def test_singleton_class():
X = iris_data
y = iris_target
# one singleton class
singleton_class = 1
ind_singleton, = np.where(y == singleton_class)
y[ind_singleton] = 2
y[ind_singleton[0]] = singleton_class
nca = NeighborhoodComponentsAnalysis(max_iter=30)
nca.fit(X, y)
# One non-singleton class
ind_1, = np.where(y == 1)
ind_2, = np.where(y == 2)
y[ind_1] = 0
y[ind_1[0]] = 1
y[ind_2] = 0
y[ind_2[0]] = 2
nca = NeighborhoodComponentsAnalysis(max_iter=30)
nca.fit(X, y)
# Only singleton classes
ind_0, = np.where(y == 0)
ind_1, = np.where(y == 1)
ind_2, = np.where(y == 2)
X = X[[ind_0[0], ind_1[0], ind_2[0]]]
y = y[[ind_0[0], ind_1[0], ind_2[0]]]
nca = NeighborhoodComponentsAnalysis(init='identity', max_iter=30)
nca.fit(X, y)
assert_array_equal(X, nca.transform(X))
def test_warm_start_effectiveness():
# A 1-iteration second fit on same data should give almost same result
# with warm starting, and quite different result without warm starting.
nca_warm = NeighborhoodComponentsAnalysis(warm_start=True, random_state=0)
nca_warm.fit(iris_data, iris_target)
transformation_warm = nca_warm.components_
nca_warm.max_iter = 1
nca_warm.fit(iris_data, iris_target)
transformation_warm_plus_one = nca_warm.components_
nca_cold = NeighborhoodComponentsAnalysis(warm_start=False, random_state=0)
nca_cold.fit(iris_data, iris_target)
transformation_cold = nca_cold.components_
nca_cold.max_iter = 1
nca_cold.fit(iris_data, iris_target)
transformation_cold_plus_one = nca_cold.components_
diff_warm = np.sum(
np.abs(transformation_warm_plus_one - transformation_warm))
diff_cold = np.sum(
np.abs(transformation_cold_plus_one - transformation_cold))
assert diff_warm < 3.0, ("Transformer changed significantly after one "
"iteration even though it was warm-started.")
assert diff_cold > diff_warm, ("Cold-started transformer changed less "
"significantly than warm-started "
"transformer after one iteration.")
def test_n_components():
rng = np.random.RandomState(42)
X = np.arange(12).reshape(4, 3)
y = [1, 1, 2, 2]
init = rng.rand(X.shape[1] - 1, 3)
# n_components = X.shape[1] != transformation.shape[0]
n_components = X.shape[1]
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
msg = ("The preferred dimensionality of the projected space "
f"`n_components` ({n_components}) does not match the output "
"dimensionality of the given linear transformation "
f"`init` ({init.shape[0]})!")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X, y)
# n_components > X.shape[1]
n_components = X.shape[1] + 2
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
msg = ("The preferred dimensionality of the projected space "
f"`n_components` ({n_components}) cannot be greater than "
f"the given data dimensionality ({X.shape[1]})!")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X, y)
# n_components < X.shape[1]
nca = NeighborhoodComponentsAnalysis(n_components=2, init='identity')
nca.fit(X, y)
def test_n_components():
rng = np.random.RandomState(42)
X = np.arange(12).reshape(4, 3)
y = [1, 1, 2, 2]
init = rng.rand(X.shape[1] - 1, 3)
# n_components = X.shape[1] != transformation.shape[0]
n_components = X.shape[1]
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
assert_raise_message(
ValueError, 'The preferred dimensionality of the '
'projected space `n_components` ({}) does not match '
'the output dimensionality of the given '
'linear transformation `init` ({})!'.format(n_components,
init.shape[0]), nca.fit, X,
y)
# n_components > X.shape[1]
n_components = X.shape[1] + 2
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
assert_raise_message(
ValueError, 'The preferred dimensionality of the '
'projected space `n_components` ({}) cannot '
'be greater than the given data '
'dimensionality ({})!'.format(n_components, X.shape[1]), nca.fit, X, y)
# n_components < X.shape[1]
nca = NeighborhoodComponentsAnalysis(n_components=2, init='identity')
nca.fit(X, y)
def test_init_transformation():
rng = np.random.RandomState(42)
X, y = make_blobs(n_samples=30, centers=6, n_features=5, random_state=0)
# Start learning from scratch
nca = NeighborhoodComponentsAnalysis(init='identity')
nca.fit(X, y)
# Initialize with random
nca_random = NeighborhoodComponentsAnalysis(init='random')
nca_random.fit(X, y)
# Initialize with auto
nca_auto = NeighborhoodComponentsAnalysis(init='auto')
nca_auto.fit(X, y)
# Initialize with PCA
nca_pca = NeighborhoodComponentsAnalysis(init='pca')
nca_pca.fit(X, y)
# Initialize with LDA
nca_lda = NeighborhoodComponentsAnalysis(init='lda')
nca_lda.fit(X, y)
init = rng.rand(X.shape[1], X.shape[1])
nca = NeighborhoodComponentsAnalysis(init=init)
nca.fit(X, y)
# init.shape[1] must match X.shape[1]
init = rng.rand(X.shape[1], X.shape[1] + 1)
nca = NeighborhoodComponentsAnalysis(init=init)
assert_raise_message(
ValueError, 'The input dimensionality ({}) of the given '
'linear transformation `init` must match the '
'dimensionality of the given inputs `X` ({}).'.format(
init.shape[1], X.shape[1]), nca.fit, X, y)
# init.shape[0] must be <= init.shape[1]
init = rng.rand(X.shape[1] + 1, X.shape[1])
nca = NeighborhoodComponentsAnalysis(init=init)
assert_raise_message(
ValueError, 'The output dimensionality ({}) of the given '
'linear transformation `init` cannot be '
'greater than its input dimensionality ({}).'.format(
init.shape[0], init.shape[1]), nca.fit, X, y)
# init.shape[0] must match n_components
init = rng.rand(X.shape[1], X.shape[1])
n_components = X.shape[1] - 2
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
assert_raise_message(
ValueError, 'The preferred dimensionality of the '
'projected space `n_components` ({}) does not match '
'the output dimensionality of the given '
'linear transformation `init` ({})!'.format(n_components,
init.shape[0]), nca.fit, X,
y)
def test_init_transformation():
rng = np.random.RandomState(42)
X, y = make_blobs(n_samples=30, centers=6, n_features=5, random_state=0)
# Start learning from scratch
nca = NeighborhoodComponentsAnalysis(init='identity')
nca.fit(X, y)
# Initialize with random
nca_random = NeighborhoodComponentsAnalysis(init='random')
nca_random.fit(X, y)
# Initialize with auto
nca_auto = NeighborhoodComponentsAnalysis(init='auto')
nca_auto.fit(X, y)
# Initialize with PCA
nca_pca = NeighborhoodComponentsAnalysis(init='pca')
nca_pca.fit(X, y)
# Initialize with LDA
nca_lda = NeighborhoodComponentsAnalysis(init='lda')
nca_lda.fit(X, y)
init = rng.rand(X.shape[1], X.shape[1])
nca = NeighborhoodComponentsAnalysis(init=init)
nca.fit(X, y)
# init.shape[1] must match X.shape[1]
init = rng.rand(X.shape[1], X.shape[1] + 1)
nca = NeighborhoodComponentsAnalysis(init=init)
msg = (f"The input dimensionality ({init.shape[1]}) of the given "
"linear transformation `init` must match the "
f"dimensionality of the given inputs `X` ({X.shape[1]}).")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X, y)
# init.shape[0] must be <= init.shape[1]
init = rng.rand(X.shape[1] + 1, X.shape[1])
nca = NeighborhoodComponentsAnalysis(init=init)
msg = (f"The output dimensionality ({init.shape[0]}) of the given "
"linear transformation `init` cannot be "
f"greater than its input dimensionality ({init.shape[1]}).")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X, y)
# init.shape[0] must match n_components
init = rng.rand(X.shape[1], X.shape[1])
n_components = X.shape[1] - 2
nca = NeighborhoodComponentsAnalysis(init=init, n_components=n_components)
msg = ("The preferred dimensionality of the "
f"projected space `n_components` ({n_components}) "
"does not match the output dimensionality of the given "
f"linear transformation `init` ({init.shape[0]})!")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X, y)
def __init__(self, X, y):
self.loss = np.inf # initialize the loss to very high
# Initialize a fake NCA and variables needed to compute the loss:
self.fake_nca = NeighborhoodComponentsAnalysis()
self.fake_nca.n_iter_ = np.inf
self.X, y, _ = self.fake_nca._validate_params(X, y)
self.same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]
def feature_reduction(x, y, n_components=2):
from sklearn.pipeline import make_pipeline
nca = make_pipeline(Normalizer(),
NeighborhoodComponentsAnalysis(init='auto',
n_components=n_components, random_state=1))
rx = nca.fit_transform(x,y)
return rx, y
def test_warm_start_validation():
X, y = make_classification(
n_samples=30,
n_features=5,
n_classes=4,
n_redundant=0,
n_informative=5,
random_state=0,
)
nca = NeighborhoodComponentsAnalysis(warm_start=True, max_iter=5)
nca.fit(X, y)
X_less_features, y = make_classification(
n_samples=30,
n_features=4,
n_classes=4,
n_redundant=0,
n_informative=4,
random_state=0,
)
msg = (f"The new inputs dimensionality ({X_less_features.shape[1]}) "
"does not match the input dimensionality of the previously learned "
f"transformation ({nca.components_.shape[1]}).")
with pytest.raises(ValueError, match=re.escape(msg)):
nca.fit(X_less_features, y)
def test_auto_init(n_samples, n_features, n_classes, n_components):
# Test that auto choose the init as expected with every configuration
# of order of n_samples, n_features, n_classes and n_components.
rng = np.random.RandomState(42)
nca_base = NeighborhoodComponentsAnalysis(init='auto',
n_components=n_components,
max_iter=1,
random_state=rng)
if n_classes >= n_samples:
pass
# n_classes > n_samples is impossible, and n_classes == n_samples
# throws an error from lda but is an absurd case
else:
X = rng.randn(n_samples, n_features)
y = np.tile(range(n_classes), n_samples // n_classes + 1)[:n_samples]
if n_components > n_features:
# this would return a ValueError, which is already tested in
# test_params_validation
pass
else:
nca = clone(nca_base)
nca.fit(X, y)
if n_components <= min(n_classes - 1, n_features):
nca_other = clone(nca_base).set_params(init='lda')
elif n_components < min(n_features, n_samples):
nca_other = clone(nca_base).set_params(init='pca')
else:
nca_other = clone(nca_base).set_params(init='identity')
nca_other.fit(X, y)
assert_array_almost_equal(nca.components_, nca_other.components_)
def test_no_verbose(capsys):
# assert by default there is no output (verbose=0)
nca = NeighborhoodComponentsAnalysis()
nca.fit(iris_data, iris_target)
out, _ = capsys.readouterr()
# check output
assert (out == '')
def __init__(self, X, y):
# Initialize a fake NCA and variables needed to call the loss
# function:
self.fake_nca = NeighborhoodComponentsAnalysis()
self.fake_nca.n_iter_ = np.inf
self.X, y, _ = self.fake_nca._validate_params(X, y)
self.same_class_mask = y[:, np.newaxis] == y[np.newaxis, :]
def knn_NCA(X_train, Y_train, X_test, K=1) -> list:
"""
Reduce the dimensionalty of the dataset using the NCA method
This is slower than using PCA or not using anything at all,
but yields better results for now
If the dataset sample is too large this takes really long to run
"""
# Scale all the output using a standard scaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Reduce the dimensionalty of the data using NCA
nca = NeighborhoodComponentsAnalysis(2).fit(X_train, Y_train)
X_train_nca = nca.transform(X_train)
X_test_nca = nca.transform(X_test)
X_train_nca = pd.DataFrame(X_train_nca)
X_test_nca = pd.DataFrame(X_test_nca)
# Classify using a KNN classifier
clf = KNeighborsClassifier(n_neighbors=K, leaf_size=2)
clf.fit(X_train_nca, Y_train)
# Return the predicted results
return clf.predict(X_test_nca)
def knnGridSearch(X_train, Y_train, X_test, Y_test) -> list:
"""
Used to run a grid search to find the best params for later usage
Only runs if the param -grid is provided
"""
# Params used for the gird search
grid_params = {
'n_neighbors': [1, 3, 5],
}
# Scale all the output using a standard scaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Reduce the dimensionalty of the data using NCA
nca = NeighborhoodComponentsAnalysis(2).fit(X_train, Y_train)
X_train_nca = nca.transform(X_train)
X_test_nca = nca.transform(X_test)
# Run the Grid search and print out the best params
classifier = KNeighborsClassifier()
gs = GridSearchCV(classifier, grid_params, verbose=1, cv=3, n_jobs=-1)
gs.fit(X_train_nca, Y_train)
print(gs.best_params_)
# Score the best found params using a confusion matrix
Y_pred = gs.predict(X_test_nca)
print(confusion_matrix(Y_test, Y_pred))
def test_parameters_valid_types(param, value):
# check that no error is raised when parameters have numpy integer or
# floating types.
nca = NeighborhoodComponentsAnalysis(**{param: value})
X = iris_data
y = iris_target
nca.fit(X, y)
def plot_nca_dim_reduction():
n_neighbors = 3
random_state = 0
# Load Digits dataset
X, y = datasets.load_digits(return_X_y=True)
# Split into train/test
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.5, stratify=y,
random_state=random_state)
dim = len(X[0])
n_classes = len(np.unique(y))
# Reduce dimension to 2 with PCA
pca = make_pipeline(StandardScaler(),
PCA(n_components=2, random_state=random_state))
# Reduce dimension to 2 with LinearDiscriminantAnalysis
lda = make_pipeline(StandardScaler(),
LinearDiscriminantAnalysis(n_components=2))
# Reduce dimension to 2 with NeighborhoodComponentAnalysis
nca = make_pipeline(
StandardScaler(),
NeighborhoodComponentsAnalysis(n_components=2,
random_state=random_state))
# Use a nearest neighbor classifier to evaluate the methods
knn = KNeighborsClassifier(n_neighbors=n_neighbors)
# Make a list of the methods to be compared
dim_reduction_methods = [('PCA', pca), ('LDA', lda), ('NCA', nca)]
# plt.figure()
for i, (name, model) in enumerate(dim_reduction_methods):
plt.figure()
# plt.subplot(1, 3, i + 1, aspect=1)
# Fit the method's model
model.fit(X_train, y_train)
# Fit a nearest neighbor classifier on the embedded training set
knn.fit(model.transform(X_train), y_train)
# Compute the nearest neighbor accuracy on the embedded test set
acc_knn = knn.score(model.transform(X_test), y_test)
# Embed the data set in 2 dimensions using the fitted model
X_embedded = model.transform(X)
# Plot the projected points and show the evaluation score
plt.scatter(X_embedded[:, 0], X_embedded[:, 1], c=y, s=30, cmap='Set1')
plt.title("{}, KNN (k={})\nTest accuracy = {:.2f}".format(
name, n_neighbors, acc_knn))
plt.show()
def KNN(df, *args, **kwargs):
unique_test_name = 'StandardScaler KNN GridSearchCV Optimised with SMOTE ENN'
# Create a temporary folder to store the transformers of the pipeline
cachedir = mkdtemp()
memory = Memory(location=cachedir, verbose=10)
y = df['QuoteConversion_Flag'].values
IDs = df.Quote_ID
X = df.drop(['QuoteConversion_Flag', 'Quote_ID'], axis=1).values
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
param_grid = {
'knn__n_neighbours': np.arange(3, 12),
'knn__algorithm': ['ball_tree', 'kd_tree', 'brute'],
'knn__leaf_size': np.arange(20, 30),
'knn__p': [1, 2, 3, 4, 5],
'nca__n_components': np.arange(2, 12),
'nca__max_iter': np.arange(1000, 2000),
'nca__tol': 10.0**-np.arange(1, 8),
}
# model classes
nca = NeighborhoodComponentsAnalysis(random_state=42, warm_start=False)
knn = KNeighborsClassifier(n_jobs=-1)
model = [make_pipeline(StandardScaler(), nca, knn, memory=memory)]
grid = GridSearchCV(model, param_grid, cv=1000, iid=False, n_jobs=-1)
grid.fit(X_train, y_train)
print("-----------------Best Param Overview--------------------")
print("Best score: %0.4f" % grid.best_score_)
print("Using the following parameters:")
print(grid.best_params_)
results = pd.DataFrame(grid.cv_results_)
results.to_csv(unique_test_name + '_cv_results.csv', index=False)
prediction = grid.predict(X_test)
print("-----------------Scoring Model--------------------")
print(classification_report(prediction, y_test))
print(confusion_matrix(prediction, y_test), "\n")
prediction = pd.DataFrame(data=prediction,
columns=['QuoteConversion_Flag'])
results = pd.concat([IDs, prediction], axis=1)
results.to_csv(unique_test_name + "ida_a3_13611165.csv", index=False)
dump(grid, "MLP[{}].joblib".format(unique_test_name))
# Delete the temporary cache before exiting
rmtree(cachedir)
return
def test_one_class():
X = iris_data[iris_target == 0]
y = iris_target[iris_target == 0]
nca = NeighborhoodComponentsAnalysis(max_iter=30,
n_components=X.shape[1],
init='identity')
nca.fit(X, y)
assert_array_equal(X, nca.transform(X))
def dim_reduc(X_train, Y_train, X_test, Y_test, K=1) -> None:
"""
Compare PCA, kernel PCA, and NCA dimensionalty reduction.
Slightly modified version of this code:
https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html
Only runs if the -dim argument is provided
KernelPCA and standard PCA give the same results
While NCA seems to have a slight edge
"""
X = pd.concat([X_train, X_test])
Y = Y_train + Y_test
random_state = 0
# Reduce dimension to 2 with PCA
pca = make_pipeline(StandardScaler(),
PCA(n_components=2, random_state=random_state))
# Reduce dimension to 2 with NeighborhoodComponentAnalysis
nca = make_pipeline(
StandardScaler(),
NeighborhoodComponentsAnalysis(n_components=2,
random_state=random_state))
# Reduce the dimensionalty using Kernel PCA
kernel_pca = make_pipeline(StandardScaler(),
KernelPCA(2, random_state=random_state))
# Use a nearest neighbor classifier to evaluate the methods
knn = KNeighborsClassifier(n_neighbors=K)
# Make a list of the methods to be compared
dim_reduction_methods = [('PCA', pca), ('NCA', nca),
('KernelPCA', kernel_pca)]
# plt.figure()
for i, (name, model) in enumerate(dim_reduction_methods):
plt.figure()
# plt.subplot(1, 3, i + 1, aspect=1)
# Fit the method's model
model.fit(X_train, Y_train)
# Fit a nearest neighbor classifier on the embedded training set
knn.fit(model.transform(X_train), Y_train)
# Compute the nearest neighbor accuracy on the embedded test set
acc_knn = knn.score(model.transform(X_test), Y_test)
print(name, acc_knn)
# Embed the data set in 2 dimensions using the fitted model
X_embedded = model.transform(X)
# Plot the projected points and show the evaluation score
plt.scatter(
X_embedded[:, 0],
X_embedded[:, 1],
c=Y,
s=30,
cmap='Set1',
)
plt.title("KNN with {}\np={}".format(name, round(acc_knn, 3)))
plt.savefig("figs/KNN_{}.png".format(name))
plt.show()
def test_transformation_dimensions():
X = np.arange(12).reshape(4, 3)
y = [1, 1, 2, 2]
# Fail if transformation input dimension does not match inputs dimensions
transformation = np.array([[1, 2], [3, 4]])
with pytest.raises(ValueError):
NeighborhoodComponentsAnalysis(init=transformation).fit(X, y)
# Fail if transformation output dimension is larger than
# transformation input dimension
transformation = np.array([[1, 2], [3, 4], [5, 6]])
# len(transformation) > len(transformation[0])
with pytest.raises(ValueError):
NeighborhoodComponentsAnalysis(init=transformation).fit(X, y)
# Pass otherwise
transformation = np.arange(9).reshape(3, 3)
NeighborhoodComponentsAnalysis(init=transformation).fit(X, y)
def knn_nca(self, X_train, X_test, y_train, y_test):
start = time.time()
nca = NeighborhoodComponentsAnalysis(random_state=1)
knn = KNeighborsClassifier(n_neighbors=3)
nca_pipe = Pipeline([('nca', nca), ('knn', knn)])
nca_pipe.fit(X_train, y_train)
self.app_metrics.nca_knnPerf = time.time() - start
score = '{}%'.format(nca_pipe.score(X_test, y_test) * 100)
print('\nKNN & NCA: {}'.format(score))
self.app_metrics.nca_knnScore = score
def run_nca(args):
nca = NeighborhoodComponentsAnalysis(n_components=2,
init=args.nca_init,
max_iter=100,
verbose=2,
random_state=42)
nca.fit(X_train, y_train)
Z = nca.transform(X_train)
Z_test = nca.transform(X_test)
return Z, Z_test
def KPPVNCA(X_train, y_train, X_test, y_test, k):
nca = NeighborhoodComponentsAnalysis()
nca.fit(X_train, y_train)
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(nca.transform(X_train), y_train)
score = knn.score(nca.transform(X_test), y_test)
return score
def test_sklearn_nca_default(self):
model, X_test = fit_classification_model(
NeighborhoodComponentsAnalysis(random_state=42), 3)
model_onnx = convert_sklearn(
model,
"NCA", [("input", FloatTensorType((None, X_test.shape[1])))],
target_opset=TARGET_OPSET)
self.assertIsNotNone(model_onnx)
dump_data_and_model(X_test,
model,
model_onnx,
basename="SklearnNCADefault")
def nearest_neighbours_classifier(training_data):
print('Generating the data model for a nearest neighbours classifier . . .\n')
X = util.drop_target_variable(training_data)
y = util.retrieve_target_variable(training_data)
X_train, X_test, y_train, y_test = train_test_split(X, y,stratify=y, test_size=0.1, random_state=1)
nca = NeighborhoodComponentsAnalysis(random_state=42)
knn = KNeighborsClassifier(n_neighbors=3)
knn=knn.fit(X_train, y_train)
nca_pipe = Pipeline([('nca', nca), ('knn', knn)])
nca_pipe.fit(X_train, y_train)
print('The data model for nearest neighbours classifier has been generated successfully!\n')
util.save_data_model(knn,'nearest_neighbours_classifier')
return knn;
def test_callback(capsys):
X = iris_data
y = iris_target
nca = NeighborhoodComponentsAnalysis(callback="my_cb")
with pytest.raises(ValueError):
nca.fit(X, y)
max_iter = 10
def my_cb(transformation, n_iter):
assert transformation.shape == (iris_data.shape[1] ** 2,)
rem_iter = max_iter - n_iter
print("{} iterations remaining...".format(rem_iter))
# assert that my_cb is called
nca = NeighborhoodComponentsAnalysis(max_iter=max_iter, callback=my_cb, verbose=1)
nca.fit(iris_data, iris_target)
out, _ = capsys.readouterr()
# check output
assert "{} iterations remaining...".format(max_iter - 1) in out
def ml_basis():
print('Welcome to the world of machine learning!')
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
stratify=y,
test_size=0.7,
random_state=42)
nca = NeighborhoodComponentsAnalysis(random_state=42)
knn = KNeighborsClassifier(n_neighbors=3)
nca_pipe = Pipeline([('nca', nca), ('knn', knn)])
nca_pipe.fit(X_train, y_train)
print(nca_pipe.score(X_test, y_test))
def test_sklearn_nca_double(self):
model, X_test = fit_classification_model(
NeighborhoodComponentsAnalysis(n_components=2,
max_iter=4,
random_state=42), 3)
X_test = X_test.astype(numpy.float64)
model_onnx = convert_sklearn(
model,
"NCA", [("input", DoubleTensorType((None, X_test.shape[1])))],
target_opset=TARGET_OPSET)
self.assertIsNotNone(model_onnx)
dump_data_and_model(X_test,
model,
model_onnx,
basename="SklearnNCADouble")
def test_nca_feature_names_out():
"""Check `get_feature_names_out` for `NeighborhoodComponentsAnalysis`."""
X = iris_data
y = iris_target
est = NeighborhoodComponentsAnalysis().fit(X, y)
names_out = est.get_feature_names_out()
class_name_lower = est.__class__.__name__.lower()
expected_names_out = np.array(
[f"{class_name_lower}{i}" for i in range(est.components_.shape[1])],
dtype=object,
)
assert_array_equal(names_out, expected_names_out)
def k_nn(train_dir, test_dir, n_neighbors, output_file, test_accuracy=False):
X_train, y_train, f_name_train = samples(train_dir, truth_file=True)
nca = NeighborhoodComponentsAnalysis(random_state=42)
knn = KNeighborsClassifier(n_neighbors=n_neighbors)
nca_pipe = Pipeline([('nca', nca), ('knn', knn)])
nca_pipe.fit(X_train, y_train)
X_test, y_test, f_name_test = samples(test_dir, truth_file=False)
predictions = nca_pipe.predict(X_test)
write_output_dsv(predictions,
f_name_test,
test_dir,
output_file=output_file)
if test_accuracy:
print(nca_pipe.score(X_test, y_test))
report_accuracy(f_name_train, predictions, test_dir, y_test, y_train)
