Implemented first version of SemiSupervised tests #175

Author: dpuenteramirez

semisupervised/__init__.py

@@ -18,7 +18,9 @@
 from .CoTraining import CoTraining
 from .TriTraining import TriTraining
 from .DemocraticCoLearning import DemocraticCoLearning
+from .DensityPeaks import STDPNF
 
 __all__ = ["CoTraining",
            "TriTraining",
-           "DemocraticCoLearning"]
+           "DemocraticCoLearning",
+           "STDPNF"]

tests/SemiSupervised.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+# @Filename:    SemiSupervised.py
+# @Author:      Daniel Puente Ramírez
+# @Time:        16/4/22 00:22
+
+import random
+
+import pytest
+import numpy as np
+import pandas as pd
+from sklearn.datasets import load_iris as load_digits
+from sklearn.model_selection import train_test_split
+
+from semisupervised import STDPNF, CoTraining, TriTraining, \
+    DemocraticCoLearning
+
+
+def to_dataframe(y):
+    if not isinstance(y, pd.DataFrame):
+        return pd.DataFrame(y)
+    return y
+
+
+@pytest.fixture
+def digits_dataset_ss():
+    x, y = load_digits(return_X_y=True, as_frame=True)
+    x = x.to_numpy()
+    y = y.to_numpy()
+    opt_labels = np.unique(y)
+    x_train, x_test, y_train, y_test = train_test_split(
+        x, y, train_size=0.75, stratify=y, random_state=42
+    )
+    x_train = pd.DataFrame(x_train)
+    x_test = pd.DataFrame(x_test)
+    y_train = pd.DataFrame(y_train)
+    y_test = pd.DataFrame(y_test)
+    li = list(set(range(x_train.shape[0])))
+    unlabeled = random.sample(li, int(x_train.shape[0] * 0.3))
+    y_train.loc[unlabeled] = -1
+
+    return x_train, x_test, y_train, y_test, opt_labels
+
+
+def base(x_train, x_test, y_train, y_test, opt_labels, algorithm, params=None):
+    assert isinstance(x_train, pd.DataFrame) and isinstance(y_train,
+                                                            pd.DataFrame)
+    assert isinstance(x_test, pd.DataFrame) and isinstance(y_test,
+                                                           pd.DataFrame)
+    model = algorithm(**params) if params is not None else algorithm()
+
+    model.fit(x_train, y_train)
+    y_pred = model.predict(x_test)
+
+    assert set(y_pred).issubset(opt_labels)
+
+
+def test_co_training(digits_dataset_ss):
+    x_train, x_test, y_train, y_test, opt_labels = digits_dataset_ss
+    base(x_train, x_test, y_train, y_test, opt_labels, CoTraining)
+
+
+def test_tri_training(digits_dataset_ss):
+    x_train, x_test, y_train, y_test, opt_labels = digits_dataset_ss
+    base(x_train, x_test, y_train, y_test, opt_labels, TriTraining)
+
+
+def test_demo_co_learning(digits_dataset_ss):
+    x_train, x_test, y_train, y_test, opt_labels = digits_dataset_ss
+    base(x_train, x_test, y_train, y_test, opt_labels, DemocraticCoLearning)
+
+
+def test_density_peaks(digits_dataset_ss):
+    x_train, x_test, y_train, y_test, opt_labels = digits_dataset_ss
+    base(x_train, x_test, y_train, y_test, opt_labels, STDPNF)