python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_marginal_3agents_config import (
FurnMoveVision3AgentUncoordinatedExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_marginal_3agents_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_mixture_nocl_norot_config import (
FurnMoveMixtureNoRotationsNoCLExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_mixture_nocl_norot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_marginal_cl_norot_config import (
FurnMoveGridExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(Sa... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_marginal_cl_norot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_bigcentral_nocl_norot_config import (
FurnMoveNoRotationsExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class Ev... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_bigcentral_nocl_norot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_marginalnocomm_nocl_rot_config import (
FurnMoveExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfi... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_marginalnocomm_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_mixture_3agents_config import (
FurnMove3AgentMixtureExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalCo... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_mixture_3agents_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_bigcentral_nocl_rot_config import (
FurnMoveBigCentralVisionExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
cla... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_bigcentral_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_bigcentral_nocl_rot_config import (
FurnMoveBigCentralNoCLExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class E... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_bigcentral_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_mixture_cl_rot_config import (
FurnMoveGridMixtureExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfi... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_mixture_cl_rot_config.py |
import os
from typing import Optional, List, Dict
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_ai2thor.ai2thor_environment import AI2ThorEnvironment
from rl_base import Episode
from rl_multi_agent.experiments.furnmove_vision_mixture_cl_rot_config import (
FurnMoveMixt... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_mixture_cl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_mixture_nocl_rot_config import (
FurnMoveExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(SaveF... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_mixture_nocl_rot_config_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_marginal_nocl_rot_config import (
FurnMoveExperimentConfig,
)
from rl_multi_agent.furnmove_eval_experiments.furnmove_vision_mixture_cl_rot_config... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_marginal_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_mixture_nocl_rot_config import (
FurnMoveGridExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(Sav... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_mixture_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_mixture4mix_cl_rot_config import (
FurnMoveExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(Sav... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_mixture4mix_cl_rot_pass_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_marginal_nocl_norot_config import (
FurnMoveMarginalNoRotationsExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
cl... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_marginal_nocl_norot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_marginal_nocl_rot_config import (
FurnMoveGridExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(Sa... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_marginal_nocl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_central_3agents_config import (
FurnMove3AgentCentralExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalCo... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_central_3agents_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_bigcentral_cl_rot_config import (
FurnMoveBigCentralExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalCon... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_bigcentral_cl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_marginal_cl_rot_config import (
FurnMoveVisionMarginalWithCLExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
cla... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_marginal_cl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_grid_bigcentral_cl_norot_config import (
FurnMoveNoRotationsExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class Eval... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_grid_bigcentral_cl_norot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_mixture2mix_cl_rot_config import (
FurnMoveExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class EvalConfig(Sav... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_mixture2mix_cl_rot_config.py |
import os
from typing import Optional
from constants import SPLIT_TO_USE_FOR_EVALUATION, ABS_PATH_TO_FINAL_FURNMOVE_CKPTS
from rl_multi_agent.experiments.furnmove_vision_bigcentral_cl_rot_config import (
FurnMoveBigCentralVisionExperimentConfig,
)
from rl_multi_agent.furnmove_utils import SaveFurnMoveMixin
class... | cordial-sync-master | rl_multi_agent/furnmove_eval_experiments/furnmove_vision_bigcentral_cl_rot_config.py |
from typing import Dict, Sequence, Tuple
import numpy as np
import constants
def manhattan_dists_between_positions(
positions: Sequence[Dict[str, float]], grid_size: float
):
dists_in_steps = [[] for _ in range(len(positions))]
for i in range(len(positions) - 1):
p0 = positions[i]
for j ... | cordial-sync-master | rl_ai2thor/ai2thor_utils.py |
cordial-sync-master | rl_ai2thor/__init__.py | |
"""A wrapper for engaging with the THOR environment."""
import copy
import math
import os
import random
import sys
import warnings
from collections import defaultdict
from typing import Tuple, Dict, List, Set, Union, Any, Optional, Mapping
import ai2thor.server
import networkx as nx
import numpy as np
from ai2thor.co... | cordial-sync-master | rl_ai2thor/ai2thor_environment.py |
from abc import abstractmethod, ABC
from typing import Dict, Any, Optional, Sequence, Tuple, List
from rl_ai2thor.ai2thor_environment import AI2ThorEnvironment
from rl_base import Episode
from rl_base.episode import MultiAgentEpisode
class AI2ThorEpisode(Episode[AI2ThorEnvironment]):
def __init__(
self,
... | cordial-sync-master | rl_ai2thor/ai2thor_episodes.py |
import copy
import itertools
import math
# noinspection PyUnresolvedReferences
import random
import re
import warnings
from typing import List, Dict, Optional, Any, Set, Tuple, Union
import ai2thor.server
import cv2
import numpy as np
from ai2thor.server import Event, MultiAgentEvent
from scipy.ndimage.measurements i... | cordial-sync-master | rl_ai2thor/ai2thor_gridworld_environment.py |
from __future__ import print_function, division
import math
from typing import Optional, List
import matplotlib
matplotlib.use("TkAgg", force=False)
import matplotlib.pyplot as plt
from matplotlib import animation
import pylab
from PIL import Image, ImageDraw
import copy
import numpy as np
import textwrap
import ... | cordial-sync-master | utils/visualization_utils.py |
from __future__ import division
import glob
import itertools
import json
import logging
import math
import os
import re
import shutil
import subprocess
from typing import Optional, Tuple, Sequence, Dict, Union
import numpy as np
import torch
import constants
try:
from reprlib import repr
except ImportError:
... | cordial-sync-master | utils/misc_util.py |
cordial-sync-master | utils/__init__.py | |
"""Contains a bunch of utilities useful during network training in PyTorch."""
import math
from collections import deque
from typing import Dict, Union, List, Tuple, Any, Callable
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from torchvision import transforms
def init_orthogonal(tensor... | cordial-sync-master | utils/net_util.py |
#!/usr/bin/env python
import subprocess
import shlex
import re
import platform
import tempfile
import os
import sys
def pci_records():
records = []
command = shlex.split('lspci -vmm')
output = subprocess.check_output(command).decode()
for devices in output.strip().split("\n\n"):
record = {}
... | cordial-sync-master | utils/startx.py |
import argparse
from constants import ABS_PATH_TO_LOCAL_THOR_BUILD
def str2bool(v):
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
def pa... | cordial-sync-master | utils/flag_parser.py |
import numpy as np
class ReservoirSampler(object):
"""Finds a random subset k elements from a stream of data in O(k) space.
See https://en.wikipedia.org/wiki/Reservoir_sampling.
"""
def __init__(self, k):
self.samples = []
self.num_seen = 0
self.k = k
def add(self, item):... | cordial-sync-master | utils/debug_util.py |
aspire-main | examples/__init__.py | |
"""
Script to demo example usage of the Aspire Multi-Vector encoder which
represents documents via contextual sentence embeddings and uses an
optimal transport based Wasserstein distance to compute document similarity:
allenai/aspire-contextualsentence-multim-biomed and
allenai/aspire-contextualsentence-multim-compsci... | aspire-main | examples/ex_aspire_consent_multimatch.py |
"""
Script to demo example usage of the Aspire Multi-Vector encoder which
represents documents via contextual sentence embeddings, i.e the models:
allenai/aspire-contextualsentence-singlem-biomed and
allenai/aspire-contextualsentence-singlem-compsci
Models released at:
https://huggingface.co/allenai/aspire-contextuals... | aspire-main | examples/ex_aspire_consent.py |
"""
Script to demo example usage of the Aspire Bi-encoder with linear mixing across
BERT layers, i.e the models: aspire-biencoder-biomed-scib-full,
aspire-biencoder-biomed-spec-full, and aspire-biencoder-compsci-spec-full.
*-all models released as zip folders alongside:
https://huggingface.co/allenai/aspire-biencoder-... | aspire-main | examples/ex_aspire_bienc.py |
# For relative imports to work in Python 3.6
import os, sys; sys.path.append(os.path.dirname(os.path.realpath(__file__))) | aspire-main | src/__init__.py |
"""
For the faceted similarity models:
Call code from everywhere, read data, initialize model, train model and make
sure training is doing something meaningful, predict with trained model and run
evaluation
"""
import argparse, os, sys
import logging
import codecs, pprint, json
import torch
from . import batchers, trai... | aspire-main | src/learning/main_sentsim.py |
"""
Classes to stream int-mapped data from file in batches, pad and sort them (as needed)
and return batch dicts for the models.
"""
import codecs
import sys
import re
import numpy as np
import torch
from transformers import AutoTokenizer
from . import data_utils as du
replace_sep = re.compile(r'\[SEP\]')
class Ge... | aspire-main | src/learning/batchers.py |
aspire-main | src/learning/__init__.py | |
"""
For the fine-grained similarity models:
Call code from everywhere, read data, initialize model, train model and make
sure training is doing something meaningful.
"""
import argparse, os, sys
import codecs, pprint, json
import datetime
import logging
import torch
import torch.multiprocessing as torch_mp
import torch... | aspire-main | src/learning/main_fsim.py |
"""
Utilities to feed and initialize the models.
"""
from __future__ import unicode_literals
from __future__ import print_function
import logging
from sklearn import metrics as skmetrics
import torch
def batched_loss_ddp(model, batcher, loss_helper, logger, batch_size, ex_fnames, num_examples):
"""
Make pred... | aspire-main | src/learning/predict_utils.py |
"""
Miscellaneous utilities to read and work with the json files and such.
Stuff multiple functions use.
"""
import sys
import os
import errno
import json
import logging
import numpy as np
# Use mpl on remote.
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def print_sorted_dict(d, out_file):
... | aspire-main | src/learning/data_utils.py |
"""
Train the passed model given the data and the batcher and save the best to disk.
"""
from __future__ import print_function
import os
import logging
import time, copy
from collections import defaultdict
import numpy as np
import torch
import torch.distributed as dist
import torch.optim as optim
import transformers
... | aspire-main | src/learning/trainer.py |
aspire-main | src/learning/facetid_models/__init__.py | |
"""
Models which learn contextual sentence representations of paper abstracts.
"""
from collections import namedtuple
import numpy as np
import torch
from torch import nn as nn
from torch.autograd import Variable
from torch.nn import functional
from transformers import AutoModel
from . import pair_distances as pair_di... | aspire-main | src/learning/facetid_models/disent_models.py |
"""
Models which learn sentence representations.
Mostly a bunch of wrappers for raw bert models which are finetuned.
"""
import torch
from torch import nn as nn
from torch.autograd import Variable
from transformers import AutoModel
class SentBERTWrapper(nn.Module):
"""
Pass sentence through encoder and minimi... | aspire-main | src/learning/facetid_models/sentsim_models.py |
"""
Functions for computing distances between documents with fine grained representations.
"""
import math
import numpy as np
import torch
from torch.autograd import Variable
from torch.nn import functional
import geomloss
from ..models_common import activations
class AllPairMaskedWasserstein:
def __init__(self,... | aspire-main | src/learning/facetid_models/pair_distances.py |
"""
Functions used across models.
"""
import numpy as np
import torch
from torch.nn import functional
from torch.autograd import Variable
def masked_softmax(batch_scores, target_lens):
"""
Given the scores for the assignments for every example in the batch apply
a masked softmax for the variable number of... | aspire-main | src/learning/models_common/activations.py |
aspire-main | src/learning/models_common/__init__.py | |
"""
Generic layers used across models.
"""
import torch
from torch import nn as nn
from torch.nn import functional
import collections
from . import activations
non_linearities = {
'tanh': torch.nn.Tanh,
'relu': torch.nn.ReLU,
'sigmoid': torch.nn.Sigmoid,
'softplus': torch.nn.Softplus
}
class FeedForw... | aspire-main | src/learning/models_common/generic_layers.py |
"""
Build abstract or sentence embeddings from own trained models or a pretrained model
and save to disk to use for ranking.
"""
import os
import sys
import logging
import re
import time
import codecs, json
import argparse
import torch
from transformers import AutoModel, AutoTokenizer
import numpy as np
from sentence_t... | aspire-main | src/pre_process/pre_proc_buildreps.py |
"""
Process the RELISH dataset.
"""
import os
import codecs
import json
import csv
import pandas as pd
import random
import spacy
scispacy_model = spacy.load("en_core_sci_sm")
scispacy_model.add_pipe('sentencizer')
def annotation_pmids(in_path):
"""
Write out pmids of the RELISH documents.
:param in_path... | aspire-main | src/pre_process/pre_proc_relish.py |
"""
Explore the GORC corpus for corpora included and such.
"""
import os
import ast
import argparse
import time
import gzip
import multiprocessing as mp
import collections
import pprint
import pickle
import codecs, json
import csv
import pandas as pd
import spacy
import data_utils as du
import pp_settings as pps
scis... | aspire-main | src/pre_process/pre_proc_gorc.py |
# For relative imports to work in Python 3.6
import os, sys; sys.path.append(os.path.dirname(os.path.realpath(__file__))) | aspire-main | src/pre_process/__init__.py |
# Constants for filtering absracts for training data.
MIN_ABS_LEN = 3
MAX_ABS_LEN = 20
MAX_NUM_TOKS = 80
MIN_NUM_TOKS = 4
| aspire-main | src/pre_process/pp_settings.py |
"""
Process the TREC-COVID dataset into a form i use.
"""
import os
import codecs
import json
import collections
import random
import sys
import xml.etree.ElementTree as ET
import pandas as pd
import csv
import spacy
import data_utils as du
scispacy_model = spacy.load("en_core_sci_sm")
scispacy_model.add_pipe('senten... | aspire-main | src/pre_process/pre_proc_treccovid.py |
"""
Miscellaneous utilities to read and work with the json files and such.
Stuff multiple functions use.
"""
import sys
import os
import errno
import pandas as pd
class DirIterator:
def __init__(self, root_path, yield_list, args=None, max_count=None, ):
"""
Generator over the file names. Typicall... | aspire-main | src/pre_process/data_utils.py |
"""
Functions to work with co-citations in each area.
"""
import os
import random
import math
import argparse
import time
import collections
import itertools
import re
import pprint
import pickle
import codecs, json
import pandas as pd
import torch
import numpy as np
# import spacy
from sentence_transformers import Sen... | aspire-main | src/pre_process/pre_proc_cocits.py |
"""
Generate rankings over randidates for queries for different datasets and trained models
or baselines. There are three types of functions here: one assumes a set of embeddings
from a model stored to disk and ranks based on distance/similarity metrics of these
embeddings, another type of function uses a more complex ... | aspire-main | src/pre_process/pp_gen_nearest.py |
"""
Process the RELISH dataset.
"""
import os
import codecs
import json
import collections
import csv
import pandas as pd
import spacy
scispacy_model = spacy.load("en_core_sci_sm")
scispacy_model.add_pipe('sentencizer')
def scidocs2myjson(in_path, out_path, dataset_name):
"""
- Write out jsonl file of abstr... | aspire-main | src/pre_process/pre_proc_scidocs.py |
from PURE.shared.const import task_ner_labels, get_labelmap
from PURE.entity.models import EntityModel
import codecs
import json
import os
from scipy.special import softmax
from collections import namedtuple
from tqdm import tqdm
import argparse
from typing import List
### constants ###
TASK_NAME = 'scierc'
NUM_LABELS... | aspire-main | src/pre_process/extract_entities.py |
"""
Read in abstracts and co-citation sentences and print similarities of
abstract sentences to co-citation sentences.
This is a quick script to examine training data placed in pre-process
so the imports work.
"""
import os
import codecs, json
import numpy as np
import scipy
from scipy import special, spatial
import to... | aspire-main | src/pre_process/print_cociteabs_sims.py |
"""
From: https://gist.github.com/bwhite/3726239#file-rank_metrics-py
"""
import numpy as np
def mean_reciprocal_rank(rs):
"""Score is reciprocal of the rank of the first relevant item
First element is 'rank 1'. Relevance is binary (nonzero is relevant).
Example from http://en.wikipedia.org/wiki/Mean_r... | aspire-main | src/evaluation/rank_metrics.py |
# For relative imports to work in Python 3.6
import os, sys; sys.path.append(os.path.dirname(os.path.realpath(__file__))) | aspire-main | src/evaluation/__init__.py |
"""
Evaluate the rankings generated by sentence similarity models.
"""
import sys
import os
import errno
import argparse
import statistics
import codecs
import json
import csv
import comet_ml as cml
from scipy import stats as scipystats
import rank_metrics as rm
facet2folds = {
"background": {"fold1_dev": ["3264... | aspire-main | src/evaluation/ranking_eval.py |
import argparse
import collections
from tqdm import tqdm
from src.evaluation.utils.models import get_model, SimilarityModel
from src.evaluation.utils.utils import *
from src.evaluation.utils.datasets import EvalDataset
from data_utils import create_dir
from typing import Union
import pandas as pd
from src.evaluation.ut... | aspire-main | src/evaluation/evaluate.py |
"""
From: https://gist.github.com/bwhite/3726239#file-rank_metrics-py
"""
import numpy as np
def mean_reciprocal_rank(rs):
"""Score is reciprocal of the rank of the first relevant item
First element is 'rank 1'. Relevance is binary (nonzero is relevant).
Example from http://en.wikipedia.org/wiki/Mean_r... | aspire-main | src/evaluation/utils/metrics.py |
from abc import ABCMeta, abstractmethod
from ex_aspire_consent import AspireConSent, AutoTokenizer, prepare_abstracts, AutoModel
from ex_aspire_consent_multimatch import AllPairMaskedWasserstein
from src.learning.facetid_models import disent_models
from src.learning import batchers
from collections import namedtuple
fr... | aspire-main | src/evaluation/utils/models.py |
import os
import codecs
import json
import pandas as pd
from typing import Dict, Union
class EvalDataset:
"""
Class for datasets used in evaluation
"""
def __init__(self, name: str, root_path: str):
"""
:param name: Name of dataset
:param root_path: Path where dataset files sit... | aspire-main | src/evaluation/utils/datasets.py |
aspire-main | src/evaluation/utils/__init__.py | |
import codecs
import os
import json
from src.evaluation.utils.datasets import EvalDataset
from data_utils import create_dir
from typing import Dict
FACETS = ('background', 'method', 'result')
def batchify(dataset: Dict, batch_size: int):
"""
Splits dataset into batch size groups
:param dataset: dict of {p... | aspire-main | src/evaluation/utils/utils.py |
from setuptools import find_packages, setup
def parse_requirements_file(path):
requirements = []
with open(path) as requirements_file:
import re
def fix_url_dependencies(req: str) -> str:
"""Pip and setuptools disagree about how URL dependencies should be handled."""
m... | catwalk-main | setup.py |
import argparse
from tango import Workspace
from tango.common.logging import initialize_logging
from catwalk.steps import PredictStep, CalculateMetricsStep
from catwalk.tasks import TASK_SETS
SHOTS = [0, 1, 2, 4, 8, 16, 32]
DEFAULT_TASKS = {
"arc_challenge",
"arc_easy",
"boolq",
"copa",
#"headqa... | catwalk-main | experiments/num_shots.py |
import argparse
from tango import Workspace
from tango.common.logging import initialize_logging
def main():
initialize_logging(log_level="WARNING")
parser = argparse.ArgumentParser()
parser.add_argument('--run', '-r', type=str, required=True)
parser.add_argument('--step', '-s', type=str, default="ta... | catwalk-main | experiments/everything/print_results.py |
import pytest
def suite_A(test_method):
return pytest.mark.suite_A(test_method)
def suite_B(test_method):
return pytest.mark.suite_B(test_method)
def suite_C(test_method):
return pytest.mark.suite_C(test_method)
def suite_D(test_method):
return pytest.mark.suite_D(test_method)
| catwalk-main | tests/util.py |
catwalk-main | tests/__init__.py | |
import pytest
from catwalk import MODELS
from catwalk.steps import PredictStep, CalculateMetricsStep
from .util import suite_A
task_names = [
"arc_challenge",
"boolq",
"copa",
"headqa_en",
"hellaswag",
"lambada",
"mc_taco",
"mrpc",
"eai::multirc",
"openbookqa",
"qnli",
... | catwalk-main | tests/test_steps.py |
import inspect
from typing import Any, Dict, cast
import pytest
import catwalk.models
import catwalk.tasks
from catwalk.task import InstanceFormat
from catwalk.tasks.huggingface import HFMCInstance
from .util import suite_B, suite_C
# These are tasks are known to fail for now due to an unreachable server.
known_fai... | catwalk-main | tests/test_all_tasks.py |
import torch
from transformers import AdamW
from catwalk import MODELS, TASKS
from catwalk.steps import CalculateMetricsStep, FinetuneStep, PredictStep
from .util import suite_D
@suite_D
def test_training():
model = MODELS["rc::tiny-gpt2"]
task = TASKS["piqa"]
instances = task.get_split("train")[:16]
... | catwalk-main | tests/test_training.py |
import pytest
import catwalk.__main__
from catwalk.steps import CalculateMetricsStep, PredictStep
from .util import suite_C
@suite_C
def test_squad():
args = catwalk.__main__._parser.parse_args(
[
"--model",
"bert-base-uncased",
"--task",
"squad",
... | catwalk-main | tests/test_spotchecks.py |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
from datetime import datetime
# -- Path setup -------------------... | catwalk-main | docs/source/conf.py |
from datetime import datetime
from pathlib import Path
from catwalk.version import VERSION
def main():
changelog = Path("CHANGELOG.md")
with changelog.open() as f:
lines = f.readlines()
insert_index: int
for i in range(len(lines)):
line = lines[i]
if line.startswith("## Unre... | catwalk-main | scripts/prepare_changelog.py |
# encoding: utf-8
"""
Prepares markdown release notes for GitHub releases.
"""
import os
from typing import List
import packaging.version
TAG = os.environ["TAG"]
ADDED_HEADER = "### Added 🎉"
CHANGED_HEADER = "### Changed ⚠️"
FIXED_HEADER = "### Fixed ✅"
REMOVED_HEADER = "### Removed 👋"
def get_change_log_notes... | catwalk-main | scripts/release_notes.py |
import math
from typing import (
Union,
Dict,
Any,
Optional,
Sequence,
Iterable,
List,
)
from collections import defaultdict
from random import Random
import tango
import transformers.optimization
from tango import Step, JsonFormat
from tango.common import Lazy, DatasetDict
from tango.commo... | catwalk-main | catwalk/steps.py |
from abc import ABC
from dataclasses import dataclass
from enum import Enum
from functools import partial
from random import Random
from typing import Dict, Any, Optional, Sequence, Union, List, Callable, Mapping, Tuple, Iterable
import torchmetrics
from mypy_extensions import KwArg
from tango.common import Registrabl... | catwalk-main | catwalk/task.py |
_MAJOR = "0"
_MINOR = "2"
# On main and in a nightly release the patch should be one ahead of the last
# released build.
_PATCH = "2"
# This is mainly for pre-releases which have the suffix "rc[0-9]+".
_SUFFIX = ""
VERSION_SHORT = "{0}.{1}".format(_MAJOR, _MINOR)
VERSION = "{0}.{1}.{2}{3}".format(_MAJOR, _MINOR, _PATC... | catwalk-main | catwalk/version.py |
import logging
from dataclasses import dataclass, field
from typing import Optional, Dict, TypeVar, Type, Any
import torch
import transformers
from cached_path import cached_path
from tango.common import det_hash
logger = logging.getLogger(__name__)
@dataclass
class TransformerSpec:
cls: type
model_name: st... | catwalk-main | catwalk/cached_transformers.py |
from catwalk.model import Model
from catwalk.models import MODELS
from catwalk.task import Task
from catwalk.tasks import TASKS
| catwalk-main | catwalk/__init__.py |
import inspect
from abc import ABC
from copy import deepcopy
from typing import Sequence, Dict, Any, Iterator, Tuple, List, Optional, Union
import torch
from tango.common import Registrable, Tqdm
from tango.common.det_hash import DetHashWithVersion
from catwalk.task import Task
Instance = Dict[str, Any]
def tenso... | catwalk-main | catwalk/model.py |
import argparse
from tango import Workspace
from tango.common.logging import initialize_logging
from catwalk.steps import TabulateMetricsStep, FinetuneStep
from catwalk.tasks import TASK_SETS
def main():
initialize_logging()
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, re... | catwalk-main | catwalk/train.py |
import argparse
from tango import Workspace
from tango.common.logging import initialize_logging
from catwalk.steps import TabulateMetricsStep
from catwalk.tasks import TASK_SETS
_parser = argparse.ArgumentParser()
_parser.add_argument('--model', type=str, required=True)
_parser.add_argument('--task', type=str, narg... | catwalk-main | catwalk/__main__.py |
from typing import Optional, cast, List
from tango.integrations.torch import TrainCallback
from catwalk import Task, Model
from catwalk.tasks import short_name_for_task_object
class CatwalkEvaluationCallback(TrainCallback):
def __init__(
self,
*args,
tasks: List[Task],
eval_limit... | catwalk-main | catwalk/training_callback.py |
from typing import Union, Optional, Dict, Any
import torch
from torchmetrics.aggregation import BaseAggregator
class PerplexityMetric(BaseAggregator):
def __init__(
self,
nan_strategy: Union[str, float] = "warn",
**kwargs: Dict[str, Any],
):
super().__init__("sum", [], nan_str... | catwalk-main | catwalk/metrics/perplexity.py |
from catwalk.metrics.entropy import EntropyMetric
from catwalk.metrics.perplexity import PerplexityMetric
from catwalk.metrics.accuracy import AccuracyMetric
from catwalk.metrics.accuracy import RelativeAccuracyImprovementMetric | catwalk-main | catwalk/metrics/__init__.py |
from typing import Union, List
import torch
from torchmetrics.aggregation import BaseAggregator
class AccuracyMetric(BaseAggregator):
"""
Unfortunately torchmetrics' multilabel accuracy makes you decide on the number of possible labels
beforehand. We need a metric that does not require this.
"""
... | catwalk-main | catwalk/metrics/accuracy.py |
import math
from typing import Union, Optional, Any, Dict
import torch
from torchmetrics.aggregation import BaseAggregator
class EntropyMetric(BaseAggregator):
def __init__(
self,
base: int = 2, # Does anyone ever use anything but 2 here?
nan_strategy: Union[str, float] = "warn",
... | catwalk-main | catwalk/metrics/entropy.py |
from catwalk.tasks import HFDatasetsTask
from datasets import load_dataset
import functools
from typing import Optional
class MrqaTask(HFDatasetsTask):
TEST_DATASETS = {"race", "drop", "bioasq", "relationextraction", "textbookqa", "duorc.paraphraserc"}
DEV_DATASETS = {"newsqa", "searchqa", "triviaqa-web", "nat... | catwalk-main | catwalk/tasks/mrqa.py |
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