def test_validation():
"""
test cross-validation
"""
# this sys.path.append are used to import knnModel inside /models/KNN
sys.path.append(".")
sys.path.append("../")
from catboostModel import PredictiveModel
X, Y = getXY()
string_cols = [
"Unnamed: 0", "dataset_type", "Name", "RescuerID", "Description",
"PetID"
]
categorical_col = [
"Type", "Gender", "Vaccinated", "Dewormed", "Sterilized", "Breed1",
"Breed2", "Color1", "Color2", "Color3", "State"
]
numerical_col = [
col for col in X.columns if col not in string_cols
and col not in categorical_col and col != "AdoptionSpeed"
]
mapping_sizes = [2, 2, 3, 3, 3, 307, 307, 7, 7, 7, 15]
cat_features = [
i for i in range(len(numerical_col),
len(numerical_col) + len(categorical_col))
]
X = pd.concat([X[numerical_col], X[categorical_col]], axis=1)
model = PredictiveModel("catboost_by_pytest")
assert model.validation(X, Y, cat_features, n_folds=2) > 0
assert model.validation(X, Y, cat_features, method=1, n_folds=2) > 0
assert model.validation(X, Y, cat_features, method=2, n_folds=2) > 0
assert model.validation(X, Y, cat_features, n_folds=1) > 0
def test_run():
"""
this test just runs the load-train-predict workflow
the code is just a script of
85f7ec7c8c0581c347a5b8034139a9ad3a6c3352../../../kNN.ipynb
"""
# this sys.path.append are used to import knnModel inside /models/KNN
sys.path.append(".")
sys.path.append("../")
from catboostModel import PredictiveModel
###########################################################
#### this can be used as an example usage of the model ####
###########################################################
X, Y = getXY()
string_cols = [
"Unnamed: 0", "dataset_type", "Name", "RescuerID", "Description",
"PetID"
]
categorical_col = [
"Type", "Gender", "Vaccinated", "Dewormed", "Sterilized", "Breed1",
"Breed2", "Color1", "Color2", "Color3", "State"
]
numerical_col = [
col for col in X.columns if col not in string_cols
and col not in categorical_col and col != "AdoptionSpeed"
]
mapping_sizes = [2, 2, 3, 3, 3, 307, 307, 7, 7, 7, 15]
cat_features = [
i for i in range(len(numerical_col),
len(numerical_col) + len(categorical_col))
]
X = pd.concat([X[numerical_col], X[categorical_col]], axis=1)
train_size = int(len(X) * 0.8)
# split in train and validation data
train_X, train_Y = X[:train_size], Y[:train_size]
validation_X, validation_Y = X[train_size:], Y[train_size:]
assert train_X.shape[0] == train_Y.shape[0]
assert validation_X.shape[0] == validation_Y.shape[0]
model = PredictiveModel("catboost_by_pytest")
model.train(train_X, train_Y, cat_features)
predictions = model.predict(validation_X)
score = model.evaluate(validation_Y)
assert score > 0 # score is less then zero means something is wrong
predictions = model.predict(validation_X, probability=True)
assert len(predictions) > 0
assert 1 - 1e6 < sum(predictions[0]) < 1 + 1e6
def test_grid_search():
"""
"""
# this sys.path.append are used to import knnModel inside /models/KNN
sys.path.append(".")
sys.path.append("../")
from catboostModel import PredictiveModel
X, Y = getXY()
string_cols = [
"Unnamed: 0", "dataset_type", "Name", "RescuerID", "Description",
"PetID"
]
categorical_col = [
"Type", "Gender", "Vaccinated", "Dewormed", "Sterilized", "Breed1",
"Breed2", "Color1", "Color2", "Color3", "State"
]
numerical_col = [
col for col in X.columns if col not in string_cols
and col not in categorical_col and col != "AdoptionSpeed"
]
mapping_sizes = [2, 2, 3, 3, 3, 307, 307, 7, 7, 7, 15]
cat_features = [
i for i in range(len(numerical_col),
len(numerical_col) + len(categorical_col))
]
X = pd.concat([X[numerical_col], X[categorical_col]], axis=1)
params = {
'depth': 3,
'iterations': 20,
'learning_rate': 0.5,
'l2_leaf_reg': 3,
'border_count': 3,
'thread_count': 4,
}
model = PredictiveModel("catboost_by_pytest", params)
assert model.validation(X, Y, cat_features, n_folds=2) > 0
def test_meta():
"""
test generate_meta, replicating validation
"""
# this sys.path.append are used to import knnModel inside /models/KNN
sys.path.append(".")
sys.path.append("../")
from catboostModel import PredictiveModel
X, Y = getXY()
string_cols = [
"Unnamed: 0", "dataset_type", "Name", "RescuerID", "Description",
"PetID"
]
categorical_col = [
"Type", "Gender", "Vaccinated", "Dewormed", "Sterilized", "Breed1",
"Breed2", "Color1", "Color2", "Color3", "State"
]
numerical_col = [
col for col in X.columns if col not in string_cols
and col not in categorical_col and col != "AdoptionSpeed"
]
mapping_sizes = [2, 2, 3, 3, 3, 307, 307, 7, 7, 7, 15]
cat_features = [
i for i in range(len(numerical_col),
len(numerical_col) + len(categorical_col))
]
X = pd.concat([X[numerical_col], X[categorical_col]], axis=1)
model = PredictiveModel("catboost_by_pytest_generate_meta")
n_folds = 3
score = model.validation(X, Y, cat_features, n_folds=n_folds)
meta_train = model.generate_meta_train(X,
Y,
cat_features,
n_folds=n_folds,
short=True)
meta_train = model.generate_meta_train(X,
Y,
cat_features,
n_folds=n_folds,
short=True,
verbose=True)
from sklearn.model_selection import KFold
splitclass = KFold(n_splits=n_folds)
for train_index, test_index in splitclass.split(X):
meta_vals = meta_train.loc[test_index] # generated from .generate_meta
train_X, train_Y = X.loc[train_index], Y.loc[train_index]
validation_X, validation_Y = X.loc[test_index], Y.loc[test_index]
assert train_X.shape[0] == train_Y.shape[0]
assert validation_X.shape[0] == validation_Y.shape[0]
model.train(train_X, train_Y, cat_features, short=True)
predictions = model.predict(validation_X, probability=True)
meta_vals = meta_vals.reset_index().drop('index', axis=1)
for i, p in enumerate(predictions):
assert p[0] == meta_vals.loc[i, 'L0']
assert p[1] == meta_vals.loc[i, 'L1']
assert p[2] == meta_vals.loc[i, 'L2']
assert p[3] == meta_vals.loc[i, 'L3']
assert p[4] == meta_vals.loc[i, 'L4']
X_test = getXY(X_test=True)
X_test = pd.concat([X_test[numerical_col], X_test[categorical_col]],
axis=1)
meta_test = model.generate_meta_test(X, Y, cat_features, X_test)
assert len(meta_test.columns) == 5
assert len(meta_test) == len(X_test)