def show_plot_by_company(company):
"""
Diplays time series plot containing the tweets about given company.
"""
plotter = DataPlotter()
graph = plotter.get_graph(company=company)
return render_template("plot.html", graph=graph)
def show_plot_by_area(area):
"""
Displays time series plot containing the tweets of given area.
"""
plotter = DataPlotter()
if area.lower() == "global":
graph = plotter.get_graph()
else:
graph = plotter.get_graph(area=area)
return render_template("plot.html", graph=graph)
def plot_overall_rating():
# reviews = extractor.pre_process_reviews()
reviews = ETLUtils.load_json_file('/Users/fpena/tmp/filtered_reviews.json')
data_frame = DataFrame(reviews)
print(data_frame)
DataPlotter.plot_data(data_frame, 'overall_rating', plot_type='bar', title='Overall Rating')
# DataPlotter.plot_data(data_frame, 'cleanliness_rating', plot_type='bar', title='Cleanliness Rating')
# DataPlotter.plot_data(data_frame, 'location_rating', plot_type='bar', title='Location Rating')
# DataPlotter.plot_data(data_frame, 'rooms_rating', plot_type='bar', title='Rooms Rating')
# DataPlotter.plot_data(data_frame, 'service_rating', plot_type='bar', title='Service Rating')
# DataPlotter.plot_data(data_frame, 'value_rating', plot_type='bar', title='Value Rating')
plt.show()
def plot_overall_rating():
# reviews = extractor.pre_process_reviews()
reviews = ETLUtils.load_json_file('/Users/fpena/tmp/filtered_reviews.json')
data_frame = DataFrame(reviews)
print(data_frame)
DataPlotter.plot_data(data_frame,
'overall_rating',
plot_type='bar',
title='Overall Rating')
# DataPlotter.plot_data(data_frame, 'cleanliness_rating', plot_type='bar', title='Cleanliness Rating')
# DataPlotter.plot_data(data_frame, 'location_rating', plot_type='bar', title='Location Rating')
# DataPlotter.plot_data(data_frame, 'rooms_rating', plot_type='bar', title='Rooms Rating')
# DataPlotter.plot_data(data_frame, 'service_rating', plot_type='bar', title='Service Rating')
# DataPlotter.plot_data(data_frame, 'value_rating', plot_type='bar', title='Value Rating')
plt.show()
def __init__(self, app, serial):
super(Window, self).__init__()
self.setWindowTitle('Tic Disorder Detection')
self.main_layout = QVBoxLayout()
self.window = QtGui.QWidget(self)
self.button_layout = QVBoxLayout()
self.window.setLayout(self.button_layout)
self.gesture_layout = QHBoxLayout()
self.gesture_container = QtGui.QWidget(self)
self.gesture_container.setLayout(self.gesture_layout)
self.data_plotters = [DataPlotter() for _ in range(4)]
for plotter in self.data_plotters:
self.gesture_layout.addWidget(plotter)
self.serial_plot = SerialPlot(app, 127, serial)
self.main_layout.addWidget(self.serial_plot)
self.main_layout.addWidget(self.window)
self.main_layout.addWidget(self.gesture_container)
self.setLayout(self.main_layout)
self.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--select', action='store_true')
parser.add_argument('-t', '--test', action='store_true')
parser.add_argument('-c', '--count', action='store_true')
parser.add_argument('-m', '--merge', '--combine', action='store_true')
parser.add_argument('-e', '--extract', action='store_true')
parser.add_argument('-d', '--debuginput', action='store_true')
parser.add_argument('-r', '--review', action='store_true')
parser.add_argument('--replace-debug', action='store_true')
parser.add_argument('-p', '--plot-data', action='store_true')
parser.add_argument('--base-data', action='store_true')
parser.add_argument('--features', default='original')
parser.add_argument('-f', '--homology-filter', action='store_true')
parser.add_argument('-y', '--classify', action='store_true')
parser.add_argument('--grid-search', action='store_true')
parser.add_argument('--plot', action='store_true')
parser.add_argument('--fit', action='store_true')
parser.add_argument('--count-total-number-of-genes', action='store_true')
args = parser.parse_args()
if args.select:
if args.test:
selector = Selector("config/Test/selection_config.json")
else:
selector = Selector("config/selection_config.json")
selector.select()
selector.selected_to_folder()
if args.count:
if args.test:
counter = Counter("config/Test/counter_config.json")
else:
counter = Counter("config/counter_config.json")
counter.count_all_viruses()
if args.merge:
if args.test:
combiner = Combiner("config/Test/combiner_config.json")
else:
combiner = Combiner("config/combiner_config.json")
combiner.combine_all_viruses()
if args.debuginput:
debug_input_collector = DebugInfoCollector("config/debug_info_collector_config.json")
if args.replace_debug:
debug_input_collector.collect(True)
else:
debug_input_collector.collect()
if args.review:
import Review
Review.run()
if args.plot_data:
data_plotter = DataPlotter("config/data_plotter_config.json")
data_plotter.plot()
if args.base_data:
base_data = BaseData("config/base_data_config.json")
base_data.create_data()
if args.homology_filter:
homology_filter = HomologyFilter('config/homology_filter.json')
homology_filter.filter()
if args.extract:
feature_extractor = FeatureExtraction("config/feature_extraction_config.json")
feature_extractor.extract(args.features)
if args.count_total_number_of_genes:
combiner = Combiner("config/combiner_config.json")
combiner.print_number_of_genes()
if args.classify:
if args.grid_search:
MLgrid = [
{
"booster": ["gblinear"],
# "lambda": [0, 0.0001, 0.001],
"lambda": [0],
# "updater": ["shotgun", "coord_descent"],
"updater": ["coord_descent", "shotgun"],
# "feature_selector": ["cyclic", "shuffle", "random", "greedy", "thrifty"]
"feature_selector": ["shuffle"]
}
# {
# "booster": ["gbtree"],
# # "max_depth": range(3, 10, 2),
# # "min_child_weight": range(1, 6, 2)
# }
]
_1vsAgrid = [
{
"estimator__booster": ["gblinear"],
"estimator__lambda": [0.1],
"estimator__updater": ["coord_descent"],
"estimator__feature_selector": ["shuffle"]
},
# {
# "estimator__booster": ["gbtree"],
# "estimator__max_depth": range(3, 10, 2),
# "estimator__min_child_weight": range(1, 6, 2)
# }
]
RRgrid = [
{
"estimator__booster": ["gblinear"],
"estimator__lambda": [0.1],
"estimator__updater": ["coord_descent"],
"estimator__feature_selector": ["shuffle"]
},
# {
# "estimator__booster": ["gbtree"]
# # "estimator__max_depth": range(3, 10, 2),
# # "estimator__min_child_weight": range(1, 6, 2)
# }
]
classification = Classification('config/classification_config.json', args.features)
classification.grid_search('ML', 'XGBoost', MLgrid, 200, 'no-pca')
else:
if args.fit:
classification = Classification('config/classification_config.json', args.features)
classification.fit_all()
if args.plot:
cp = ClassificationPlotter('config/classification_config.json', args.features)
cp.plot_all()
