ianlcassidy

ianlcassidy

import pandas as pd
from sklearn.metrics import euclidean_distances
from sklearn.preprocessing import StandardScaler


def get_hotel_recommendations(df: pd.DataFrame, anchor_id: int) -> pd.DataFrame:
    # features used to compute the similarity
    features = ['lat', 'lng', 'avg_rate', 'star_rating', 'user_rating']
    
    # create the features - make the anchor be the first row in the dataframe

ianlcassidy

from typing import Dict

import pandas as pd
from sklearn.metrics import euclidean_distances


def get_personalized_hotel_recommendations(df: pd.DataFrame, user_features: Dict) -> pd.DataFrame:
    # features used to compute the similarity
    features = ['distance', 'avg_rate', 'star_rating', 'user_rating']
    

ianlcassidy

import typing as T

import numpy as np
from pydantic import BaseModel
from pydantic.types import conint, confloat


class FeatureIsNoneError(Exception):
    pass

ianlcassidy

import typing as T

import pandas as pd
import numpy as np


def create_features_container_from_dataframe(
    df: pd.DataFrame,
    class_name: str = "Features",
) -> str:

ianlcassidy

import numpy as np
from pydantic import ValidationError

from some.path.features import AnimalFeatures, FeatureIsNoneError
from some.other.path.models import ML_MODEL
from some.logger import log


def create_features() -> np.ndarray:
    # instantiate the features container