python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
"""
Django settings for elementary project.
Generated by 'django-admin startproject' using Django 1.8.
For more information on this file, see
https://docs.djangoproject.com/en/1.8/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.8/ref/settings/
"""
from elemen... | elementary-master | django/elementary/settings/base.py |
from setuptools import setup
_REQUIRED = [
"tqdm",
"openai",
"manifest-ml",
]
setup(
name="evaporate",
version="0.0.1",
description="evaporating data lakes with foundation models",
author="simran brandon sabri avanika andrew immanuel chris",
packages=["evaporate"],
install_requires... | evaporate-main | setup.py |
import re
import argparse
import random
from bs4 import BeautifulSoup
from collections import Counter, defaultdict
def set_profiler_args(profiler_args):
parser = argparse.ArgumentParser(
"LLM profiler.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
... | evaporate-main | evaporate/profiler_utils.py |
import argparse
import os
def get_args(database_name, BASE_DATA_DIR = "/data/evaporate/"):
parser = argparse.ArgumentParser(
"LLM explorer.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--overwrite_cache",
type=bool,
default=... | evaporate-main | evaporate/configs.py |
import numpy as np
from collections import Counter, defaultdict
def text_f1(preds=[], golds=[], attribute= ''):
"""Compute average F1 of text spans.
Taken from Squad without prob threshold for no answer.
"""
total_f1 = 0
total_recall = 0
total_prec = 0
f1s = []
for pred, gold in zip(pre... | evaporate-main | evaporate/evaluate_synthetic_utils.py |
import os
import math
import json
import pickle
import html
from bs4 import BeautifulSoup
from collections import Counter, defaultdict
from utils import get_file_attribute
from evaluate_synthetic_utils import text_f1
# Compute recall from two sets
def set_recall(pred, gt):
return len(set(pred) & set(gt)) / len(s... | evaporate-main | evaporate/evaluate_synthetic.py |
import json
import math
import statistics
import random
from tqdm import tqdm
from collections import Counter, defaultdict
from typing import List, Dict, Tuple, Set
from prompts import Step, SCHEMA_ID_PROMPTS
from utils import apply_prompt
from profiler_utils import clean_metadata
def directly_extract_from_chunks_w_... | evaporate-main | evaporate/schema_identification.py |
############################ SCHEMA ID PROMPTS ############################
SCHEMA_ID_PROMPTS = [
f"""Sample text:
<tr class="mergedrow"><th scope="row" class="infobox-label"><div style="text-indent:-0.9em;margin-left:1.2em;font-weight:normal;">• <a href="/wiki/Monarchy_of_Canada" title="Monarchy of Canada">Monarc... | evaporate-main | evaporate/prompts.py |
import os
import json
from collections import Counter, defaultdict
from manifest import Manifest
from configs import get_args
from prompts import Step
cur_idx = 0
def apply_prompt(step : Step, max_toks = 50, do_print=False, manifest=None, overwrite_cache=False):
global cur_idx
manifest_lst = manifest.copy()... | evaporate-main | evaporate/utils.py |
import os
import random
import pickle
from tqdm import tqdm
from functools import partial
from multiprocessing import Pool
from collections import Counter, defaultdict
import signal
from contextlib import contextmanager
import re
import json
import math
import time
import pandas as pd
import numpy as np
from bs4 impor... | evaporate-main | evaporate/profiler.py |
from collections import defaultdict, Counter
import numpy as np
from prompts import (PICK_VALUE, Step,)
from utils import apply_prompt
def clean_comparison(responses, field):
clean_responses = []
if type(responses) == str:
responses = [responses]
for response in responses:
response = respo... | evaporate-main | evaporate/evaluate_profiler.py |
import os
import time
import random
import json
import datetime
from tqdm import tqdm
import pickle
import argparse
from collections import defaultdict, Counter
from utils import get_structure, get_manifest_sessions, get_file_attribute
from profiler_utils import chunk_file, sample_scripts, set_profiler_args
from schem... | evaporate-main | evaporate/run_profiler.py |
import numpy as np
import itertools
import matplotlib.pyplot as plt
import scipy.stats
class Ising():
def __init__(self, m, potentials, thetas = None, vals = [-1, 1], ) -> None:
self.m = m
self.v = m + 1 # total number of vertices
self.potentials = potentials
self.va... | evaporate-main | evaporate/weak_supervision/pgm.py |
import numpy as np
import itertools
def get_probabilties(num_lfs, num_examples, predictions, label_name_to_int):
lf_array = np.zeros((num_lfs, num_examples))
golds = []
# Collect golds and preds
for i, (k, item) in enumerate(predictions.items()):
preds = item['chos... | evaporate-main | evaporate/weak_supervision/ws_utils.py |
import networkx as nx
import numpy as np
from itertools import chain, product, combinations
from scipy.sparse import issparse
import more_itertools
import torch
class DependentPGM:
"""
This class describes a PGM learned from labeled data with specified edge structure.
Args:
edges: li... | evaporate-main | evaporate/weak_supervision/binary_deps.py |
"""This script contains code to execute different methods"""
from readline import append_history_file
from sklearn.metrics import accuracy_score
import numpy as np
from snorkel.labeling.model import LabelModel
from snorkel.utils import probs_to_preds
import itertools
import math
import torch
import collections
from ... | evaporate-main | evaporate/weak_supervision/methods.py |
import argparse
import numpy as np
import json
import sys
import pickle
import random
import cvxpy as cp
import scipy as sp
from tqdm import tqdm
from methods import Aggregator
from metal.label_model import LabelModel
from collections import defaultdict, Counter
sys.path.append("../")
from evaluate_synthetic import cl... | evaporate-main | evaporate/weak_supervision/run_ws.py |
import numpy as np
import itertools
import scipy.stats
import math
import networkx as nx
from itertools import chain
from methods import Aggregator
from binary_deps import structure_learning
from binary_deps import DependentPGM
from sklearn.metrics import log_loss, accuracy_score
class Ising():
def _... | evaporate-main | evaporate/weak_supervision/make_pgm.py |
import numpy as np
import pandas as pd
from emptyheaded import *
class ResultError(Exception):
pass
def lollipop_agg(db):
lolli_agg = \
"""
LollipopAgg(;z) :- Edge(a,b),Edge(b,c),Edge(a,c),Edge(a,x),z:long<-[COUNT(*)].
"""
print "\nQUERY: LOLLIPOP AGG"
db.eval(lolli_agg)
def barbell_agg(db):
b_agg = \
""... | EmptyHeaded-master | test/graph/perf.py |
import numpy as np
import pandas as pd
from emptyheaded import *
check_big_out = False
## TODO:
## 4-Clique SQL
## Fix Barbell and 4-Clique Selection Order
class ResultError(Exception):
pass
def lollipop_agg(db):
lolli_agg = \
"""
LollipopAgg(;z) :- Edge(a,b),Edge(b,c),Edge(a,c),Edge(a,x),z:long<-[COUNT(*)]... | EmptyHeaded-master | test/graph/travis.py |
import sys
import os
import re
logdir = os.path.expandvars("$EMPTYHEADED_HOME") + "/logs"
def get_query_times(filename):
dataset = ""
queryname = ""
time = ""
writefile = open(logdir+"/"+filename + ".csv","w")
for line in open(filename+ ".log","r"):
matchObj = re.match(r'.*DATASET: (.*)', line, re.M|re.... | EmptyHeaded-master | test/graph/parse.py |
import time
import os
import argparse
from pyspark import SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.types import Row
def test_aggregation_query(query, query_name):
if sql_context is not None:
print("\nTESTING {0}\nSPARK SQL\n".format(query_name) + "#" * 80)
result_set = sq... | EmptyHeaded-master | test/graph/spark_sql.py |
import numpy as np
import pandas as pd
from emptyheaded import *
class ResultError(Exception):
pass
def lubm1(db):
lbm1 = \
"""
lubm1(a) :- b='http://www.Department0.University0.edu/GraduateCourse0',
c='http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#GraduateStudent',
takesCourse(a,b),rdftype(a,c).
"""... | EmptyHeaded-master | test/rdf/perf.py |
import numpy as np
import pandas as pd
from emptyheaded import *
class ResultError(Exception):
pass
def lubm1(db):
lubm1 = \
"""
lubm1(a) :- b='http://www.Department0.University0.edu/GraduateCourse0',
c='http://www.lehigh.edu/~zhp2/2004/0401/univ-bench.owl#GraduateStudent',
takesCourse(a,b),rdftype(a,c).
""... | EmptyHeaded-master | test/rdf/travis.py |
import numpy as np
import pandas as pd
from emptyheaded import *
def triangle():
return datalog("""
Triangle(a,b,c) :- Edge(a,b),Edge(b,c),Edge(a,c).
""").ir
def triangle_counting():
return datalog("""
Triangle(a;z) :- Edge(a,b),Edge(b,c),Edge(a,c),z:uint64<-[COUNT(b,c)].
""").ir
def triangle_agg():
... | EmptyHeaded-master | python/test_parser.py |
import numpy as np
import pandas as pd
from emptyheaded import *
def triangle():
return optimize("""
Triangle(a,b,c) :- Edge(a,b),Edge(b,c),Edge(a,c).
""").ir
def triangle_counting():
return optimize("""
Triangle(a;z) :- Edge(a,b),Edge(b,c),Edge(a,c),z:long<-[COUNT(b,c)].
""").ir
def triangle_agg():
... | EmptyHeaded-master | python/test_optimizer.py |
## Stores the relations. Each relation has:
## 1) A file (csv or tsv) the data comes from
## 2) A schema
## IMPORTANT
## The order the types are specified in the schema must be
## the same as the order in the CSV or TSV file. The annotations
## (if specified) must come last.
from schema import Schema
import pandas as... | EmptyHeaded-master | python/relation.py |
import sys
from schema import Schema
from relation import Relation
from database import Database
from config import Config
from parsers import *
from ir import *
import glob
import jpype
import os
import numpy as np
import pandas as pd
#launch the JVM
def start():
ehhome = os.path.expandvars("$EMPTYHEADED_HOME")
... | EmptyHeaded-master | python/emptyheaded.py |
## Stores the configuration of the database
class Config:
def __init__(self,system="emptyheaded",num_threads=1,num_sockets=4,layout="hybrid",memory="RAM"):
self.system = system#delite/spark
self.num_threads = num_threads
self.num_sockets = num_sockets
self.layout = layout #EmptyHeaded only
self.m... | EmptyHeaded-master | python/config.py |
## High-level class to store the database
## The database contains a filename and relations
## Relations contain schemas
## Database -> Relations -> Schema
## Only one database should be created per python process.
## Database spins up the JVM which serves as our Query Compiler.
import jpype
from config import Config... | EmptyHeaded-master | python/database.py |
## Contains the bridge for each front-end parser.
## The parser pontentially spins up the JVM
## creates the respective object in scala
## Sends the string to the object which returns an IR
import jpype
from ir import *
class Parser:
def __init__(self):
self.duncecap = jpype.JPackage('duncecap')
class sql(Pars... | EmptyHeaded-master | python/parsers.py |
## Maintains the interface for the intermediate representation
## This can be sent in and out of both code generators and
## the GHD optimizer.
import jpype
def strip_unicode(values):
return [str(x) for x in values]
#Convinance class for expressing relations
class RELATION:
def __init__(self,name,attributes,anno... | EmptyHeaded-master | python/ir.py |
## Stores the schemas for each relation.
## Enables users to define a schema.
## Note: defining a schema does not add a
## relation to the database. A user must define
## all schemas they wish to add to a database
## before creating the database. Once the database
## is created they can execute queries over the respe... | EmptyHeaded-master | python/schema.py |
import os
import platform
import sys
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
EH_PATH=os.path.expandvars("$EMPTYHEADED_HOME")
if platform.uname()[0] == "Darwin":
clibs = ["-arch","x86_64","-mavx",'-Wno-unused-function',
'-s... | EmptyHeaded-master | cython/createDB/setup.py |
import os
import platform
import sys
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
EH_PATH=os.path.expandvars("$EMPTYHEADED_HOME")
if platform.uname()[0] == "Darwin":
clibs = ["-arch","x86_64","-mavx",'-Wno-unused-function',
'-s... | EmptyHeaded-master | cython/trie/setup.py |
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
import sys
import numpy
import platform
import os
EH_PATH=os.path.expandvars("$EMPTYHEADED_HOME")
if platform.uname()[0] == "Darwin":
clibs = ["-arch","x86_64","-mavx",'-Wno-unused-function',
'-s... | EmptyHeaded-master | cython/db/setup.py |
import os
import platform
import sys
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
EH_PATH=os.path.expandvars("$EMPTYHEADED_HOME")
clibs = [ "-mavx2",
"-fPIC",
"-std=c++0x",
"-pedantic",
"-O3",
... | EmptyHeaded-master | cython/query/setup.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
# coding: utf-8
"""Dataset Loader for Memory Dialogs.
Author(s): noctli, skottur
(c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
"""
import json
import logging
import os
import pickle
import re
from itertools import cha... | comet_memory_dialog-main | models/gpt2_mm/dataset_memory.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import copy
import json
import logging
import random
import time
from argparse import ArgumentParser
from itertools import chain
import os
from pprint import pformat
import numpy as np
import torch
import torch.nn.functional as F
import tqdm
... | comet_memory_dialog-main | models/gpt2_mm/generate.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
from transformers import *
import math
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
def gelu(x):
return (
0.5
* x
* (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch... | comet_memory_dialog-main | models/gpt2_mm/VideoGPT2.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
# coding: utf-8
# author: noctli
import json
import pickle
from itertools import chain
import numpy as np
import torch
import torch.utils.data
from torch.utils.data import Dataset
# from train import SPECIAL_TOKENS, MODEL_INPUTS, PADDED_INPUT... | comet_memory_dialog-main | models/gpt2_mm/dataset.py |
# Copyright (c) 2019-present, HuggingFace Inc.
# All rights reserved. This source code is licensed under the BSD-style license found in the LICENSE file in the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
import json
import logging
import math
import os
f... | comet_memory_dialog-main | models/gpt2_mm/train.py |
#! /usr/bin/env python
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Create API and MM-DST result JSONS from model result file.
Author(s): Satwik Kottur
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import collections
import copy
... | comet_memory_dialog-main | models/gpt2_mm/utils/create_result_jsons.py |
#! /usr/bin/env python
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Extract BUTD features for memories.
Author(s): Satwik Kottur
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import base64
import json
import os
import pickle
im... | comet_memory_dialog-main | models/gpt2_mm/utils/extract_memory_features.py |
#! /usr/bin/env python
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Preprocess the memory dialog dataset.
Author(s): Satwik Kottur
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import json
import os
MM_CONTEXT = "<MM>"
START_AP... | comet_memory_dialog-main | models/gpt2_mm/utils/preprocess_memory_dataset.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#!/usr/bin/env python3
"""
Script for converting the main SIMMC datasets (.JSON format)
into the line-by-line stringified format (and back).
The reformatted data is used as input for the GPT-2 based
DST model baseline.
"""
import ... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/utils/convert.py |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "Lic... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/run_language_modeling.py |
#!/usr/bin/env python3
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Scripts for converting the main SIMMC datasets (.JSON format)
into the line-by-line stringified format (and back).
The reformatted data is used as input for the GPT-2 based
DST model baseline.
"""
from gpt... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/preprocess_input.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#!/usr/bin/env python3
"""
Scripts for evaluating the GPT-2 DST model predictions.
First, we parse the line-by-line stringified format into responses
and compute BLEU score.
"""
import argparse
import json
from gpt2_dst.utils.convert ... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/evaluate_response.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#! /usr/bin/env python
"""
Gets the best model given all the checkpoints.
Author(s): Satwik Kottur
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import os
import re
def main(args):
f... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/get_best_model.py |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#
# Licensed under the Apache Lice... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/run_generation.py |
#!/usr/bin/env python3
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Scripts for evaluating the GPT-2 DST model predictions.
First, we parse the line-by-line stringified format into responses
and compute BLEU score.
"""
import argparse
import ast
import copy
import json
import... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/reformat_dst_response_outputs.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
#!/usr/bin/env python3
"""
Scripts for evaluating the GPT-2 DST model predictions.
First, we parse the line-by-line stringified format into
the structured DST output.
We then run the main DST Evaluation script to get results.
"""... | comet_memory_dialog-main | models/gpt2_text/gpt2_dst/scripts/evaluate.py |
"""
Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
Script evaluates response generation using GT responses.
Expected JSON format:
[
"dialog_id": <dialog_id>,
"predictions": [
{
"turn_id": <turn_id>,
"response": <str; model output>,
}
...... | comet_memory_dialog-main | models/gpt2_text/utils/response_evaluation.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
# !/usr/bin/env python3
"""
Util functions for evaluating the DST model predictions.
The script includes a main function which takes
the original JSON data file and the predicted model output file
(in the same format), and outputs ... | comet_memory_dialog-main | models/gpt2_text/utils/evaluate_dst.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
"""
Description: merges the synthetically generated dialogs (.json, .p)
and the tab-separated Appen annotations (.txt)
to putput the merged dialogs in both .json and .p formats
"""
import os
import json
import... | comet_memory_dialog-main | dialog_simulator/merge_synth_and_appen.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import random
import numpy as np
from typing import List, Tuple
from SimulatorBase import SimulatorBase
from constants import GoalType, DialogAct, GoalMemoryRefType
from Data import MemoryDialog, Goal, GoalParameter, Fram... | comet_memory_dialog-main | dialog_simulator/UserSimulator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from constants import API_CALL_TYPE, TurnSpeaker, DialogAct
from Data import Turn, Frame, ActAttributes, MemoryDialog, APIResponse, APIRequest
from typing import Dict, Tuple
import sys
sys.path.append("/Users/shanemoon/w... | comet_memory_dialog-main | dialog_simulator/MemoryDialogModel.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import json, random, traceback, os
from typing import List, Tuple
from constants import TurnSpeaker, DialogAct, API_STATUS
from Data import Dialog, MemoryDialog, MemoryGraph, Turn, Goal
from UserSimulator import PilotUser... | comet_memory_dialog-main | dialog_simulator/MemoryDialogSimulator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import random
import json
from MemoryDialogModel import PilotMemoryDialogModel
from Data import MemoryGraph, MemoryDialog, Turn
from MemoryServiceAPI import MemoryServiceAPI
import sys
sys.path.append("/Users/shanemoon/... | comet_memory_dialog-main | dialog_simulator/InteractiveDialogHandler.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
"""
Merges multiple batches of SIMMC 2.0 files into one,
and also outputs train, dev, devtest, and test sets.
"""
import os
import json
import csv
import random
import pickle
import numpy as np
from utils import l... | comet_memory_dialog-main | dialog_simulator/get_user_utterances.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from enum import Enum
class GoalType(Enum):
UNKNOWN = "unknown"
SEARCH = "search"
REFINE_SEARCH = "refine_search"
GET_RELATED = "get_related"
GET_INFO = "get_info"
GET_AGGREGATED_INFO = "get_aggr... | comet_memory_dialog-main | dialog_simulator/constants.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from constants import API_CALL_TYPE, TurnSpeaker, DialogAct
from Data import Turn, Frame, ActAttributes, MemoryDialog, APIResponse, APIRequest
from typing import Dict, Tuple
class DummyMemoryDialogModel(MemoryDialogModel... | comet_memory_dialog-main | dialog_simulator/DummyMemoryDialogModel.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
"""
Merges multiple batches of SIMMC 2.0 files into one,
and also outputs train, dev, devtest, and test sets.
"""
import os
import json
import csv
import random
import pickle
import numpy as np
from utils import l... | comet_memory_dialog-main | dialog_simulator/merge_data_json.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from constants import visual_slots, all_slots
import random
random.seed(0)
def build_parameter_ontology(memory_graph, metadata, domain=None, ontology=None):
if ontology is None:
ontology = {
"v... | comet_memory_dialog-main | dialog_simulator/utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import random
from typing import List, Tuple
from SimulatorBase import SimulatorBase
from constants import GoalType, DialogAct, API_STATUS, API_CALL_TYPE
from Data import (
MemoryDialog,
Goal,
Frame,
ActAt... | comet_memory_dialog-main | dialog_simulator/AssistantSimulator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import os
import copy
import json
import csv
import random
import pickle
from MemoryDialogSimulator import MemoryDialogSimulator
from UserSimulator import PilotUserSimulator
from AssistantSimulator import PilotAssistantSi... | comet_memory_dialog-main | dialog_simulator/main.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import random
from constants import (
GoalType,
GoalMemoryRefType,
numeric_slots,
non_visual_slots,
visual_slots,
all_slots,
)
from Data import Goal, GoalParameter, MemoryTime
from utils import wei... | comet_memory_dialog-main | dialog_simulator/GoalGenerator.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from Data import MemoryDialog, Goal, Frame
from typing import List
class SimulatorBase:
def register_memory_service_api(self, memory_service_api):
self.memory_service_api = memory_service_api
def fit_go... | comet_memory_dialog-main | dialog_simulator/SimulatorBase.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
from __future__ import annotations
from constants import GoalType, GoalMemoryRefType, DialogAct
from utils import str_memories, int_memory_ids, get_slot_values_simple_from_json
import pickle
from datetime import datetime
... | comet_memory_dialog-main | dialog_simulator/Data.py |
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved.
#!/usr/bin/env python3
import random
from typing import Dict, Tuple
from Data import APIRequest, APIResponse, MemoryTime, MemoryLocation
from constants import API_CALL_TYPE, API_STATUS, GoalType
from utils import str_memory
from datetime import... | comet_memory_dialog-main | dialog_simulator/MemoryServiceAPI.py |
import argparse
def main():
parser = argparse.ArgumentParser(description='Make seeds')
parser.add_argument('--script', type=str, default='')
parser.add_argument('--num_seeds', type=int, default=5)
args = parser.parse_args()
seed = int(args.script.split('--seed ')[-1].split(' --')[0])
... | spacetime-main | make_seeds.py |
"""
Model loss functions and objectives
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import L1Loss as MAE
from torch.nn import MSELoss as MSE
from torch.nn import CrossEntropyLoss
def get_loss(loss, reduction='none', ignore_index=-100):
"""
Different loss functions dep... | spacetime-main | loss.py |
"""
Model optimizer and scheduler
"""
import torch
def get_optimizer(model, configs):
optim_configs = {k: v for k, v in configs.items() if k != '_name_'}
if configs['_name_'] == 'adamw':
return torch.optim.AdamW(model.parameters(), **optim_configs)
elif configs['_name_'] == 'sgd':
return t... | spacetime-main | optimizer.py |
import os
import copy
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from os.path import join
from omegaconf import OmegaConf
from dataloaders import initialize_data_functions, get_evaluation_loaders
from utils.logging import print_header, print_args, print_config
from optimizer i... | spacetime-main | main.py |
import importlib
from torch.utils.data import DataLoader
def initialize_data_functions(args):
"""
Retrieve dataloaders and visualization function.
Example:
load_data, visualize_data = initialize_data_functions(args)
dataloaders, dataset = load_data(config.dataset, config.loader)
... | spacetime-main | dataloaders/__init__.py |
import numpy as np
import matplotlib.pyplot as plt
from dataloaders.datasets.informer import ETTHour, ETTMinute, ECL, Exchange, ILI, Traffic, Weather
def get_dataset(name):
if name == 'etth':
return ETTHour
elif name == 'ettm':
return ETTMinute
elif name == 'ecl':
return ECL
e... | spacetime-main | dataloaders/informer.py |
"""
Parent dataset for sequential data.
Code from https://github.com/HazyResearch/state-spaces/blob/main/src/dataloaders/base.py
"""
from functools import partial
import os
import io
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F... | spacetime-main | dataloaders/datasets/sequence.py |
from .sequence import SequenceDataset, default_data_path | spacetime-main | dataloaders/datasets/__init__.py |
"""
Informer benchmark datasets from Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting (AAAI'21 Best Paper)
- Authors: Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang
Code from https://github.com/HazyResearch/state-spaces/blob/main/src/dataloader... | spacetime-main | dataloaders/datasets/informer.py |
from .args import initialize_args
from .configs import load_main_config, load_model_config
from .experiment import format_arg, seed_everything, initialize_experiment | spacetime-main | setup/__init__.py |
import os
import random
import torch
import numpy as np
from os.path import join
def format_arg(arg_name, cutoff=2):
arg_name = str(arg_name)
if arg_name is None:
return arg_name
# Hardcode to handle backslash
name_splits = arg_name.split('/')
if len(name_splits) > 1:
return ... | spacetime-main | setup/experiment.py |
import argparse
def initialize_args():
parser = argparse.ArgumentParser(description='SpaceTime arguments')
# Model
parser.add_argument('--model', type=str, default='spacetime')
parser.add_argument('--embedding_config', type=str, default='embedding/repeat')
parser.add_argument('--preprocess_co... | spacetime-main | setup/args.py |
"""
Load default configs
"""
from .data import get_dataset_config, get_dataloader_config
from .optimizer import get_optimizer_config, get_scheduler_config
from .model import load_model_config
def load_main_config(args, config_dir='./configs'):
configs = {'dataset': get_dataset_config(args, config_dir),
... | spacetime-main | setup/configs/__init__.py |
"""
Load and update model configs
"""
from os.path import join
from omegaconf import OmegaConf
# SpaceTime model
def load_model_config(config, config_dir='./configs/model', args=None):
for k in ['embedding_config', 'encoder_config',
'decoder_config', 'output_config']:
_config = OmegaConf.lo... | spacetime-main | setup/configs/model.py |
from os.path import join
from omegaconf import OmegaConf
def get_optimizer_config(args, config_dir='./configs'):
config = OmegaConf.load(
join(config_dir, 'optimizer', f'{args.optimizer}.yaml'))
if args.lr is not None:
config.lr = args.lr
if args.weight_decay is not None:
config.we... | spacetime-main | setup/configs/optimizer.py |
from os.path import join
from omegaconf import OmegaConf
from dataloaders import get_data_module
def get_dataset_config(args, config_dir='./configs'):
get_data_module(args) # Initialize args.dataset_type
fpath = join(config_dir, 'datasets', args.dataset_type, f'{args.dataset}.yaml')
config = OmegaConf.l... | spacetime-main | setup/configs/data.py |
"""
Logging utilities
"""
import rich.syntax
import rich.tree
from omegaconf import OmegaConf, DictConfig, ListConfig
def print_header(x, border='both'):
print('-' * len(x))
print(x)
print('-' * len(x))
def print_args(args, return_dict=False, verbose=True):
attributes = [a for a in dir(args... | spacetime-main | utils/logging.py |
"""
Code from https://github.com/HazyResearch/state-spaces/blob/main/src/utils/config.py
"""
import rich.syntax
import rich.tree
from omegaconf import OmegaConf, DictConfig
from typing import Sequence, Mapping
def print_config(config: DictConfig,
resolve: bool = True,) -> None:
"""Prints content ... | spacetime-main | utils/config.py |
import copy
def update_args_from_checkpoint_name(args, fname):
_args = copy.deepcopy(args)
fname = fname.replace('=no-', '=normal-').replace('=xa-', '=xavier-').replace('.pth', '').replace('=tc-', '=timm_cosine-').replace('=ir-', '=informer_rmse-')
all_args = []
for f in fname.split('-')[2:]:
... | spacetime-main | utils/checkpoint.py |
spacetime-main | utils/__init__.py | |
spacetime-main | model/__init__.py | |
import torch.nn as nn
from einops import rearrange
from model.components import Activation, DropoutNd
def init_mlp(config):
if config['method'] == 'mlp':
return MLP(**config['kwargs'])
else:
return nn.Identity()
class MLP(nn.Module):
def __init__(self,
input_dim: int, ... | spacetime-main | model/mlp.py |
"""
SpaceTime Network
"""
import torch.nn as nn
from model.embedding import init_embedding
from model.block import Encoder, Decoder
from model.mlp import init_mlp
class SpaceTime(nn.Module):
def __init__(self,
embedding_config: dict,
encoder_config: dict,
decode... | spacetime-main | model/network.py |
"""
Basic neural net components
OurModule from: https://github.com/HazyResearch/state-spaces/blob/main/src/models/sequence/ss/kernel.py (OptimModule)
Activation and DropoutND from: https://github.com/HazyResearch/state-spaces/blob/main/src/models/nn/components.py
"""
import torch
import torch.nn as nn
from einops imp... | spacetime-main | model/components.py |
"""
SpaceTime blocks, stacked into encoder and decoder of architecture
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.components import OurModule
from model.mlp import init_mlp
from model.ssm import init_ssm
from model.ssm.preprocess import init_preprocess_ssm as init_pre
class Blo... | spacetime-main | model/block.py |
import torch
import torch.nn.functional as F
import opt_einsum as oe
from einops import repeat, rearrange
from model.functional.krylov import krylov
from model.ssm.base import SSM
class CompanionSSM(SSM):
"""
Open-loop implementation of Companion SSM:
-> y_t = C \sum_{i = 0}^{k - 1 - i} A^k B u_i
... | spacetime-main | model/ssm/companion.py |
from .companion import CompanionSSM
from .shift import ShiftSSM
from .closed_loop import ClosedLoopCompanionSSM, ClosedLoopShiftSSM
def init_ssm(config):
supported_methods = ['companion', 'closed_loop_companion',
'shift', 'closed_loop_shift']
if config['method'] == 'companion':
... | spacetime-main | model/ssm/__init__.py |
import torch
import opt_einsum as oe
from einops import repeat, rearrange
from model.functional.krylov import krylov
from model.ssm.companion import CompanionSSM
class ShiftSSM(CompanionSSM):
"""
Open-loop implementation of Shift SSM:
-> y_t = C \sum_{i = 0}^{k - 1 - i} S^k B u_i
where S is shift ... | spacetime-main | model/ssm/shift.py |
import torch
import torch.nn as nn
import opt_einsum as oe
from einops import rearrange, repeat
from model.components import OurModule
class SSM(OurModule):
def __init__(self,
model_dim: int,
n_kernels: int, # Number of kernels / scales
kernel_dim: int,
... | spacetime-main | model/ssm/base.py |
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