def test_evaluate_classification_optimiser():
"""Test evaluate method of Optimiser class for classication."""
reader = Reader(sep=",")
dict = reader.train_test_split(
Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
target_name="Survived")
drift_thresholder = Drift_thresholder()
drift_thresholder = drift_thresholder.fit_transform(dict)
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring=None, n_folds=3)
assert len(record) == 1
score = opt.evaluate(None, dict)
assert -np.Inf <= score
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring="roc_auc", n_folds=3)
assert len(record) == 1
score = opt.evaluate(None, dict)
assert 0. <= score <= 1.
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring="wrong_scoring", n_folds=3)
assert len(record) == 1
with pytest.warns(UserWarning) as record:
score = opt.evaluate(None, dict)
assert opt.scoring == "neg_log_loss"
def test_evaluate_regression_optimiser():
"""Test evaluate method of Optimiser class for regression."""
reader = Reader(sep=",")
dict = reader.train_test_split(Lpath=[
"data_for_tests/train_regression.csv",
"data_for_tests/test_regression.csv"
],
target_name="SalePrice")
drift_thresholder = Drift_thresholder()
drift_thresholder = drift_thresholder.fit_transform(dict)
mape = make_scorer(lambda y_true, y_pred: 100 * np.sum(
np.abs(y_true - y_pred) / y_true) / len(y_true),
greater_is_better=False,
needs_proba=False)
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring=mape, n_folds=3)
assert len(record) == 1
score = opt.evaluate(None, dict)
assert -np.Inf <= score
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring=None, n_folds=3)
assert len(record) == 1
score = opt.evaluate(None, dict)
assert -np.Inf <= score
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring="wrong_scoring", n_folds=3)
assert len(record) == 1
with pytest.warns(UserWarning) as record:
score = opt.evaluate(None, dict)
assert -np.Inf <= score
def test_fit_predict_predictor_regression(mock_show):
"""Test fit_predict method of Predictor class for regression."""
rd = Reader(sep=',')
dt = rd.train_test_split(Lpath=[
"data_for_tests/train_regression.csv",
"data_for_tests/test_regression.csv"
],
target_name="SalePrice")
drift_thresholder = Drift_thresholder()
df = drift_thresholder.fit_transform(dt)
mape = make_scorer(lambda y_true, y_pred: 100 * np.sum(
np.abs(y_true - y_pred) / y_true) / len(y_true),
greater_is_better=False,
needs_proba=False)
opt = Optimiser(scoring=mape, n_folds=3)
opt.evaluate(None, df)
space = {
'ne__numerical_strategy': {
"search": "choice",
"space": [0]
},
'ce__strategy': {
"search":
"choice",
"space":
["label_encoding", "random_projection", "entity_embedding"]
},
'fs__threshold': {
"search": "uniform",
"space": [0.01, 0.3]
},
'est__max_depth': {
"search": "choice",
"space": [3, 4, 5, 6, 7]
}
}
best = opt.optimise(space, df, 1)
prd = Predictor(verbose=True)
prd.fit_predict(best, df)
pred_df = pd.read_csv("save/SalePrice_predictions.csv")
assert np.all(
list(pred_df.columns) == ['Unnamed: 0', 'SalePrice_predicted'])
assert np.shape(pred_df) == (1459, 2)
def test_evaluate_and_optimise_classification():
"""Test evaluate_and_optimise method of Optimiser class."""
reader = Reader(sep=",")
dict = reader.train_test_split(
Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
target_name="Survived")
drift_thresholder = Drift_thresholder()
drift_thresholder = drift_thresholder.fit_transform(dict)
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring='accuracy', n_folds=3)
assert len(record) == 1
dict_error = dict.copy()
dict_error["target"] = dict_error["target"].astype(str)
with pytest.raises(ValueError):
score = opt.evaluate(None, dict_error)
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring='accuracy', n_folds=3)
assert len(record) == 1
score = opt.evaluate(None, dict)
assert 0. <= score <= 1.
space = {
'ne__numerical_strategy': {
"search": "choice",
"space": [0]
},
'ce__strategy': {
"search": "choice",
"space":
["label_encoding", "random_projection", "entity_embedding"]
},
'fs__threshold': {
"search": "uniform",
"space": [0.01, 0.3]
},
'est__max_depth': {
"search": "choice",
"space": [3, 4, 5, 6, 7]
}
}
best = opt.optimise(space, dict, 1)
assert type(best) == type(dict)
def test_init_drift_thresholder():
"""Test init method of Drift_thresholder class."""
drift_thresholder = Drift_thresholder()
assert drift_thresholder.threshold == 0.6
assert not drift_thresholder.inplace
assert drift_thresholder.verbose
assert drift_thresholder.to_path == "save"
assert drift_thresholder._Drift_thresholder__Ddrifts == {}
assert not drift_thresholder._Drift_thresholder__fitOK
def test_fit_predict_predictor_classification():
"""Test fit_predict method of Predictor class for classification."""
reader = Reader(sep=",")
dict = reader.train_test_split(
Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
target_name="Survived")
drift_thresholder = Drift_thresholder()
drift_thresholder = drift_thresholder.fit_transform(dict)
with pytest.warns(UserWarning) as record:
opt = Optimiser(scoring='accuracy', n_folds=3)
assert len(record) == 1
space = {
'ne__numerical_strategy': {
"search": "choice",
"space": [0]
},
'ce__strategy': {
"search": "choice",
"space":
["label_encoding", "random_projection", "entity_embedding"]
},
'fs__threshold': {
"search": "uniform",
"space": [0.01, 0.3]
},
'est__max_depth': {
"search": "choice",
"space": [3, 4, 5, 6, 7]
}
}
optimal_hyper_parameters = opt.optimise(space, dict, 1)
predictor = Predictor(verbose=False)
predictor.fit_predict(optimal_hyper_parameters, dict)
pred_df = pd.read_csv("save/Survived_predictions.csv")
assert np.all(
list(pred_df.columns) ==
['Unnamed: 0', '0.0', '1.0', 'Survived_predicted'])
assert np.shape(pred_df) == (418, 4)
def test_drifts():
"""Test drifts method of Drift_thresholder class."""
drift_thresholder = Drift_thresholder()
with pytest.raises(ValueError):
drift_thresholder.drifts()
reader = Reader(sep=",")
dict = reader.train_test_split(
Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
target_name="Survived")
drift_thresholder.fit_transform(dict)
drifts = drift_thresholder.drifts()
assert drifts != {}
def test_fit_transform():
"""Test fit transform method of Drift_thresholder class."""
drift_thresholder = Drift_thresholder()
reader = Reader(sep=",")
dict = reader.train_test_split(Lpath=["data_for_tests/train.csv"],
target_name="Survived")
drift_thresholder.fit_transform(dict)
assert not drift_thresholder._Drift_thresholder__fitOK
dict = reader.train_test_split(
Lpath=["data_for_tests/train.csv", "data_for_tests/test.csv"],
target_name="Survived")
drift_thresholder.fit_transform(dict)
assert drift_thresholder._Drift_thresholder__fitOK
dict = reader.train_test_split(Lpath=[
"data_for_tests/inplace_train.csv", "data_for_tests/inplace_test.csv"
],
target_name="Survived")
drift_thresholder.inplace = True
drift_thresholder.fit_transform(dict)
assert drift_thresholder._Drift_thresholder__fitOK
from mlbox.preprocessing.reader import Reader
from mlbox.preprocessing.drift_thresholder import Drift_thresholder
from mlbox.optimisation.optimiser import Optimiser
from mlbox.prediction.predictor import Predictor
#from mlbox.encoding import Categorical_encoder
from mlbox.model.classification import StackingClassifier, Classifier
import pandas as pd
paths = ["train_1.csv", "test.csv"]
target_name = "Class"
rd = Reader(sep=",")
df = rd.train_test_split(paths, target_name)
print(df["train"].head())
dft = Drift_thresholder()
df = dft.fit_transform(df)
opt = Optimiser()
warnings.filterwarnings('ignore', category=DeprecationWarning)
score = opt.evaluate(None, df)
space = {
'ne__numerical_strategy':{"search":"choice",
"space":[0, "mean"]},
'ce__strategy':{"search":"choice",
"space":["label_encoding", "random_projection", "entity_embedding"]},
'fs__threshold':{"search":"uniform",
"space":[0.001, 0.2]},
'est__strategy':{"search":"choice",
"space":["RandomForest", "ExtraTrees", "LightGBM"]},