sys.path.append("..")
import pandas
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.dataset import load_dataset
from evaluation.metrics import QualityMetricsCalculator
# configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
# data = load_dataset("../datasets/houseB.csv", "../datasets/houseB.config")
# run the classifier on the whole dataset and calculate confusion matrix
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
results = cls.predict(data.data)
matrix = QualityMetricsCalculator(data.target, results).confusion_matrix()
# format confusion matrix for pretty printing
letters = list(map(chr, list(range(97, 123)))) + list(map(chr, list(range(65, 91))))
action_to_letter = {action: letter for action, letter in zip(matrix.index, letters)}
matrix.columns = [action_to_letter[action] for action in matrix.columns]
matrix.index = ["(%s) %s" % (action_to_letter[action], action) for action in matrix.index]
matrix.index.name = "Actual action"
pandas.set_option("expand_frame_repr", False)
pandas.set_option("max_columns", 40)
print matrix
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.dataset import load_dataset
from evaluation.metrics import QualityMetricsCalculator
#configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
#data = load_dataset("../datasets/houseB.csv", "../datasets/houseB.config")
#run the classifier on the whole dataset and calculate confusion matrix
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
results = cls.predict(data.data)
matrix = QualityMetricsCalculator(data.target, results).confusion_matrix()
#format confusion matrix for pretty printing
letters = list(map(chr, list(range(97, 123)))) + list(
map(chr, list(range(65, 91))))
action_to_letter = {
action: letter
for action, letter in zip(matrix.index, letters)
}
matrix.columns = [action_to_letter[action] for action in matrix.columns]
matrix.index = [
"(%s) %s" % (action_to_letter[action], action) for action in matrix.index
]
matrix.index.name = "Actual action"
pandas.set_option('expand_frame_repr', False)
pandas.set_option('max_columns', 40)
print matrix
import pandas
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.classifiers.bayes import NaiveBayesClassifier
from recsys.dataset import load_dataset
from evaluation import plot
from evaluation.metrics import QualityMetricsCalculator
import config
#configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
to_compare = [1, 2, 3, 4]
#run classifier and count true positives
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
results = cls.predict(data.data)
results = QualityMetricsCalculator(data.target,
results).true_positives_for_all()
#only use the interesting cutoffs
results = results.transpose()[to_compare]
results.columns = ["cutoff=%s" % c for c in results.columns]
conf = plot.plot_config(config.plot_directory,
sub_dirs=[data.name],
prefix="histogram_cutoffs",
img_type=config.img_type)
plot.comparison_histogram(results, conf)
print "Results can be found in the \"%s\" directory" % config.plot_directory
import sys
sys.path.append("..")
import pandas
from recsys.classifiers.temporal import TemporalEvidencesClassifier
from recsys.classifiers.bayes import NaiveBayesClassifier
from recsys.dataset import load_dataset
from evaluation import plot
from evaluation.metrics import QualityMetricsCalculator
import config
#configuration
data = load_dataset("../datasets/houseA.csv", "../datasets/houseA.config")
to_compare = [1, 2, 3, 4]
#run classifier and count true positives
cls = TemporalEvidencesClassifier(data.features, data.target_names)
cls = cls.fit(data.data, data.target)
results = cls.predict(data.data)
results = QualityMetricsCalculator(data.target, results).true_positives_for_all()
#only use the interesting cutoffs
results = results.transpose()[to_compare]
results.columns = ["cutoff=%s" % c for c in results.columns]
conf = plot.plot_config(config.plot_directory, sub_dirs=[data.name], prefix="histogram_cutoffs", img_type=config.img_type)
plot.comparison_histogram(results, conf)
print "Results can be found in the \"%s\" directory" % config.plot_directory
