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test_wrapper.py
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import os
import sys
import pandas as pd
import numpy as np
import pytest
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_breast_cancer
from sslearn.datasets import read_csv
from sslearn.model_selection import artificial_ssl_dataset
from sslearn.wrapper import (
CoTraining, CoForest, CoTrainingByCommittee, DemocraticCoLearning, Rasco, RelRasco,
SelfTraining, Setred, TriTraining, DeTriTraining
)
X, y = read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "example_files", "abalone.csv"), format="pandas")
X2, y2 = read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "example_files", "abalone.csv"), format="numpy")
X_l, y_l = load_breast_cancer(return_X_y=True)
multiples_estimator = [
DecisionTreeClassifier(max_depth=1),
DecisionTreeClassifier(max_depth=2),
KNeighborsClassifier(n_neighbors=5),
GaussianNB(),
KNeighborsClassifier(n_neighbors=3),
]
def check_random(estimator, estimator_key="base_estimator", **kwargs):
for i in range(5):
clf = eval(f"estimator({estimator_key}=KNeighborsClassifier(), random_state=i, **kwargs)")
clf.fit(X, y)
y1 = clf.predict(X)
clf = eval(f"estimator({estimator_key}=KNeighborsClassifier(), random_state=i, **kwargs)")
clf.fit(X, y)
y2 = clf.predict(X)
assert np.all(y1 == y2)
def check_multiple(estimator, **kwargs):
clf = estimator(base_estimator=multiples_estimator, n_estimators=5, **kwargs)
clf.fit(X, y)
clf.predict(X)
clf.predict_proba(X)
def check_all_label(estimator, **kwargs):
clf = estimator(**kwargs)
clf.fit(X_l, y_l)
clf.predict(X_l)
clf.predict_proba(X_l)
def check_pandas(estimator, **kwargs):
clf = estimator(**kwargs)
clf.fit(X, y)
clf.predict(X)
clf.predict_proba(X)
def check_numpy(estimator, **kwargs):
clf = estimator(**kwargs)
clf.fit(X2, y2)
clf.predict(X2)
clf.predict_proba(X2)
def check_basic(estimator, **kwargs):
check_pandas(estimator, **kwargs)
check_numpy(estimator, **kwargs)
class TestCoForest:
def test_basic(self):
check_basic(CoForest)
def test_multiple(self):
check_multiple(CoForest)
def test_random_state(self):
check_random(CoForest)
def test_all_label(self):
check_all_label(CoForest)
class TestCoTrainingByCommittee:
def test_basic(self):
check_basic(CoTrainingByCommittee)
def test_random_state(self):
check_random(CoTrainingByCommittee, estimator_key="ensemble_estimator")
def test_all_label(self):
check_all_label(CoTrainingByCommittee)
class TestCoTraining:
def test_basic(self):
check_basic(CoTraining, force_second_view=False)
def test_multiview(self):
clf = CoTraining()
clf.fit(X.iloc[:,:len(X.columns)//2], y, X2=X.iloc[:, len(X.columns)//2:])
clf.predict(X.iloc[:, :len(X.columns)//2], X2=X.iloc[:, len(X.columns)//2:])
clf.predict_proba(X.iloc[:, :len(X.columns)//2], X2=X.iloc[:, len(X.columns)//2:])
clf = CoTraining()
clf.fit(X, y, features=[[i for i in range(len(X.columns)//2)], [i for i in range(len(X.columns)//2, len(X.columns))]])
clf.predict(X)
clf.predict_proba(X)
def test_multiple(self):
clf = CoTraining(force_second_view=False,
base_estimator=DecisionTreeClassifier(max_depth=1),
second_base_estimator=DecisionTreeClassifier(max_depth=2))
clf.fit(X, y)
clf.predict(X)
clf.predict_proba(X)
def test_random_state(self):
check_random(CoTraining, force_second_view=False)
def test_all_label(self):
check_all_label(CoTraining, force_second_view=False)
class TestDemocraticCoLearning:
def test_basic(self):
check_basic(DemocraticCoLearning)
def test_multiple(self):
check_multiple(DemocraticCoLearning)
def test_random_state(self):
check_random(DemocraticCoLearning, n_estimators=3)
def test_all_label(self):
check_all_label(DemocraticCoLearning)
class TestRasco:
def test_basic(self):
check_basic(Rasco)
def test_multiple(self):
check_multiple(Rasco)
def test_random_state(self):
check_random(Rasco, n_estimators=10)
def test_all_label(self):
check_all_label(Rasco)
class TestRelRasco:
def test_basic(self):
check_basic(RelRasco)
def test_multiple(self):
check_multiple(RelRasco)
def test_random_state(self):
check_random(RelRasco, n_estimators=10)
def test_all_label(self):
check_all_label(RelRasco)
class TestSelfTraining:
def test_basic(self):
check_basic(SelfTraining, base_estimator=KNeighborsClassifier())
def test_all_label(self):
check_all_label(SelfTraining, base_estimator=KNeighborsClassifier())
class TestSetred:
def test_basic(self):
check_basic(Setred)
def test_random_state(self):
check_random(Setred)
def test_all_label(self):
check_all_label(Setred)
class TestTriTraining:
def test_basic(self):
check_basic(TriTraining)
def test_random_state(self):
check_random(TriTraining)
def test_multiple(self):
clf = TriTraining(base_estimator=[DecisionTreeClassifier(max_depth=1), DecisionTreeClassifier(max_depth=2), DecisionTreeClassifier(max_depth=3)])
clf.fit(X, y)
clf.predict(X)
clf.predict_proba(X)
def test_no_more_three(self):
with pytest.raises(AttributeError):
_ = TriTraining(base_estimator=multiples_estimator)
def test_all_label(self):
check_all_label(TriTraining)
class TestDeTriTraining:
def test_basic(self):
check_basic(DeTriTraining, max_iterations=1)
def test_random_state(self):
check_random(DeTriTraining, max_iterations=1)
def test_all_label(self):
check_all_label(DeTriTraining, max_iterations=1)
# Create a fake groups
groups = list()
for order, i in enumerate(range(0, len(X), 5)):
groups.extend([order for _ in range(5)])
groups = np.array(groups)
groups = groups[:X.shape[0]]
# class TestWiWTriTraining:
# def test_basic(self):
# clf = WiWTriTraining(base_estimator=DecisionTreeClassifier())
# clf.fit(X, y, instance_group=groups)
# clf.predict(X, instance_group=groups)
# clf.predict_proba(X)
# clf = WiWTriTraining(DecisionTreeClassifier())
# clf.fit(X2, y2, instance_group=groups)
# clf.predict(X2, instance_group=groups)
# clf.predict_proba(X2)
# def test_multiple(self):
# clf = WiWTriTraining(base_estimator=[DecisionTreeClassifier(max_depth=1), DecisionTreeClassifier(max_depth=2), DecisionTreeClassifier(max_depth=3)])
# clf.fit(X, y, instance_group=groups)
# clf.predict(X, instance_group=groups)
# clf.predict_proba(X)
# def test_random_state(self):
# for i in range(10):
# clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
# clf.fit(X, y, instance_group=groups)
# y1 = clf.predict(X, instance_group=groups)
# clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
# clf.fit(X, y, instance_group=groups)
# y2 = clf.predict(X, instance_group=groups)
# assert np.all(y1 == y2)
# def test_all_label(self):
# clf = WiWTriTraining(base_estimator=KNeighborsClassifier())
# clf.fit(X, y, instance_group=groups)
# clf.predict(X, instance_group=groups)
# clf.predict_proba(X)