272 lines
10 KiB
Python
272 lines
10 KiB
Python
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# Load libraries ---------------------------------------------
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from datetime import datetime, timedelta
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from dateutil.easter import easter
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from data_preprocessing.dataset_specification import DatasetSpecification
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import pandas as pd
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import numpy as np
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# ------------------------------------------------------------
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class DataPreprocessingToolkit(object):
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def __init__(self):
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dataset_specification = DatasetSpecification()
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self.sum_columns = dataset_specification.get_sum_columns()
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self.mean_columns = dataset_specification.get_mean_columns()
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self.mode_columns = dataset_specification.get_mode_columns()
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self.first_columns = dataset_specification.get_first_columns()
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self.nights_buckets = dataset_specification.get_nights_buckets()
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self.npeople_buckets = dataset_specification.get_npeople_buckets()
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self.room_segment_buckets = dataset_specification.get_room_segment_buckets()
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self.arrival_terms = dataset_specification.get_arrival_terms()
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self.item_features_columns = dataset_specification.get_items_df_feature_columns()
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# #########################
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# Entire datasets functions
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# #########################
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def fix_date_to(self, df):
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df.loc[:, "date_to"] = df["date_to"].apply(lambda x: x + timedelta(days=1))
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return df
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def add_length_of_stay(self, df):
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# Write your code here
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return df
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def add_book_to_arrival(self, df):
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df.loc[:, "book_to_arrival"] = (df["date_from"] - df["booking_date"]).dt.days
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return df
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def add_nrooms(self, df):
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df.loc[:, "n_rooms"] = 1
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return df
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def add_weekend_stay(self, df):
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s = df["date_from"].dt.dayofweek
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e = df["date_to"].dt.dayofweek
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dt = (df["date_to"] - df["date_from"]).dt.days
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df.loc[:, "weekend_stay"] = (((s >= 4) & (s != 6)) | (e >= 5) | ((e < s) & (s != 6)) | (dt >= 6))
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df.loc[:, "weekend_stay"] = df["weekend_stay"].replace({True: 'True', False: 'False'})
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return df
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def add_night_price(self, df):
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# Write your code here
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return df
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def clip_book_to_arrival(self, df):
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df.loc[:, "book_to_arrival"] = np.maximum(df["book_to_arrival"], 0)
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return df
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def sum_npeople(self, df):
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df.loc[:, "n_people"] = np.maximum(df["n_people"] + df["n_children_1"] + df["n_children_2"] + df["n_children_3"], 1)
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return df
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def filter_out_company_clients(self, df):
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df = df.loc[df["is_company"] == 0]
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return df
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def filter_out_long_stays(self, df):
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df = df.loc[df["length_of_stay"] <= 21]
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return df
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def leave_one_from_group_reservations(self, df):
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unique_group_rows = []
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df.loc[:, "group_id"] = df["group_id"].fillna(-1)
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group_ids = []
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for idx, row in df.iterrows():
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if row["group_id"] != -1:
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if row["group_id"] not in group_ids:
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unique_group_rows.append(row)
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group_ids.append(row["group_id"])
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else:
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unique_group_rows.append(row)
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cleaned_dataset = pd.DataFrame(unique_group_rows, columns=df.columns)
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return df
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def aggregate_group_reservations(self, df):
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non_group_reservations = df.loc[df["group_id"] == "",
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self.sum_columns + self.mean_columns + self.mode_columns + self.first_columns]
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group_reservations = df.loc[df["group_id"] != ""]
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agg_datasets = [group_reservations.loc[:, ["group_id"] + self.sum_columns].groupby("group_id").sum(),
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group_reservations.loc[:, ["group_id"] + self.mean_columns].groupby("group_id").mean(),
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group_reservations.loc[:, ["group_id"] + self.mode_columns].groupby("group_id").agg(lambda x: x.value_counts().index[0]),
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group_reservations.loc[:, ["group_id"] + self.first_columns].groupby("group_id").first()]
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group_reservations = agg_datasets[0]
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for i in range(1, len(agg_datasets)):
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group_reservations = group_reservations.merge(agg_datasets[i], on="group_id")
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group_reservations = group_reservations.reset_index(drop=True)
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df = pd.concat([non_group_reservations, group_reservations])
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return df
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def leave_only_ota(self, df):
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df = df.loc[df.loc[:, "Source"].apply(lambda x: "booking" in x.lower() or "expedia" in x.lower())]
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return df
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def map_date_to_term_datasets(self, df):
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df.loc[:, "date_from"] = df["date_from"].astype(str).apply(lambda x: x[:10])
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df.loc[:, 'term'] = df['date_from'].apply(lambda x: self.map_date_to_term(x))
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return df
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def map_length_of_stay_to_nights_buckets(self, df):
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df.loc[:, 'length_of_stay_bucket'] = df['length_of_stay'].apply(lambda x: self.map_value_to_bucket(x, self.nights_buckets))
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return df
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def map_night_price_to_room_segment_buckets(self, df):
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# Write your code here
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return df
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# def map_night_price_to_room_segment_buckets(self, df):
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# night_prices = df.loc[df['accomodation_price'] > 1]\
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# .groupby(['term', 'room_group_id'])['night_price'].mean().reset_index()
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# night_prices.columns = ['term', 'room_group_id', 'termnight_price']
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# df = pd.merge(df, night_prices, on=['term', 'room_group_id'], how='left')
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# df.loc[:, 'room_segment'] = df['termnight_price'].apply(
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# lambda x: self.map_value_to_bucket(x, self.room_segment_buckets))
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# df = df.drop(columns=['termnight_price'])
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# return df
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def map_npeople_to_npeople_buckets(self, df):
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df.loc[:, 'n_people_bucket'] = df['n_people'].apply(lambda x: self.map_value_to_bucket(x, self.npeople_buckets))
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return df
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def map_item_to_item_id(self, df):
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df.loc[:, 'item'] = df[self.item_features_columns].astype(str).agg(' '.join, axis=1)
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ids = df['item'].unique().tolist()
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mapping = {ids[i]: i for i in range(len(ids))}
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df['item_id'] = df['item'].apply(lambda x: mapping[x])
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return df
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def add_interaction_id(self, df):
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df.loc[:, 'interaction_id'] = range(df.shape[0])
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return df
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# ################
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# Column functions
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# ################
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def bundle_period(self, diff):
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diff = float(diff)
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if int(diff) < 0:
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return "<0"
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elif int(diff) <= 7:
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return diff
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elif 7 < int(diff) <= 14:
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return "<14"
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elif 14 < int(diff) <= 30:
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return "<30"
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elif 30 < int(diff) <= 60:
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return "<60"
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elif 60 < int(diff) <= 180:
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return "<180"
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elif int(diff) > 180:
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return ">180"
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def bundle_price(self, price):
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mod = 300.0
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return int((price + mod / 2) / mod) * mod
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def map_date_to_season(self, date):
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day = int(date[8:10])
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month = int(date[5:7])
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if (month == 12 and day >= 21) or (month == 1) or (month == 2) or (month == 3 and day <= 19):
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return "Winter"
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if (month == 3 and day >= 20) or (month == 4) or (month == 5) or (month == 6 and day <= 20):
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return "Spring"
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if (month == 6 and day >= 21) or (month == 7) or (month == 8) or (month == 9 and day <= 22):
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return "Summer"
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if (month == 9 and day >= 23) or (month == 10) or (month == 11) or (month == 12 and day <= 20):
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return "Autumn"
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def map_value_to_bucket(self, value, buckets):
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if value == "":
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return str(buckets[0]).replace(", ", "-")
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for bucket in buckets:
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if bucket[0] <= value <= bucket[1]:
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return str(bucket).replace(", ", "-")
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def map_date_to_term(self, date):
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m = int(date[5:7])
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d = int(date[8:10])
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term = None
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for arrival_term in self.arrival_terms:
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if arrival_term == "Easter":
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year = int(date[:4])
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easter_date = easter(year)
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easter_start = easter_date + timedelta(days=-4)
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easter_end = easter_date + timedelta(days=1)
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esm = easter_start.month
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esd = easter_start.day
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eem = easter_end.month
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eed = easter_end.day
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if ((m > esm) or (m == esm and d >= esd)) and ((m < eem) or (m == eem and d <= eed)):
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term = arrival_term
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break
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elif arrival_term == "NewYear":
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sm = self.arrival_terms[arrival_term][0]["start"]["m"]
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sd = self.arrival_terms[arrival_term][0]["start"]["d"]
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em = self.arrival_terms[arrival_term][0]["end"]["m"]
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ed = self.arrival_terms[arrival_term][0]["end"]["d"]
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if ((m > sm) or (m == sm and d >= sd)) or ((m < em) or (m == em and d <= ed)):
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term = arrival_term
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break
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else:
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is_match = False
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for i in range(len(self.arrival_terms[arrival_term])):
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sm = self.arrival_terms[arrival_term][i]["start"]["m"]
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sd = self.arrival_terms[arrival_term][i]["start"]["d"]
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em = self.arrival_terms[arrival_term][i]["end"]["m"]
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ed = self.arrival_terms[arrival_term][i]["end"]["d"]
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if ((m > sm) or (m == sm and d >= sd)) and ((m < em) or (m == em and d <= ed)):
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term = arrival_term
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is_match = True
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break
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if is_match:
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break
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return term
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def map_dates_to_terms(self, dates):
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terms = []
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for date in dates:
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term = self.map_date_to_term(date)
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terms.append(term)
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return terms
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def filter_out_historical_dates(self, date_list):
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"""
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Filters out past dates from a list of dates.
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"""
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future_dates = []
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for date in date_list:
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if date >= datetime.now():
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future_dates.append(date.strftime("%Y-%m-%d"))
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return future_dates
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