def logreg_predict(input_dict):
from discomll.classification import logistic_regression
predictions_url = logistic_regression.predict(input_dict["dataset"],
fitmodel_url=input_dict["fitmodel_url"],
save_results=True)
return {"string": predictions_url}
def test_log_reg(self):
# python tests_classification.py Tests_Classification.test_log_reg
from discomll.classification import logistic_regression
train_data1, test_data1 = datasets.breastcancer_cont_orange()
train_data2, test_data2 = datasets.breastcancer_cont_discomll()
learner = Orange.classification.logreg.LogRegLearner(fitter=Orange.classification.logreg.LogRegFitter_Cholesky)
classifier = learner(train_data1)
thetas1 = classifier.beta
predictions1 = []
probabilities1 = []
for inst in test_data1:
target, probs = classifier(inst, Orange.classification.Classifier.GetBoth)
predictions1.append(target.value)
probabilities1.append(probs.values())
thetas_url = logistic_regression.fit(train_data2, alpha=1e-8, max_iterations=10)
thetas2 = [v for k, v in result_iterator(thetas_url["logreg_fitmodel"]) if k == "thetas"]
results_url = logistic_regression.predict(test_data2, thetas_url)
predictions2 = []
probabilities2 = []
for k, v in result_iterator(results_url):
predictions2.append(v[0])
probabilities2.append(v[1])
self.assertTrue(np.allclose(thetas1, thetas2))
self.assertTrue(np.allclose(probabilities1, probabilities2, atol=1e-5))
self.assertListEqual(predictions1, predictions2)
def test_log_reg(self):
# python tests_classification.py Tests_Classification.test_log_reg
from discomll.classification import logistic_regression
train_data1, test_data1 = datasets.breastcancer_cont_orange()
train_data2, test_data2 = datasets.breastcancer_cont_discomll()
learner = Orange.classification.logreg.LogRegLearner(fitter=Orange.classification.logreg.LogRegFitter_Cholesky)
classifier = learner(train_data1)
thetas1 = classifier.beta
predictions1 = []
probabilities1 = []
for inst in test_data1:
target, probs = classifier(inst, Orange.classification.Classifier.GetBoth)
predictions1.append(target.value)
probabilities1.append(probs.values())
thetas_url = logistic_regression.fit(train_data2, alpha=1e-8, max_iterations=10)
thetas2 = [v for k, v in result_iterator(thetas_url["logreg_fitmodel"]) if k == "thetas"]
results_url = logistic_regression.predict(test_data2, thetas_url)
predictions2 = []
probabilities2 = []
for k, v in result_iterator(results_url):
predictions2.append(v[0])
probabilities2.append(v[1])
self.assertTrue(np.allclose(thetas1, thetas2))
self.assertTrue(np.allclose(probabilities1, probabilities2, atol=1e-5))
self.assertListEqual(predictions1, predictions2)
def logreg_predict(input_dict):
from discomll.classification import logistic_regression
predictions_url = logistic_regression.predict(
input_dict["dataset"],
fitmodel_url=input_dict["fitmodel_url"],
save_results=True)
return {"string": predictions_url}
def log_reg_predict(input_dict):
from discomll.classification import logistic_regression
predictions_url = logistic_regression.predict(input_dict["dataset"],
fitmodel_url = input_dict["fitmodel_url"],
save_results =True)
from disco.core import result_iterator
pred = "ID__Pred__Real__Probs\n"
for X_id, (y_predicted, y_real, probs) in result_iterator(predictions_url):
probs = [round(p, 4) for p in probs]
pred += str(X_id) + " " + str(y_predicted) + " " + str(y_real) + " " + str(probs) + "\n"
return {"string" : pred}
def log_reg_predict(input_dict):
from discomll.classification import logistic_regression
predictions_url = logistic_regression.predict(
input_dict["dataset"],
fitmodel_url=input_dict["fitmodel_url"],
save_results=True)
from disco.core import result_iterator
pred = "ID__Pred__Real__Probs\n"
for X_id, (y_predicted, y_real, probs) in result_iterator(predictions_url):
probs = [round(p, 4) for p in probs]
pred += str(X_id) + " " + str(y_predicted) + " " + str(
y_real) + " " + str(probs) + "\n"
return {"string": pred}
from discomll import dataset
from discomll.classification import logistic_regression
train = dataset.Data(data_tag=["http://ropot.ijs.si/data/ionosphere/train/xaaaaa.gz",
"http://ropot.ijs.si/data/ionosphere/train/xaaabj.gz"],
data_type="gzip",
generate_urls=True,
id_index=0,
X_indices=range(1, 35),
X_meta=["c" for i in range(1, 35)],
y_index=35,
delimiter=",",
y_map=["b", "g"])
test = dataset.Data(data_tag=["http://ropot.ijs.si/data/ionosphere/test/xaaaaa.gz",
"http://ropot.ijs.si/data/ionosphere/test/xaaabj.gz"],
data_type="gzip",
generate_urls=True,
id_index=0,
X_indices=range(1, 35),
X_meta=["c" for i in range(1, 35)],
y_index=35,
delimiter=",",
y_map=["b", "g"])
fit_model = logistic_regression.fit(train, max_iterations=18, alpha=1)
predictions = logistic_regression.predict(test, fit_model)
print predictions
train = dataset.Data(data_tag=[
"http://ropot.ijs.si/data/sonar/train/xaaaaa.gz",
"http://ropot.ijs.si/data/sonar/train/xaaabj.gz"
],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
test = dataset.Data(data_tag=[
"http://ropot.ijs.si/data/sonar/test/xaaaaa.gz",
"http://ropot.ijs.si/data/sonar/test/xaaabj.gz"
],
data_type="gzip",
generate_urls=True,
X_indices=range(1, 61),
id_index=0,
y_index=61,
X_meta=["c" for i in range(1, 61)],
y_map=["R", "M"],
delimiter=",")
fit_model = logistic_regression.fit(train, max_iterations=10)
predictions = logistic_regression.predict(test, fit_model)
print predictions
