mbednarski

from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.tools import BaseTool
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper
from spotipy.oauth2 import SpotifyClientCredentials, SpotifyOAuth
import azapi
import spotipy
from langchain.utilities import GoogleSearchAPIWrapper
from urllib.parse import quote_plus

mbednarski

from pathlib import Path

import lark
import rich


class Parser:
    def __init__(
        self, grammar_path: Path = Path("grammar.lark"), start: str = "program"
    ) -> None:

mbednarski

from pathlib import Path

import rich
import typer
from lark.tree import pydot__tree_to_png

from xdlang.pipeline import Parser
from xdlang.pipeline.ast_builder import AstBuilder

app = typer.Typer()

mbednarski

from pathlib import Path
import lark
import rich


class Parser:
    def __init__(
        self, grammar_path: Path = Path("grammar.lark"), start: str = "program"
    ) -> None:
        with grammar_path.open("rt") as f:

mbednarski

from lark import Lark, tree
from lark.visitors import CollapseAmbiguities
import matplotlib.pyplot as plt

def plot_trees(grammar:str,  text:str, start='expr'):
    parser = Lark(grammar=grammar, start=start,ambiguity='explicit')  
    parsed = parser.parse(text)
    trees = CollapseAmbiguities().transform(parsed)
    for t in trees:
        tree.pydot__tree_to_png(t, filename='tree.png', rankdir='TB')

mbednarski

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import optuna


X, y = load_iris(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20, stratify=y)

mbednarski

from setuptools import setup

setup(name='mlp',
      packages=['mlp'],
      version='0.0.1dev1',
      entry_points={
            'console_scripts': ['mlp-cli=mlp.cmd:main']
      }
)

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Usage:
    mlp-cli train <dataset-dir> <model-file> [--vocab-size=<vocab-size>]
    mlp-cli ask <model-file> <question>
    mlp-cli (-h | --help)

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def main():
    arguments = docopt(__doc__)

    if arguments['train']:
        train_model(arguments['<dataset-dir>'],
                    arguments['<model-file>'],
                    int(arguments['--vocab-size'])
        )
    elif arguments['ask']:
        ask_model(arguments['<model-file>'],

mbednarski

"""MLP - machine-learning-production

Usage:
    mlp.py train <dataset-dir> <model-file> [--vocab-size=<vocab-size>]
    mlp.py ask <model-file> <question>
    mlp.py (-h | --help)

Arguments:
    <dataset-dir>  Directory with dataset.
    <model-file>   Serialized model file.