def get_displacement_model_traces(load_data, visualised_load_data, traces, model, model_name, model_color, y_axis, start_date, end_date):
predicted_load_data = displacement_model.predict(
load_data, start_date, end_date, model)
assert (len(visualised_load_data) == len(predicted_load_data)) # prevents plotting a model when not all displaced data is available
predicted_load_traces = generate_traces(
visualised_load_data['Date'], predicted_load_data[y_axis], model_name, predicted_load_data['Date'].tolist(), color=model_color)
traces.extend(predicted_load_traces)
return traces
def get_regression_model_traces(load_data, visualised_load_data, traces, model, model_name, model_color, y_axis, start_date, end_date):
try:
predicted_load_data = regression_model.predict(
load_data, start_date, end_date, model, y_axis)
except Exception as predict_error:
raise predict_error
predicted_load_traces = generate_traces(
visualised_load_data['Date'], predicted_load_data, model_name, color=model_color)
traces.extend(predicted_load_traces)
return traces
def get_highlighted_traces(traces, highlight_variable, highlight_value, load_data, start_date, end_date, y_axis):
visualised_load_data = load_data[(
load_data.Date >= start_date) & (load_data.Date <= end_date)]
if (highlight_variable != 'None' and highlight_value != 'None'): #if there is highlighted data points specified
highlighted_load_data = pd.DataFrame() #structure to hold data entries that are highlighted
highlighted_load_data['Date'], highlighted_load_data['y_axis'] = zip(*visualised_load_data.apply(
lambda row: get_highlighted_entries_dataframe(row, highlight_variable, highlight_value, y_axis), axis=1))
highlighted_traces = generate_traces(
highlighted_load_data['Date'], highlighted_load_data['y_axis'], 'Highlighted Load', color=highlight_color, line_width=2)
traces.extend(highlighted_traces)
return traces
def generate_model_error_traces(load_data, graph_figure, models,
test_data_start_date, test_data_end_date,
highlight_variable, highlight_value):
actual_load_y = graph_figure['data'][0]['y']
# if there is highlighted data points, models index >= 2
if (graph_figure['data'][1]['name'] == 'Highlighted Load'):
start_index = 2
highlighted_dates = graph_figure['data'][1][
'x'] # list of dates that are highlighted in the visualised models graph
else:
start_index = 1
highlighted_dates = [
] #no highlighted dates in the model visualisation graph
if (test_data_start_date == None or test_data_end_date
== None): #if there is test data chosen, need to visualise this
is_test_data = False
else:
is_test_data = True
model_error_traces = [] #list of traces to plot
highlighted_data_points = pd.DataFrame(
columns=['APE', 'Percentage', 'Text']
) # structure to contain the highlighted data error points to superimpose onto the graph
for i in range(start_index, len(graph_figure['data'])
): #iterate through all model visualisation points on graph
plotted_model = graph_figure['data'][
i] #the model forecast plotted in the load visualisation graph
model_error_traces, highlighted_data_points = generate_visualised_error_traces(
plotted_model, actual_load_y, model_error_traces,
highlighted_dates, highlighted_data_points)
if (
is_test_data == True
): #test data is chosen, generate test data model error visualisation points
model_error_traces, highlighted_data_points = generate_test_error_traces(
plotted_model, models, load_data, test_data_start_date,
test_data_end_date, highlight_variable, highlight_value,
model_error_traces, highlighted_data_points)
if (len(highlighted_data_points)
): #if there is highlighted points generate their superimposed traces
highlighted_traces = generate_traces(
highlighted_data_points['APE'],
highlighted_data_points['Percentage'],
'Highlighted',
highlighted_data_points['Text'].tolist(),
color=highlight_color,
marker_size=4,
mode='markers')
model_error_traces.extend(highlighted_traces)
return model_error_traces
def generate_error_traces(model_error_traces, model_error_data, legend_name,
model_color, highlighted_data_points):
error_traces = generate_traces(model_error_data['APE'],
model_error_data['Percentage'],
legend_name,
model_error_data['Text'].tolist(),
color=model_color,
marker_size=1.5,
mode='lines+markers',
line_width=2)
model_error_traces.extend(error_traces)
highlighted_data_points = highlighted_data_points.append(
model_error_data[model_error_data['Highlight'] == True], sort=False)
return model_error_traces, highlighted_data_points
def get_visualised_traces(load_data, start_date, end_date, y_axis):
visualised_load_data = load_data[(
load_data.Date >= start_date) & (load_data.Date <= end_date)]
visualised_traces = generate_traces(
visualised_load_data['Date'], visualised_load_data[y_axis], 'Actual Load', color=actual_load_color)
return visualised_traces