modified
ds_custom = Dataset(ds.samples, sa={'custom': ds.targets})
r = oa(ds)
assert_array_equal(ds.samples, ds_samples_orig) # no inplace changes!
self.assertRaises(KeyError, oa_custom, ds)
r_custom = oa_custom(ds_custom)
self.assertTrue(np.allclose(r.samples, r_custom.samples))
# we should get the same results on subsequent runs
r2 = oa(ds)
r_custom2 = oa_custom(ds_custom)
self.assertTrue(np.allclose(r.samples, r2.samples))
self.assertTrue(np.allclose(r_custom.samples, r_custom2.samples))
skip_if_no_external('scipy')
from scipy.stats.stats import f_oneway
# compare against scipy implementation
# we need to create groups of those target samples
groups = [
ds[ds.targets == ut]
for ut in ds.sa['targets'].unique
]
spf, spp = f_oneway(*groups)
assert_array_almost_equal(r.samples[0], spf)
def test_anova(self, do_int):
"""Additional aspects of OnewayAnova
"""
oa = OneWayAnova()
oa_custom = OneWayAnova(space='custom')
ds = datasets['uni4large'].copy()
if do_int:
ds.samples = (ds.samples * 1000).astype(np.int)
ds_samples_orig = ds.samples.copy(
) # to verify that nothing was modified
ds_custom = Dataset(ds.samples, sa={'custom': ds.targets})
r = oa(ds)
assert_array_equal(ds.samples, ds_samples_orig) # no inplace changes!
self.assertRaises(KeyError, oa_custom, ds)
r_custom = oa_custom(ds_custom)
self.assertTrue(np.allclose(r.samples, r_custom.samples))
# we should get the same results on subsequent runs
r2 = oa(ds)
r_custom2 = oa_custom(ds_custom)
self.assertTrue(np.allclose(r.samples, r2.samples))
self.assertTrue(np.allclose(r_custom.samples, r_custom2.samples))
skip_if_no_external('scipy')
from scipy.stats.stats import f_oneway
# compare against scipy implementation
# we need to create groups of those target samples
groups = [ds[ds.targets == ut] for ut in ds.sa['targets'].unique]
spf, spp = f_oneway(*groups)
assert_array_almost_equal(r.samples[0], spf)
def anova(frame, qualitative):
anv = pd.DataFrame()
anv['feature'] = qualitative
pvals = []
for c in qualitative:
samples = []
for cls in frame[c].unique():
s = frame[frame[c] == cls]['SalePrice'].values
samples.append(s)
pval = stats.f_oneway(*samples)[1] #pval值大于0.05就可以认为该变量对SalePrice没有作用
pvals.append(pval)
anv['pval'] = pvals
return anv.sort_values('pval')
def doAnovaTest(allData):
fileIndex = 0
for data in allData:
print('***************************** FILE: test_', fileIndex,
'******************************')
# One - Way ANOVA
fvalue, pvalue = stats.f_oneway(data['v0'], data['v1'], data['v2'],
data['v3'], data['v4'], data['v5'],
data['v6'], data['v7'])
resultOneWay = "One way ANOVA: fvalue: " + str(
fvalue), " pvalue: " + str(pvalue)
print(resultOneWay)
#Two - Way ANOVA
for variable in data.columns:
model = ols('{} ~ v0'.format(variable), data=data).fit()
anova_table = sm.stats.anova_lm(model, typ=2)
print(anova_table)
def _transform(self, X, y=None):
notify.entering(__class__.__name__, "_fit")
results = pd.DataFrame()
all_columns = X.columns.tolist()
categorical = self._ordinal + self._nominal
columns = list(set(X.columns.tolist()).intersection(categorical))
# Measure variance between predictor levels w.r.t. the response
self.remaining_ = pd.DataFrame()
self.features_removed_ = []
for column in columns:
f, p = f_oneway(X[column], y)
if p > self._alpha:
self.features_removed_.append(column)
else:
d = {"Feature": column, "F-statistic": f, "p-value": p}
df = pd.DataFrame(data=d, index=[0])
self.remaining_ = pd.concat((self.remaining_, df), axis=0)
# Drop features
self.X_ = X.drop(columns=self.features_removed_)
notify.leaving(__class__.__name__, "_fit")
return self.X_
def plt_distribution(var):
path = './figs/distributions/%s.png' % var
if os.path.isfile(path):
return # File already exists
black_list = ['use', 'activity']
if var in black_list or 'description' in var:
return # Don't try to plot text variables
try:
lower_bound = int(math.floor(min([del_loans[var].min(), def_loans[var].min()])))
upper_bound = int(math.ceil(max([del_loans[var].max(), def_loans[var].max()])))
binwidth = int(math.ceil((upper_bound - lower_bound)/20))
binwidth = 1 if binwidth == 0 else binwidth
fig = plt.figure()
ax = fig.add_axes([0.1, 0.1,0.75, 0.75])
if del_loans[var].dtype.name == 'float64' or del_loans[var].dtype.name == 'int64':
fig = del_loans[var].hist(alpha=.5,
color='green',
bins=xrange(lower_bound, upper_bound+binwidth, binwidth),
weights=np.zeros_like(del_loans[var]) + 1. / del_loans[var].size,
label='Repaid')
if var != 'dollar_days_late_metric':
def_loans[var].hist(alpha=.5,
color='red',
bins=xrange(lower_bound, upper_bound+binwidth, binwidth),
weights=np.zeros_like(def_loans[var]) + 1. / def_loans[var].size,
label='Defaulted')
if del_loans[var].dtype.name == 'object':
fig = del_loans[var].plot(kind='bar',
alpha=.5,
color='green',
bins=xrange(lower_bound, upper_bound+binwidth, binwidth),
weights=np.zeros_like(del_loans[var]) + 1. / del_loans[var].size,
label='Repaid')
if var != 'dollar_days_late_metric':
def_loans[var].plot(kind='bar',
alpha=.5,
color='red',
bins=xrange(lower_bound, upper_bound+binwidth, binwidth),
weights=np.zeros_like(def_loans[var]) + 1. / def_loans[var].size,
label='Defaulted')
mu = np.average(del_loans[var])
sigma = np.std(del_loans[var])
textstr = 'Repaid\n$\mu=%.3f$\n$\sigma=%.3f$'%(mu, sigma)
props = dict(boxstyle='round', facecolor='#336600', alpha=0.5)
ax.text(1.02, 0.95, textstr, fontsize=14, transform=ax.transAxes,
verticalalignment='top', bbox=props)
plt.axvline(x=mu, color='#336600', linewidth=3.0)
plt.axvline(x=mu - sigma, color='#336600', linewidth=1.0, alpha=.5)
plt.axvline(x=mu + sigma, color='#336600', linewidth=1.0, alpha=.5)
mu = np.average(def_loans[var])
sigma = np.std(def_loans[var])
ignore_default = ['dollar_days_late_metric', 'actual_days_to_pay']
if var not in ignore_default:
textstr = 'Defaulted\n$\mu=%.3f$\n$\sigma=%.3f$'%(mu, sigma)
props = dict(boxstyle='round', facecolor='#990000', alpha=0.5)
ax.text(1.02, 0.72, textstr, fontsize=14, transform=ax.transAxes,
verticalalignment='top', bbox=props)
plt.axvline(x=mu, color='#990000', linewidth=3.0)
plt.axvline(x=mu-sigma, color='#990000', linewidth=1.0, alpha=.5)
plt.axvline(x=mu+sigma, color='#990000', linewidth=1.0, alpha=.5)
#One Way ANOVA Between Defaulted and Repaid
f_val, p_val = f_oneway(del_loans[var], def_loans[var])
textstr = 'ANOVA\np=%.3f'%(p_val)
props = dict(boxstyle='round', facecolor='white')
ax.text(1.02, 0.5, textstr, fontsize=14, transform=ax.transAxes,
verticalalignment='top', bbox=props)
plt.title('%s Distribution' % ' '.join([s.capitalize() for s in var.split('_')]))
plt.grid(False)
path = './figs/distributions/%s.png' % var
fig.get_figure().savefig(path)
except Exception as e:
log.error('Could not make a dist plot for %(var)s because of %(e)s' % {'var': var, 'e': e})
ANalysis Of VAriace(anova) - pode ser usada para encontrar a correlacao entre diferentes grupos de uma variavel categorica.
ex.:podemos usar anova para ver se há alguma diferença de preco medio para diferentes marcas de carro.
o teste anova apresenta dois valores:
-F-test score: variacao entre medias de amostras do grupo dividido pela variacao dentro de cada amostra do grupo.
-p-value: grau de confiança.
o f-test calcula a relacao de variação entre as medias de grupos sobre a variação dentro de cada umas das medias de grupos de amostra
'''
#anova entre honda e subaru
#pegar os dados de 'make' e 'price'
df_anova = df[["make", "price"]]
#agrupar os dados por diferentes marcas
grouped_anova = df_anova.groupby(["make"])
#teste anova
from scipy.stats import stats
anova_results_1 = stats.f_oneway(
grouped_anova.get_group("honda")["price"],
grouped_anova.get_group("subaru")["price"])
print(anova_results_1)
#statistic=0.19744030127462606, pvalue=0.6609478240622193
#os precos entre honda nao sao muito diferentes, porque o f-test é menor do que 1 e p-value é maior do que 0,05.
#anova entre honda e jaguar
from scipy.stats import stats
anova_results_1 = stats.f_oneway(
grouped_anova.get_group("honda")["price"],
grouped_anova.get_group("jaguar")["price"])
print(anova_results_1)
#statistic=400.925870564337, pvalue=1.0586193512077862e-11
#nesse caso há grande correlacao entre uma variavel categórica e outras variaveis, pois o teste anova apresentou um f-test grande e um p-value pequeno
##
#Model Development
# calculate Pearson's correlation
corr, _ = pearsonr(col1, col2)
print('Pearsons correlation: %.3f' % corr)
corr, _ = spearmanr(col1, col2)
print('Spearmans p: %.3f' % corr)
corr, _ = kendalltau(col1, col2)
print('Kendall\'s tau: %.3f' % corr)
elif part == 2:
# reading csv file
data = pd.read_csv("Part2.csv")
# stats f_oneway functions takes the groups as input and returns F and P-value
fvalue, pvalue = stats.f_oneway(data['Before'], data['After'])
print(fvalue, pvalue)
# Create a boxplot
# data[["Before", "After"]].plot(kind='box')
# plt.savefig('part2_box.png')
data["Before"].plot(kind='hist', title='histogram of 0')
data["After"].plot(kind='hist', title='Distribution of data')
plt.savefig('part2_distr.png')
stat, p = ttest_ind(data["Before"], data["After"], equal_var=False)
print('T-Test individual: t=%.3f, p=%.3f' % (stat, p))
print("Mean Before %.2f(%.2f)" %
(np.mean(data["Before"]), np.std(data["Before"])))
print("Mean After %.2f(%.2f)" %
def check_anova():
store = mongo_driver.MongoStore(props.DATASET)
LOGGER.info("Processing for R-Py")
diffs = store.load_differences(projection={"diff": False})
rpy_asts = []
rpy_ngrams = []
rpy_levenshteins = []
for i, diff in enumerate(diffs):
if i % 10000 == 0:
LOGGER.info("Processed R-Py: %d ..." % i)
# if i == 50000:
# break
ast = diff.get('d_ast', None)
ngram = diff.get('d_n_gram', None)
lev = diff.get('d_levenshtein', None)
if ast is None or ngram is None or lev is None:
continue
rpy_asts.append(ast)
rpy_ngrams.append(ngram)
rpy_levenshteins.append(lev)
LOGGER.info("Processing for Python")
py_diffs = store.load_self_syntactic_differences(language=props.TYPE_PYTHON)
py_asts = []
py_ngrams = []
py_levenshteins = []
for i, diff in enumerate(py_diffs):
if i % 10000 == 0:
LOGGER.info("Processed Python: %d ..." % i)
# if i == 50000:
# break
ast = diff.get('d_ast', None)
ngram = diff.get('d_n_gram', None)
lev = diff.get('d_levenshtein', None)
if ast is None or ngram is None or lev is None:
continue
py_asts.append(ast)
py_ngrams.append(ngram)
py_levenshteins.append(lev)
LOGGER.info("Processing for R")
r_diffs = store.load_self_syntactic_differences(language=props.TYPE_R)
r_asts = []
r_ngrams = []
r_levenshteins = []
for i, diff in enumerate(r_diffs):
if i % 10000 == 0:
LOGGER.info("Processed R: %d ..." % i)
# if i == 50000:
# break
ast = diff.get('d_ast', None)
ngram = diff.get('d_n_gram', None)
lev = diff.get('d_levenshtein', None)
if ast is None or ngram is None or lev is None:
continue
r_asts.append(ast)
r_ngrams.append(ngram)
r_levenshteins.append(lev)
print("\n### AST distance")
f_measure, p_value = f_oneway(rpy_asts, py_asts, r_asts)
print("F-Measure: %f, p-value: %f" % (f_measure, p_value))
print("R-Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(rpy_asts)), np.asscalar(np.var(rpy_asts))))
print("Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(py_asts)), np.asscalar(np.var(py_asts))))
print("R => Mean: %f, Std: %f" % (np.asscalar(np.mean(r_asts)), np.asscalar(np.var(r_asts))))
f_measure, p_value = f_oneway(rpy_asts, py_asts)
print("Rpy-Py => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(rpy_asts, r_asts)
print("Rpy-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(py_asts, r_asts)
print("Py-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
print("\n### N-Gram distance")
f_measure, p_value = f_oneway(rpy_ngrams, py_ngrams, r_ngrams)
print("F-Measure: %f, p-value: %f" % (f_measure, p_value))
print("R-Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(rpy_ngrams)), np.asscalar(np.var(rpy_ngrams))))
print("Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(py_ngrams)), np.asscalar(np.var(py_ngrams))))
print("R => Mean: %f, Std: %f" % (np.asscalar(np.mean(r_ngrams)), np.asscalar(np.var(r_ngrams))))
f_measure, p_value = f_oneway(rpy_ngrams, py_ngrams)
print("Rpy-Py => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(rpy_ngrams, r_ngrams)
print("Rpy-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(py_ngrams, r_ngrams)
print("Py-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
print("\n### Levenshtein distance")
f_measure, p_value = f_oneway(rpy_levenshteins, py_levenshteins, r_levenshteins)
print("F-Measure: %f, p-value: %f" % (f_measure, p_value))
print("R-Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(rpy_levenshteins)), np.asscalar(np.var(rpy_levenshteins))))
print("Py => Mean: %f, Std: %f" % (np.asscalar(np.mean(py_levenshteins)), np.asscalar(np.var(py_levenshteins))))
print("R => Mean: %f, Std: %f" % (np.asscalar(np.mean(r_levenshteins)), np.asscalar(np.var(r_levenshteins))))
f_measure, p_value = f_oneway(rpy_levenshteins, py_levenshteins)
print("Rpy-Py => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(rpy_levenshteins, r_levenshteins)
print("Rpy-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
f_measure, p_value = f_oneway(py_levenshteins, r_levenshteins)
print("Py-R => F-Measure: %f, p-value: %f" % (f_measure, p_value))
