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
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def _create_learning_rate_scheduler(learning_rate_config, optimizer, total_step):
"""Create optimizer learning rate scheduler based on config.
Args:
learning_rate_config: A LearningRate proto message.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
... | Create optimizer learning rate scheduler based on config.
Args:
learning_rate_config: A LearningRate proto message.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
| _create_learning_rate_scheduler | python | traveller59/second.pytorch | second/pytorch/builder/lr_scheduler_builder.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/builder/lr_scheduler_builder.py | MIT |
def second_box_encode(boxes, anchors, encode_angle_to_vector=False, smooth_dim=False):
"""box encode for VoxelNet
Args:
boxes ([N, 7] Tensor): normal boxes: x, y, z, l, w, h, r
anchors ([N, 7] Tensor): anchors
"""
box_ndim = anchors.shape[-1]
cas, cgs = [], []
if box_ndim > 7:
... | box encode for VoxelNet
Args:
boxes ([N, 7] Tensor): normal boxes: x, y, z, l, w, h, r
anchors ([N, 7] Tensor): anchors
| second_box_encode | python | traveller59/second.pytorch | second/pytorch/core/box_torch_ops.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/box_torch_ops.py | MIT |
def corners_nd(dims, origin=0.5):
"""generate relative box corners based on length per dim and
origin point.
Args:
dims (float array, shape=[N, ndim]): array of length per dim
origin (list or array or float): origin point relate to smallest point.
dtype (output dtype, optional)... | generate relative box corners based on length per dim and
origin point.
Args:
dims (float array, shape=[N, ndim]): array of length per dim
origin (list or array or float): origin point relate to smallest point.
dtype (output dtype, optional): Defaults to np.float32
Return... | corners_nd | python | traveller59/second.pytorch | second/pytorch/core/box_torch_ops.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/box_torch_ops.py | MIT |
def center_to_corner_box2d(centers, dims, angles=None, origin=0.5):
"""convert kitti locations, dimensions and angles to corners
Args:
centers (float array, shape=[N, 2]): locations in kitti label file.
dims (float array, shape=[N, 2]): dimensions in kitti label file.
angles (float ... | convert kitti locations, dimensions and angles to corners
Args:
centers (float array, shape=[N, 2]): locations in kitti label file.
dims (float array, shape=[N, 2]): dimensions in kitti label file.
angles (float array, shape=[N]): rotation_y in kitti label file.
Returns:
... | center_to_corner_box2d | python | traveller59/second.pytorch | second/pytorch/core/box_torch_ops.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/box_torch_ops.py | MIT |
def _compute_loss(self,
prediction_tensor,
target_tensor,
weights,
class_indices=None):
""" Args:
input [batch_num, class_num]:
The direct prediction of classification fc layer.
target [ba... | Args:
input [batch_num, class_num]:
The direct prediction of classification fc layer.
target [batch_num, class_num]:
Binary target (0 or 1) for each sample each class. The value is -1
when the sample is ignored.
| _compute_loss | python | traveller59/second.pytorch | second/pytorch/core/ghm_loss.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/ghm_loss.py | MIT |
def indices_to_dense_vector(indices,
size,
indices_value=1.,
default_value=0,
dtype=np.float32):
"""Creates dense vector with indices set to specific value and rest to zeros.
This function exists because... | Creates dense vector with indices set to specific value and rest to zeros.
This function exists because it is unclear if it is safe to use
tf.sparse_to_dense(indices, [size], 1, validate_indices=False)
with indices which are not ordered.
This function accepts a dynamic size (e.g. tf.shape(tensor)[0])
Args... | indices_to_dense_vector | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def __call__(self,
prediction_tensor,
target_tensor,
ignore_nan_targets=False,
scope=None,
**params):
"""Call the loss function.
Args:
prediction_tensor: an N-d tensor of shape [batch, anchors, ...]
representing predicted ... | Call the loss function.
Args:
prediction_tensor: an N-d tensor of shape [batch, anchors, ...]
representing predicted quantities.
target_tensor: an N-d tensor of shape [batch, anchors, ...] representing
regression or classification targets.
ignore_nan_targets: whether to ignore nan... | __call__ | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the (encoded) predicted locations of objects.
target_tensor: A float tensor of shape [batch_size, ... | Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the (encoded) predicted locations of objects.
target_tensor: A float tensor of shape [batch_size, num_anchors,
code_size] representing the regression targets
w... | _compute_loss | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def _compute_loss(self,
prediction_tensor,
target_tensor,
weights,
class_indices=None):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing th... | Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing one-hot encoded classification targe... | _compute_loss | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def __init__(self, gamma=2.0, alpha=0.25):
"""Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
all_zero_negative: bool. if True, will treat all zero as background.
else, will treat ... | Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
all_zero_negative: bool. if True, will treat all zero as background.
else, will treat first label as background. only affect alpha.
| __init__ | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def __init__(self, gamma=2.0, alpha=0.25):
"""Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
"""
self._alpha = alpha
self._gamma = gamma | Constructor.
Args:
gamma: exponent of the modulating factor (1 - p_t) ^ gamma.
alpha: optional alpha weighting factor to balance positives vs negatives.
| __init__ | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anch... | Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing one-hot encoded classification targe... | _compute_loss | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def __init__(self, alpha, bootstrap_type='soft'):
"""Constructor.
Args:
alpha: a float32 scalar tensor between 0 and 1 representing interpolation
weight
bootstrap_type: set to either 'hard' or 'soft' (default)
Raises:
ValueError: if bootstrap_type is not either 'hard' or 'soft'
... | Constructor.
Args:
alpha: a float32 scalar tensor between 0 and 1 representing interpolation
weight
bootstrap_type: set to either 'hard' or 'soft' (default)
Raises:
ValueError: if bootstrap_type is not either 'hard' or 'soft'
| __init__ | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anch... | Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing the predicted logits for each class
target_tensor: A float tensor of shape [batch_size, num_anchors,
num_classes] representing one-hot encoded classification targe... | _compute_loss | python | traveller59/second.pytorch | second/pytorch/core/losses.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/core/losses.py | MIT |
def __init__(self,
in_channels,
out_channels,
use_norm=True,
last_layer=False):
"""
Pillar Feature Net Layer.
The Pillar Feature Net could be composed of a series of these layers, but the PointPillars paper results only
... |
Pillar Feature Net Layer.
The Pillar Feature Net could be composed of a series of these layers, but the PointPillars paper results only
used a single PFNLayer. This layer performs a similar role as second.pytorch.voxelnet.VFELayer.
:param in_channels: <int>. Number of input channels.
... | __init__ | python | traveller59/second.pytorch | second/pytorch/models/pointpillars.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/pointpillars.py | MIT |
def __init__(self,
num_input_features=4,
use_norm=True,
num_filters=(64, ),
with_distance=False,
voxel_size=(0.2, 0.2, 4),
pc_range=(0, -40, -3, 70.4, 40, 1)):
"""
Pillar Feature Net.
The networ... |
Pillar Feature Net.
The network prepares the pillar features and performs forward pass through PFNLayers. This net performs a
similar role to SECOND's second.pytorch.voxelnet.VoxelFeatureExtractor.
:param num_input_features: <int>. Number of input features, either x, y, z or x, y, z, r.... | __init__ | python | traveller59/second.pytorch | second/pytorch/models/pointpillars.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/pointpillars.py | MIT |
def __init__(self,
output_shape,
use_norm=True,
num_input_features=64,
num_filters_down1=[64],
num_filters_down2=[64, 64],
name='SpMiddle2K'):
"""
Point Pillar's Scatter.
Converts learned featur... |
Point Pillar's Scatter.
Converts learned features from dense tensor to sparse pseudo image. This replaces SECOND's
second.pytorch.voxelnet.SparseMiddleExtractor.
:param output_shape: ([int]: 4). Required output shape of features.
:param num_input_features: <int>. Number of input... | __init__ | python | traveller59/second.pytorch | second/pytorch/models/pointpillars.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/pointpillars.py | MIT |
def __init__(self,
use_norm=True,
num_class=2,
layer_nums=(3, 5, 5),
layer_strides=(2, 2, 2),
num_filters=(128, 128, 256),
upsample_strides=(1, 2, 4),
num_upsample_filters=(256, 256, 256),
... | deprecated. exists for checkpoint backward compilability (SECOND v1.0)
| __init__ | python | traveller59/second.pytorch | second/pytorch/models/rpn.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/rpn.py | MIT |
def __init__(self,
use_norm=True,
num_class=2,
layer_nums=(3, 5, 5),
layer_strides=(2, 2, 2),
num_filters=(128, 128, 256),
upsample_strides=(1, 2, 4),
num_upsample_filters=(256, 256, 256),
... | upsample_strides support float: [0.25, 0.5, 1]
if upsample_strides < 1, conv2d will be used instead of convtranspose2d.
| __init__ | python | traveller59/second.pytorch | second/pytorch/models/rpn.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/rpn.py | MIT |
def network_forward(self, voxels, num_points, coors, batch_size):
"""this function is used for subclass.
you can add custom network architecture by subclass VoxelNet class
and override this function.
Returns:
preds_dict: {
box_preds: ...
cls_p... | this function is used for subclass.
you can add custom network architecture by subclass VoxelNet class
and override this function.
Returns:
preds_dict: {
box_preds: ...
cls_preds: ...
dir_cls_preds: ...
}
| network_forward | python | traveller59/second.pytorch | second/pytorch/models/voxelnet.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/voxelnet.py | MIT |
def forward(self, example):
"""module's forward should always accept dict and return loss.
"""
voxels = example["voxels"]
num_points = example["num_points"]
coors = example["coordinates"]
if len(num_points.shape) == 2: # multi-gpu
num_voxel_per_batch = exampl... | module's forward should always accept dict and return loss.
| forward | python | traveller59/second.pytorch | second/pytorch/models/voxelnet.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/voxelnet.py | MIT |
def predict(self, example, preds_dict):
"""start with v1.6.0, this function don't contain any kitti-specific code.
Returns:
predict: list of pred_dict.
pred_dict: {
box3d_lidar: [N, 7] 3d box.
scores: [N]
label_preds: [N]
... | start with v1.6.0, this function don't contain any kitti-specific code.
Returns:
predict: list of pred_dict.
pred_dict: {
box3d_lidar: [N, 7] 3d box.
scores: [N]
label_preds: [N]
metadata: meta-data which contains dataset-sp... | predict | python | traveller59/second.pytorch | second/pytorch/models/voxelnet.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/voxelnet.py | MIT |
def convert_norm_to_float(net):
'''
BatchNorm layers to have parameters in single precision.
Find all layers and convert them back to float. This can't
be done with built in .apply as that function will apply
fn to all modules, parameters, and buffers. Thus we wouldn't
be... |
BatchNorm layers to have parameters in single precision.
Find all layers and convert them back to float. This can't
be done with built in .apply as that function will apply
fn to all modules, parameters, and buffers. Thus we wouldn't
be able to guard the float conversion based o... | convert_norm_to_float | python | traveller59/second.pytorch | second/pytorch/models/voxelnet.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/voxelnet.py | MIT |
def get_paddings_indicator(actual_num, max_num, axis=0):
"""Create boolean mask by actually number of a padded tensor.
Args:
actual_num ([type]): [description]
max_num ([type]): [description]
Returns:
[type]: [description]
"""
actual_num = torch.unsqueeze(actual_num, axis ... | Create boolean mask by actually number of a padded tensor.
Args:
actual_num ([type]): [description]
max_num ([type]): [description]
Returns:
[type]: [description]
| get_paddings_indicator | python | traveller59/second.pytorch | second/pytorch/models/voxel_encoder.py | https://github.com/traveller59/second.pytorch/blob/master/second/pytorch/models/voxel_encoder.py | MIT |
def box3d_overlap_kernel(boxes,
qboxes,
rinc,
criterion=-1,
z_axis=1,
z_center=1.0):
"""
z_axis: the z (height) axis.
z_center: unified z (height) center of box.
"""
... |
z_axis: the z (height) axis.
z_center: unified z (height) center of box.
| box3d_overlap_kernel | python | traveller59/second.pytorch | second/utils/eval.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/eval.py | MIT |
def calculate_iou_partly(gt_annos,
dt_annos,
metric,
num_parts=50,
z_axis=1,
z_center=1.0):
"""fast iou algorithm. this function can be used independently to
do result analysis.
Args... | fast iou algorithm. this function can be used independently to
do result analysis.
Args:
gt_annos: dict, must from get_label_annos() in kitti_common.py
dt_annos: dict, must from get_label_annos() in kitti_common.py
metric: eval type. 0: bbox, 1: bev, 2: 3d
num_parts: int. a para... | calculate_iou_partly | python | traveller59/second.pytorch | second/utils/eval.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/eval.py | MIT |
def eval_class_v3(gt_annos,
dt_annos,
current_classes,
difficultys,
metric,
min_overlaps,
compute_aos=False,
z_axis=1,
z_center=1.0,
num_parts=50):
"""Kit... | Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.
Args:
gt_annos: dict, must from get_label_annos() in kitti_common.py
dt_annos: dict, must from get_label_annos() in kitti_common.py
current_class: int, 0: car, 1: pedestrian, 2: cyclist
difficulty: int. eval diffi... | eval_class_v3 | python | traveller59/second.pytorch | second/utils/eval.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/eval.py | MIT |
def get_official_eval_result(gt_annos,
dt_annos,
current_classes,
difficultys=[0, 1, 2],
z_axis=1,
z_center=1.0):
"""
gt_annos and dt_annos must contains following... |
gt_annos and dt_annos must contains following keys:
[bbox, location, dimensions, rotation_y, score]
| get_official_eval_result | python | traveller59/second.pytorch | second/utils/eval.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/eval.py | MIT |
def flat_nested_json_dict(json_dict, sep=".") -> dict:
"""flat a nested json-like dict. this function make shadow copy.
"""
flatted = {}
for k, v in json_dict.items():
if isinstance(v, dict):
_flat_nested_json_dict(v, flatted, sep, str(k))
else:
flatted[str(k)] = ... | flat a nested json-like dict. this function make shadow copy.
| flat_nested_json_dict | python | traveller59/second.pytorch | second/utils/log_tool.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/log_tool.py | MIT |
def log_text(self, text, step, tag="regular log"):
"""This function only add text to log.txt and tensorboard texts
"""
print(text)
print(text, file=self.log_file)
if step > self._text_current_gstep and self._text_current_gstep != -1:
total_text = '\n'.join(self._tb_te... | This function only add text to log.txt and tensorboard texts
| log_text | python | traveller59/second.pytorch | second/utils/log_tool.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/log_tool.py | MIT |
def points_to_bev(points,
voxel_size,
coors_range,
with_reflectivity=False,
density_norm_num=16,
max_voxels=40000):
"""convert kitti points(N, 4) to a bev map. return [C, H, W] map.
this function based on algorithm in poin... | convert kitti points(N, 4) to a bev map. return [C, H, W] map.
this function based on algorithm in points_to_voxel.
takes 5ms in a reduced pointcloud with voxel_size=[0.1, 0.1, 0.8]
Args:
points: [N, ndim] float tensor. points[:, :3] contain xyz points and
points[:, 3] contain reflectiv... | points_to_bev | python | traveller59/second.pytorch | second/utils/simplevis.py | https://github.com/traveller59/second.pytorch/blob/master/second/utils/simplevis.py | MIT |
def scatter_nd(indices, updates, shape):
"""pytorch edition of tensorflow scatter_nd.
this function don't contain except handle code. so use this carefully
when indice repeats, don't support repeat add which is supported
in tensorflow.
"""
ret = torch.zeros(*shape, dtype=updates.dtype, device=up... | pytorch edition of tensorflow scatter_nd.
this function don't contain except handle code. so use this carefully
when indice repeats, don't support repeat add which is supported
in tensorflow.
| scatter_nd | python | traveller59/second.pytorch | torchplus/ops/array_ops.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/ops/array_ops.py | MIT |
def latest_checkpoint(model_dir, model_name):
"""return path of latest checkpoint in a model_dir
Args:
model_dir: string, indicate your model dir(save ckpts, summarys,
logs, etc).
model_name: name of your model. we find ckpts by name
Returns:
path: None if isn't exist or ... | return path of latest checkpoint in a model_dir
Args:
model_dir: string, indicate your model dir(save ckpts, summarys,
logs, etc).
model_name: name of your model. we find ckpts by name
Returns:
path: None if isn't exist or latest checkpoint path.
| latest_checkpoint | python | traveller59/second.pytorch | torchplus/train/checkpoint.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/checkpoint.py | MIT |
def save(model_dir,
model,
model_name,
global_step,
max_to_keep=8,
keep_latest=True):
"""save a model into model_dir.
Args:
model_dir: string, indicate your model dir(save ckpts, summarys,
logs, etc).
model: torch.nn.Module instance.
... | save a model into model_dir.
Args:
model_dir: string, indicate your model dir(save ckpts, summarys,
logs, etc).
model: torch.nn.Module instance.
model_name: name of your model. we find ckpts by name
global_step: int, indicate current global step.
max_to_keep: int,... | save | python | traveller59/second.pytorch | torchplus/train/checkpoint.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/checkpoint.py | MIT |
def split_bn_bias(layer_groups):
"Split the layers in `layer_groups` into batchnorm (`bn_types`) and non-batchnorm groups."
split_groups = []
for l in layer_groups:
l1, l2 = [], []
for c in l.children():
if isinstance(c, bn_types): l2.append(c)
else: l1.append(c)
... | Split the layers in `layer_groups` into batchnorm (`bn_types`) and non-batchnorm groups. | split_bn_bias | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def get_master(layer_groups, flat_master: bool = False):
"Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
split_groups = split_bn_bias(layer_groups)
model_params = [[
param for param in lg.parameters() if param.requires_grad
] for lg in split_gr... | Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32. | get_master | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def model_g2master_g(model_params, master_params,
flat_master: bool = False) -> None:
"Copy the `model_params` gradients to `master_params` for the optimizer step."
if flat_master:
for model_group, master_group in zip(model_params, master_params):
if len(master_group) !=... | Copy the `model_params` gradients to `master_params` for the optimizer step. | model_g2master_g | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def listify(p=None, q=None):
"Make `p` listy and the same length as `q`."
if p is None: p = []
elif isinstance(p, str): p = [p]
elif not isinstance(p, Iterable): p = [p]
n = q if type(q) == int else len(p) if q is None else len(q)
if len(p) == 1: p = p * n
assert len(p) == n, f'List len mism... | Make `p` listy and the same length as `q`. | listify | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def trainable_params(m: nn.Module):
"Return list of trainable params in `m`."
res = filter(lambda p: p.requires_grad, m.parameters())
return res | Return list of trainable params in `m`. | trainable_params | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def create(cls, opt_func, lr, layer_groups, **kwargs):
"Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."
split_groups = split_bn_bias(layer_groups)
opt = opt_func([{
'params': trainable_params(l),
'lr': 0
} for l in split_groups])
... | Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`. | create | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def new(self, layer_groups):
"Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
opt_func = getattr(self, 'opt_func', self.opt.__class__)
split_groups = split_bn_bias(layer_groups)
opt = opt_func([{
'params': trainable_params(l... | Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters. | new | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def step(self) -> None:
"Set weight decay and step optimizer."
# weight decay outside of optimizer step (AdamW)
if self.true_wd:
for lr, wd, pg1, pg2 in zip(self._lr, self._wd,
self.opt.param_groups[::2],
... | Set weight decay and step optimizer. | step | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def clear(self):
"Reset the state of the inner optimizer."
sd = self.state_dict()
sd['state'] = {}
self.load_state_dict(sd) | Reset the state of the inner optimizer. | clear | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def beta(self, val: float) -> None:
"Set beta (or alpha as makes sense for given optimizer)."
if val is None: return
if 'betas' in self.opt_keys:
self.set_val('betas', (self._mom, listify(val, self._beta)))
elif 'alpha' in self.opt_keys:
self.set_val('alpha', list... | Set beta (or alpha as makes sense for given optimizer). | beta | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def read_defaults(self) -> None:
"Read the values inside the optimizer for the hyper-parameters."
self._beta = None
if 'lr' in self.opt_keys: self._lr = self.read_val('lr')
if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum')
if 'alpha' in self.opt_keys: self._be... | Read the values inside the optimizer for the hyper-parameters. | read_defaults | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def set_val(self, key: str, val, bn_groups: bool = True):
"Set `val` inside the optimizer dictionary at `key`."
if is_tuple(val): val = [(v1, v2) for v1, v2 in zip(*val)]
for v, pg1, pg2 in zip(val, self.opt.param_groups[::2],
self.opt.param_groups[1::2]):
... | Set `val` inside the optimizer dictionary at `key`. | set_val | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def read_val(self, key: str):
"Read a hyperparameter `key` in the optimizer dictionary."
val = [pg[key] for pg in self.opt.param_groups[::2]]
if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for o in val]
return val | Read a hyperparameter `key` in the optimizer dictionary. | read_val | python | traveller59/second.pytorch | torchplus/train/fastai_optim.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/fastai_optim.py | MIT |
def annealing_cos(start, end, pct):
# print(pct, start, end)
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = np.cos(np.pi * pct) + 1
return end + (start - end) / 2 * cos_out | Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0. | annealing_cos | python | traveller59/second.pytorch | torchplus/train/learning_schedules_fastai.py | https://github.com/traveller59/second.pytorch/blob/master/torchplus/train/learning_schedules_fastai.py | MIT |
def install_with_constraints(session, *args, **kwargs):
"""Install packages constrained by Poetry's lock file.
This function is a wrapper for nox.sessions.Session.install. It
invokes pip to install packages inside of the session's virtualenv.
Additionally, pip is passed a constraints file generated fro... | Install packages constrained by Poetry's lock file.
This function is a wrapper for nox.sessions.Session.install. It
invokes pip to install packages inside of the session's virtualenv.
Additionally, pip is passed a constraints file generated from
Poetry's lock file, to ensure that the packages are pinne... | install_with_constraints | python | JakobGM/patito | noxfile.py | https://github.com/JakobGM/patito/blob/master/noxfile.py | MIT |
def test(session):
"""Run test suite using pytest + coverage + xdoctest."""
if session.python == "3.9":
# Only run test coverage and docstring tests on python 3.10
args = session.posargs # or ["--cov", "--xdoctest"]
else:
args = session.posargs
session.run(
"poetry",
... | Run test suite using pytest + coverage + xdoctest. | test | python | JakobGM/patito | noxfile.py | https://github.com/JakobGM/patito/blob/master/noxfile.py | MIT |
def type_check(session):
"""Run type-checking on project using pyright."""
args = session.posargs or locations
session.run(
"poetry",
"install",
"--only=main",
"--extras",
"caching pandas",
external=True,
)
install_with_constraints(
session, "m... | Run type-checking on project using pyright. | type_check | python | JakobGM/patito | noxfile.py | https://github.com/JakobGM/patito/blob/master/noxfile.py | MIT |
def format(session):
"""Run the ruff formatter on the entire code base."""
args = session.posargs or locations
install_with_constraints(session, "ruff")
session.run("ruff format", *args) | Run the ruff formatter on the entire code base. | format | python | JakobGM/patito | noxfile.py | https://github.com/JakobGM/patito/blob/master/noxfile.py | MIT |
def __init__(self, exc: Exception, loc: Union[str, "Loc"]) -> None:
"""Wrap an error in an ErrorWrapper."""
self.exc = exc
self._loc = loc | Wrap an error in an ErrorWrapper. | __init__ | python | JakobGM/patito | src/patito/exceptions.py | https://github.com/JakobGM/patito/blob/master/src/patito/exceptions.py | MIT |
def collect(
self,
*args,
**kwargs,
) -> DataFrame[ModelType]: # noqa: DAR101, DAR201
"""Collect into a DataFrame.
See documentation of polars.DataFrame.collect for full description of
parameters.
"""
background = kwargs.pop("background", False)
... | Collect into a DataFrame.
See documentation of polars.DataFrame.collect for full description of
parameters.
| collect | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def from_existing(cls: type[LDF], lf: pl.LazyFrame) -> LDF:
"""Construct a patito.DataFrame object from an existing polars.DataFrame object."""
if getattr(cls, "model", False):
return cls.model.LazyFrame._from_pyldf(super().lazy()._ldf) # type: ignore
return LazyFrame._from_pyldf(l... | Construct a patito.DataFrame object from an existing polars.DataFrame object. | from_existing | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def lazy(self: DataFrame[ModelType]) -> LazyFrame[ModelType]:
"""Convert DataFrame into LazyFrame.
See documentation of polars.DataFrame.lazy() for full description.
Returns:
A new LazyFrame object.
"""
if getattr(self, "model", False):
return self.mode... | Convert DataFrame into LazyFrame.
See documentation of polars.DataFrame.lazy() for full description.
Returns:
A new LazyFrame object.
| lazy | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def validate(self, columns: Sequence[str] | None = None, **kwargs: Any):
"""Validate the schema and content of the dataframe.
You must invoke ``.set_model()`` before invoking ``.validate()`` in order
to specify how the dataframe should be validated.
Returns:
DataFrame[Model... | Validate the schema and content of the dataframe.
You must invoke ``.set_model()`` before invoking ``.validate()`` in order
to specify how the dataframe should be validated.
Returns:
DataFrame[Model]: The original patito dataframe, if correctly validated.
Raises:
... | validate | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def get(self, predicate: pl.Expr | None = None) -> ModelType:
"""Fetch the single row that matches the given polars predicate.
If you expect a data frame to already consist of one single row,
you can use ``.get()`` without any arguments to return that row.
Raises:
RowDoesNo... | Fetch the single row that matches the given polars predicate.
If you expect a data frame to already consist of one single row,
you can use ``.get()`` without any arguments to return that row.
Raises:
RowDoesNotExist: If zero rows evaluate to true for the given predicate.
... | get | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def iter_models(
self, validate_df: bool = True, validate_model: bool = False
) -> ModelGenerator[ModelType]:
"""Iterate over all rows in the dataframe as pydantic models.
Args:
validate_df: If set to ``True``, the dataframe will be validated before
making models... | Iterate over all rows in the dataframe as pydantic models.
Args:
validate_df: If set to ``True``, the dataframe will be validated before
making models out of each row. If set to ``False``, beware that columns
need to be the exact same as the model fields.
... | iter_models | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def _pydantic_model(self) -> type[Model]:
"""Dynamically construct patito model compliant with dataframe.
Returns:
A pydantic model class where all the rows have been specified as
`typing.Any` fields.
"""
from patito.pydantic import Model
pydantic_a... | Dynamically construct patito model compliant with dataframe.
Returns:
A pydantic model class where all the rows have been specified as
`typing.Any` fields.
| _pydantic_model | python | JakobGM/patito | src/patito/polars.py | https://github.com/JakobGM/patito/blob/master/src/patito/polars.py | MIT |
def __init__(cls, name: str, bases: tuple, clsdict: dict, **kwargs) -> None:
"""Construct new patito model.
Args:
name: Name of model class.
bases: Tuple of superclasses.
clsdict: Dictionary containing class properties.
**kwargs: Additional keyword argume... | Construct new patito model.
Args:
name: Name of model class.
bases: Tuple of superclasses.
clsdict: Dictionary containing class properties.
**kwargs: Additional keyword arguments.
| __init__ | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def valid_dtypes(
cls: type[Model],
) -> Mapping[str, frozenset[DataTypeClass | DataType]]:
"""Return a list of polars dtypes which Patito considers valid for each field.
The first item of each list is the default dtype chosen by Patito.
Returns:
A dictionary mapping ea... | Return a list of polars dtypes which Patito considers valid for each field.
The first item of each list is the default dtype chosen by Patito.
Returns:
A dictionary mapping each column string name to a list of valid dtypes.
Raises:
NotImplementedError: If one or more m... | valid_dtypes | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def defaults(cls: type[Model]) -> dict[str, Any]:
"""Return default field values specified on the model.
Returns:
Dictionary containing fields with their respective default values.
Example:
>>> from typing_extensions import Literal
>>> import patito as pt
... | Return default field values specified on the model.
Returns:
Dictionary containing fields with their respective default values.
Example:
>>> from typing_extensions import Literal
>>> import patito as pt
>>> class Product(pt.Model):
... na... | defaults | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def non_nullable_columns(cls: type[Model]) -> set[str]:
"""Return names of those columns that are non-nullable in the schema.
Returns:
Set of column name strings.
Example:
>>> from typing import Optional
>>> import patito as pt
>>> class MyModel(... | Return names of those columns that are non-nullable in the schema.
Returns:
Set of column name strings.
Example:
>>> from typing import Optional
>>> import patito as pt
>>> class MyModel(pt.Model):
... nullable_field: Optional[int]
... | non_nullable_columns | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def unique_columns(cls: type[Model]) -> set[str]:
"""Return columns with uniqueness constraint.
Returns:
Set of column name strings.
Example:
>>> from typing import Optional
>>> import patito as pt
>>> class Product(pt.Model):
... ... | Return columns with uniqueness constraint.
Returns:
Set of column name strings.
Example:
>>> from typing import Optional
>>> import patito as pt
>>> class Product(pt.Model):
... product_id: int = pt.Field(unique=True)
... ... | unique_columns | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def derived_columns(cls: type[Model]) -> set[str]:
"""Return set of columns which are derived from other columns."""
infos = cls.column_infos
return {
column for column in cls.columns if infos[column].derived_from is not None
} | Return set of columns which are derived from other columns. | derived_columns | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def validate_schema(cls: type[ModelType]):
"""Users should run this after defining or edit a model. We withhold the checks at model definition time to avoid expensive queries of the model schema."""
for column in cls.columns:
col_info = cls.column_infos[column]
field_info = cls.m... | Users should run this after defining or edit a model. We withhold the checks at model definition time to avoid expensive queries of the model schema. | validate_schema | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def from_row(
cls: type[ModelType],
row: pd.DataFrame | pl.DataFrame,
validate: bool = True,
) -> ModelType:
"""Represent a single data frame row as a Patito model.
Args:
row: A dataframe, either polars and pandas, consisting of a single row.
validate... | Represent a single data frame row as a Patito model.
Args:
row: A dataframe, either polars and pandas, consisting of a single row.
validate: If ``False``, skip pydantic validation of the given row data.
Returns:
Model: A patito model representing the given row data.... | from_row | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def _from_polars(
cls: type[ModelType],
dataframe: pl.DataFrame,
validate: bool = True,
) -> ModelType:
"""Construct model from a single polars row.
Args:
dataframe: A polars dataframe consisting of one single row.
validate: If ``True``, run the pydan... | Construct model from a single polars row.
Args:
dataframe: A polars dataframe consisting of one single row.
validate: If ``True``, run the pydantic validators. If ``False``, pydantic
will not cast any types in the resulting object.
Returns:
Model: A ... | _from_polars | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def validate(
cls: type[ModelType],
dataframe: pd.DataFrame | pl.DataFrame,
columns: Sequence[str] | None = None,
allow_missing_columns: bool = False,
allow_superfluous_columns: bool = False,
drop_superfluous_columns: bool = False,
) -> DataFrame[ModelType]:
"... | Validate the schema and content of the given dataframe.
Args:
dataframe: Polars DataFrame to be validated.
columns: Optional list of columns to validate. If not provided, all columns
of the dataframe will be validated.
allow_missing_columns: If True, missing ... | validate | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def iter_models(
cls: type[ModelType], dataframe: pd.DataFrame | pl.DataFrame
) -> ModelGenerator[ModelType]:
"""Validate the dataframe and iterate over the rows, yielding Patito models.
Args:
dataframe: Polars or pandas DataFrame to be validated.
Returns:
L... | Validate the dataframe and iterate over the rows, yielding Patito models.
Args:
dataframe: Polars or pandas DataFrame to be validated.
Returns:
ListableIterator: An iterator of patito models over the validated data.
Raises:
patito.exceptions.DataFrameValida... | iter_models | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def example_value( # noqa: C901
cls,
field: str | None = None,
properties: dict[str, Any] | None = None,
) -> date | datetime | time | timedelta | float | int | str | None | Mapping | list:
"""Return a valid example value for the given model field.
Args:
field: ... | Return a valid example value for the given model field.
Args:
field: Field name identifier.
properties: Pydantic v2-style properties dict
Returns:
A single value which is consistent with the given field definition.
Raises:
NotImplementedError: I... | example_value | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def example(
cls: type[ModelType],
**kwargs: Any, # noqa: ANN401
) -> ModelType:
"""Produce model instance with filled dummy data for all unspecified fields.
The type annotation of unspecified field is used to fill in type-correct
dummy data, e.g. ``-1`` for ``int``, ``"dum... | Produce model instance with filled dummy data for all unspecified fields.
The type annotation of unspecified field is used to fill in type-correct
dummy data, e.g. ``-1`` for ``int``, ``"dummy_string"`` for ``str``, and so
on...
The first item of ``typing.Literal`` annotations are used... | example | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def pandas_examples(
cls: type[ModelType],
data: dict | Iterable,
columns: Iterable[str] | None = None,
) -> pd.DataFrame:
"""Generate dataframe with dummy data for all unspecified columns.
Offers the same API as the pandas.DataFrame constructor.
Non-iterable values,... | Generate dataframe with dummy data for all unspecified columns.
Offers the same API as the pandas.DataFrame constructor.
Non-iterable values, besides strings, are repeated until they become as long as
the iterable arguments.
Args:
data: Data to populate the dummy dataframe ... | pandas_examples | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def join(
cls: type[Model],
other: type[Model],
how: Literal["inner", "left", "outer", "asof", "cross", "semi", "anti"],
) -> type[Model]:
"""Dynamically create a new model compatible with an SQL Join operation.
For instance, ``ModelA.join(ModelB, how="left")`` will create a... | Dynamically create a new model compatible with an SQL Join operation.
For instance, ``ModelA.join(ModelB, how="left")`` will create a model containing
all the fields of ``ModelA`` and ``ModelB``, but where all fields of ``ModelB``
has been made ``Optional``, i.e. nullable. This is consistent wi... | join | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def select(cls: type[ModelType], fields: str | Iterable[str]) -> type[Model]:
"""Create a new model consisting of only a subset of the model fields.
Args:
fields: A single field name as a string or a collection of strings.
Returns:
A new model containing only the fields... | Create a new model consisting of only a subset of the model fields.
Args:
fields: A single field name as a string or a collection of strings.
Returns:
A new model containing only the fields specified by ``fields``.
Raises:
ValueError: If one or more non-exi... | select | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def drop(cls: type[ModelType], name: str | Iterable[str]) -> type[Model]:
"""Return a new model where one or more fields are excluded.
Args:
name: A single string field name, or a list of such field names,
which will be dropped.
Returns:
New model class ... | Return a new model where one or more fields are excluded.
Args:
name: A single string field name, or a list of such field names,
which will be dropped.
Returns:
New model class where the given fields have been removed.
Examples:
>>> class My... | drop | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def prefix(cls: type[ModelType], prefix: str) -> type[Model]:
"""Return a new model where all field names have been prefixed.
Args:
prefix: String prefix to add to all field names.
Returns:
New model class with all the same fields only prefixed with the given prefix.
... | Return a new model where all field names have been prefixed.
Args:
prefix: String prefix to add to all field names.
Returns:
New model class with all the same fields only prefixed with the given prefix.
Example:
>>> class MyModel(Model):
... ... | prefix | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def suffix(cls: type[ModelType], suffix: str) -> type[Model]:
"""Return a new model where all field names have been suffixed.
Args:
suffix: String suffix to add to all field names.
Returns:
New model class with all the same fields only suffixed with the given
... | Return a new model where all field names have been suffixed.
Args:
suffix: String suffix to add to all field names.
Returns:
New model class with all the same fields only suffixed with the given
suffix.
Example:
>>> class MyModel(Model):
... | suffix | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def rename(cls: type[ModelType], mapping: dict[str, str]) -> type[Model]:
"""Return a new model class where the specified fields have been renamed.
Args:
mapping: A dictionary where the keys are the old field names
and the values are the new names.
Returns:
... | Return a new model class where the specified fields have been renamed.
Args:
mapping: A dictionary where the keys are the old field names
and the values are the new names.
Returns:
A new model class where the given fields have been renamed.
Raises:
... | rename | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def with_fields(
cls: type[ModelType],
**field_definitions: Any, # noqa: ANN401
) -> type[Model]:
"""Return a new model class where the given fields have been added.
Args:
**field_definitions: the keywords are of the form:
``field_name=(field_type, field... | Return a new model class where the given fields have been added.
Args:
**field_definitions: the keywords are of the form:
``field_name=(field_type, field_default)``.
Specify ``...`` if no default value is provided.
For instance, ``column_name=(int, ..... | with_fields | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def _derive_model(
cls: type[ModelType],
model_name: str,
field_mapping: dict[str, Any],
) -> type[Model]:
"""Derive a new model with new field definitions.
Args:
model_name: Name of new model class.
field_mapping: A mapping where the keys represent f... | Derive a new model with new field definitions.
Args:
model_name: Name of new model class.
field_mapping: A mapping where the keys represent field names and the values
represent field definitions. String field definitions are used as
pointers to the origin... | _derive_model | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def Field(
*args: Any, **kwargs: Any
) -> Any: # annotate with Any to make the downstream type annotations happy
"""Annotate model field with additional type and validation information.
This class is built on ``pydantic.Field`` and you can find the list of parameters
in the `API reference <https://doc... | Annotate model field with additional type and validation information.
This class is built on ``pydantic.Field`` and you can find the list of parameters
in the `API reference <https://docs.pydantic.dev/latest/api/fields/>`_.
Patito adds additional parameters which are used when validating dataframes,
th... | Field | python | JakobGM/patito | src/patito/pydantic.py | https://github.com/JakobGM/patito/blob/master/src/patito/pydantic.py | MIT |
def _transform_df(dataframe: pl.DataFrame, schema: type[Model]) -> pl.DataFrame:
"""Transform any properties of the dataframe according to the model.
Currently only supports using AliasGenerator to transform column names to match a model.
Args:
dataframe: Polars DataFrame to be validated.
... | Transform any properties of the dataframe according to the model.
Currently only supports using AliasGenerator to transform column names to match a model.
Args:
dataframe: Polars DataFrame to be validated.
schema: Patito model which specifies how the dataframe should be structured.
| _transform_df | python | JakobGM/patito | src/patito/validators.py | https://github.com/JakobGM/patito/blob/master/src/patito/validators.py | MIT |
def _find_errors( # noqa: C901
dataframe: pl.DataFrame,
schema: type[Model],
columns: Sequence[str] | None = None,
allow_missing_columns: bool = False,
allow_superfluous_columns: bool = False,
) -> list[ErrorWrapper]:
"""Validate the given dataframe.
Args:
dataframe: Polars DataFra... | Validate the given dataframe.
Args:
dataframe: Polars DataFrame to be validated.
schema: Patito model which specifies how the dataframe should be structured.
columns: If specified, only validate the given columns. Missing columns will
check if any specified columns are missing f... | _find_errors | python | JakobGM/patito | src/patito/validators.py | https://github.com/JakobGM/patito/blob/master/src/patito/validators.py | MIT |
def validate(
dataframe: pd.DataFrame | pl.DataFrame,
schema: type[Model],
columns: Sequence[str] | None = None,
allow_missing_columns: bool = False,
allow_superfluous_columns: bool = False,
drop_superfluous_columns: bool = False,
) -> pl.DataFrame:
"""Validate the given dataframe.
Args... | Validate the given dataframe.
Args:
dataframe: Polars DataFrame to be validated.
schema: Patito model which specifies how the dataframe should be structured.
columns: Optional list of columns to validate. If not provided, all columns
of the dataframe will be validated.
a... | validate | python | JakobGM/patito | src/patito/validators.py | https://github.com/JakobGM/patito/blob/master/src/patito/validators.py | MIT |
def expr_deserializer(
expr: str | pl.Expr | list[pl.Expr] | None,
) -> pl.Expr | list[pl.Expr] | None:
"""Deserialize a polars expression or list thereof from json.
This is applied both during deserialization and validation.
"""
if expr is None:
return None
elif isinstance(expr, pl.Exp... | Deserialize a polars expression or list thereof from json.
This is applied both during deserialization and validation.
| expr_deserializer | python | JakobGM/patito | src/patito/_pydantic/column_info.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/column_info.py | MIT |
def expr_or_col_name_deserializer(expr: str | pl.Expr | None) -> pl.Expr | str | None:
"""Deserialize a polars expression or column name from json.
This is applied both during deserialization and validation.
"""
if expr is None:
return None
elif isinstance(expr, pl.Expr):
return exp... | Deserialize a polars expression or column name from json.
This is applied both during deserialization and validation.
| expr_or_col_name_deserializer | python | JakobGM/patito | src/patito/_pydantic/column_info.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/column_info.py | MIT |
def __repr__(self) -> str:
"""Print only Field attributes whose values are not default (mainly None)."""
not_default_field = {
field: getattr(self, field)
for field in self.model_fields
if getattr(self, field) is not self.model_fields[field].default
}
... | Print only Field attributes whose values are not default (mainly None). | __repr__ | python | JakobGM/patito | src/patito/_pydantic/column_info.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/column_info.py | MIT |
def __repr_args__(self) -> "ReprArgs":
"""Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
Can either return:
* name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
* or, just values, e.g.: `[(None, 'foo'), (No... | Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
Can either return:
* name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
* or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
| __repr_args__ | python | JakobGM/patito | src/patito/_pydantic/repr.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/repr.py | MIT |
def __pretty__(
self, fmt: Callable[[Any], Any], **kwargs: Any
) -> Generator[Any, None, None]:
"""Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects."""
yield self.__repr_name__() + "("
yield 1
for name, value in ... | Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects. | __pretty__ | python | JakobGM/patito | src/patito/_pydantic/repr.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/repr.py | MIT |
def display_as_type(obj: Any) -> str:
"""Pretty representation of a type, should be as close as possible to the original type definition string.
Takes some logic from `typing._type_repr`.
"""
if isinstance(obj, types.FunctionType):
return obj.__name__
elif obj is ...:
return "..."
... | Pretty representation of a type, should be as close as possible to the original type definition string.
Takes some logic from `typing._type_repr`.
| display_as_type | python | JakobGM/patito | src/patito/_pydantic/repr.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/repr.py | MIT |
def schema_for_model(cls: type[ModelType]) -> dict[str, dict[str, Any]]:
"""Return schema properties where definition references have been resolved.
Returns:
Field information as a dictionary where the keys are field names and the
values are dictionaries containing metadata information abou... | Return schema properties where definition references have been resolved.
Returns:
Field information as a dictionary where the keys are field names and the
values are dictionaries containing metadata information about the field
itself.
Raises:
TypeError: if a field is an... | schema_for_model | python | JakobGM/patito | src/patito/_pydantic/schema.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/schema.py | MIT |
def validate_polars_dtype(
annotation: type[Any] | None,
dtype: DataType | DataTypeClass | None,
column: str | None = None,
) -> None:
"""Check that the polars dtype is valid for the given annotation. Raises ValueError if not.
Args:
annotation (type[Any] | None): python type annotation
... | Check that the polars dtype is valid for the given annotation. Raises ValueError if not.
Args:
annotation (type[Any] | None): python type annotation
dtype (DataType | DataTypeClass | None): polars dtype
column (Optional[str], optional): column name. Defaults to None.
| validate_polars_dtype | python | JakobGM/patito | src/patito/_pydantic/dtypes/dtypes.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/dtypes/dtypes.py | MIT |
def validate_annotation(
annotation: type[Any] | Any | None, column: str | None = None
) -> None:
"""Check that the provided annotation has polars/patito support (we can resolve it to a default dtype). Raises ValueError if not.
Args:
annotation (type[Any] | None): python type annotation
col... | Check that the provided annotation has polars/patito support (we can resolve it to a default dtype). Raises ValueError if not.
Args:
annotation (type[Any] | None): python type annotation
column (Optional[str], optional): column name. Defaults to None.
| validate_annotation | python | JakobGM/patito | src/patito/_pydantic/dtypes/dtypes.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/dtypes/dtypes.py | MIT |
def is_optional(type_annotation: type[Any] | Any | None) -> bool:
"""Return True if the given type annotation is an Optional annotation.
Args:
type_annotation: The type annotation to be checked.
Returns:
True if the outermost type is Optional.
"""
return (get_origin(type_annotatio... | Return True if the given type annotation is an Optional annotation.
Args:
type_annotation: The type annotation to be checked.
Returns:
True if the outermost type is Optional.
| is_optional | python | JakobGM/patito | src/patito/_pydantic/dtypes/utils.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/dtypes/utils.py | MIT |
def unwrap_optional(type_annotation: type[Any] | Any) -> type:
"""Return the inner, wrapped type of an Optional.
Is a no-op for non-Optional types.
Args:
type_annotation: The type annotation to be dewrapped.
Returns:
The input type, but with the outermost Optional removed.
"""
... | Return the inner, wrapped type of an Optional.
Is a no-op for non-Optional types.
Args:
type_annotation: The type annotation to be dewrapped.
Returns:
The input type, but with the outermost Optional removed.
| unwrap_optional | python | JakobGM/patito | src/patito/_pydantic/dtypes/utils.py | https://github.com/JakobGM/patito/blob/master/src/patito/_pydantic/dtypes/utils.py | MIT |
def test_valids_basic_annotations() -> None:
"""Test type annotations match polars dtypes."""
# base types
assert DtypeResolver(str).valid_polars_dtypes() == STRING_DTYPES
assert DtypeResolver(int).valid_polars_dtypes() == DataTypeGroup(INTEGER_DTYPES)
assert DtypeResolver(float).valid_polars_dtypes... | Test type annotations match polars dtypes. | test_valids_basic_annotations | python | JakobGM/patito | tests/test_dtypes.py | https://github.com/JakobGM/patito/blob/master/tests/test_dtypes.py | MIT |
def test_valids_nested_annotations() -> None:
"""Test type annotations match nested polars types like List."""
assert len(DtypeResolver(list).valid_polars_dtypes()) == 0 # needs inner annotation
assert (
DtypeResolver(tuple).valid_polars_dtypes()
== DtypeResolver(list).valid_polars_dtypes()... | Test type annotations match nested polars types like List. | test_valids_nested_annotations | python | JakobGM/patito | tests/test_dtypes.py | https://github.com/JakobGM/patito/blob/master/tests/test_dtypes.py | MIT |
def test_dtype_validation() -> None:
"""Ensure python types match polars types."""
validate_polars_dtype(int, pl.Int16) # no issue
with pytest.raises(ValueError, match="Invalid dtype"):
validate_polars_dtype(int, pl.Float64)
with pytest.raises(ValueError, match="Invalid dtype"):
valid... | Ensure python types match polars types. | test_dtype_validation | python | JakobGM/patito | tests/test_dtypes.py | https://github.com/JakobGM/patito/blob/master/tests/test_dtypes.py | MIT |
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