def test_util():
"""Test utility module."""
x_train, y_train = Dataset.load_from_files(dataset_path, folder_label=True)
rd = RecursiveDefaultDict()
rd["a"]["new"]["element"] = "assigned"
Print.set_verbosity(VERBOSITY.VERBOSE)
Print.verbosity_region_begin(VERBOSITY.VERBOSE)
print(Print.style.header("this is a header!"))
Print.warn("This is a warning!")
Print.info("This is an informative message!")
Print.show("This is a message!")
with pytest.raises(Exception):
Print.warn("This is a warning!", raises=Exception)
Print.set_decorator_info(">", "<")
Print.set_decorator_warn("|", "|")
Print.set_decorator_error("*", "*")
Print.verbosity_region_end()
def test_evaluation(mocker):
"""Test Evaluation class."""
mocker.patch("webbrowser.open")
mocker.patch("matplotlib.pyplot.show")
kfold_validation = Evaluation.kfold_cross_validation
Evaluation.__cache__ = None
Evaluation.__cache_file__ = None
Evaluation.__clf__ = None
Evaluation.__last_eval_tag__ = None
Evaluation.__last_eval_method__ = None
Evaluation.__last_eval_def_cat__ = None
ss = [0, 0.5]
ll = [0, 1.5]
pp = [0, 2]
x_data, y_data = Dataset.load_from_files(DATASET_PATH)
clf = SS3()
clf.set_model_path("tests")
# no classifier assigned case
Evaluation.clear_cache()
with pytest.raises(ValueError):
Evaluation.get_best_hyperparameters()
with pytest.raises(ValueError):
Evaluation.remove()
with pytest.raises(ValueError):
Evaluation.show_best()
with pytest.raises(ValueError):
Evaluation.plot(TMP_FOLDER)
# Not-yet-trained model case
Evaluation.set_classifier(clf)
Evaluation.clear_cache()
Evaluation.remove()
Evaluation.show_best()
assert Evaluation.plot(TMP_FOLDER) is False
with pytest.raises(pyss3.EmptyModelError):
Evaluation.test(clf, x_data, y_data)
with pytest.raises(pyss3.EmptyModelError):
kfold_validation(clf, x_data, y_data)
with pytest.raises(pyss3.EmptyModelError):
Evaluation.grid_search(clf, x_data, y_data)
with pytest.raises(LookupError):
Evaluation.get_best_hyperparameters()
# default argument values
clf.train(x_data, y_data)
assert Evaluation.test(clf, x_data, y_data, plot=PY3) == 1
assert Evaluation.test(clf, ['bla bla bla'], ['pos'], plot=PY3) == 0
assert Evaluation.test(clf, ['bla bla bla', "I love this love movie!"],
['pos', 'pos'],
plot=PY3) == 0.5
assert kfold_validation(clf, x_data, y_data, plot=PY3) > 0
s, l, p, a = clf.get_hyperparameters()
s0, l0, p0, a0 = Evaluation.grid_search(clf, x_data, y_data)
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters()
s2, l2, p2, a2 = Evaluation.get_best_hyperparameters("recall")
assert s0 == s and l0 == l and p0 == p and a0 == a
assert s0 == s1 and l0 == l1 and p0 == p1 and a0 == a1
assert s0 == s2 and l0 == l2 and p0 == p2 and a0 == a2
assert Evaluation.plot(TMP_FOLDER) is True
Evaluation.remove()
Evaluation.show_best()
assert Evaluation.plot(TMP_FOLDER) is False
# test
# OK
assert Evaluation.test(clf, x_data, y_data, def_cat='unknown',
plot=PY3) == 1
assert Evaluation.test(clf, x_data, y_data, def_cat='neg', plot=PY3) == 1
assert Evaluation.test(clf, x_data, y_data, metric="f1-score",
plot=PY3) == 1
assert Evaluation.test(clf,
x_data,
y_data,
plot=PY3,
metric="recall",
metric_target="weighted avg") == 1
assert Evaluation.test(clf,
x_data,
y_data,
plot=PY3,
metric="recall",
metric_target="neg") == 1
# Not OK
with pytest.raises(InvalidCategoryError):
Evaluation.test(clf, x_data, y_data, def_cat='xxx', plot=PY3)
with pytest.raises(KeyError):
Evaluation.test(clf, x_data, y_data, metric="xxx", plot=PY3)
with pytest.raises(KeyError):
Evaluation.test(clf,
x_data,
y_data,
metric="recall",
metric_target="xxx",
plot=PY3)
# k-fold
# OK
assert kfold_validation(clf, x_data, y_data, n_grams=3, plot=PY3) > 0
assert kfold_validation(clf, x_data, y_data, k=10, plot=PY3) > 0
assert kfold_validation(
clf, x_data, y_data, k=10, def_cat='unknown', plot=PY3) > 0
assert kfold_validation(clf, x_data, y_data, k=10, def_cat='neg',
plot=PY3) > 0
assert kfold_validation(clf, x_data, y_data, metric="f1-score",
plot=PY3) > 0
assert kfold_validation(clf,
x_data,
y_data,
plot=PY3,
metric="recall",
metric_target="weighted avg") > 0
assert kfold_validation(
clf, x_data, y_data, plot=PY3, metric="recall",
metric_target="neg") > 0
# Not OK
with pytest.raises(ValueError):
kfold_validation(clf, x_data, y_data, n_grams=-1, plot=PY3)
with pytest.raises(ValueError):
kfold_validation(clf, x_data, y_data, n_grams=clf, plot=PY3)
with pytest.raises(ValueError):
kfold_validation(clf, x_data, y_data, k=-1, plot=PY3)
with pytest.raises(ValueError):
kfold_validation(clf, x_data, y_data, k=clf, plot=PY3)
with pytest.raises(ValueError):
kfold_validation(clf, x_data, y_data, k=None, plot=PY3)
with pytest.raises(InvalidCategoryError):
kfold_validation(clf, x_data, y_data, def_cat='xxx', plot=PY3)
with pytest.raises(KeyError):
kfold_validation(clf, x_data, y_data, metric="xxx", plot=PY3)
with pytest.raises(KeyError):
kfold_validation(clf,
x_data,
y_data,
metric="recall",
metric_target="xxx",
plot=PY3)
# grid_search
# OK
s0, l0, p0, a0 = Evaluation.grid_search(clf, x_data, y_data, s=ss)
s1, l1, p1, a1 = Evaluation.grid_search(clf,
x_data,
y_data,
s=ss,
l=ll,
p=pp)
assert s0 == s1 and l0 == l1 and p0 == p1 and a0 == a1
s0, l0, p0, a0 = Evaluation.grid_search(clf, x_data, y_data, k_fold=4)
s0, l0, p0, a0 = Evaluation.grid_search(clf,
x_data,
y_data,
def_cat='unknown',
p=pp)
s1, l1, p1, a1 = Evaluation.grid_search(clf,
x_data,
y_data,
def_cat='neg',
p=pp)
assert s0 == s1 and l0 == l1 and p0 == p1 and a0 == a1
s0, l0, p0, a0 = Evaluation.grid_search(clf,
x_data,
y_data,
metric="f1-score",
p=pp)
s1, l1, p1, a1 = Evaluation.grid_search(clf,
x_data,
y_data,
p=pp,
metric="recall",
metric_target="weighted avg")
s1, l1, p1, a1 = Evaluation.grid_search(clf,
x_data,
y_data,
p=pp,
metric="recall",
metric_target="neg")
assert s0 == s1 and l0 == l1 and p0 == p1 and a0 == a1
# Not OK
with pytest.raises(TypeError):
Evaluation.grid_search(clf, x_data, y_data, s='asd')
with pytest.raises(TypeError):
Evaluation.grid_search(clf, x_data, y_data, s=clf)
with pytest.raises(TypeError):
Evaluation.grid_search(clf, x_data, y_data, k_fold=clf)
with pytest.raises(TypeError):
Evaluation.grid_search(clf, x_data, y_data, k_fold="xxx")
with pytest.raises(InvalidCategoryError):
Evaluation.grid_search(clf, x_data, y_data, def_cat='xxx')
with pytest.raises(KeyError):
Evaluation.grid_search(clf, x_data, y_data, metric="xxx")
with pytest.raises(KeyError):
Evaluation.grid_search(clf,
x_data,
y_data,
metric="recall",
metric_target="xxx")
# get_best_hyperparameters
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters()
s2, l2, p2, a2 = Evaluation.get_best_hyperparameters("recall")
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters(
"recall", "weighted avg")
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters("recall", "pos")
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters(method="10-fold")
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters(method="10-fold",
def_cat="neg")
s1, l1, p1, a1 = Evaluation.get_best_hyperparameters(method="10-fold",
def_cat="unknown")
assert s0 == s1 and l0 == l1 and p0 == p1 and a0 == a1
assert s0 == s2 and l0 == l2 and p0 == p2 and a0 == a2
# Not OK
with pytest.raises(KeyError):
Evaluation.get_best_hyperparameters("xxx")
with pytest.raises(KeyError):
Evaluation.get_best_hyperparameters("recall", "xxx")
with pytest.raises(LookupError):
Evaluation.get_best_hyperparameters(method="xxx")
with pytest.raises(LookupError):
Evaluation.get_best_hyperparameters(def_cat="xxx")
with pytest.raises(LookupError):
Evaluation.get_best_hyperparameters(method="4-fold", def_cat="unknown")
# plot OK
assert Evaluation.plot(TMP_FOLDER) is True
# remove
# OK
assert Evaluation.remove(s, l, p, a)[0] == 10
assert Evaluation.remove(def_cat="neg")[0] == 2
assert Evaluation.remove(method="test")[0] == 12
assert Evaluation.remove(s=-10)[0] == 0
assert Evaluation.remove(def_cat="xxx")[0] == 0
assert Evaluation.remove(method="xxx")[0] == 0
assert Evaluation.remove()[0] == 1
assert Evaluation.plot(TMP_FOLDER) is False # plot not OK (no evaluations)
# not OK
with pytest.raises(TypeError):
Evaluation.remove("xxx")
with pytest.raises(TypeError):
Evaluation.remove(clf)
Evaluation.show_best()
Evaluation.show_best(method="test")
Evaluation.show_best(def_cat="unknown")
Evaluation.show_best(metric="f1-score")
Evaluation.show_best(metric="f1-score", avg="weighted avg")
# different tag
rmtree("./tests/ss3_models", ignore_errors=True)
"""Tests for pytest."""
from os import path
from pyss3 import SS3, STR_NORM_GV_XAI, STR_XAI
from pyss3 import STR_UNKNOWN, STR_MOST_PROBABLE, STR_UNKNOWN_CATEGORY
from pyss3.util import Dataset
import pyss3
import pytest
DATASET_FOLDER = "dataset"
dataset_path = path.join(path.abspath(path.dirname(__file__)), DATASET_FOLDER)
x_train, y_train = Dataset.load_from_files(dataset_path, folder_label=False)
x_test = [
"sports nfl nba superbowl soccer football team. learns jersey air bowl hockey.\n"
"baseball helmet mccutchen jordan curry poker",
"travel pictures images moment glamour canvas photoshoot lens dslr portrait "
"beautiful seasons lines colours snap usm af eos painter gallery museum "
"flower kinkade",
"hairstyles boutique handbag dress trends womens menswear luxury claudiepierlot "
"rustic wedding bride collection signed patrick ista streetstyle cocksox purse "
"trending status brush cosmetic stretchy gucci leather cream trendy "
"bargains victoria",
"finance business development fiverr hiring job social debt logos stationary "
"read bad media mlm uganda entrepreneurship strategy mistake 1st employee "
"financial inbound habits coupon",
"cooking cook chef food drink rice kitchen cold organic yummy yum bread "
"strawberry bbq pepper beverages grocery cupcakes easter gurpreet sushi "
"dining meal chicken lime mushrooms restaurant whiskey",
"vitamins calcium minerals workout weightloss fit skin spa motivation care "
# Ok, now we are ready to begin. Let's create a new SS3 instance
clf = SS3()
# What are the default hyperparameter values? let's see
s, l, p, _ = clf.get_hyperparameters()
print("Smoothness(s):", s)
print("Significance(l):", l)
print("Sanction(p):", p)
# Ok, now let's load the training and the test set using the `load_from_files` function
# from `pyss3.util`. Since, in this dataset, there's a single file for each category,
# we will use the argument ``folder_label=False`` to tell PySS3 to use each file as a different
# category and each line inside of it as a different document:
x_train, y_train = Dataset.load_from_files("datasets/topic/train",
folder_label=False)
x_test, y_test = Dataset.load_from_files("datasets/topic/test",
folder_label=False)
# Let's train our model...
clf.fit(x_train, y_train)
# Note that we don't have to create any document-term matrix! we are using just the
# plain `x_train` documents :D cool uh? (SS3 creates a language model for each category
# and therefore it doesn't need to create any document-term matrices)
#
# Now that the model has been trained, let's test it using the documents in `x_test`
y_pred = clf.predict(x_test)
# Let's see how good our model performed
print("Accuracy:", accuracy_score(y_pred, y_test))
# ... and unzip the "movie_review.zip" dataset inside the `datasets` folder.
system('unzip -u datasets/movie_review.zip -d datasets/')
# Ok, now we are ready to begin. Let's create a new SS3 instance.
clf = SS3()
# What are the default hyper-parameter values? let's see
s, l, p, _ = clf.get_hyperparameters()
print("Smoothness(s):", s)
print("Significance(l):", l)
print("Sanction(p):", p)
# Ok, now let's load the training and the test set using the `load_from_files`
# function from `pyss3.util` as follow:
x_train, y_train = Dataset.load_from_files("datasets/movie_review/train")
x_test, y_test = Dataset.load_from_files("datasets/movie_review/test")
# Let's train our model...
clf.fit(x_train, y_train)
# Note that we don't have to create any document-term matrix! we are using just
# the plain `x_train` documents :D cool uh?
# (SS3 creates a language model for each category and therefore it doesn't need
# to create any document-term matrices)
# Now that the model has been trained, let's test it using the documents in `x_test`
y_pred = clf.predict(x_test)
# Let's see how good our model performed
print("Accuracy:", accuracy_score(y_pred, y_test))
