import numpy as np
np.random.seed(0)

from bokeh.io import curdoc
from bokeh.models import ColumnDataSource, VBox, HBox, Select, Slider
from bokeh.plotting import Figure
from bokeh.palettes import Spectral6

from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler

# define some helper functions
def clustering(X, algorithm, n_clusters):
    # normalize dataset for easier parameter selection
    X = StandardScaler().fit_transform(X)

    # estimate bandwidth for mean shift
    bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)

    # connectivity matrix for structured Ward
    connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)

    # make connectivity symmetric
    connectivity = 0.5 * (connectivity + connectivity.T)

    # Generate the new colors:
    if algorithm=='MiniBatchKMeans':
        model = cluster.MiniBatchKMeans(n_clusters=n_clusters)

    elif algorithm=='Birch':
        model = cluster.Birch(n_clusters=n_clusters)

    elif algorithm=='DBSCAN':
        model = cluster.DBSCAN(eps=.2)

    elif algorithm=='AffinityPropagation':
        model = cluster.AffinityPropagation(damping=.9,
                                            preference=-200)

    elif algorithm=='MeanShift':
        model = cluster.MeanShift(bandwidth=bandwidth,
                                  bin_seeding=True)

    elif algorithm=='SpectralClustering':
        model = cluster.SpectralClustering(n_clusters=n_clusters,
                                           eigen_solver='arpack',
                                           affinity="nearest_neighbors")

    elif algorithm=='Ward':
        model = cluster.AgglomerativeClustering(n_clusters=n_clusters,
                                                linkage='ward',
                                                connectivity=connectivity)

    elif algorithm=='AgglomerativeClustering':
        model = cluster.AgglomerativeClustering(linkage="average",
                                                affinity="cityblock",
                                                n_clusters=n_clusters,
                                                connectivity=connectivity)

    model.fit(X)

    if hasattr(model, 'labels_'):
            y_pred = model.labels_.astype(np.int)
    else:
            y_pred = model.predict(X)

    return X, y_pred

def get_dataset(dataset, n_samples):
    if dataset == 'Noisy Circles':
        return datasets.make_circles(n_samples=n_samples,
                                    factor=0.5,
                                    noise=0.05)

    elif dataset == 'Noisy Moons':
        return datasets.make_moons(n_samples=n_samples,
                                   noise=0.05)

    elif dataset == 'Blobs':
        return datasets.make_blobs(n_samples=n_samples,
                                   random_state=8)

    elif dataset == "No Structure":
        return np.random.rand(n_samples, 2), None

# set up initial data
n_samples = 1500
n_clusters = 2
algorithm = 'MiniBatchKMeans'
dataset = 'Noisy Circles'

X, y = get_dataset(dataset, n_samples)
X, y_pred = clustering(X, algorithm, n_clusters)
spectral = np.hstack([Spectral6] * 20)
colors = [spectral[i] for i in y]

# set up plot (styling in theme.yaml)
plot = Figure(toolbar_location=None, title=algorithm)
source = ColumnDataSource(data=dict(x=X[:, 0], y=X[:, 1], colors=colors))
plot.circle('x', 'y', fill_color='colors', line_color=None, source=source)

# set up widgets
clustering_algorithms= [
    'MiniBatchKMeans',
    'AffinityPropagation',
    'MeanShift',
    'SpectralClustering',
    'Ward',
    'AgglomerativeClustering',
    'DBSCAN',
    'Birch'
]

datasets_names = [
    'Noisy Circles',
    'Noisy Moons',
    'Blobs',
    'No Structure'
]

algorithm_select = Select(value='MiniBatchKMeans',
                          title='Select algorithm:',
                          options=clustering_algorithms)

dataset_select = Select(value='Noisy Circles',
                        title='Select dataset:',
                        options=datasets_names)

samples_slider = Slider(title="Number of samples",
                        value=1500.0,
                        start=1000.0,
                        end=3000.0,
                        step=100)

clusters_slider = Slider(title="Number of clusters",
                         value=2.0,
                         start=2.0,
                         end=10.0,
                         step=1)

# set up callbacks
def update_algorithm_or_clusters(attrname, old, new):
    global X

    algorithm = algorithm_select.value
    n_clusters = int(clusters_slider.value)

    X, y_pred = clustering(X, algorithm, n_clusters)
    colors = [spectral[i] for i in y_pred]

    source.data['colors'] = colors
    source.data['x'] = X[:, 0]
    source.data['y'] = X[:, 1]

    plot.title = algorithm

def update_samples_or_dataset(attrname, old, new):
    global X, y

    dataset = dataset_select.value
    algorithm = algorithm_select.value
    n_clusters = int(clusters_slider.value)
    n_samples = int(samples_slider.value)

    X, y = get_dataset(dataset, n_samples)
    X, y_pred = clustering(X, algorithm, n_clusters)
    colors = [spectral[i] for i in y_pred]

    source.data['x'] = X[:, 0]
    source.data['y'] = X[:, 1]
    source.data['colors'] = colors

algorithm_select.on_change('value', update_algorithm_or_clusters)
clusters_slider.on_change('value', update_algorithm_or_clusters)

dataset_select.on_change('value', update_samples_or_dataset)
samples_slider.on_change('value', update_samples_or_dataset)

# set up layout
selects = HBox(dataset_select, algorithm_select)
inputs = VBox(samples_slider, clusters_slider, selects)

# add to document
curdoc().add_root(HBox(inputs, plot, width=800))
