From f8a948914b928d9fd3c0e32c034bd90315caa389 Mon Sep 17 00:00:00 2001 From: Maxim Smolskiy Date: Mon, 1 Apr 2024 22:39:31 +0300 Subject: [PATCH] Enable ruff NPY002 rule (#11336) --- linear_algebra/src/conjugate_gradient.py | 6 ++++-- machine_learning/decision_tree.py | 3 ++- machine_learning/k_means_clust.py | 6 +++--- machine_learning/sequential_minimum_optimization.py | 5 +++-- neural_network/back_propagation_neural_network.py | 8 +++++--- neural_network/convolution_neural_network.py | 13 +++++++------ neural_network/input_data.py | 6 +++--- neural_network/two_hidden_layers_neural_network.py | 9 +++++---- pyproject.toml | 1 - 9 files changed, 32 insertions(+), 25 deletions(-) diff --git a/linear_algebra/src/conjugate_gradient.py b/linear_algebra/src/conjugate_gradient.py index 4c0b58deb..45da35813 100644 --- a/linear_algebra/src/conjugate_gradient.py +++ b/linear_algebra/src/conjugate_gradient.py @@ -61,7 +61,8 @@ def _create_spd_matrix(dimension: int) -> Any: >>> _is_matrix_spd(spd_matrix) True """ - random_matrix = np.random.randn(dimension, dimension) + rng = np.random.default_rng() + random_matrix = rng.normal(size=(dimension, dimension)) spd_matrix = np.dot(random_matrix, random_matrix.T) assert _is_matrix_spd(spd_matrix) return spd_matrix @@ -157,7 +158,8 @@ def test_conjugate_gradient() -> None: # Create linear system with SPD matrix and known solution x_true. dimension = 3 spd_matrix = _create_spd_matrix(dimension) - x_true = np.random.randn(dimension, 1) + rng = np.random.default_rng() + x_true = rng.normal(size=(dimension, 1)) b = np.dot(spd_matrix, x_true) # Numpy solution. diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py index 7f129919a..e48905eea 100644 --- a/machine_learning/decision_tree.py +++ b/machine_learning/decision_tree.py @@ -187,7 +187,8 @@ def main(): tree = DecisionTree(depth=10, min_leaf_size=10) tree.train(x, y) - test_cases = (np.random.rand(10) * 2) - 1 + rng = np.random.default_rng() + test_cases = (rng.random(10) * 2) - 1 predictions = np.array([tree.predict(x) for x in test_cases]) avg_error = np.mean((predictions - test_cases) ** 2) diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py index 9f6646944..a926362fc 100644 --- a/machine_learning/k_means_clust.py +++ b/machine_learning/k_means_clust.py @@ -55,12 +55,12 @@ TAG = "K-MEANS-CLUST/ " def get_initial_centroids(data, k, seed=None): """Randomly choose k data points as initial centroids""" - if seed is not None: # useful for obtaining consistent results - np.random.seed(seed) + # useful for obtaining consistent results + rng = np.random.default_rng(seed) n = data.shape[0] # number of data points # Pick K indices from range [0, N). - rand_indices = np.random.randint(0, n, k) + rand_indices = rng.integers(0, n, k) # Keep centroids as dense format, as many entries will be nonzero due to averaging. # As long as at least one document in a cluster contains a word, diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py index be16baca1..408d59ab5 100644 --- a/machine_learning/sequential_minimum_optimization.py +++ b/machine_learning/sequential_minimum_optimization.py @@ -289,12 +289,13 @@ class SmoSVM: if cmd is None: return - for i2 in np.roll(self.unbound, np.random.choice(self.length)): + rng = np.random.default_rng() + for i2 in np.roll(self.unbound, rng.choice(self.length)): cmd = yield i1, i2 if cmd is None: return - for i2 in np.roll(self._all_samples, np.random.choice(self.length)): + for i2 in np.roll(self._all_samples, rng.choice(self.length)): cmd = yield i1, i2 if cmd is None: return diff --git a/neural_network/back_propagation_neural_network.py b/neural_network/back_propagation_neural_network.py index 7e0bdbbe2..6131a13e9 100644 --- a/neural_network/back_propagation_neural_network.py +++ b/neural_network/back_propagation_neural_network.py @@ -51,8 +51,9 @@ class DenseLayer: self.is_input_layer = is_input_layer def initializer(self, back_units): - self.weight = np.asmatrix(np.random.normal(0, 0.5, (self.units, back_units))) - self.bias = np.asmatrix(np.random.normal(0, 0.5, self.units)).T + rng = np.random.default_rng() + self.weight = np.asmatrix(rng.normal(0, 0.5, (self.units, back_units))) + self.bias = np.asmatrix(rng.normal(0, 0.5, self.units)).T if self.activation is None: self.activation = sigmoid @@ -174,7 +175,8 @@ class BPNN: def example(): - x = np.random.randn(10, 10) + rng = np.random.default_rng() + x = rng.normal(size=(10, 10)) y = np.asarray( [ [0.8, 0.4], diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py index 07cc456b7..3c5519244 100644 --- a/neural_network/convolution_neural_network.py +++ b/neural_network/convolution_neural_network.py @@ -41,15 +41,16 @@ class CNN: self.size_pooling1 = size_p1 self.rate_weight = rate_w self.rate_thre = rate_t + rng = np.random.default_rng() self.w_conv1 = [ - np.asmatrix(-1 * np.random.rand(self.conv1[0], self.conv1[0]) + 0.5) + np.asmatrix(-1 * rng.random((self.conv1[0], self.conv1[0])) + 0.5) for i in range(self.conv1[1]) ] - self.wkj = np.asmatrix(-1 * np.random.rand(self.num_bp3, self.num_bp2) + 0.5) - self.vji = np.asmatrix(-1 * np.random.rand(self.num_bp2, self.num_bp1) + 0.5) - self.thre_conv1 = -2 * np.random.rand(self.conv1[1]) + 1 - self.thre_bp2 = -2 * np.random.rand(self.num_bp2) + 1 - self.thre_bp3 = -2 * np.random.rand(self.num_bp3) + 1 + self.wkj = np.asmatrix(-1 * rng.random((self.num_bp3, self.num_bp2)) + 0.5) + self.vji = np.asmatrix(-1 * rng.random((self.num_bp2, self.num_bp1)) + 0.5) + self.thre_conv1 = -2 * rng.random(self.conv1[1]) + 1 + self.thre_bp2 = -2 * rng.random(self.num_bp2) + 1 + self.thre_bp3 = -2 * rng.random(self.num_bp3) + 1 def save_model(self, save_path): # save model dict with pickle diff --git a/neural_network/input_data.py b/neural_network/input_data.py index 9d4195487..d189e3f9e 100644 --- a/neural_network/input_data.py +++ b/neural_network/input_data.py @@ -153,7 +153,7 @@ class _DataSet: """ seed1, seed2 = random_seed.get_seed(seed) # If op level seed is not set, use whatever graph level seed is returned - np.random.seed(seed1 if seed is None else seed2) + self._rng = np.random.default_rng(seed1 if seed is None else seed2) dtype = dtypes.as_dtype(dtype).base_dtype if dtype not in (dtypes.uint8, dtypes.float32): raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype) @@ -211,7 +211,7 @@ class _DataSet: # Shuffle for the first epoch if self._epochs_completed == 0 and start == 0 and shuffle: perm0 = np.arange(self._num_examples) - np.random.shuffle(perm0) + self._rng.shuffle(perm0) self._images = self.images[perm0] self._labels = self.labels[perm0] # Go to the next epoch @@ -225,7 +225,7 @@ class _DataSet: # Shuffle the data if shuffle: perm = np.arange(self._num_examples) - np.random.shuffle(perm) + self._rng.shuffle(perm) self._images = self.images[perm] self._labels = self.labels[perm] # Start next epoch diff --git a/neural_network/two_hidden_layers_neural_network.py b/neural_network/two_hidden_layers_neural_network.py index dea7e2342..d488de590 100644 --- a/neural_network/two_hidden_layers_neural_network.py +++ b/neural_network/two_hidden_layers_neural_network.py @@ -28,19 +28,20 @@ class TwoHiddenLayerNeuralNetwork: # Random initial weights are assigned. # self.input_array.shape[1] is used to represent number of nodes in input layer. # First hidden layer consists of 4 nodes. - self.input_layer_and_first_hidden_layer_weights = np.random.rand( - self.input_array.shape[1], 4 + rng = np.random.default_rng() + self.input_layer_and_first_hidden_layer_weights = rng.random( + (self.input_array.shape[1], 4) ) # Random initial values for the first hidden layer. # First hidden layer has 4 nodes. # Second hidden layer has 3 nodes. - self.first_hidden_layer_and_second_hidden_layer_weights = np.random.rand(4, 3) + self.first_hidden_layer_and_second_hidden_layer_weights = rng.random((4, 3)) # Random initial values for the second hidden layer. # Second hidden layer has 3 nodes. # Output layer has 1 node. - self.second_hidden_layer_and_output_layer_weights = np.random.rand(3, 1) + self.second_hidden_layer_and_output_layer_weights = rng.random((3, 1)) # Real output values provided. self.output_array = output_array diff --git a/pyproject.toml b/pyproject.toml index c8a8744ab..50cd38005 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,6 @@ lint.ignore = [ # `ruff rule S101` for a description of that rule "EXE001", # Shebang is present but file is not executable" -- FIX ME "G004", # Logging statement uses f-string "INP001", # File `x/y/z.py` is part of an implicit namespace package. Add an `__init__.py`. -- FIX ME - "NPY002", # Replace legacy `np.random.choice` call with `np.random.Generator` -- FIX ME "PGH003", # Use specific rule codes when ignoring type issues -- FIX ME "PLC1901", # `{}` can be simplified to `{}` as an empty string is falsey "PLW060", # Using global for `{name}` but no assignment is done -- DO NOT FIX