def tukeys_range_test(table, response_cols, factor_col, alpha=0.05):
rb = ReportBuilder()
rb.addMD("""## Tukey's range test Result""")
for response_col in response_cols:
data = table[response_col]
posthoc = pairwise_tukeyhsd(data, table[factor_col], alpha=alpha)
posthoc_html = posthoc._results_table.as_html()
posthoc.plot_simultaneous()
rb.addMD("""### {response_col}""".format(response_col=response_col))
rb.addHTML(posthoc_html)
rb.addPlt(plt)
plt.clf()
return {'result': {'report': rb.get()}}
def default(self, o):
# TODO add more support types
if isinstance(o, set):
return {'__set__': list(o)}
elif isinstance(o, numpy.ndarray):
return {'__numpy__': _to_default_list(o)}
elif hasattr(o, '_repr_html_'):
rb = ReportBuilder()
rb.addHTML(o._repr_html_())
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
elif hasattr(o, 'savefig'):
rb = ReportBuilder()
rb.addPlt(o)
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
else:
rb = ReportBuilder()
rb.addRawTextMD(str(o))
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
def _correlation(table, vars, method='pearson', height=2.5, corr_prec=2):
size = len(vars)
s_default = plt.rcParams['lines.markersize']**2.
scatter_kws = {"s": s_default * height / 6.4}
corr_arr = np.ones((size, size)) # TODO variable name dict
p_arr = np.zeros((size, size))
for i in range(size):
for j in range(i):
if method == 'pearson':
r, p = stats.pearsonr(table[vars[i]], table[vars[j]])
elif method == 'spearman':
r, p = stats.spearmanr(table[vars[i]], table[vars[j]])
elif method == 'kendal':
r, p = stats.kendalltau(table[vars[i]], table[vars[j]])
corr_arr[i][j] = r
p_arr[i][j] = p
for i in range(size):
for j in range(i, size):
corr_arr[i][j] = corr_arr[j][i]
p_arr[i][j] = p_arr[j][i]
def corr(x, y, **kwargs):
if kwargs['method'] == 'pearson':
r, p = stats.pearsonr(x, y)
elif kwargs['method'] == 'spearman':
r, p = stats.spearmanr(x, y)
elif kwargs['method'] == 'kendal':
r, p = stats.kendalltau(x, y)
p_stars = ''
if p <= 0.05:
p_stars = '*'
if p <= 0.01:
p_stars = '**'
if p <= 0.001:
p_stars = '***'
corr_text = '{:.{prec}f}'.format(r, prec=corr_prec)
print(type(corr_prec))
font_size = abs(r) * 15 * 2 / corr_prec + 5
ax = plt.gca()
ax.annotate(corr_text, [
.5,
.5,
],
xycoords="axes fraction",
ha='center',
va='center',
fontsize=font_size * height)
ax.annotate(p_stars,
xy=(0.65, 0.6),
xycoords=ax.transAxes,
color='red',
fontsize=17 * height)
g = sns.PairGrid(table, vars=vars, height=height)
g.map_diag(sns.distplot)
if method == 'pearson':
g.map_lower(sns.regplot, scatter_kws=scatter_kws)
else:
g.map_lower(sns.regplot, lowess=True, scatter_kws=scatter_kws)
g.map_upper(corr, method=method)
rb = ReportBuilder()
rb.addPlt(plt)
plt.clf()
params = {'vars': vars, 'method': method, 'height': height}
res = dict()
res['params'] = params
res['corr'] = corr_arr
res['pvalue'] = p_arr
res['report'] = rb.get()
return {'result': res}
return super(DefaultEncoder, self).encode(hint_tuples(obj))
def default(self, o):
<<<<<<< HEAD
# TODO add more support types
if isinstance(o, set):
return {'__set__': list(o)}
elif isinstance(o, numpy.ndarray):
return {'__numpy__': _to_default_list(o)}
elif hasattr(o, '_repr_html_'):
rb = ReportBuilder()
rb.addHTML(o._repr_html_())
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
elif hasattr(o, 'savefig'):
rb = ReportBuilder()
rb.addPlt(o)
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
else:
rb = ReportBuilder()
rb.addRawTextMD(str(o))
return {'report': rb.get(), '__pickled__': list(pickle.dumps(o))}
=======
if isinstance(o, set):
return {'__set__': list(o)}
elif isinstance(o, numpy.ndarray):
return {'__numpy__': o.tolist()}
# TODO add more support types
# return {'__pickled__': list(pickle.dumps(o))}
elif hasattr(o, '_repr_html_'):
rb = BrtcReprBuilder()
rb.addHTML(o._repr_html_())