micahmelling

import pandas as pd
from scipy.stats import norm
import numpy as np
import matplotlib.pyplot as plt


np.random.seed(42)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)

micahmelling

import mimetypes
import pulumi_aws as aws
import pulumi

from pulumi import FileAsset
from pulumi_aws import s3


def main(bucket_name: str, index_html_path: str, package_dist_path: str, aliases: list, certificate_arn: str,
         domain_name: str, hosted_zone_id: str, ip_addresses: list):

micahmelling

from functools import reduce
import warnings

import jenkspy
import pandas as pd

warnings.filterwarnings('ignore')
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)

micahmelling

import pandas as pd
import networkx as nx
from sklearn.base import BaseEstimator, TransformerMixin


class CentralityEncoder(BaseEstimator, TransformerMixin):
    def __init__(self, node_column, edge_column):
        self.mapping_dict = {}
        self.node_column = node_column
        self.edge_column = edge_column

micahmelling

from sklearn.base import ClassifierMixin, RegressorMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer

from helpers.wrangling import drop_features
from modeling.config import CATEGORICAL_FEATURES, FEATURES_TO_DROP
from modeling.embedding import EmbeddingsEncoder


def get_pipeline(model: RegressorMixin or ClassifierMixin) -> Pipeline:

micahmelling

from copy import deepcopy

import pandas as pd
from keras import models
from keras.layers import Dense, Embedding, Flatten
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder, StandardScaler


class EmbeddingsEncoder(BaseEstimator, TransformerMixin):

micahmelling

import boto3
import json
import string
import random
import warnings

from botocore.config import Config
from ds_helpers.aws import get_secrets_manager_secret
from ds_helpers.db import connect_to_mysql

micahmelling

import pandas as pd
import joblib
import os

from sklearn.model_selection import cross_val_score
from hyperopt import fmin, tpe, Trials, space_eval


def train_model(x_train, y_train, get_pipeline_function, model_uid, model, param_space, iterations, cv_strategy,
                cv_scoring, static_param_space):

micahmelling

import mimetypes
import pulumi_aws as aws
import pulumi

from pulumi import FileAsset
from pulumi_aws import s3


def main(bucket_name, index_html_path, aliases, certificate_arn, domain_name, hosted_zone_id, ip_address):
    """

micahmelling

import pulumi_aws as aws


def main(database_name, allocated_gb_storage, db_engine, db_engine_version, instance_class, root_password,
         root_username, subnet_group_name, subnet_id_list, security_group_name, vpc_id, allowed_ip_list,
         security_group_port):
    """
    Creates an RDS instance and associated security group.

    :param database_name: name of the database