def mainregress(selection, alpha):
if len(selection) < 2:
return
x = xdown.get()['value']
y = ydown.get()['value']
tabdata = []
mldatax = []
mldatay = []
species = iris.Species.unique()
for i, p in enumerate(selection['points']):
mldatax.append(p['x'])
mldatay.append(p['y'])
tabdata.append({
x: p['x'],
y: p['y'],
'species': species[p['curve']]
})
X = np.c_[mldatax, np.array(mldatax) ** 2]
ridge = KernelRidge(alpha=alpha).fit(X, mldatay)
xspace = np.linspace(min(mldatax)-1, max(mldatax)+1, 100)
plot = pw.scatter(mldatax, mldatay, label='train', markersize=15)
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot += pw.line(xspace, ridge.predict(np.c_[xspace, xspace**2]), label='model', mode='lines')
plot.xlabel = x
plot.ylabel = y
linear.do_all(plot.dict)
table1.do_data(pd.DataFrame(tabdata))
def gen_stars(label):
buss1 = business[business.categories.str.contains(label)]
cache['bids'] = buss1.business_id
chart = pw.scatter(buss1.review_count, buss1.stars, text=buss1.name)
chart.xlabel('reviews')
chart.ylabel('stars')
chart.title(label)
chart.layout['hovermode'] = 'closest'
return chart
def pairplot(x, y):
if x is None or y is None:
return
x = x['value']
y = y['value']
plot = pw.Chart()
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot.xlabel = x
plot.ylabel = y
mainplot.do_all(plot.dict)
def pairplot(x, y):
print('hellox')
if x is None or y is None:
return
x = x['value']
y = y['value']
plot = pw.Chart()
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot.xlabel(x)
plot.ylabel(y)
mainplot.do_all(plot.to_json())
def vizplace(place):
bid = cache['bids'][place['point']]
name = business[business.business_id == bid].name.values[0]
chart = gen_busy(bid)
chart.title(name)
busy.do_all(chart.dict)
revbid = reviews[reviews.business_id == bid]
chart = pw.scatter(revbid.date, revbid.stars)
chart.data[0]['marker'] = {'opacity': 1 / np.log(revbid.shape[0])}
chart.ylabel('stars')
chart.title(name)
chart.layout['hovermode'] = 'closest'
revdate.do_all(chart.dict)
def mainregress(selection, alpha):
if len(selection) < 2:
return
x = xdown.get()['value']
y = ydown.get()['value']
tabdata = []
mldatax = []
mldatay = []
species = iris.Species.unique()
for i, p in enumerate(selection['points']):
mldatax.append(p['x'])
mldatay.append(p['y'])
tabdata.append({
x: p['x'],
y: p['y'],
'species': species[p['curveNumber']]
})
X = np.c_[mldatax, np.array(mldatax)**2]
ridge = KernelRidge(alpha=alpha).fit(X, mldatay)
xspace = np.linspace(min(mldatax) - 1, max(mldatax) + 1, 100)
plot = pw.scatter(mldatax, mldatay, label='train', markersize=15)
for i, df in iris.groupby('Species'):
plot += pw.scatter(df[x], df[y], label=i)
plot += pw.line(xspace,
ridge.predict(np.c_[xspace, xspace**2]),
label='model',
mode='lines')
plot.xlabel(x)
plot.ylabel(y)
linear.do_all(plot.to_json())
table1.do_update(tabdata)
def bubble():
chart = pw.scatter(data, markersize=np.arange(1, 6) * 10)
chart.save('fig_bubble.html', **options)
def scatter():
pw.scatter(data).save('fig_scatter.html', **options)
def bubble():
chart = pw.scatter(data, markersize=np.arange(1, 6) * 10)
chart.save('fig_bubble.html', **options)
def scatter():
pw.scatter(data).save('fig_scatter.html', **options)