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
