Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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prices.bidlo,
prices.ret,
prices.vol,
prices.ask,
prices.bid,
prices.retx,
trends.trend
FROM prices LEFT JOIN trends
ON prices.permno = trends.permno
AND prices.date >= trends.start_date
AND prices.date <= trends.end_date
WHERE prices.permno = :permno
AND prices.date >= :start
AND prices.date <= :end
ORDER BY prices.date
""", dict(
permno = permno,
start = start or "0000-00-00",
end = end or "9999-99-99"))
# constuct features from data rows
row = cursor.fetchone()
lastrow = row
while row:
yield row["date"], self.Features(
askhi = self._change(row["askhi"], lastrow["askhi"]),
bidlo = self._change(row["bidlo"], lastrow["bidlo"]),
vol = self._change(row["vol"], lastrow["vol"]),
ret = util.tofloat(row["ret"]),
ask = self._change(row["ask"], lastrow["ask"]),
bid = self._change(row["bid"], lastrow["bid"]),
retx = util.tofloat(row["retx"]),
trend = util.tofloat(row["trend"]) / 100)
lastrow = row
row = cursor.fetchone()
cursor.close()
def multiseries(self, permnos, start = None, end = None):
"""Return the timeseries of features for a set of issues.
Parameters:
permnos -- sequence of CRSP permnos
start -- start date (None = start of time)
end -- end date (None = end of time)
"""
return self._synchronize(
[self.timeseries(p, start = start, end = end) for p in permnos])
def _change(self, x, y):
"""Return the relative change between x and y."""
if None in (x, y):
return 0
change = (x - y) / y if y else 0
return min(max(change, -1), 1)
def _synchronize(self, timeseries_sequence):
"""Synchronize a sequence of timeseries."""
# join sequences on date
for items in util.join(timeseries_sequence, key = lambda x: x[0]):
# construct a row with one date and default vectors as needed
date = next(filter(None, items))[0]
values = list(itertools.chain.from_iterable(
[i[1:] if i else [self.default_features] for i in items]))
yield [date] + values
# <FILESEP>
from langchain import PromptTemplate, LLMChain
from langchain.llms import CTransformers
import os
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceBgeEmbeddings
from io import BytesIO
from langchain.document_loaders import PyPDFLoader
import gradio as gr
local_llm = "zephyr-7b-beta.Q5_K_S.gguf"
config = {
'max_new_tokens': 1024,
'repetition_penalty': 1.1,
'temperature': 0.1,
'top_k': 50,
'top_p': 0.9,
'stream': True,
'threads': int(os.cpu_count() / 2)
}
llm = CTransformers(
model=local_llm,
model_type="mistral",
lib="avx2", #for CPU use
**config
)