def test_run(self):
path = EnvironmentSettings.tmp_test_path / "mlapplicationtest/"
PathBuilder.build(path)
dataset = RandomDatasetGenerator.generate_repertoire_dataset(50, {5: 1}, {5: 1}, {"l1": {1: 0.5, 2: 0.5}}, path / 'dataset/')
ml_method = LogisticRegression()
encoder = KmerFreqRepertoireEncoder(NormalizationType.RELATIVE_FREQUENCY, ReadsType.UNIQUE, SequenceEncodingType.CONTINUOUS_KMER, 3,
scale_to_zero_mean=True, scale_to_unit_variance=True)
label_config = LabelConfiguration([Label("l1", [1, 2])])
enc_dataset = encoder.encode(dataset, EncoderParams(result_path=path, label_config=label_config, filename="tmp_enc_dataset.pickle", pool_size=4))
ml_method.fit(enc_dataset.encoded_data, 'l1')
hp_setting = HPSetting(encoder, {"normalization_type": "relative_frequency", "reads": "unique", "sequence_encoding": "continuous_kmer",
"k": 3, "scale_to_zero_mean": True, "scale_to_unit_variance": True}, ml_method, {}, [], 'enc1', 'ml1')
PathBuilder.build(path / 'result/instr1/')
shutil.copy(path / 'dict_vectorizer.pickle', path / 'result/instr1/dict_vectorizer.pickle')
shutil.copy(path / 'scaler.pickle', path / 'result/instr1/scaler.pickle')
ml_app = MLApplicationInstruction(dataset, label_config, hp_setting, 4, "instr1", False)
ml_app.run(path / 'result/')
predictions_path = path / "result/instr1/predictions.csv"
self.assertTrue(os.path.isfile(predictions_path))
df = pd.read_csv(predictions_path)
self.assertEqual(50, df.shape[0])
shutil.rmtree(path)
def test_run(self):
path = EnvironmentSettings.root_path / "test/tmp/mlmethodassessment/"
PathBuilder.build(path)
dataset = RepertoireDataset(repertoires=RepertoireBuilder.build(
[["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"], ["AA"], ["CC"],
["AA"], ["CC"], ["AA"], ["CC"]], path)[0])
dataset.encoded_data = EncodedData(
examples=np.array([[1, 1], [1, 1], [3, 3], [1, 1], [1, 1], [3, 3],
[1, 1], [1, 1], [3, 3], [1, 1], [1, 1], [3,
3]]),
labels={
"l1": [1, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 3],
"l2": [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]
})
label_config = LabelConfiguration()
label_config.add_label("l1", [1, 3])
label = Label(name='l1', values=[1, 2])
method1 = LogisticRegression()
method1.fit(dataset.encoded_data, label=label)
res = MLMethodAssessment.run(
MLMethodAssessmentParams(
dataset=dataset,
method=method1,
metrics={
Metric.ACCURACY, Metric.BALANCED_ACCURACY, Metric.F1_MACRO
},
optimization_metric=Metric.LOG_LOSS,
predictions_path=EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/predictions.csv",
label=label,
ml_score_path=EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/ml_score.csv",
split_index=1,
path=EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/"))
self.assertTrue(isinstance(res, dict))
self.assertTrue(res[Metric.LOG_LOSS.name.lower()] <= 0.1)
self.assertTrue(
os.path.isfile(EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/ml_score.csv"))
df = pd.read_csv(EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/ml_score.csv")
self.assertTrue(df.shape[0] == 1)
df = pd.read_csv(EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/predictions.csv")
self.assertEqual(12, df.shape[0])
shutil.rmtree(EnvironmentSettings.root_path /
"test/tmp/mlmethodassessment/")
def test_predict(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"test1": [1, 0, 2, 0], "test2": [1, 0, 2, 0]}
lr = LogisticRegression()
lr.fit(EncodedData(x, y), Label("test2"))
test_x = np.array([[0, 1, 0], [1, 0, 0]])
y = lr.predict(EncodedData(test_x), Label("test2"))
self.assertTrue(len(y["test2"]) == 2)
self.assertTrue(y["test2"][1] in [0, 1, 2])
def test_store(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
lr = LogisticRegression()
lr.fit(EncodedData(x, y), Label("default"))
path = EnvironmentSettings.root_path / "test/tmp/lr/"
lr.store(path, ["f1", "f2", "f3"])
self.assertTrue(os.path.isfile(path / "logistic_regression.pickle"))
with open(path / "logistic_regression.pickle", "rb") as file:
lr2 = pickle.load(file)
self.assertTrue(isinstance(lr2, SklearnLogisticRegression))
shutil.rmtree(path)
def test_load(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"default": np.array([1, 0, 2, 0])}
lr = LogisticRegression()
lr.fit(EncodedData(x, y), Label("default"))
path = EnvironmentSettings.root_path / "test/tmp/lr2/"
PathBuilder.build(path)
with open(path / "logistic_regression.pickle", "wb") as file:
pickle.dump(lr.model, file)
lr2 = LogisticRegression()
lr2.load(path)
self.assertTrue(isinstance(lr2.model, SklearnLogisticRegression))
shutil.rmtree(path)
def test_fit(self):
x = np.array([[1, 0, 0], [0, 1, 1], [1, 1, 1], [0, 1, 1]])
y = {"test": np.array([1, 0, 2, 0])}
lr = LogisticRegression()
lr.fit(EncodedData(x, y), Label("test"))