repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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knodle-develop | knodle-develop/examples/data_preprocessing/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/entity_pairs_aka_lfs/tac_based_re_dataset_preprocessor.py | import argparse
import sys
import os
from pathlib import Path
import logging
from typing import Dict
import numpy as np
import pandas as pd
import scipy.sparse as sp
from joblib import dump
from knodle.trainer.utils import log_section
from examples.data_preprocessing.tac_based_dataset.utils.utils import count_file_li... | 10,977 | 36.986159 | 119 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/utils/utils.py | import itertools
import json
import logging
import re
from typing import Union, Tuple
import spacy
import pandas as pd
from pandas import DataFrame
ARG1 = "$ARG1"
ARG2 = "$ARG2"
PRINT_EVERY = 10000
UNKNOWN_RELATIONS_ID = 404 # id which will be assigned to unknown relations, i.e. relations that wasn't seen in KB
log... | 9,799 | 37.28125 | 120 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/utils/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/utils/lfs_reconstructor.py | import argparse
import csv
import os
import sys
from pathlib import Path
import logging
from typing import Dict, Tuple, List
from tutorials.data_preprocessing.tac_based_dataset.conll_relation_extraction_dataset.utils import (
count_file_lines, get_id, update_dict, convert_to_tacred_rel
)
logger = logging.getLogge... | 4,842 | 34.874074 | 125 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/tac_based_dataset/[WIP]_patterns_aka_lfs/tac_based_re_dataset_preprocessor.py | import argparse
import logging
import os
import re
import sys
from typing import Union
from pathlib import Path
from joblib import dump
import numpy as np
import pandas as pd
from tutorials.data_preprocessing.tac_based_dataset.utils.utils import (
get_analysed_conll_data, get_id, update_dict, get_match_matrix_row... | 6,126 | 42.764286 | 123 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/imdb_dataset/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/examples/data_preprocessing/imdb_dataset/prepare_weak_imdb_data.py | #!/usr/bin/env python
# coding: utf-8
"""
IMDB Dataset - Create Weak Supervision Sources and Get the Weak Data Annotations
This notebook shows how to use keywords as a weak supervision source on the example of a well-known IMDB Movie Review dataset, which targets a binary sentiment analysis task.
The original datase... | 10,029 | 41.863248 | 649 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/police_killing_dataset/preprocessing_with_kb.py | #!/usr/bin/env python
# coding: utf-8
"""
This tutorial shows how to find names of people killed by the police in a corpus of newspaper articles.
The corpus was created by Katherine A. Keith et al. (2017) for a similar task using distant supervision.
This dataset contains mentions of people (based on keywords related ... | 18,908 | 44.563855 | 290 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/police_killing_dataset/data_preprocessing_with_regex.py | #!/usr/bin/env python
# coding: utf-8
# Police Killing Dataset: Data Preprocessing using RegEx as rules
"""
This tutorial shows how to find names of people killed by the police in a corpus of newspaper articles.
The corpus was created by Katherine A. Keith et al. (2017) for a similar task using distant supervision.
... | 18,647 | 43.505967 | 348 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/MIMIC_CXR_dataset/prepare_mimic_cxr.py | # -*- coding: utf-8
"""
Preprocessing of MIMIC-CXR dataset
This file illustrates how weak supervision can be applied on medical images
and the corresponding reports. Since there are two sources of data (images and
reports) we establish a double layer weak supervision.
In this example the MIMIC-CXR dataset is used... | 21,914 | 37.179443 | 126 | py |
knodle-develop | knodle-develop/examples/data_preprocessing/wiennerisches_diarium_historical_dataset/data_preprocessing_wiener_diarum_toponym.py | # import libraries
import os
import re
import pandas as pd
import numpy as np
from typing import List, Tuple, Dict
from tqdm import tqdm
from joblib import dump
from minio import Minio
client = Minio("knodle.cc", secure=False)
# define the path to the folder where the data will be stored
data_path = "../../../data_f... | 7,673 | 32.077586 | 171 | py |
knodle-develop | knodle-develop/tests/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/tests/trainer/test_multi_trainer.py | from tests.trainer.generic import std_trainer_input_1
from knodle.trainer.multi_trainer import MultiTrainer
def test_auto_train(std_trainer_input_1):
(
model,
model_input_x, rule_matches_z, mapping_rules_labels_t,
y_labels
) = std_trainer_input_1
trainers = ["majority", "snorkel"... | 689 | 24.555556 | 62 | py |
knodle-develop | knodle-develop/tests/trainer/test_auto_trainer.py | from tests.trainer.generic import std_trainer_input_1
from knodle.trainer.auto_trainer import AutoTrainer
def test_auto_train(std_trainer_input_1):
(
model,
model_input_x, rule_matches_z, mapping_rules_labels_t,
y_labels
) = std_trainer_input_1
for name in AutoTrainer.registry:
... | 791 | 27.285714 | 62 | py |
knodle-develop | knodle-develop/tests/trainer/test_multi_label.py | from torch.nn import BCEWithLogitsLoss
from knodle.trainer import MajorityVoteTrainer, MajorityConfig
from tests.trainer.generic import std_trainer_input_1
def test_auto_train(std_trainer_input_1):
(
model,
model_input_x, rule_matches_z, mapping_rules_labels_t,
_
) = std_trainer_input... | 849 | 27.333333 | 106 | py |
knodle-develop | knodle-develop/tests/trainer/generic.py | import pytest
import numpy as np
import torch
from torch.utils.data import TensorDataset
from knodle.model.logistic_regression_model import LogisticRegressionModel
@pytest.fixture
def std_trainer_input_1():
num_samples = 64
num_features = 16
num_rules = 6
num_classes = 2
x_np = np.ones((num_sam... | 1,813 | 28.737705 | 92 | py |
knodle-develop | knodle-develop/tests/trainer/cleanlab/test_cl.py | from torch.nn import CrossEntropyLoss
from tests.trainer.generic import std_trainer_input_2
from knodle.trainer.cleanlab.cleanlab import CleanLabTrainer
from knodle.trainer.cleanlab.config import CleanLabConfig
def test_cleanlab_base_test(std_trainer_input_2):
(
model,
inputs_x, mapping_rules_la... | 875 | 27.258065 | 101 | py |
knodle-develop | knodle-develop/tests/trainer/wscrossweigh/test_wscw_data_preparation.py | import torch
from torch.utils.data import TensorDataset
import pytest
import numpy as np
from knodle.trainer.wscrossweigh.data_splitting_by_rules import get_rules_sample_ids, get_samples_labels_idx_by_rule_id
@pytest.fixture(scope='session')
def get_test_data():
rule_assignments_t = np.array([[1, 0, 0],
... | 5,521 | 39.903704 | 119 | py |
knodle-develop | knodle-develop/tests/trainer/wscrossweigh/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/tests/trainer/wscrossweigh/test_wscw.py | from tests.trainer.generic import std_trainer_input_2
from knodle.trainer.wscrossweigh.wscrossweigh_weights_calculator import WSCrossWeighWeightsCalculator
def test_dscw_base_test(std_trainer_input_2):
(
model,
inputs_x, mapping_rules_labels_t, train_rule_matches_z,
test_dataset, test_lab... | 695 | 26.84 | 101 | py |
knodle-develop | knodle-develop/tests/trainer/utils/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/tests/trainer/utils/test_denoise.py | import pytest
import numpy as np
from numpy.testing import assert_array_equal
from knodle.trainer.utils.denoise import activate_neighbors
def test_denoise_knn():
## case 1 ##
test_array = np.array([[0, 1], [1, 0]])
right_result = np.array([[1, 1], [1, 1]])
indices = np.array([[0, 1], [1, 0]])
de... | 1,541 | 28.09434 | 87 | py |
knodle-develop | knodle-develop/tests/trainer/snorkel/test_utils.py | import numpy as np
from scipy import sparse as ss
import torch
from torch.utils.data import TensorDataset
from knodle.trainer.snorkel.utils import (
z_t_matrix_to_snorkel_matrix,
prepare_empty_rule_matches,
add_labels_for_empty_examples
)
def test_z_t_matrix_to_snorkel_matrix():
# test dense case
... | 2,626 | 24.019048 | 111 | py |
knodle-develop | knodle-develop/tests/evaluation/majority.py | import numpy as np
from knodle.evaluation.majority import majority_sklearn_report
def test_majority_vote_no_match():
z = np.zeros((2, 4))
t = np.zeros((4, 2))
z[0, 0] = 1
z[1, 1] = 1
t[0, 0] = 1
t[1, 0] = 1
t[2, 0] = 1
t[3, 0] = 1
y = np.array([1, 1])
report = majority_skle... | 1,213 | 18.269841 | 62 | py |
knodle-develop | knodle-develop/tests/data/test_statistics.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/tests/data/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/tests/transformation/test_labels.py | import numpy as np
from knodle.transformation.labels import label_ids_to_labels
def test_label_ids_to_labels():
prediction_ids = np.array([0, 1, 1, 0])
gold_label_ids = np.array([1, 0, 0, 1])
labels2ids = {
0: "first",
1: "second"
}
predictions_truth = ["first", "second", "secon... | 581 | 25.454545 | 94 | py |
knodle-develop | knodle-develop/tests/transformation/test_rule_label_format.py | import numpy as np
from knodle.transformation.rule_label_format import transform_snorkel_matrix_to_z_t
def test_transform_snorkel_matrix_to_z_t():
lambda_matrix = np.array([[-1, 2, -1, 1], [1, -1, -1, 1], [1, 2, 0, -1]])
correct_z_matrix = np.array([[0, 1, 0, 1], [1, 0, 0, 1], [1, 1, 1, 0]])
correct_t... | 525 | 28.222222 | 83 | py |
knodle-develop | knodle-develop/tests/transformation/test_majority.py | import random
import pytest
import numpy as np
from knodle.transformation.majority import (
probabilities_to_majority_vote, z_t_matrices_to_majority_vote_probs, z_t_matrices_to_majority_vote_labels,
probabilities_to_binary_multi_labels
)
def test_probabilies_to_majority_vote_fixed():
# format: (probabi... | 4,849 | 30.493506 | 120 | py |
knodle-develop | knodle-develop/tests/transformation/test_filter.py | import numpy as np
import torch
from torch.utils.data import TensorDataset
from knodle.transformation.filter import filter_empty_probabilities, filter_probability_threshold
def test_filter_empty_probabilities():
input_ids = np.ones((3, 4))
input_ids[0, 0] = 0
input_mask = np.ones((3, 4))
input_mask[1... | 1,837 | 28.174603 | 118 | py |
knodle-develop | knodle-develop/tests/transformation/generic.py | import pytest
import torch
from torch.utils.data import TensorDataset
import numpy as np
@pytest.fixture
def filter_input():
input_ids = np.ones((3, 4))
input_ids[0, 0] = 0
input_mask = np.ones((3, 4))
input_mask[1, 1] = 0
class_probs = np.array([
[0.5, 0.5],
[0.3, 0.7],
[... | 932 | 16.942308 | 92 | py |
knodle-develop | knodle-develop/tests/transformation/test_rule_reduction.py | import numpy as np
from scipy.sparse import csr_matrix
from knodle.transformation.rule_reduction import _get_merged_matrix, reduce_rule_matches, _get_rule_by_label_iterator
def test_reduction():
# test rule iterator
mapping_rule_class_t = np.array([
[1, 0],
[0, 1],
[1, 0],
[1,... | 6,514 | 29.162037 | 117 | py |
knodle-develop | knodle-develop/tests/transformation/torch_input.py | import numpy as np
from numpy.testing import assert_array_equal
from torch import Tensor, equal
from torch.utils.data import TensorDataset
from knodle.transformation.torch_input import input_labels_to_tensordataset
def test_input_labels_to_tensordataset():
a = np.ones((4, 4))
b = np.ones((4, 3))
labels... | 604 | 27.809524 | 86 | py |
knodle-develop | knodle-develop/knodle/version.py | # This is a placeholder file and is not supposed to be maintained.
# This file will be replaced with an up-to-date value during package build via github action.
__version__ = '0.0.0'
| 183 | 45 | 93 | py |
knodle-develop | knodle-develop/knodle/__init__.py | """knodle - Knowledge infused deep learning framework"""
__author__ = "knodle <knodle@cs.univie.ac.at>"
__all__ = []
from knodle.version import __version__
import logging
logger = logging.getLogger()
handler = logging.StreamHandler()
formatter = logging.Formatter("%(asctime)s %(name)-12s %(levelname)-8s %(message)s"... | 445 | 25.235294 | 84 | py |
knodle-develop | knodle-develop/knodle/trainer/auto_trainer.py | from typing import Callable
import numpy as np
from torch.utils.data import TensorDataset
from knodle.trainer.trainer import Trainer
class AutoTrainer:
""" The factory class for creating training executors
See See https://medium.com/@geoffreykoh/implementing-the-factory-
pattern-via-dynamic-registry-and... | 1,469 | 30.956522 | 102 | py |
knodle-develop | knodle-develop/knodle/trainer/config.py | import pathlib
from typing import Callable, Dict
import os
import logging
from snorkel.classification import cross_entropy_with_probs
import torch
from torch import Tensor
from torch.optim import SGD
from torch.optim.optimizer import Optimizer
from knodle.trainer.utils.utils import check_and_return_device, set_seed
... | 6,907 | 44.447368 | 121 | py |
knodle-develop | knodle-develop/knodle/trainer/auto_config.py | from typing import Callable
from knodle.trainer.config import TrainerConfig
class AutoConfig:
""" The factory class for creating Config classes of training executors
See See https://medium.com/@geoffreykoh/implementing-the-factory-
pattern-via-dynamic-registry-and-python-decorators-479fc1537bbe
"""
... | 921 | 27.8125 | 75 | py |
knodle-develop | knodle-develop/knodle/trainer/multi_trainer.py | import copy
import logging
from typing import Callable, List, Dict
import numpy as np
from torch.utils.data import TensorDataset
from knodle.trainer import AutoTrainer
from knodle.trainer.utils import log_section
logger = logging.getLogger(__name__)
class MultiTrainer:
""" The factory class for creating train... | 1,699 | 33.693878 | 92 | py |
knodle-develop | knodle-develop/knodle/trainer/__init__.py | from knodle.trainer.config import TrainerConfig
from knodle.trainer.baseline.majority import MajorityVoteTrainer, MajorityConfig
from knodle.trainer.knn_aggregation.knn import KNNAggregationTrainer, KNNConfig
from knodle.trainer.snorkel.snorkel import SnorkelKNNAggregationTrainer, SnorkelTrainer, SnorkelConfig, Snorke... | 526 | 57.555556 | 120 | py |
knodle-develop | knodle-develop/knodle/trainer/trainer.py | import logging
import os
from abc import ABC, abstractmethod
from typing import Union, Dict, Tuple, List
import numpy as np
import skorch
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import classification_report
from torch import Tensor
from torch.nn import Module
from torch.... | 14,273 | 42.386018 | 119 | py |
knodle-develop | knodle-develop/knodle/trainer/baseline/config.py | from knodle.trainer.auto_config import AutoConfig
from knodle.trainer.config import BaseTrainerConfig
@AutoConfig.register("majority")
class MajorityConfig(BaseTrainerConfig):
def __init__(
self,
use_probabilistic_labels: bool = True,
**kwargs
):
super().__init__(**... | 435 | 28.066667 | 64 | py |
knodle-develop | knodle-develop/knodle/trainer/baseline/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/trainer/baseline/majority.py | import logging
import numpy as np
import torch.nn as nn
from torch.optim import SGD
from torch.utils.data import TensorDataset
from knodle.transformation.majority import input_to_majority_vote_input
from knodle.transformation.torch_input import input_labels_to_tensordataset
from knodle.trainer.trainer import BaseTr... | 2,436 | 37.68254 | 100 | py |
knodle-develop | knodle-develop/knodle/trainer/cleanlab/config.py | from knodle.trainer.baseline.config import MajorityConfig
from knodle.trainer.auto_config import AutoConfig
@AutoConfig.register("cleanlab")
class CleanLabConfig(MajorityConfig):
def __init__(
self,
cv_n_folds=5,
prune_method: str = 'prune_by_noise_rate',
converge_... | 2,724 | 45.982759 | 119 | py |
knodle-develop | knodle-develop/knodle/trainer/cleanlab/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/trainer/cleanlab/latent_estimation.py | import copy
import numpy as np
from sklearn.base import RegressorMixin
from tqdm import tqdm
from knodle.trainer.wscrossweigh.data_splitting_by_rules import (
k_folds_splitting_by_rules, k_folds_splitting_by_signatures
)
def estimate_cv_predicted_probabilities_split_by_rules(
model_input_x: np.ndarray,
... | 2,515 | 36.552239 | 114 | py |
knodle-develop | knodle-develop/knodle/trainer/cleanlab/cleanlab.py | import logging
import numpy as np
from cleanlab.classification import LearningWithNoisyLabels
from skorch import NeuralNetClassifier
from torch.utils.data import TensorDataset
from knodle.trainer import MajorityVoteTrainer
from knodle.trainer.auto_trainer import AutoTrainer
from knodle.trainer.cleanlab.config import ... | 4,376 | 44.59375 | 119 | py |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/wscrossweigh_weights_calculator.py | import copy
import logging
import os
import torch
from torch.utils.data import DataLoader
from joblib import dump
from knodle.trainer.baseline.majority import MajorityVoteTrainer
from knodle.trainer.utils import log_section
from knodle.trainer.wscrossweigh.data_splitting_by_rules import k_folds_splitting_by_rules
fro... | 5,533 | 43.99187 | 120 | py |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/utils.py | import logging
import random
from typing import Dict
import numpy as np
import torch
from torch.utils.data import TensorDataset
logger = logging.getLogger(__name__)
def get_labels_randomly(
rule_matches_z: np.ndarray, rule_assignments_t: np.ndarray
) -> np.ndarray:
""" Calculates sample labels basing on... | 4,180 | 36.666667 | 119 | py |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/config.py | from torch.optim import Optimizer
from knodle.trainer.baseline.config import MajorityConfig
from knodle.trainer.auto_config import AutoConfig
@AutoConfig.register("wscrossweigh")
class WSCrossWeighConfig(MajorityConfig):
def __init__(
self,
partitions: int = 2,
folds: int = 10... | 3,386 | 39.807229 | 123 | py |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/data_splitting_by_rules.py | import logging
import random
from typing import List, Dict, Union, Tuple
import scipy.sparse as sp
import numpy as np
import torch
from torch.utils.data import TensorDataset
from knodle.trainer.wscrossweigh.utils import return_unique
from knodle.transformation.torch_input import input_info_labels_to_tensordataset, in... | 14,185 | 49.483986 | 120 | py |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/trainer/wscrossweigh/wscrossweigh.py | import logging
import os
from copy import copy
import numpy as np
import torch
from joblib import load
from torch.nn import Module
from torch.optim import SGD
from torch.utils.data import TensorDataset
from knodle.trainer.auto_trainer import AutoTrainer
from knodle.trainer.baseline.majority import MajorityVoteTrainer... | 6,409 | 45.115108 | 119 | py |
knodle-develop | knodle-develop/knodle/trainer/knn_aggregation/knn.py | import os
import logging
import joblib
import numpy as np
from torch.optim import SGD
from torch.utils.data import TensorDataset
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors
from annoy import AnnoyIndex
from knodle.transformation.majority import input_to_majority_vote_input
fro... | 6,376 | 40.953947 | 115 | py |
knodle-develop | knodle-develop/knodle/trainer/knn_aggregation/config.py | import os
from knodle.trainer.baseline.config import MajorityConfig
from knodle.trainer.auto_config import AutoConfig
@AutoConfig.register("knn")
class KNNConfig(MajorityConfig):
def __init__(
self,
k: int = None,
radius: float = None,
use_approximation: bool = Fal... | 2,653 | 36.380282 | 118 | py |
knodle-develop | knodle-develop/knodle/trainer/knn_aggregation/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/trainer/utils/checks.py | import logging
from numpy import ndarray
from knodle.trainer.config import BaseTrainerConfig
def check_other_class_id(trainer_config: BaseTrainerConfig, mapping_rules_labels_t: ndarray):
# check and derive other_class_id from class mappings if neccessary
if trainer_config.other_class_id is None:
if n... | 775 | 47.5 | 115 | py |
knodle-develop | knodle-develop/knodle/trainer/utils/utils.py | import random
import logging
import torch
from torch import Tensor, argmax
from torch.utils.data import TensorDataset
import numpy as np
import matplotlib.pyplot as plt
def log_section(text: str, logger: logging, additional_info: {} = None) -> None:
"""
Prints a section
Args:
text: Text to print
... | 2,163 | 26.392405 | 88 | py |
knodle-develop | knodle-develop/knodle/trainer/utils/denoise.py | import logging
from typing import Iterable
from tqdm import tqdm
import numpy as np
import scipy.sparse as ss
logger = logging.getLogger(__name__)
def activate_neighbors(
rule_matches_z: np.ndarray, indices: Iterable[np.ndarray]
) -> np.ndarray:
"""
Take provided closest neighbors and add their rule... | 1,682 | 31.365385 | 96 | py |
knodle-develop | knodle-develop/knodle/trainer/utils/__init__.py | from knodle.trainer.utils.utils import log_section
| 51 | 25 | 50 | py |
knodle-develop | knodle-develop/knodle/trainer/snorkel/snorkel.py | from typing import Tuple
import numpy as np
from snorkel.labeling.model import LabelModel
from torch.optim import SGD
from torch.utils.data import TensorDataset
from knodle.transformation.torch_input import input_labels_to_tensordataset
from knodle.trainer.auto_trainer import AutoTrainer
from knodle.trainer.baselin... | 6,133 | 43.129496 | 115 | py |
knodle-develop | knodle-develop/knodle/trainer/snorkel/utils.py | from typing import Tuple
import numpy as np
from scipy import sparse as ss
def z_t_matrix_to_snorkel_matrix(rule_matches_z: np.ndarray, mapping_rules_labels_t: np.ndarray) -> np.ndarray:
snorkel_matrix = -1 * np.ones(rule_matches_z.shape)
if isinstance(rule_matches_z, ss.csr_matrix):
rule_matches_z ... | 2,559 | 37.208955 | 111 | py |
knodle-develop | knodle-develop/knodle/trainer/snorkel/config.py | from knodle.trainer.baseline.config import MajorityConfig
from knodle.trainer.knn_aggregation.config import KNNConfig
from knodle.trainer.auto_config import AutoConfig
@AutoConfig.register("snorkel")
class SnorkelConfig(MajorityConfig):
"""Config class for the Snorkel wrapper.
"""
def __init__(
... | 1,202 | 32.416667 | 120 | py |
knodle-develop | knodle-develop/knodle/trainer/snorkel/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/evaluation/plotting.py | import matplotlib.pyplot as plt
def draw_loss_accuracy_plot(curves: dict) -> None:
""" The function creates a plot of 4 curves and displays it"""
colors = "bgrcmyk"
color_index = 0
epochs = range(1, len(next(iter(curves.values()))) + 1)
for label, value in curves.items():
plt.plot(epochs,... | 505 | 28.764706 | 76 | py |
knodle-develop | knodle-develop/knodle/evaluation/multi_label_metrics.py | import numpy as np
from typing import Dict, List
from sklearn.metrics import precision_recall_fscore_support
def encode_to_binary(labels: List[List[int]], num_labels: int) -> np.array:
"""
Encodes the labels of each instance to binary vectors
Args:
labels: List with the labels for each instance (... | 2,256 | 33.19697 | 107 | py |
knodle-develop | knodle-develop/knodle/evaluation/statistics.py | from typing import Dict, Union, List
import pandas as pd
import numpy as np
def get_y_statistics(y_gold: Union[List, np.ndarray]) -> pd.DataFrame:
"""Returns a few statistics for a label vector.
"""
y = pd.Series(y_gold)
stats_dict = [
["num_classes", y.nunique()],
["num_samples", y_g... | 2,648 | 34.32 | 114 | py |
knodle-develop | knodle-develop/knodle/evaluation/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/evaluation/majority.py | from typing import Dict
import numpy as np
from sklearn.metrics.classification import classification_report
from knodle.transformation.majority import probabilities_to_majority_vote, z_t_matrices_to_majority_vote_probs
def majority_sklearn_report(
rule_matches_z: np.array, mapping_rules_labels_t: np.array, ... | 1,311 | 35.444444 | 110 | py |
knodle-develop | knodle-develop/knodle/evaluation/other_class_metrics.py | import logging
from collections import Counter
from typing import Dict, List
import numpy as np
from knodle.transformation.labels import label_ids_to_labels
logger = logging.getLogger(__name__)
def classification_report_other_class(
y_true: np.array, y_pred: np.array, ids2labels: Dict, other_class_id: int... | 4,095 | 38.76699 | 116 | py |
knodle-develop | knodle-develop/knodle/utils/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/model/logistic_regression_model.py | import torch
from torch import nn
class LogisticRegressionModel(nn.Module):
def __init__(self, input_dim: int, output_classes: int):
super(LogisticRegressionModel, self).__init__()
self.linear = torch.nn.Linear(input_dim, output_classes)
def forward(self, x):
x = x.float()
out... | 365 | 25.142857 | 64 | py |
knodle-develop | knodle-develop/knodle/model/logisitc_regression_with_emb_layer.py | import torch
from torch import nn
import numpy as np
class LogisticRegressionModel(nn.Module):
def __init__(
self,
input_size: int,
word_input_dim: int,
word_output_dim: int,
word_embedding_matrix: np.ndarray,
output_classes: int,
):
... | 1,040 | 30.545455 | 78 | py |
knodle-develop | knodle-develop/knodle/model/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/model/bidirectional_lstm_model.py | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence
from torch.nn.utils.rnn import pad_packed_sequence
class BidirectionalLSTM(nn.Module):
def __init__(
self,
word_input_dim,
word_output_dim,
word_embedding_matrix,
num_... | 2,448 | 31.653333 | 100 | py |
knodle-develop | knodle-develop/knodle/model/EarlyStopping/__init__.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0, name="checkpoint"):
"""
Args:
patience (int): How long to wait after last time vali... | 1,854 | 34.673077 | 108 | py |
knodle-develop | knodle-develop/knodle/transformation/labels.py | from typing import Dict
import numpy as np
def label_ids_to_labels(predictions: np.ndarray, labels: np.ndarray, ids2labels: Dict) -> [np.ndarray, np.ndarray]:
"""
Prepare data for string label based evaluation.
Args:
predictions: predicted label ids
labels: gold label ids
ids2labe... | 716 | 31.590909 | 115 | py |
knodle-develop | knodle-develop/knodle/transformation/filter.py | from typing import Tuple, Union, List
import numpy as np
from torch.utils.data import TensorDataset
def filter_tensor_dataset_by_indices(dataset: TensorDataset, filter_ids: Union[np.ndarray, List[int]]) -> TensorDataset:
"""Filters each tensor of a TensorDataset, given some "filter_ids".
Args:
datas... | 2,543 | 39.380952 | 120 | py |
knodle-develop | knodle-develop/knodle/transformation/rule_label_format.py | import numpy as np
def transform_snorkel_matrix_to_z_t(class_matrix: np.ndarray) -> [np.ndarray, np.ndarray]:
"""Takes a matrix in format used by e.g. Snorkel (https://github.com/snorkel-team/snorkel)
and transforms it to z / t matrices. Format
- class_matrix_ij = -1, iff the rule doesn't apply
... | 1,188 | 36.15625 | 94 | py |
knodle-develop | knodle-develop/knodle/transformation/__init__.py | 0 | 0 | 0 | py | |
knodle-develop | knodle-develop/knodle/transformation/majority.py | import logging
import random
import warnings
import numpy as np
import scipy.sparse as sp
from torch.utils.data import TensorDataset
from knodle.transformation.filter import filter_empty_probabilities, filter_probability_threshold
logger = logging.getLogger(__name__)
def probabilities_to_majority_vote(
pro... | 10,110 | 46.027907 | 125 | py |
knodle-develop | knodle-develop/knodle/transformation/torch_input.py | import numpy as np
import torch
from torch.utils.data import TensorDataset
def input_labels_to_tensordataset(model_input_x: TensorDataset, labels: np.ndarray) -> TensorDataset:
"""
This function takes Dataset with data features (num_samples x features dimension x features) and
labels (num_samples x labels... | 1,667 | 42.894737 | 115 | py |
knodle-develop | knodle-develop/knodle/transformation/rule_reduction.py | import logging
from typing import Union, Dict, Iterable
import numpy as np
from scipy import sparse as ss
matrix = Union[np.ndarray, ss.csr_matrix]
logger = logging.getLogger(__name__)
def reduce_rule_matches(
rule_matches_z: matrix, mapping_rules_labels_t: matrix, drop_rules: bool = False,
max_rules: int ... | 11,722 | 44.088462 | 122 | py |
FantasticNetworksNoData | FantasticNetworksNoData-main/train.py | from __future__ import print_function
import os
import json
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.optim as optim
import datetime
from datetime import datetime
import numpy as np
import copy
from torchvision import datasets, transforms, models... | 18,369 | 35.161417 | 180 | py |
FantasticNetworksNoData | FantasticNetworksNoData-main/models/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import linalg as LA
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, HW, stride=1):
super(BasicBlock, self).__init__()
#self.kwargs = kwargs
self.conv1 = nn.Conv2d(in_planes,... | 5,019 | 32.691275 | 110 | py |
klayout | klayout-master/setup.py | """
KLayout standalone Python module setup script
Copyright (C) 2006-2023 Matthias Koefferlein
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
... | 35,093 | 34.592292 | 161 | py |
klayout | klayout-master/build4mac.py | macbuild/build4mac.py | 21 | 21 | 21 | py |
klayout | klayout-master/makeDMG4mac.py | macbuild/makeDMG4mac.py | 23 | 23 | 23 | py |
klayout | klayout-master/src/pymod/distutils_src/pya/__init__.py | # import all packages from klayout, such as klayout.db and klayout.tl
# WARNING: doing it manually until it becomes impractical
# TODO: We need a specification document explaining what should go into pya
from klayout.db import * # noqa
from klayout.lib import * # noqa
from klayout.tl import * # noqa
from klayout.rd... | 373 | 36.4 | 75 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/__init__.py |
from .tl import __version__
| 30 | 6.75 | 27 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/db/__init__.py | import functools
from typing import Type
import klayout.dbcore
from klayout.dbcore import *
from klayout.db.pcell_declaration_helper import PCellDeclarationHelper
__all__ = klayout.dbcore.__all__ + ["PCellDeclarationHelper"] # type: ignore
# If class has from_s, to_s, and assign, use them to
# enable serialization.... | 718 | 33.238095 | 82 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/db/pcell_declaration_helper.py | from klayout.db import Trans, PCellDeclaration, PCellParameterDeclaration
class _PCellDeclarationHelperLayerDescriptor(object):
"""
A descriptor object which translates the PCell parameters into class attributes
"""
def __init__(self, param_index):
self.param_index = param_index
def __ge... | 8,886 | 28.623333 | 104 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/tl/__init__.py | import klayout.tlcore
from klayout.tlcore import *
__all__ = klayout.tlcore.__all__
| 85 | 16.2 | 32 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/lay/__init__.py | import klayout.dbcore # enables stream reader plugins
import klayout.laycore
from klayout.laycore import *
__all__ = klayout.laycore.__all__
| 144 | 19.714286 | 54 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/lib/__init__.py | import klayout.libcore
from klayout.libcore import *
__all__ = klayout.libcore.__all__
| 88 | 16.8 | 33 | py |
klayout | klayout-master/src/pymod/distutils_src/klayout/rdb/__init__.py | import klayout.rdbcore
from klayout.rdbcore import *
__all__ = klayout.rdbcore.__all__
| 88 | 16.8 | 33 | py |
klayout | klayout-master/src/lay/lay/macro_templates/new_python_file.py |
# Enter your Python code here
| 32 | 7.25 | 29 | py |
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