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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) |
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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 |
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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: |
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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): |
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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 |
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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): |
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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): | |
""" |
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from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier | |
from xgboost import XGBClassifier | |
from lightgbm import LGBMClassifier | |
from hyperopt import hp | |
from sklearn.metrics import log_loss, brier_score_loss, roc_auc_score, f1_score, balanced_accuracy_score | |
from collections import namedtuple | |
TARGET = 'target' | |
TEST_SET_START_YEAR = 2017 |
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import os | |
from tqdm import tqdm | |
def upload_directory_to_s3(local_directory, bucket): | |
""" | |
Uploads an entire directory to S3. | |
:param local_directory: name of the local directory |
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import shap | |
import pandas as pd | |
import numpy as np | |
import os | |
import matplotlib.pyplot as plt | |
import multiprocessing as mp | |
from functools import partial | |
from statistics import mean |
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