def update_lle(m, k_neighbours):
main_stats.apply_lle(m=m, k=k_neighbours)
lle_vis = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_lle,
column_name=main_stats.label_column).plot_data()
return [lle_vis]
def update_pca_box(box_dim):
box_plot = VisualizationPlotly(
pd_data_frame=main_stats.
reduced_pandas_dataframe_isomap
).box_plot_classifications(dim=box_dim - 1)
return [box_plot]
def update_pca(m):
main_stats.apply_pca(m=m)
pca_vis = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_pca,
column_name=main_stats.label_column).plot_data()
return [pca_vis]
def update_isomap(m):
main_stats.apply_mds(m=m)
mds_vis = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_mds,
column_name=main_stats.label_column).plot_data()
return [mds_vis]
def update_kmap(m, k_neighbours, a):
main_stats.apply_kmap(m=m, k=k_neighbours, a=a)
kmap_vis = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_kmap,
column_name=main_stats.label_column).plot_data()
return [kmap_vis]
def update_tsne(m, perplexity):
main_stats.apply_tsne(m=m, perplexity=perplexity)
tsne_vis = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_tsne,
column_name=main_stats.label_column).plot_data()
return [tsne_vis]
def update_pca_box(box_dim):
print(box_dim)
box_plot = VisualizationPlotly(
pd_data_frame=main_stats.reduced_pandas_dataframe_pca,
column_name=main_stats.label_column
).box_plot_classifications(dim=box_dim - 1)
return [box_plot]
def _get_graph(self):
''' dashboard with the graphs plots
'''
# PCA CASE HERE, right now just 2 plots (scatter and boxplot)
if self.method == 'PCA':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_pca,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
pca_plots = []
if 'scatter' in self.plot_options:
scatter_fig = visualisation.plot_data()
pca_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_pca', figure=scatter_fig)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig = visualisation.box_plot_classifications()
pca_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_pca', figure=box_fig)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
pca_dendo = visualisation.plot_dendrogram()
pca_plots.append(html.Div([
dcc.Graph(id='dendrogram_pca', figure=pca_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
pca_density = visualisation.plot_data_density()
pca_plots.append(html.Div([
dcc.Graph(id='density_pca', figure=pca_density),
], className='five columns'
))
if 'heat' in self.plot_options:
pca_heat = visualisation.plot_data_heat()
pca_plots.append(html.Div([
dcc.Graph(id='heat_pca', figure=pca_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='pca') # this should be done somewhere else
pca_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
pca_plots.append(html.Div([
dcc.Graph(id='connected_graph_figure_pca', figure=pca_graph),
], className='five columns'
))
dashboard = html.Div(children=pca_plots, className="row")
if self.method == 'LLE':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_lle,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
lle_plots = []
if 'scatter' in self.plot_options:
scatter_fig_lle = visualisation.plot_data()
lle_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_lle', figure=scatter_fig_lle)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig_lle = visualisation.box_plot_classifications()
lle_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_lle', figure=box_fig_lle)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
lle_dendo = visualisation.plot_dendrogram()
lle_plots.append(html.Div([
dcc.Graph(id='dendrogram_lle', figure=lle_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
lle_density = visualisation.plot_data_density()
lle_plots.append(html.Div([
dcc.Graph(id='density_lle', figure=lle_density),
], className='five columns'
))
if 'heat' in self.plot_options:
lle_heat = visualisation.plot_data_heat()
lle_plots.append(html.Div([
dcc.Graph(id='heat_lle', figure=lle_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='lle') # this should be done somewhere else
lle_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
lle_plots.append( html.Div([
dcc.Graph(id='connected_graph_figure_lle', figure=lle_graph),
], className='five columns'
))
dashboard = html.Div(children=lle_plots, className="row")
if self.method == 'TSNE':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_tsne,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
tsne_plots = []
if 'scatter' in self.plot_options:
scatter_fig_tsne = visualisation.plot_data()
tsne_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_tsne', figure=scatter_fig_tsne)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig_tsne = visualisation.box_plot_classifications()
tsne_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_tsne', figure=box_fig_tsne)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
tsne_dendo = visualisation.plot_dendrogram()
tsne_plots.append(html.Div([
dcc.Graph(id='dendrogram_tsne', figure=tsne_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
tsne_density = visualisation.plot_data_density()
tsne_plots.append(html.Div([
dcc.Graph(id='density_tsne', figure=tsne_density),
], className='five columns'
))
if 'heat' in self.plot_options:
tsne_heat = visualisation.plot_data_heat()
tsne_plots.append(html.Div([
dcc.Graph(id='heat_tsne', figure=tsne_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='tsne') # this should be done somewhere else
tsne_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
tsne_plots.append( html.Div([
dcc.Graph(id='connected_graph_figure_tsne', figure=tsne_graph),
], className='five columns'
))
dashboard = html.Div(children=tsne_plots, className="row")
if self.method == 'UMAP':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_umap,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
umap_plots = []
if 'scatter' in self.plot_options:
scatter_fig = visualisation.plot_data()
umap_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_umap', figure=scatter_fig)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig = visualisation.box_plot_classifications()
umap_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_umap', figure=box_fig)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
umap_dendo = visualisation.plot_dendrogram()
umap_plots.append(html.Div([
dcc.Graph(id='dendrogram_umap', figure=umap_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
umap_density = visualisation.plot_data_density()
umap_plots.append(html.Div([
dcc.Graph(id='density_umap', figure=umap_density),
], className='five columns'
))
if 'heat' in self.plot_options:
umap_heat = visualisation.plot_data_heat()
umap_plots.append(html.Div([
dcc.Graph(id='heat_umap', figure=umap_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='umap') # this should be done somewhere else
umap_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
umap_plots.append(html.Div([
dcc.Graph(id='connected_graph_figure_umap', figure=umap_graph),
], className='five columns'
))
dashboard = html.Div(children=umap_plots, className="row")
if self.method == 'KMAP':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_kmap,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
kmap_plots = []
if 'scatter' in self.plot_options:
scatter_fig = visualisation.plot_data()
kmap_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_kmap', figure=scatter_fig)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig = visualisation.box_plot_classifications()
kmap_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_kmap', figure=box_fig)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
kmap_dendo = visualisation.plot_dendrogram()
kmap_plots.append(html.Div([
dcc.Graph(id='dendrogram_kmap', figure=kmap_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
kmap_density = visualisation.plot_data_density()
kmap_plots.append(html.Div([
dcc.Graph(id='density_kmap', figure=kmap_density),
], className='five columns'
))
if 'heat' in self.plot_options:
kmap_heat = visualisation.plot_data_heat()
kmap_plots.append(html.Div([
dcc.Graph(id='heat_kmap', figure=kmap_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='kmap') # this should be done somewhere else
kmap_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
kmap_plots.append(html.Div([
dcc.Graph(id='connected_graph_figure_kmap', figure=kmap_graph),
], className='five columns'
))
dashboard = html.Div(children=kmap_plots, className="row")
if self.method == 'ISOMAP':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_isomap,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
isomap_plots = []
if 'scatter' in self.plot_options:
scatter_fig = visualisation.plot_data()
isomap_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_isomap', figure=scatter_fig)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig = visualisation.box_plot_classifications()
isomap_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_isomap', figure=box_fig)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
isomap_dendo = visualisation.plot_dendrogram()
isomap_plots.append(html.Div([
dcc.Graph(id='dendrogram_isomap', figure=isomap_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
isomap_density = visualisation.plot_data_density()
isomap_plots.append(html.Div([
dcc.Graph(id='density_isomap', figure=isomap_density),
], className='five columns'
))
if 'heat' in self.plot_options:
isomap_heat = visualisation.plot_data_heat()
isomap_plots.append(html.Div([
dcc.Graph(id='heat_isomap', figure=isomap_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='isomap') # this should be done somewhere else
isomap_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
isomap_plots.append(html.Div([
dcc.Graph(id='connected_graph_figure_isomap', figure=isomap_graph),
], className='five columns'
))
dashboard = html.Div(children=isomap_plots, className="row")
if self.method == 'MDS':
visualisation = VisualizationPlotly(pd_data_frame=self.stats.reduced_pandas_dataframe_mds,
column_name=self.stats.label_column)
# Depending on the plot_options the user selected, produce corresponding plots and append them to the
# list
mds_plots = []
if 'scatter' in self.plot_options:
scatter_fig = visualisation.plot_data()
mds_plots.append(html.Div([
dcc.Graph(id='reduced_data_plot_mds', figure=scatter_fig)
], className='five columns'
))
if 'box' in self.plot_options:
box_fig = visualisation.box_plot_classifications()
mds_plots.append(html.Div([
dcc.Graph(id='box_outliers_plot_mds', figure=box_fig)
], className='five columns'
))
if 'dendrogram' in self.plot_options:
mds_dendo = visualisation.plot_dendrogram()
mds_plots.append(html.Div([
dcc.Graph(id='dendrogram_mds', figure=mds_dendo),
], className='five columns'
))
if 'density' in self.plot_options:
mds_density = visualisation.plot_data_density()
mds_plots.append(html.Div([
dcc.Graph(id='density_mds', figure=mds_density),
], className='five columns'
))
if 'heat' in self.plot_options:
mds_heat = visualisation.plot_data_heat()
mds_plots.append(html.Div([
dcc.Graph(id='heat_mds', figure=mds_heat),
], className='five columns'
))
if 'k' in self.plot_options:
self.stats.graph_neighbours(n_neighbours=4, algorithm='mds') # this should be done somewhere else
mds_graph = visualisation.graph_neighbours(self.stats.edges, self.stats.nodes)
mds_plots.append(html.Div([
dcc.Graph(id='connected_graph_figure_mds', figure=mds_graph),
], className='five columns'
))
dashboard = html.Div(children=mds_plots, className="row")
return dashboard