def pipeline_save_best_model(fileName, logFile):
print(
"Pipeline: Will save the model with the best parameters and all of the data."
)
conf = Configurator(fileName)
data_reader = PlaylistDataReader()
data_reader.generate_datasets()
l = Logger(data_reader.targetData, logFile)
rec_sys = conf.extract_models(data_reader)
evaluator = SequentialEvaluator(data_reader.get_URM_all(), [10],
exclude_seen=True)
for model in rec_sys:
model.fit(save_model=True, best_parameters=True, location="submission")
def pipeline_stable(fileName, logFile):
print(
"Pipeline: Will predict recommendations using current parameters and URM test"
)
conf = Configurator(fileName)
data_reader = PlaylistDataReader()
data_reader.generate_datasets()
l = Logger(data_reader.targetData, logFile)
rec_sys = conf.extract_models(data_reader)
evaluator = SequentialEvaluator(data_reader.get_URM_test(), [10],
exclude_seen=True)
for model in rec_sys:
model.fit()
results_run, results_run_string = evaluator.evaluateRecommender(model)
print("Algorithm: {}, results: \n{}".format(str(model),
results_run_string))
l.export_experiments(model, results_run_string)
l.log_experiment()
def pipeline_save_training_model(fileName, logFile):
print(
"Pipeline: Will save the model with the defined parameters and URM test"
)
conf = Configurator(fileName)
data_reader = PlaylistDataReader()
data_reader.generate_datasets()
l = Logger(data_reader.targetData, logFile)
rec_sys = conf.extract_models(data_reader)
evaluator = SequentialEvaluator(data_reader.get_URM_test(), [10],
exclude_seen=True)
for model in rec_sys:
model.fit(save_model=True, best_parameters=False, location="training")
results_run, results_run_string = evaluator.evaluateRecommender(model)
print("Algorithm: {}, results: \n{}".format(str(model),
results_run_string))
l.export_experiments(model, results_run_string)
l.log_experiment()
def pipeline_submission_hybrid(fileName, logFile):
print(
"Pipeline: Will save the recommendations using best parameters and all of the data"
)
conf = Configurator(fileName)
data_reader = PlaylistDataReader()
data_reader.generate_datasets()
l = Logger(data_reader.targetData, logFile)
rec_sys = conf.extract_models(data_reader, submission=True)
evaluator = SequentialEvaluator(data_reader.get_URM_all(), [10],
exclude_seen=True)
for model in rec_sys:
model.fit(submission=True, best_parameters=True)
# results_run, results_run_string = evaluator.evaluateRecommender(model)
# print("Algorithm: {}, results: \n{}".format(str(model), results_run_string))
result_string = "Full Submission No MAP"
l.export_experiments(model, result_string)
l.log_experiment(submission=True)
from path_generator import PathGenerator
from visibility.graphs import Graphs
import math
import numpy as np
import matplotlib.pyplot as plt
from utils.config import Configurator
from pathlib import Path
import os
graphs = Graphs()
file_path = Path(__file__)
config_fn = 'default.yaml'
yaml_fp = os.path.join(str(file_path.parent.parent), 'configs', config_fn)
configurator = Configurator(yaml_fp)
config = configurator.configurate()
runtime = []
overhead = []
for i in range(1, 12):
print(f'STARTING NUMBER {i}')
path_gen = PathGenerator(config, build=False)
g = graphs.get_graph(complexity=i)
start = list(g.start)
end = list(g.end)
xx, xy, uv, uomega, solver_times, overhead_times = path_gen.run(
g, start, end)
loop_times = [x + y for x, y in zip(solver_times, overhead_times)]
overhead += overhead_times
runtime += solver_times
def generate_plot(config_filename):
yaml_fp = os.path.join(str(file_path.parent.parent), 'configs',
config_filename)
configurator = Configurator(yaml_fp)
config = configurator.configurate()
path_gen = PathGenerator(config, build=False)
start = list(g.start)
end = list(g.end)
xx, xy, uv, uomega, _, _ = path_gen.run(g, start, end)
fig, ax = plt.subplots(2, 1)
fig.set_figheight(6)
fig.set_figwidth(10)
vel_ax = ax[0]
path_gen.mpc_generator.plot_vel(vel_ax, uv)
#vel_ax.set_xlabel('Time [s]')
vel_ax.set_ylabel('Linear elocity [m/s]')
vel_ax.legend(['Linear velocity'])
vel_ax.grid('on')
omega_ax = ax[1]
path_gen.mpc_generator.plot_omega(omega_ax, uomega)
omega_ax.set_xlabel('Time [s]')
omega_ax.set_ylabel('Angular velocity [rad/s]')
omega_ax.legend(['Angular velocity'])
omega_ax.grid('on')
fig2, ax2 = plt.subplots(1, 1)
fig2.set_figheight(15)
fig2.set_figwidth(15)
path_ax = ax2
path_gen.ppp.plot_all(path_ax)
path_ax.plot(xx, xy, c='b', label='Path', marker='o', alpha=0.5)
path_ax.plot(start[0], start[1], marker='*', color='g', markersize=15)
path_ax.plot(end[0], end[1], marker='*', color='r', markersize=15)
path_ax.set_xlabel('X [m]', fontsize=12)
path_ax.set_ylabel('Y [m]', fontsize=12)
legend_elems = [
Line2D([0], [0], color='k', label='Original Boundary'),
Line2D([0], [0], color='g', label='Padded Boundary'),
Line2D([0], [0], color='r', label='Original Obstacles'),
Line2D([0], [0], color='b', label='Padded Obstacles'),
Line2D([0], [0], linestyle='--', color='k', label='A-Star Path'),
Line2D([0], [0],
marker='o',
color='b',
label='Generated Path',
alpha=0.5),
Line2D([0], [0], color='r', marker='*', label='Start Position'),
Line2D([0], [0], color='g', marker='*', label='End Position'),
]
path_ax.legend(handles=legend_elems)
path_ax.axis('equal')
fig.savefig(os.path.join(folder_name,
config_filename + f'_vel_omega_{str(t)}.png'),
dpi=600,
format='png')
fig2.savefig(os.path.join(folder_name,
config_filename + f'_path_{str(t)}.png'),
dpi=600,
format='png')
plt.close('all')