sys.path.append("..") import pandas from recsys.classifiers.temporal import TemporalEvidencesClassifier from recsys.classifiers.binning import initialize_bins from recsys.dataset import load_dataset from evaluation import plot import config #configuration data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config") #fit classifier to dataset cls = TemporalEvidencesClassifier(data.features, data.target_names, bins=initialize_bins(0, 300, 10)) cls = cls.fit(data.data, data.target) #create visualizations of habits around each user action plot_conf = plot.plot_config(config.plot_directory, sub_dirs=[data.name, "habits"], img_type=config.img_type) for source in cls.sources.values(): observations = pandas.DataFrame(source.temporal_counts) observations.columns = data.target_names observations.index = cls.bins plot.plot_observations(source.name(), observations, plot_conf) print "Results can be found in the \"%s\" directory" % config.plot_directory
""" import sys sys.path.append("..") import pandas from evaluation.experiment import Experiment from recsys.classifiers.temporal import TemporalEvidencesClassifier from recsys.classifiers.binning import initialize_bins from recsys.dataset import load_dataset #configuration data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config") intervals_to_test = [#test various settings for delta t_max ("Delta t_max=1200s", initialize_bins(start=0, end=60, width=10) + initialize_bins(start=60, end=1200, width=30)), ("Delta t_max=120s", initialize_bins(start=0, end=60, width=10) + initialize_bins(start=60, end=120, width=30)), ("Delta t_max=60s", initialize_bins(start=0, end=60, width=10)), ("Delta t_max=30s", initialize_bins(start=0, end=30, width=10)), ("Delta t_max=10s", initialize_bins(start=0, end=10, width=10)), #test various interval widths ("all intervals 2s wide", initialize_bins(start=0, end=300, width=2)), ("all intervals 4s wide", initialize_bins(start=0, end=300, width=4)), ("all intervals 6s wide", initialize_bins(start=0, end=300, width=6)), ("all intervals 8s wide", initialize_bins(start=0, end=300, width=8)), ("all intervals 30s wide", initialize_bins(start=0, end=300, width=30)), ("all intervals 50s wide", initialize_bins(start=0, end=300, width=50)), ("all intervals 100s wide", initialize_bins(start=0, end=300, width=100))]
""" import sys sys.path.append("..") import pandas from evaluation.experiment import Experiment from recsys.classifiers.temporal import TemporalEvidencesClassifier from recsys.classifiers.binning import initialize_bins from recsys.dataset import load_dataset #configuration data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config") intervals_to_test = [ #test various settings for delta t_max ("Delta t_max=1200s", initialize_bins(start=0, end=60, width=10) + initialize_bins(start=60, end=1200, width=30)), ("Delta t_max=120s", initialize_bins(start=0, end=60, width=10) + initialize_bins(start=60, end=120, width=30)), ("Delta t_max=60s", initialize_bins(start=0, end=60, width=10)), ("Delta t_max=30s", initialize_bins(start=0, end=30, width=10)), ("Delta t_max=10s", initialize_bins(start=0, end=10, width=10)), #test various interval widths ("all intervals 2s wide", initialize_bins(start=0, end=300, width=2)), ("all intervals 4s wide", initialize_bins(start=0, end=300, width=4)), ("all intervals 6s wide", initialize_bins(start=0, end=300, width=6)), ("all intervals 8s wide", initialize_bins(start=0, end=300, width=8)), ("all intervals 30s wide", initialize_bins(start=0, end=300, width=30)), ("all intervals 50s wide", initialize_bins(start=0, end=300, width=50)), ("all intervals 100s wide", initialize_bins(start=0, end=300, width=100)) ]
of the figure still stands: the user has some observable habits after closing the frontdoor. """ import sys sys.path.append("..") import pandas from recsys.classifiers.temporal import TemporalEvidencesClassifier from recsys.classifiers.binning import initialize_bins from recsys.dataset import load_dataset from evaluation import plot import config #configuration data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config") #fit classifier to dataset cls = TemporalEvidencesClassifier(data.features, data.target_names, bins=initialize_bins(0, 300, 10)) cls = cls.fit(data.data, data.target) #create visualizations of habits around each user action plot_conf = plot.plot_config(config.plot_directory, sub_dirs=[data.name, "habits"], img_type=config.img_type) for source in cls.sources.values(): observations = pandas.DataFrame(source.temporal_counts) observations.columns = data.target_names observations.index = cls.bins plot.plot_observations(source.name(), observations, plot_conf) print "Results can be found in the \"%s\" directory" % config.plot_directory