def plot_clusters(path_to_cluster_results,
file_name,
plot_medoid_summary=True,
plot_each_cluster=True,
n_trajectories_per_cluster=4,
plot_heatmap=True,
save_outputs=True):
"""
Function to plot the results of the sequence analysis. These are the old matplotlib plots (deprecated, do not use)
Parameters:
path_to_cluster_results: path to the cluster results
file_name: name of the cluster results file
plot_medoid_summary: Boolean flag to plot of the medoids of all clusters
plot_each_cluster: Boolean flag to plot sample trajectories of each cluster
n_trajectories_per_cluster: Number of sample trajectories to plot in the clusterwise sample
plot_heatmap: Boolean flag to plot of the Tuscany heatmap for each cluster
save_outputs: Boolean flag to save the results
Returns:
saves the plots in the results folder
"""
## loading the cluster data
cluster_results = pd.read_csv(path_to_cluster_results + file_name + ".csv")
gp.str_to_list(cluster_results)
r_path = os.path.dirname(os.path.realpath(__file__))
## loading the shape files
path_shapefiles, regions, provinces, territories, municipalities, crs = read_files.read_shapefile_data(
r_path + '/../read_shapefiles/', 'shape_files_path.json')
df_mun = read_files.read_shapefiles_in(False, path_shapefiles, regions,
crs)
df_mun_tus = read_files.read_shapefiles_in(True, path_shapefiles,
municipalities, crs)
r_path = "../viz/"
## Initilizing the plot
t = TrajectoryClustermap(df_mun,
path_to_centroids=r_path + "comune_centroids.csv")
## Plotting required ones
if plot_medoid_summary:
t.plot_medoids(cluster_results,
path_to_save=path_to_cluster_results,
file_name=file_name,
save=True)
if plot_each_cluster:
t.plot_samples(cluster_results,
n_trajectories_per_cluster,
path_to_save=path_to_cluster_results,
file_name=file_name,
save=True)
if plot_heatmap:
t.plot_trajectories_heatmap(cluster_results,
df_mun_tus,
path_to_save=path_to_cluster_results,
file_name=file_name,
save=True)
def read_tusc(path):
"""Helper function to read in the Tuscany shapefile"""
path_shapefiles, regions, provinces, territories, municipalities, crs = read_files.read_shapefile_data(
path, 'shape_files_path.json')
df_reg_tus = read_files.read_shapefiles_in(True, path_shapefiles, regions,
crs)
return df_reg_tus
def plot_cluster_heatmaps(path_to_cluster_results, file_name):
"""
Function to plot the interactive heatmap results of the sequence analysis.
Parameters:
path_to_cluster_results: path to the cluster results
file_name: name of the cluster results file
Returns:
saves the plots in the results folder
"""
r_path = os.path.dirname(os.path.realpath(__file__))
path_shapefiles, regions, provinces, territories, municipalities, crs = read_files.read_shapefile_data(
r_path + '/../read_shapefiles/', 'shape_files_path.json')
df_mun = read_files.read_shapefiles_in(False, path_shapefiles, regions,
crs)
df_mun_tus = read_files.read_shapefiles_in(True, path_shapefiles,
municipalities, crs)
# expand multipolygons
shp_expanded = df_mun_tus.set_index(['PRO_COM'])['geometry'].apply(
pd.Series).stack().reset_index()
shp_expanded.rename(columns={0: 'geometry'}, inplace=True)
df_mun_tus_exp = shp_expanded.merge(df_mun_tus.drop(columns='geometry'),
on='PRO_COM',
how='left')
df_clusters = pd.read_csv(path_to_cluster_results + file_name + ".csv")
gp.str_to_list(df_clusters)
# create heatmaps gdfs
for c in df_clusters['cluster'].unique():
df_clus = df_clusters[df_clusters['cluster'] == c]
# create list of municipalities for all the trips in a single cluster
trips = []
for t in range(df_clus.shape[0]):
trips.extend(
list(map(int, np.unique(df_clus['locations_list'].iloc[t]))))
df_trips = pd.DataFrame(data={'pro_com': trips})
df_counts = df_trips['pro_com'].value_counts().rename_axis(
'pro_com').reset_index(name='counts')
# counts for each municipality
heatmap_gdf = df_mun_tus_exp.merge(df_counts,
how='left',
left_on='PRO_COM',
right_on='pro_com').fillna(0)
# Get lat lon from geometry to plot
heatmap_toplot = heatmap_gdf.drop('geometry', axis=1).copy()
heatmap_toplot['x'] = heatmap_gdf.apply(getGeometryCoords,
geom='geometry',
coord_type='x',
shape_type='polygon',
axis=1)
heatmap_toplot['y'] = heatmap_gdf.apply(getGeometryCoords,
geom='geometry',
coord_type='y',
shape_type='polygon',
axis=1)
# Make heatmap
colors = brewer['Reds'][9][::-1]
mapper = LinearColorMapper(palette=colors,
high=heatmap_toplot['counts'].max(),
low=heatmap_toplot['counts'].min())
source = ColumnDataSource(data=dict(x=heatmap_toplot['x'],
y=heatmap_toplot['y'],
name=heatmap_toplot['COMUNE'],
count=heatmap_toplot['counts']))
p = figure(x_axis_location=None,
y_axis_location=None,
plot_width=800,
plot_height=700)
p.grid.grid_line_color = None
p.outline_line_color = None
p.title.align = "center"
p.title.text_font_size = "40px"
p.patches('x',
'y',
source=source,
fill_color=transform('count', mapper),
fill_alpha=0.8,
line_color="gray",
line_width=0.3)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size='10pt',
label_standoff=12,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
#Add tools
hover = HoverTool(tooltips=[("Comune",
"@name"), ("Number of visitors",
"@count")])
p.add_tools(PanTool(), WheelZoomTool(), hover)
output_file(path_to_cluster_results + "/clusterwise_heatmaps/" +
file_name + "_heatmap_cluster_" + str(c) + ".html")
save(p)
def run(params):
"""
Runs the Geo2vec model based on the params input
"""
username = params['username']
season = params['season']
country = params['country']
EMB_SIZE = params['EMB_SIZE']
WINDOW_SIZE = params['WINDOW_SIZE']
N_EPOCHS = params['N_EPOCHS']
N_CLUSTERS = params['N_CLUSTERS']
MIN_LENGTH = params['MIN_LENGTH']
MIN_COUNT = params['MIN_COUNT']
train_model = params['train_model']
apply_tsne = params['apply_tsne']
plot_clusters_italy=params['plot_clusters_italy']
plot_clusters_tuscany=params['plot_clusters_tuscany']
path_to_shapefiles = "../src/utils/read_shapefiles/"
path_to_final_plots = "../results/geo2vec/"
#####################################
# Preprocessing #
#####################################
# load locations data
if country == 'all':
df_trips = load_df.get_geo2vec_data_all_country(username, season).rename(columns={'com_locs_trunc':'locations'})
else:
df_trips = load_df.get_geo2vec_data(username, season, country).rename(columns={'com_locs_trunc':'locations'})
preprocessing.str_to_list(df_trips)
df_trips_red = preprocessing.filter_short_trips(df_trips, min_length=MIN_LENGTH)
preprocessing.descriptive_sanity_check(df_trips_red)
####################################
# Load data from shapefiles #
####################################
path_shapefiles, regions, provinces, territories, municipalities, crs = read_files.read_shapefile_data(path_to_shapefiles, 'shape_files_path.json')
df_mun = read_files.read_shapefiles_in(False, path_shapefiles, municipalities, crs)
df_mun_tus = read_files.read_shapefiles_in(True, path_shapefiles, municipalities, crs)
#df_ter_tus = read_files.read_shapefiles_in(True, path_shapefiles, territories, crs, apply_crs=False)
###################################
# Geo2vec model #
###################################
g2v = Geo2vec(EMB_SIZE, WINDOW_SIZE, season, country)
# initialize model
g2v.initialize(sequences=df_trips_red['locations_list'], min_count=MIN_COUNT)
# train model
if train_model:
g2v.train(df_trips_red['locations_list'], n_epochs=N_EPOCHS)
g2v.print_params()
# cluster model
g2v.create_clusters(n_clusters=N_CLUSTERS)
# save clusters for Italy
# careful here: plotting Italy generates a HUGE html file
if plot_clusters_italy:
print('\nITALY -- plotting...')
g2v.merge_gdf(df_mun)
g2v.get_most_similar()
g2v.pickle_cluster_labels(tag='Italy')
plt_loc(g2v.gdf_clusters,
path_to_file=path_to_final_plots+'{}_{}/'.format(country, season),
file_name='Location_cluster_Italy_EMB{}_WIN{}_EPO{}_CLU{}_MinL{}_MinC{}.html'.format(EMB_SIZE, WINDOW_SIZE, N_EPOCHS, N_CLUSTERS, MIN_LENGTH, MIN_COUNT))
# save clusters for Tuscany
if plot_clusters_tuscany:
print('\nTUSCANY -- plotting...\n\n')
g2v.merge_gdf(df_mun_tus)
g2v.get_most_similar()
g2v.pickle_cluster_labels(tag='Tuscany')
plt_loc(g2v.gdf_clusters,
path_to_file=path_to_final_plots+'{}_{}/'.format(country, season),
file_name='Location_cluster_Tuscany_EMB{}_WIN{}_EPO{}_CLU{}_MinL{}_MinC{}.html'.format(EMB_SIZE, WINDOW_SIZE, N_EPOCHS, N_CLUSTERS, MIN_LENGTH, MIN_COUNT))
# Apply t-SNE to visualize the clusters
if apply_tsne:
g2v.apply_tsne_2D()
g2v.plot_tsne_2D(path_to_save=path_to_final_plots+'{}_{}/'.format(country, season))