code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
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def test_infer_online_handles_content_type_text_plain():
"""Test that the engine can handle text/plain responses and parse them as JSON."""
with aioresponses() as m:
m.post(
_TARGET_SERVER,
status=200,
body=json.dumps(
{
"choices": ... | Test that the engine can handle text/plain responses and parse them as JSON. | test_infer_online_handles_content_type_text_plain | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_infer_online_handles_invalid_content():
"""Test that the engine properly handles invalid content responses."""
with aioresponses() as m:
m.post(
_TARGET_SERVER,
status=200,
body=json.dumps({"error": {"message": "Invalid JSON content"}}),
content_t... | Test that the engine properly handles invalid content responses. | test_infer_online_handles_invalid_content | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_infer_online_exponential_backoff():
"""Test that the engine implements exponential backoff correctly."""
sleep_calls = []
async def mock_sleep(delay):
sleep_calls.append(delay)
def callback(url, **kwargs):
# Fail until the last attempt
if len(sleep_calls) < 3:
... | Test that the engine implements exponential backoff correctly. | test_infer_online_exponential_backoff | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_non_retriable_errors(mock_asyncio_sleep):
"""Test that certain HTTP status codes are not retried."""
non_retriable_codes = [400, 401, 403, 404, 422]
error_messages = {
400: "Bad request error",
401: "Unauthorized error",
403: "Forbidden error",
404: "Not found error"... | Test that certain HTTP status codes are not retried. | test_non_retriable_errors | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_response_processing_error(mock_asyncio_sleep):
"""Test handling of errors during response processing."""
with aioresponses() as m:
m.post(
_TARGET_SERVER,
status=200,
payload={"choices": [{"invalid": "response"}]}, # Missing required fields
)
... | Test handling of errors during response processing. | test_response_processing_error | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_malformed_json_response(mock_asyncio_sleep):
"""Test handling of malformed JSON responses."""
with aioresponses() as m:
m.post(
_TARGET_SERVER,
status=200,
body="Invalid JSON {",
content_type="application/json",
)
m.post(
... | Test handling of malformed JSON responses. | test_malformed_json_response | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_unexpected_error_handling(mock_asyncio_sleep):
"""Test handling of unexpected errors during API calls."""
def raise_unexpected(*args, **kwargs):
raise ValueError("Unexpected internal error")
with aioresponses() as m:
m.post(_TARGET_SERVER, callback=raise_unexpected)
engin... | Test handling of unexpected errors during API calls. | test_unexpected_error_handling | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_list_response_error_handling():
"""Test handling of list-type error responses."""
with aioresponses() as m:
m.post(
_TARGET_SERVER,
status=500,
payload=[{"error": {"message": "Internal server error"}}],
)
engine = RemoteInferenceEngine(
... | Test handling of list-type error responses. | test_list_response_error_handling | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_retry_with_different_errors():
"""Test retry behavior with different types of errors on each attempt."""
attempt = 0
def get_response(*args, **kwargs):
nonlocal attempt
attempt += 1
if attempt == 1:
raise aiohttp.ClientError("Network error")
elif attemp... | Test retry behavior with different types of errors on each attempt. | test_retry_with_different_errors | python | oumi-ai/oumi | tests/unit/inference/test_remote_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_remote_inference_engine.py | Apache-2.0 |
def test_convert_conversation_to_api_input(sambanova_engine):
"""Test conversion of conversation to SambaNova API input format."""
conversation = Conversation(
messages=[
Message(content="System message", role=Role.SYSTEM),
Message(content="User message", role=Role.USER),
... | Test conversion of conversation to SambaNova API input format. | test_convert_conversation_to_api_input | python | oumi-ai/oumi | tests/unit/inference/test_sambanova_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_sambanova_inference_engine.py | Apache-2.0 |
def test_convert_api_output_to_conversation(sambanova_engine):
"""Test conversion of SambaNova API output to conversation."""
original_conversation = Conversation(
messages=[
Message(content="User message", role=Role.USER),
],
metadata={"key": "value"},
conversation_i... | Test conversion of SambaNova API output to conversation. | test_convert_api_output_to_conversation | python | oumi-ai/oumi | tests/unit/inference/test_sambanova_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_sambanova_inference_engine.py | Apache-2.0 |
def test_convert_api_output_to_conversation_error_handling(sambanova_engine):
"""Test error handling in API output conversion."""
original_conversation = Conversation(
messages=[Message(content="User message", role=Role.USER)]
)
# Test empty choices
with pytest.raises(RuntimeError, match="N... | Test error handling in API output conversion. | test_convert_api_output_to_conversation_error_handling | python | oumi-ai/oumi | tests/unit/inference/test_sambanova_inference_engine.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/inference/test_sambanova_inference_engine.py | Apache-2.0 |
async def test_get_failure_reason_from_response_with_json_response():
"""Test handling of non-retryable errors with JSON response."""
mock_response = AsyncMock(spec=aiohttp.ClientResponse)
mock_response.status = 400
mock_response.json.return_value = {"error": {"message": "Invalid request"}}
result ... | Test handling of non-retryable errors with JSON response. | test_get_failure_reason_from_response_with_json_response | python | oumi-ai/oumi | tests/unit/utils/test_http.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/utils/test_http.py | Apache-2.0 |
async def test_get_failure_reason_from_response_with_list_response():
"""Test handling of non-retryable errors with list response."""
mock_response = AsyncMock(spec=aiohttp.ClientResponse)
mock_response.status = 400
mock_response.json.return_value = [{"error": {"message": "Invalid request"}}]
resul... | Test handling of non-retryable errors with list response. | test_get_failure_reason_from_response_with_list_response | python | oumi-ai/oumi | tests/unit/utils/test_http.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/utils/test_http.py | Apache-2.0 |
async def test_get_failure_reason_from_response_with_empty_response():
"""Test handling of non-retryable errors with empty response."""
mock_response = AsyncMock(spec=aiohttp.ClientResponse)
mock_response.status = 400
mock_response.json.return_value = {}
result = await get_failure_reason_from_respo... | Test handling of non-retryable errors with empty response. | test_get_failure_reason_from_response_with_empty_response | python | oumi-ai/oumi | tests/unit/utils/test_http.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/utils/test_http.py | Apache-2.0 |
async def test_get_failure_reason_from_response_with_json_error():
"""Test handling of non-retryable errors when JSON parsing fails."""
mock_response = AsyncMock(spec=aiohttp.ClientResponse)
mock_response.status = 400
mock_response.json.side_effect = Exception("JSON decode error")
result = await ge... | Test handling of non-retryable errors when JSON parsing fails. | test_get_failure_reason_from_response_with_json_error | python | oumi-ai/oumi | tests/unit/utils/test_http.py | https://github.com/oumi-ai/oumi/blob/master/tests/unit/utils/test_http.py | Apache-2.0 |
def face_distance(face_encodings, face_to_compare):
"""
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
for each comparison face. The distance tells you how similar the faces are.
:param faces: List of face encodings to compare
:param face_to_compar... |
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
for each comparison face. The distance tells you how similar the faces are.
:param faces: List of face encodings to compare
:param face_to_compare: A face encoding to compare against
:return: A numpy ... | face_distance | python | davidsandberg/facenet | contributed/clustering.py | https://github.com/davidsandberg/facenet/blob/master/contributed/clustering.py | MIT |
def _chinese_whispers(encoding_list, threshold=0.55, iterations=20):
""" Chinese Whispers Algorithm
Modified from Alex Loveless' implementation,
http://alexloveless.co.uk/data/chinese-whispers-graph-clustering-in-python/
Inputs:
encoding_list: a list of facial encodings from face_recognition
... | Chinese Whispers Algorithm
Modified from Alex Loveless' implementation,
http://alexloveless.co.uk/data/chinese-whispers-graph-clustering-in-python/
Inputs:
encoding_list: a list of facial encodings from face_recognition
threshold: facial match threshold,default 0.6
iterations: sin... | _chinese_whispers | python | davidsandberg/facenet | contributed/clustering.py | https://github.com/davidsandberg/facenet/blob/master/contributed/clustering.py | MIT |
def cluster_facial_encodings(facial_encodings):
""" Cluster facial encodings
Intended to be an optional switch for different clustering algorithms, as of right now
only chinese whispers is available.
Input:
facial_encodings: (image_path, facial_encoding) dictionary of facial en... | Cluster facial encodings
Intended to be an optional switch for different clustering algorithms, as of right now
only chinese whispers is available.
Input:
facial_encodings: (image_path, facial_encoding) dictionary of facial encodings
Output:
sorted_clusters: a... | cluster_facial_encodings | python | davidsandberg/facenet | contributed/clustering.py | https://github.com/davidsandberg/facenet/blob/master/contributed/clustering.py | MIT |
def compute_facial_encodings(sess,images_placeholder,embeddings,phase_train_placeholder,image_size,
embedding_size,nrof_images,nrof_batches,emb_array,batch_size,paths):
""" Compute Facial Encodings
Given a set of images, compute the facial encodings of each face detected in the images a... | Compute Facial Encodings
Given a set of images, compute the facial encodings of each face detected in the images and
return them. If no faces, or more than one face found, return nothing for that image.
Inputs:
image_paths: a list of image paths
Outputs:
facia... | compute_facial_encodings | python | davidsandberg/facenet | contributed/clustering.py | https://github.com/davidsandberg/facenet/blob/master/contributed/clustering.py | MIT |
def main(args):
""" Main
Given a list of images, save out facial encoding data files and copy
images into folders of face clusters.
"""
from os.path import join, basename, exists
from os import makedirs
import numpy as np
import shutil
import sys
if not exists(args.output):
... | Main
Given a list of images, save out facial encoding data files and copy
images into folders of face clusters.
| main | python | davidsandberg/facenet | contributed/clustering.py | https://github.com/davidsandberg/facenet/blob/master/contributed/clustering.py | MIT |
def triplet_loss(anchor, positive, negative, alpha):
"""Calculate the triplet loss according to the FaceNet paper
Args:
anchor: the embeddings for the anchor images.
positive: the embeddings for the positive images.
negative: the embeddings for the negative images.
Returns:
t... | Calculate the triplet loss according to the FaceNet paper
Args:
anchor: the embeddings for the anchor images.
positive: the embeddings for the positive images.
negative: the embeddings for the negative images.
Returns:
the triplet loss according to the FaceNet paper as a float te... | triplet_loss | python | davidsandberg/facenet | src/facenet.py | https://github.com/davidsandberg/facenet/blob/master/src/facenet.py | MIT |
def center_loss(features, label, alfa, nrof_classes):
"""Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"
(http://ydwen.github.io/papers/WenECCV16.pdf)
"""
nrof_features = features.get_shape()[1]
centers = tf.get_variable('centers', [nrof_class... | Center loss based on the paper "A Discriminative Feature Learning Approach for Deep Face Recognition"
(http://ydwen.github.io/papers/WenECCV16.pdf)
| center_loss | python | davidsandberg/facenet | src/facenet.py | https://github.com/davidsandberg/facenet/blob/master/src/facenet.py | MIT |
def _add_loss_summaries(total_loss):
"""Add summaries for losses.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages ... | Add summaries for losses.
Generates moving average for all losses and associated summaries for
visualizing the performance of the network.
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses.
| _add_loss_summaries | python | davidsandberg/facenet | src/facenet.py | https://github.com/davidsandberg/facenet/blob/master/src/facenet.py | MIT |
def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
"""Detects faces in an image, and returns bounding boxes and points for them.
img: input image
minsize: minimum faces' size
pnet, rnet, onet: caffemodel
threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
fac... | Detects faces in an image, and returns bounding boxes and points for them.
img: input image
minsize: minimum faces' size
pnet, rnet, onet: caffemodel
threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
factor: the factor used to create a scaling pyramid of face sizes to detect in... | detect_face | python | davidsandberg/facenet | src/align/detect_face.py | https://github.com/davidsandberg/facenet/blob/master/src/align/detect_face.py | MIT |
def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
"""Detects faces in a list of images
images: list containing input images
detection_window_size_ratio: ratio of minimum face size to smallest image dimension
pnet, rnet, onet: caffemodel
threshold: thresh... | Detects faces in a list of images
images: list containing input images
detection_window_size_ratio: ratio of minimum face size to smallest image dimension
pnet, rnet, onet: caffemodel
threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
factor: the factor used to create a scal... | bulk_detect_face | python | davidsandberg/facenet | src/align/detect_face.py | https://github.com/davidsandberg/facenet/blob/master/src/align/detect_face.py | MIT |
def generateBoundingBox(imap, reg, scale, t):
"""Use heatmap to generate bounding boxes"""
stride=2
cellsize=12
imap = np.transpose(imap)
dx1 = np.transpose(reg[:,:,0])
dy1 = np.transpose(reg[:,:,1])
dx2 = np.transpose(reg[:,:,2])
dy2 = np.transpose(reg[:,:,3])
y, x = np.where(imap ... | Use heatmap to generate bounding boxes | generateBoundingBox | python | davidsandberg/facenet | src/align/detect_face.py | https://github.com/davidsandberg/facenet/blob/master/src/align/detect_face.py | MIT |
def pad(total_boxes, w, h):
"""Compute the padding coordinates (pad the bounding boxes to square)"""
tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)
tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)
numbox = total_boxes.shape[0]
dx = np.ones((numbox), dtype=np.int32)
d... | Compute the padding coordinates (pad the bounding boxes to square) | pad | python | davidsandberg/facenet | src/align/detect_face.py | https://github.com/davidsandberg/facenet/blob/master/src/align/detect_face.py | MIT |
def inception_resnet_v1(inputs, is_training=True,
dropout_keep_prob=0.8,
bottleneck_layer_size=128,
reuse=None,
scope='InceptionResnetV1'):
"""Creates the Inception Resnet V1 model.
Args:
inputs: a 4-D tensor ... | Creates the Inception Resnet V1 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the netw... | inception_resnet_v1 | python | davidsandberg/facenet | src/models/inception_resnet_v1.py | https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py | MIT |
def inception_resnet_v2(inputs, is_training=True,
dropout_keep_prob=0.8,
bottleneck_layer_size=128,
reuse=None,
scope='InceptionResnetV2'):
"""Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor o... | Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the netw... | inception_resnet_v2 | python | davidsandberg/facenet | src/models/inception_resnet_v2.py | https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v2.py | MIT |
def __init__(self, facePredictor):
"""
Instantiate an 'AlignDlib' object.
:param facePredictor: The path to dlib's
:type facePredictor: str
"""
assert facePredictor is not None
#pylint: disable=no-member
self.detector = dlib.get_frontal_face_detector()
... |
Instantiate an 'AlignDlib' object.
:param facePredictor: The path to dlib's
:type facePredictor: str
| __init__ | python | davidsandberg/facenet | tmp/align_dlib.py | https://github.com/davidsandberg/facenet/blob/master/tmp/align_dlib.py | MIT |
def getAllFaceBoundingBoxes(self, rgbImg):
"""
Find all face bounding boxes in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:return: All face bounding boxes in an image.
:rtype: dlib.rectangles
"""
a... |
Find all face bounding boxes in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:return: All face bounding boxes in an image.
:rtype: dlib.rectangles
| getAllFaceBoundingBoxes | python | davidsandberg/facenet | tmp/align_dlib.py | https://github.com/davidsandberg/facenet/blob/master/tmp/align_dlib.py | MIT |
def getLargestFaceBoundingBox(self, rgbImg, skipMulti=False):
"""
Find the largest face bounding box in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param skipMulti: Skip image if more than one face detected.
:type... |
Find the largest face bounding box in an image.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param skipMulti: Skip image if more than one face detected.
:type skipMulti: bool
:return: The largest face bounding box in an ima... | getLargestFaceBoundingBox | python | davidsandberg/facenet | tmp/align_dlib.py | https://github.com/davidsandberg/facenet/blob/master/tmp/align_dlib.py | MIT |
def findLandmarks(self, rgbImg, bb):
"""
Find the landmarks of a face.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param bb: Bounding box around the face to find landmarks for.
:type bb: dlib.rectangle
:return: Dete... |
Find the landmarks of a face.
:param rgbImg: RGB image to process. Shape: (height, width, 3)
:type rgbImg: numpy.ndarray
:param bb: Bounding box around the face to find landmarks for.
:type bb: dlib.rectangle
:return: Detected landmark locations.
:rtype: list of... | findLandmarks | python | davidsandberg/facenet | tmp/align_dlib.py | https://github.com/davidsandberg/facenet/blob/master/tmp/align_dlib.py | MIT |
def align(self, imgDim, rgbImg, bb=None,
landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP,
skipMulti=False, scale=1.0):
r"""align(imgDim, rgbImg, bb=None, landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP)
Transform and align a face in an image.
:pa... | align(imgDim, rgbImg, bb=None, landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP)
Transform and align a face in an image.
:param imgDim: The edge length in pixels of the square the image is resized to.
:type imgDim: int
:param rgbImg: RGB image to process. Shape: (height, width... | align | python | davidsandberg/facenet | tmp/align_dlib.py | https://github.com/davidsandberg/facenet/blob/master/tmp/align_dlib.py | MIT |
def tffunc(*argtypes):
'''Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
'''
placeholders = list(map(tf.placeholder, argtypes))
def wrap(f):
out = f(*placeholders)
def wrapper(*args, **kw):
... | Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
| tffunc | python | davidsandberg/facenet | tmp/deepdream.py | https://github.com/davidsandberg/facenet/blob/master/tmp/deepdream.py | MIT |
def calc_grad_tiled(img, t_grad, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
sz = tile_size
h, w = img.shape[:2]
sx, sy = np.random.randin... | Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations. | calc_grad_tiled | python | davidsandberg/facenet | tmp/deepdream.py | https://github.com/davidsandberg/facenet/blob/master/tmp/deepdream.py | MIT |
def lap_split(img):
'''Split the image into lo and hi frequency components'''
with tf.name_scope('split'):
lo = tf.nn.conv2d(img, k5x5, [1,2,2,1], 'SAME')
lo2 = tf.nn.conv2d_transpose(lo, k5x5*4, tf.shape(img), [1,2,2,1])
hi = img-lo2
return lo, hi | Split the image into lo and hi frequency components | lap_split | python | davidsandberg/facenet | tmp/deepdream.py | https://github.com/davidsandberg/facenet/blob/master/tmp/deepdream.py | MIT |
def normalize_std(img, eps=1e-10):
'''Normalize image by making its standard deviation = 1.0'''
with tf.name_scope('normalize'):
std = tf.sqrt(tf.reduce_mean(tf.square(img)))
return img/tf.maximum(std, eps) | Normalize image by making its standard deviation = 1.0 | normalize_std | python | davidsandberg/facenet | tmp/deepdream.py | https://github.com/davidsandberg/facenet/blob/master/tmp/deepdream.py | MIT |
def data_type():
"""Return the type of the activations, weights, and placeholder variables."""
if FLAGS.use_fp16:
return tf.float16
else:
return tf.float32 | Return the type of the activations, weights, and placeholder variables. | data_type | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def maybe_download(filename):
"""Download the data from Yann's website, unless it's already here."""
if not tf.gfile.Exists(WORK_DIRECTORY):
tf.gfile.MakeDirs(WORK_DIRECTORY)
filepath = os.path.join(WORK_DIRECTORY, filename)
if not tf.gfile.Exists(filepath):
filepath, _ = urllib.request.... | Download the data from Yann's website, unless it's already here. | maybe_download | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def extract_data(filename, num_images):
"""Extract the images into a 4D tensor [image index, y, x, channels].
Values are rescaled from [0, 255] down to [-0.5, 0.5].
"""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(IMA... | Extract the images into a 4D tensor [image index, y, x, channels].
Values are rescaled from [0, 255] down to [-0.5, 0.5].
| extract_data | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def extract_labels(filename, num_images):
"""Extract the labels into a vector of int64 label IDs."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_images)
labels = np.frombuffer(buf, dtype=np.uint8).astype(np.in... | Extract the labels into a vector of int64 label IDs. | extract_labels | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def fake_data(num_images):
"""Generate a fake dataset that matches the dimensions of MNIST."""
data = np.ndarray(
shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),
dtype=np.float32)
labels = np.zeros(shape=(num_images,), dtype=np.int64)
for image in range(num_images):
lab... | Generate a fake dataset that matches the dimensions of MNIST. | fake_data | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def error_rate(predictions, labels):
"""Return the error rate based on dense predictions and sparse labels."""
return 100.0 - (
100.0 *
np.sum(np.argmax(predictions, 1) == labels) /
predictions.shape[0]) | Return the error rate based on dense predictions and sparse labels. | error_rate | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def batch_norm(x, phase_train): #pylint: disable=unused-variable
"""
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Variable, true indicates training p... |
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Variable, true indicates training phase
scope: string, variable scope
affn: w... | batch_norm | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def eval_in_batches(data, sess):
"""Get all predictions for a dataset by running it in small batches."""
size = data.shape[0]
if size < EVAL_BATCH_SIZE:
raise ValueError("batch size for evals larger than dataset: %d" % size)
predictions = np.ndarray(shape=(size, NUM_LABELS), ... | Get all predictions for a dataset by running it in small batches. | eval_in_batches | python | davidsandberg/facenet | tmp/mnist_center_loss.py | https://github.com/davidsandberg/facenet/blob/master/tmp/mnist_center_loss.py | MIT |
def l2_loss(tensor, weight=1.0, scope=None):
"""Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for op_scope.
Returns:
the L2 loss op.
"""
with tf.name_scope(... | Define a L2Loss, useful for regularize, i.e. weight decay.
Args:
tensor: tensor to regularize.
weight: an optional weight to modulate the loss.
scope: Optional scope for op_scope.
Returns:
the L2 loss op.
| l2_loss | python | davidsandberg/facenet | tmp/network.py | https://github.com/davidsandberg/facenet/blob/master/tmp/network.py | MIT |
def batch_norm(x, phase_train):
"""
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Variable, true indicates training phase
scope: string, variable scope
a... |
Batch normalization on convolutional maps.
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Variable, true indicates training phase
scope: string, variable scope
affn: whether to affn-transform out... | batch_norm | python | davidsandberg/facenet | tmp/network.py | https://github.com/davidsandberg/facenet/blob/master/tmp/network.py | MIT |
def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
""" Define an inference network for face recognition based
on inception modules using batch normalization
Args:
images: The images to run inference on, dimensions batch_size x height x width x channels
phas... | Define an inference network for face recognition based
on inception modules using batch normalization
Args:
images: The images to run inference on, dimensions batch_size x height x width x channels
phase_train: True if batch normalization should operate in training mode
| inference | python | davidsandberg/facenet | tmp/nn2.py | https://github.com/davidsandberg/facenet/blob/master/tmp/nn2.py | MIT |
def gradients(ys, xs, grad_ys=None, checkpoints='collection', **kwargs):
'''
Authors: Tim Salimans & Yaroslav Bulatov
memory efficient gradient implementation inspired by "Training Deep Nets with Sublinear Memory Cost"
by Chen et al. 2016 (https://arxiv.org/abs/1604.06174)
ys,xs,grad_ys,kwargs are... |
Authors: Tim Salimans & Yaroslav Bulatov
memory efficient gradient implementation inspired by "Training Deep Nets with Sublinear Memory Cost"
by Chen et al. 2016 (https://arxiv.org/abs/1604.06174)
ys,xs,grad_ys,kwargs are the arguments to standard tensorflow tf.gradients
(https://www.tensorflow.o... | gradients | python | openai/glow | memory_saving_gradients.py | https://github.com/openai/glow/blob/master/memory_saving_gradients.py | MIT |
def capture_ops():
"""Decorator to capture ops created in the block.
with capture_ops() as ops:
# create some ops
print(ops) # => prints ops created.
"""
micros = int(time.time()*10**6)
scope_name = str(micros)
op_list = []
with tf.name_scope(scope_name):
yield op_list
... | Decorator to capture ops created in the block.
with capture_ops() as ops:
# create some ops
print(ops) # => prints ops created.
| capture_ops | python | openai/glow | memory_saving_gradients.py | https://github.com/openai/glow/blob/master/memory_saving_gradients.py | MIT |
def debug_print(s, *args):
"""Like logger.log, but also replaces all TensorFlow ops/tensors with their
names. Sensitive to value of DEBUG_LOGGING, see enable_debug/disable_debug
Usage:
debug_print("see tensors %s for %s", tensorlist, [1,2,3])
"""
if DEBUG_LOGGING:
formatted_args = [f... | Like logger.log, but also replaces all TensorFlow ops/tensors with their
names. Sensitive to value of DEBUG_LOGGING, see enable_debug/disable_debug
Usage:
debug_print("see tensors %s for %s", tensorlist, [1,2,3])
| debug_print | python | openai/glow | memory_saving_gradients.py | https://github.com/openai/glow/blob/master/memory_saving_gradients.py | MIT |
def format_ops(ops, sort_outputs=True):
"""Helper method for printing ops. Converts Tensor/Operation op to op.name,
rest to str(op)."""
if hasattr(ops, '__iter__') and not isinstance(ops, str):
l = [(op.name if hasattr(op, "name") else str(op)) for op in ops]
if sort_outputs:
re... | Helper method for printing ops. Converts Tensor/Operation op to op.name,
rest to str(op). | format_ops | python | openai/glow | memory_saving_gradients.py | https://github.com/openai/glow/blob/master/memory_saving_gradients.py | MIT |
def _symmetric_matrix_square_root(mat, eps=1e-10):
"""Compute square root of a symmetric matrix.
Note that this is different from an elementwise square root. We want to
compute M' where M' = sqrt(mat) such that M' * M' = mat.
Also note that this method **only** works for symmetric matrices.
Args:
... | Compute square root of a symmetric matrix.
Note that this is different from an elementwise square root. We want to
compute M' where M' = sqrt(mat) such that M' * M' = mat.
Also note that this method **only** works for symmetric matrices.
Args:
mat: Matrix to take the square root of.
eps: Sma... | _symmetric_matrix_square_root | python | openai/glow | tfops.py | https://github.com/openai/glow/blob/master/tfops.py | MIT |
def forward(self, x: Tensor, edge_index: Adj,
edge_attr: OptTensor = None, batch: Adj = None,
angle_data: List = None, size: Size = None) -> Tensor:
""" Inputs:
* x: (n_points, d) where d is pos_dims + feat_dims
* edge_index: (2, n_edges)
* ... | Inputs:
* x: (n_points, d) where d is pos_dims + feat_dims
* edge_index: (2, n_edges)
* edge_attr: tensor (n_edges, n_feats) excluding basic distance feats.
* batch: (n_points,) long tensor. specifies xloud belonging for each point
* angle_data: list of tens... | forward | python | lucidrains/egnn-pytorch | egnn_pytorch/egnn_pytorch_geometric.py | https://github.com/lucidrains/egnn-pytorch/blob/master/egnn_pytorch/egnn_pytorch_geometric.py | MIT |
def make_jobfile_from_command_list(jobfile_path, commands):
"""
Save a jobfile containing commands to run.
Parameters
----------
jobfile_path : str or path
commands : list of str
"""
# Creating a jobfile containing all commands to run
jobfile_content = ''.join('%s\n' % com for com in... |
Save a jobfile containing commands to run.
Parameters
----------
jobfile_path : str or path
commands : list of str
| make_jobfile_from_command_list | python | WassimTenachi/PhySO | benchmarking/utils.py | https://github.com/WassimTenachi/PhySO/blob/master/benchmarking/utils.py | MIT |
def assess_equivalence (pareto_df, Feynman_pb, check_only_most_acc = False, verbose = False):
"""
Checks if at least one expression in the Pareto front is symbolically equivalent to target expression, following a
similar methodology as SRBench (see https://github.com/cavalab/srbench).
I.e, an expression... |
Checks if at least one expression in the Pareto front is symbolically equivalent to target expression, following a
similar methodology as SRBench (see https://github.com/cavalab/srbench).
I.e, an expression is deemed equivalent if:
- the symbolic difference simplifies to 0
- OR the symbolic... | assess_equivalence | python | WassimTenachi/PhySO | benchmarking/FeynmanBenchmark/feynman_results_analysis.py | https://github.com/WassimTenachi/PhySO/blob/master/benchmarking/FeynmanBenchmark/feynman_results_analysis.py | MIT |
def assess_metric_test (pareto_df, Feynman_pb, metric_func, i_pareto=-1):
"""
Computes metric value of the best Pareto front expression on noiseless test data.
Parameters
----------
pareto_df : pd.DataFrame
Pareto front dataframe generated by PhySO.
Feynman_pb : physo.benchmark.FeynmanDa... |
Computes metric value of the best Pareto front expression on noiseless test data.
Parameters
----------
pareto_df : pd.DataFrame
Pareto front dataframe generated by PhySO.
Feynman_pb : physo.benchmark.FeynmanDataset.FeynmanProblem.FeynmanProblem
Related Feynman problem.
metric_f... | assess_metric_test | python | WassimTenachi/PhySO | benchmarking/FeynmanBenchmark/feynman_results_analysis.py | https://github.com/WassimTenachi/PhySO/blob/master/benchmarking/FeynmanBenchmark/feynman_results_analysis.py | MIT |
def get_symbolic_result (pareto_df, Feynman_pb, i_pareto = -1):
"""
Produces an SRBench style dictionary characterizing the best expression found.
Parameters
----------
pareto_df : pd.DataFrame
Pareto front dataframe generated by PhySO.
Feynman_pb : physo.benchmark.FeynmanDataset.Feynman... |
Produces an SRBench style dictionary characterizing the best expression found.
Parameters
----------
pareto_df : pd.DataFrame
Pareto front dataframe generated by PhySO.
Feynman_pb : physo.benchmark.FeynmanDataset.FeynmanProblem.FeynmanProblem
Related Feynman problem.
i_pareto : ... | get_symbolic_result | python | WassimTenachi/PhySO | benchmarking/FeynmanBenchmark/feynman_results_analysis.py | https://github.com/WassimTenachi/PhySO/blob/master/benchmarking/FeynmanBenchmark/feynman_results_analysis.py | MIT |
def load_run_data (pb_folder_prefix):
"""
Safely loads pareto front .csv and curves data .csv into dataframes if possible return None otherwise.
Also returns noise level encoded into folder name.
Parameters
----------
pb_folder_prefix : str or path
Starting name of folder containing run ... |
Safely loads pareto front .csv and curves data .csv into dataframes if possible return None otherwise.
Also returns noise level encoded into folder name.
Parameters
----------
pb_folder_prefix : str or path
Starting name of folder containing run data (there should only be one folder startin... | load_run_data | python | WassimTenachi/PhySO | benchmarking/FeynmanBenchmark/feynman_results_analysis.py | https://github.com/WassimTenachi/PhySO/blob/master/benchmarking/FeynmanBenchmark/feynman_results_analysis.py | MIT |
def load_class_equations_csv (filepath_eqs ="ClassEquations.csv"):
"""
Loads ClassEquations.csv into a pd.DataFrame.
Parameters
----------
filepath_eqs : str
Path to ClassEquations.csv.
Returns
-------
eqs_class_df : pd.DataFrame
"""
eqs_class_df = pd.read_csv(filepath_e... |
Loads ClassEquations.csv into a pd.DataFrame.
Parameters
----------
filepath_eqs : str
Path to ClassEquations.csv.
Returns
-------
eqs_class_df : pd.DataFrame
| load_class_equations_csv | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def get_units (i_eq, i_var = 0, output_var = False):
"""
Gets units of variable.
Parameters
----------
i_eq : int
Equation number in the set of equations.
i_var : int
Variable id in its equation line.
output_var : bool
If True, returns units of output variable, otherw... |
Gets units of variable.
Parameters
----------
i_eq : int
Equation number in the set of equations.
i_var : int
Variable id in its equation line.
output_var : bool
If True, returns units of output variable, otherwise returns units of input variable specified by i_var.
... | get_units | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def __init__(self, i_eq = None, eq_name = None, original_var_names = False):
"""
Loads a Class problem based on its number in the set or its equation name.
Parameters
----------
i_eq : int
Equation number in the set of equations.
eq_name : str
Equa... |
Loads a Class problem based on its number in the set or its equation name.
Parameters
----------
i_eq : int
Equation number in the set of equations.
eq_name : str
Equation name in the set of equations (e.g. 'Harmonic Oscillator').
original_var_nam... | __init__ | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def target_function(self, X, K):
"""
Evaluates X with target function, using K values.
Parameters
----------
X : numpy.array of shape (n_vars, ?,) of floats
Input variables.
K : numpy.array of shape (n_spe,) of floats
Spe free consts.
Retur... |
Evaluates X with target function, using K values.
Parameters
----------
X : numpy.array of shape (n_vars, ?,) of floats
Input variables.
K : numpy.array of shape (n_spe,) of floats
Spe free consts.
Returns
-------
y : numpy.array o... | target_function | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def generate_data_points (self, n_samples = 1_000, n_realizations = 10, return_K = False):
"""
Generates data points accordingly for this Class problem.
Parameters
----------
n_samples : int
Number of samples to draw. By default, 1e3.
n_realizations : int
... |
Generates data points accordingly for this Class problem.
Parameters
----------
n_samples : int
Number of samples to draw. By default, 1e3.
n_realizations : int
Number of realizations to draw. By default, 10.
return_K : bool
If True, r... | generate_data_points | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def get_sympy(self, K_vals=None):
"""
Gets sympy expression of the formula evaluated with spe free consts.
Parameters
----------
K_vals : numpy.array of shape (?, n_spe,) of floats or None
Values to evaluate spe free consts with, if None, uses random values and return... |
Gets sympy expression of the formula evaluated with spe free consts.
Parameters
----------
K_vals : numpy.array of shape (?, n_spe,) of floats or None
Values to evaluate spe free consts with, if None, uses random values and returns only one realization.
Returns
... | get_sympy | python | WassimTenachi/PhySO | physo/benchmark/ClassDataset/ClassProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/ClassDataset/ClassProblem.py | MIT |
def get_units (var_name):
"""
Gets units of variable var_name. Example: get_units("kb")
Parameters
----------
var_name : str
original variable name.
Returns
-------
units : numpy.array of shape (FEYN_UNITS_VECTOR_SIZE,) of floats
Units of variable.
"""
assert not ... |
Gets units of variable var_name. Example: get_units("kb")
Parameters
----------
var_name : str
original variable name.
Returns
-------
units : numpy.array of shape (FEYN_UNITS_VECTOR_SIZE,) of floats
Units of variable.
| get_units | python | WassimTenachi/PhySO | physo/benchmark/FeynmanDataset/FeynmanProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/FeynmanDataset/FeynmanProblem.py | MIT |
def __init__(self, i_eq = None, eq_name = None, original_var_names = False):
"""
Loads a Feynman problem based on its number in the set or its equation name
Parameters
----------
i_eq : int
Equation number in the whole set of equations (0 to 99 for bulk eqs and 100 to... |
Loads a Feynman problem based on its number in the set or its equation name
Parameters
----------
i_eq : int
Equation number in the whole set of equations (0 to 99 for bulk eqs and 100 to 119 for bonus eqs).
eq_name : str
Equation name in the set of equat... | __init__ | python | WassimTenachi/PhySO | physo/benchmark/FeynmanDataset/FeynmanProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/FeynmanDataset/FeynmanProblem.py | MIT |
def target_function(self, X):
"""
Evaluates X with target function.
Parameters
----------
X : numpy.array of shape (n_vars, ?,) of floats
Returns
-------
y : numpy.array of shape (?,) of floats
"""
# Getting sympy function
f = sympy... |
Evaluates X with target function.
Parameters
----------
X : numpy.array of shape (n_vars, ?,) of floats
Returns
-------
y : numpy.array of shape (?,) of floats
| target_function | python | WassimTenachi/PhySO | physo/benchmark/FeynmanDataset/FeynmanProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/FeynmanDataset/FeynmanProblem.py | MIT |
def compare_expression (self, trial_expr,
handle_trigo = True,
prevent_zero_frac = True,
prevent_inf_equivalence = True,
round_decimal = 2,
verbose=False... |
Checks if trial_expr is symbolically equivalent to the target expression of this Feynman problem, following a
similar methodology as SRBench (see https://github.com/cavalab/srbench).
I.e, it is deemed equivalent if:
- the symbolic difference simplifies to 0
- OR the symb... | compare_expression | python | WassimTenachi/PhySO | physo/benchmark/FeynmanDataset/FeynmanProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/FeynmanDataset/FeynmanProblem.py | MIT |
def trial_function (self, trial_expr, X):
"""
Evaluates X on a trial expression mapping X to input variables names in sympy.
Parameters
----------
trial_expr : Sympy Expression
Trial sympy expression with evaluated numeric free constants and assumptions regarding vari... |
Evaluates X on a trial expression mapping X to input variables names in sympy.
Parameters
----------
trial_expr : Sympy Expression
Trial sympy expression with evaluated numeric free constants and assumptions regarding variables
(positivity etc.) encoded in expres... | trial_function | python | WassimTenachi/PhySO | physo/benchmark/FeynmanDataset/FeynmanProblem.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/FeynmanDataset/FeynmanProblem.py | MIT |
def read_pareto_csv (pareto_csv_path, sympy_X_symbols_dict = None, return_df = False):
"""
Loads a Pareto front csv generated by PhySO into sympy expressions with evaluated free constants.
Only works for expressions not using dataset spe free constants (ie. Class SR tasks), in those cases, pkl loading
i... |
Loads a Pareto front csv generated by PhySO into sympy expressions with evaluated free constants.
Only works for expressions not using dataset spe free constants (ie. Class SR tasks), in those cases, pkl loading
is recommended instead (physo.read_pareto_pkl).
Parameters
----------
pareto_csv_pa... | read_pareto_csv | python | WassimTenachi/PhySO | physo/benchmark/utils/read_logs.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/read_logs.py | MIT |
def get_pareto_expressions_from_df (pareto_df, sympy_X_symbols_dict = None):
"""
Loads a Pareto front dataframe generated by PhySO into sympy expressions with evaluated free constants.
Only works for expressions not using dataset spe free constants (ie. Class SR tasks).
Parameters
----------
par... |
Loads a Pareto front dataframe generated by PhySO into sympy expressions with evaluated free constants.
Only works for expressions not using dataset spe free constants (ie. Class SR tasks).
Parameters
----------
pareto_df : pd.DataFrame
Pareto front dataframe generated by PhySO.
sympy_X... | get_pareto_expressions_from_df | python | WassimTenachi/PhySO | physo/benchmark/utils/read_logs.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/read_logs.py | MIT |
def replace_sin_by_cos (expr):
"""
Replaces sin(...) by cos(pi/2 - ...) in a sympy expression.
Parameters
----------
expr : Sympy Expression
Returns
-------
ex1 : Sympy Expression
"""
ex1 = expr
# If sin(...) is encountered, replacing it by cos(pi/2 - ...)
for a in sympy.... |
Replaces sin(...) by cos(pi/2 - ...) in a sympy expression.
Parameters
----------
expr : Sympy Expression
Returns
-------
ex1 : Sympy Expression
| replace_sin_by_cos | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def replace_cos_by_sin (expr):
"""
Replaces cos(...) by sin(pi/2 - ...) in a sympy expression.
Parameters
----------
expr : Sympy Expression
Returns
-------
ex1 : Sympy Expression
"""
ex1 = expr
# If cos(...) is encountered, replacing it by sin(pi/2 - ...)
for a in sympy.... |
Replaces cos(...) by sin(pi/2 - ...) in a sympy expression.
Parameters
----------
expr : Sympy Expression
Returns
-------
ex1 : Sympy Expression
| replace_cos_by_sin | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def round_floats(expr, round_decimal = 2):
"""
Rounds the floats in a sympy expression as in SRBench (see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
round_decimal : int
Rounding up to this decimal.
Use round_decimal = 2 for SRBench-like beh... |
Rounds the floats in a sympy expression as in SRBench (see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
round_decimal : int
Rounding up to this decimal.
Use round_decimal = 2 for SRBench-like behavior (as they actually round up to 2 decimals).
... | round_floats | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def clean_sympy_expr(expr, round_decimal = 2):
"""
Cleans (rounds floats, simplifies) sympy expression for symbolic comparison purposes as in SRBench
(see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
round_decimal : int
Rounding up to this decima... |
Cleans (rounds floats, simplifies) sympy expression for symbolic comparison purposes as in SRBench
(see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
round_decimal : int
Rounding up to this decimal.
Use round_decimal = 2 for SRBench-like beha... | clean_sympy_expr | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def compare_expression (trial_expr,
target_expr,
handle_trigo = True,
prevent_zero_frac = True,
prevent_inf_equivalence = True,
round_decimal = 2,
verbose=Fals... |
Checks if trial_expr is symbolically equivalent to target_expr, following a similar methodology as
SRBench (see https://github.com/cavalab/srbench).
I.e, it is deemed equivalent if:
- the symbolic difference simplifies to 0
- OR the symbolic difference is a constant
- OR the symboli... | compare_expression | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def expression_size(expr):
"""
Evaluates complexity as in SRBench
(see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
Returns
-------
c : int
"""
c=0
for arg in sympy.preorder_traversal(expr):
c += 1
return c |
Evaluates complexity as in SRBench
(see https://github.com/cavalab/srbench).
Parameters
----------
expr : Sympy Expression
Returns
-------
c : int
| expression_size | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def sympy_to_prefix(sympy_expr):
"""
Converts a sympy expression to prefix notation.
Parameters
----------
sympy_expr : sympy.core
Sympy expression
Returns
-------
dict :
tokens_str : numpy.array of str
List of tokens in the expression.
arities : numpy... |
Converts a sympy expression to prefix notation.
Parameters
----------
sympy_expr : sympy.core
Sympy expression
Returns
-------
dict :
tokens_str : numpy.array of str
List of tokens in the expression.
arities : numpy.array of int
List of aritie... | sympy_to_prefix | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def sympy_symbol_with_assumptions_from_range(name, low, high):
"""
Returns a sympy symbol with assumptions from its data range.
Parameters
----------
name : str
Name of the variable.
low : float
Lowest value taken by the variable.
high : float
Highest value taken by t... |
Returns a sympy symbol with assumptions from its data range.
Parameters
----------
name : str
Name of the variable.
low : float
Lowest value taken by the variable.
high : float
Highest value taken by the variable.
Returns
-------
sympy.Symbol
| sympy_symbol_with_assumptions_from_range | python | WassimTenachi/PhySO | physo/benchmark/utils/symbolic_utils.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/symbolic_utils.py | MIT |
def timeout(seconds=10, error_message=os.strerror(errno.ETIME)):
"""
# Works on UNIX only
# https://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish
Demo:
@timeout(20)
def myfunc(n):
time.sleep(n)
return True
myfunc(n>20) will be killed
... |
# Works on UNIX only
# https://stackoverflow.com/questions/2281850/timeout-function-if-it-takes-too-long-to-finish
Demo:
@timeout(20)
def myfunc(n):
time.sleep(n)
return True
myfunc(n>20) will be killed
| timeout | python | WassimTenachi/PhySO | physo/benchmark/utils/timeout_unix.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/benchmark/utils/timeout_unix.py | MIT |
def loss_func(logits_train, ideal_probs_train, R_train, baseline, lengths, gamma_decay, entropy_weight, ):
"""
Loss function for reinforcing symbolic programs.
Parameters
----------
logits_train : torch.tensor of shape (max_time_step, n_train, n_choices,)
Probabilities generated by the... |
Loss function for reinforcing symbolic programs.
Parameters
----------
logits_train : torch.tensor of shape (max_time_step, n_train, n_choices,)
Probabilities generated by the rnn (for each step along program length, for each program in training sub-batch,
for each choosable token... | loss_func | python | WassimTenachi/PhySO | physo/learn/loss.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/learn/loss.py | MIT |
def save_pareto_pkl (pareto_progs, fpath):
"""
Save pareto programs to pickle file.
Parameters
----------
pareto_progs : list of Program.Program
List of pareto programs.
fpath : str
Path to pkl file.
"""
with open(fpath, 'wb') as f:
pickle.dump(pareto_progs, f)
... |
Save pareto programs to pickle file.
Parameters
----------
pareto_progs : list of Program.Program
List of pareto programs.
fpath : str
Path to pkl file.
| save_pareto_pkl | python | WassimTenachi/PhySO | physo/learn/monitoring.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/learn/monitoring.py | MIT |
def read_pareto_pkl (fpath):
"""
Load pareto programs from pickle file.
Parameters
----------
fpath : str
Path to pkl file.
Returns
-------
pareto_progs : list of Program.Program
List of pareto programs.
"""
with open(fpath, 'rb') as f:
pareto_progs = pick... |
Load pareto programs from pickle file.
Parameters
----------
fpath : str
Path to pkl file.
Returns
-------
pareto_progs : list of Program.Program
List of pareto programs.
| read_pareto_pkl | python | WassimTenachi/PhySO | physo/learn/monitoring.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/learn/monitoring.py | MIT |
def __init__(self,
library_args,
priors_config,
multi_X,
multi_y,
rewards_computer,
batch_size,
max_time_step,
multi_y_weights = 1.,
free_const_opti_args = None,
... |
Parameters
----------
library_args: dict
Arguments passed to library.__init__
priors_config : list of couples (str : dict)
List of priors. List containing couples with prior name as first item in couple (see prior.PRIORS_DICT for list
of available pri... | __init__ | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_sibling_one_hot (self, step = None):
"""
Get siblings one hot of tokens at step. 0 one hot vectors for dummies.
Parameters
----------
step : int
Step of token from which sibling one hot should be returned.
By default, step = current step
Re... |
Get siblings one hot of tokens at step. 0 one hot vectors for dummies.
Parameters
----------
step : int
Step of token from which sibling one hot should be returned.
By default, step = current step
Returns
-------
one_hot : numpy.array of s... | get_sibling_one_hot | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_parent_one_hot (self, step = None):
"""
Get parents one hot of tokens at step.
Parameters
----------
step : int
Step of token from which parent one hot should be returned.
By default, step = current step
Returns
-------
one_... |
Get parents one hot of tokens at step.
Parameters
----------
step : int
Step of token from which parent one hot should be returned.
By default, step = current step
Returns
-------
one_hot : numpy.array of shape (batch_size, n_choices) of i... | get_parent_one_hot | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_previous_tokens_one_hot(self):
"""
Get previous step tokens as one hot.
Returns
-------
one_hot : numpy.array of shape (batch_size, n_choices) of int
One hot.
"""
# Return 0 if 0th step
if self.programs.curr_step == 0:
one_h... |
Get previous step tokens as one hot.
Returns
-------
one_hot : numpy.array of shape (batch_size, n_choices) of int
One hot.
| get_previous_tokens_one_hot | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_sibling_units_obs (self, step = None):
"""
Get (required) units of sibling of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units
are not available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNIT... |
Get (required) units of sibling of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units
are not available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_AVAILABLE where units are available and equal to INTERFA... | get_sibling_units_obs | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_parent_units_obs (self, step = None):
"""
Get (required) units of parent of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units
are not available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_... |
Get (required) units of parent of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units
are not available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_AVAILABLE where units are available and equal to INTERFAC... | get_parent_units_obs | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_previous_tokens_units_obs (self, step = None):
"""
Get (required) units of tokens before step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units are not
available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNI... |
Get (required) units of tokens before step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units are not
available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_AVAILABLE where units are available and equal to INTERFACE_UNIT... | get_previous_tokens_units_obs | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_tokens_units_obs (self, step = None):
"""
Get (required) units of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units are not
available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_AVAILABLE ... |
Get (required) units of tokens at step. Filling using INTERFACE_UNITS_UNAVAILABLE_FILLER where units are not
available. Adding a vector in addition to the units indicating if units are available or not (equal to
INTERFACE_UNITS_AVAILABLE where units are available and equal to INTERFACE_UNITS_UN... | get_tokens_units_obs | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_obs(self):
"""
Computes observation of current step for symbolic regression task.
Returns
-------
obs : numpy.array of shape (batch_size, 3*n_choices+1,) of float
"""
# Relatives one-hots
parent_one_hot = self.get_parent_one_hot() ... |
Computes observation of current step for symbolic regression task.
Returns
-------
obs : numpy.array of shape (batch_size, 3*n_choices+1,) of float
| get_obs | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def get_rewards (self):
"""
Computes rewards of programs contained in batch.
Returns
-------
rewards : numpy.array of shape (batch_size,) of float
Rewards of programs.
"""
rewards = self.rewards_computer(programs = self.programs,
... |
Computes rewards of programs contained in batch.
Returns
-------
rewards : numpy.array of shape (batch_size,) of float
Rewards of programs.
| get_rewards | python | WassimTenachi/PhySO | physo/physym/batch.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch.py | MIT |
def ParallelExeAvailability(verbose=False):
"""
Checks if parallel run is available on this system and produces a recommended config.
Parameters
----------
verbose : bool
Prints log.
Returns
-------
recommended_config : dict
bool recommended_config[parallel_mode] : will p... |
Checks if parallel run is available on this system and produces a recommended config.
Parameters
----------
verbose : bool
Prints log.
Returns
-------
recommended_config : dict
bool recommended_config[parallel_mode] : will parallel mode work on this system ?
int rec... | ParallelExeAvailability | python | WassimTenachi/PhySO | physo/physym/batch_execute.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch_execute.py | MIT |
def BatchExecution (progs, X,
# Realization related
i_realization = 0,
n_samples_per_dataset = None,
# Mask
mask = None,
pad_with = np.NaN,
# Parallel mode related
... |
Executes prog(X) for each prog in progs and returns the results.
NB: Parallel execution is typically slower because of communication time (parallel_mode = False is recommended).
Parallel mode causes inter-process communication errors on some systems (probably due to the large number of
information to p... | BatchExecution | python | WassimTenachi/PhySO | physo/physym/batch_execute.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch_execute.py | MIT |
def BatchExecutionReduceGather (progs, X, reduce_wrapper,
# Realization related
i_realization = 0,
n_samples_per_dataset = None,
# Mask
mask = None,... |
Executes prog(X) for each prog in progs and gathers reduce_wrapper(prog(X)) as a result.
NB: Parallel execution is typically slower because of communication time (even just gathering a float).
Parameters
----------
progs : vect_programs.VectPrograms
Programs in the batch.
X : torch.tens... | BatchExecutionReduceGather | python | WassimTenachi/PhySO | physo/physym/batch_execute.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch_execute.py | MIT |
def BatchExecutionReward (progs, X, y_target, reward_function, y_weights = 1.,
# Realization related
i_realization = 0,
n_samples_per_dataset = None,
# Mask
mask = None,
... |
Executes prog(X) for each prog in progs and gathers reward_function(y_target, prog(X), y_weights) as a result.
NB: Parallel execution is typically slower because of communication time (even just gathering a float).
Parameters
----------
progs : vect_programs.VectPrograms
Programs in the bat... | BatchExecutionReward | python | WassimTenachi/PhySO | physo/physym/batch_execute.py | https://github.com/WassimTenachi/PhySO/blob/master/physo/physym/batch_execute.py | MIT |
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