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import pytest
import pandas as pd
import numpy as np
import os
from syntetic_issue_report_data_generation.utils.clustering_sampling import ClusteringSampling
from syntetic_issue_report_data_generation.utils.text_encoder import Encoder
from syntetic_issue_report_data_generation.config import RANDOM_SEED, DATASET_CONFIGs, RAW_DATA_DIR, EMBEDDING_DIR
from sklearn.datasets import make_blobs
@pytest.fixture
def dataset():
"""
Fixture used to load the dataset used to test the clustering functions
"""
# load the test dataset
df = pd.read_csv(
RAW_DATA_DIR / f"{DATASET_CONFIGs['nlbse24_test']['data_path']}",
sep=DATASET_CONFIGs["nlbse24_test"].get('sep', ','),
encoding="utf-8",
)
return df
@pytest.fixture
def clustering_sampling():
"""
Fixture used to create the Clustering sampling model that needs to be tested
"""
return ClusteringSampling(n_clusters=5, n_samples=20, method='medoid')
class TestClustering:
"""
Test class to test the clustering functions of the class ClusteringSampling.
The model is tested on its best hyperparameters configuration found in the notebook 'clustering-sampling-techniques-comparison.ipynb'.
"""
def test_minimum_functionality(self, dataset : pd.DataFrame, clustering_sampling : ClusteringSampling):
"""
Test method that checks if the clustering model works with some basic samples
"""
# Create a basic datset from sklearn make blobs
X, y = make_blobs(n_samples=200, n_features=2, centers=5, random_state=RANDOM_SEED)
# Train the model
sample_dict = clustering_sampling.forward(X)
# evaluate the model
evaluation = clustering_sampling.evaluate(sample_dict["sample"], X)
# Check if the clustering metrics are satisfied
assert evaluation["silhouette"] > 0.5
assert evaluation["mse"] < 1.5
def test_dataset(self, dataset : pd.DataFrame, clustering_sampling : ClusteringSampling):
"""
Test the clustering model on the test dataset
"""
# Extract the bodies of the dataset
bodies = [str(x) for x in dataset[DATASET_CONFIGs["nlbse24_test"]["body_col"]].tolist()]
# Extract embeddings of the bodies
embeddings_path = EMBEDDING_DIR / "issue-report-classification" / "nlbse24" / "nlbse24_test_embeddings.npy"
if not os.path.exists(embeddings_path):
encoder = Encoder()
embeddings = encoder.encode(bodies, save=True, save_path=embeddings_path)
else:
embeddings = np.load(embeddings_path)
# Train the model
sample_dict = clustering_sampling.forward(embeddings)
# evaluate the model
evaluation = clustering_sampling.evaluate(sample_dict["sample"], embeddings)
# Check if the clustering metrics are satisfied
assert evaluation["silhouette"] > 0.5
assert evaluation["mse"] < 1.5
class TestSampling:
"""
Test class to test the sampling functions of the class ClusteringSampling.
The model is tested on its best hyperparameters configuration found in the notebook 'clustering-sampling-techniques-comparison.ipynb'.
"""
def test_minimum_functionality(self, dataset : pd.DataFrame, clustering_sampling : ClusteringSampling):
"""
Test method that checks if the sampling method works with a basid dataset
"""
# Create a basic datset from a normal distribution
X = np.random.normal(0,1,(200,2))
# Train the model
sample_dict = clustering_sampling.forward(X)
# evaluate the model
evaluation = clustering_sampling.evaluate(sample_dict["sample"], X)
# Check if the sampling metrics are satisfied
assert evaluation["distributions_mean_norm_sim"] < 0.5
assert evaluation["distributions_std_norm_sim"] < 0.5
assert evaluation["kst"] == False
def test_dataset(self, dataset : pd.DataFrame, clustering_sampling : ClusteringSampling):
"""
Test the sampling method on the test dataset
"""
# Extract the bodies of the dataset
bodies = [str(x) for x in dataset[DATASET_CONFIGs["nlbse24_test"]["body_col"]].tolist()]
# Extract embeddings of the bodies
embeddings_path = EMBEDDING_DIR / "issue-report-classification" / "nlbse24" / "nlbse24_test_embeddings.npy"
if not os.path.exists(embeddings_path):
encoder = Encoder()
embeddings = encoder.encode(bodies, save=True, save_path=embeddings_path)
else:
embeddings = np.load(embeddings_path)
# Train the model
sample_dict = clustering_sampling.forward(embeddings)
# evaluate the model
evaluation = clustering_sampling.evaluate(sample_dict["sample"], embeddings)
# Check if the sampling metrics are satisfied
assert evaluation["distributions_mean_norm_sim"] < 0.5
assert evaluation["distributions_std_norm_sim"] < 0.5
assert evaluation["kst"] == False |