Описание проекта

Компания «Чётенькое такси» собрала исторические данные о заказах такси в аэропортах. Чтобы привлекать больше водителей в период пиковой нагрузки, нужно спрогнозировать количество заказов такси на следующий час. Постройте модель для такого предсказания.

Значение метрики RMSE на тестовой выборке должно быть не больше 48.

Вам нужно:

Загрузить данные и выполнить их ресемплирование по одному часу.
Проанализировать данные.
Обучить разные модели с различными гиперпараметрами. Сделать тестовую выборку размером 10% от исходных данных.
Проверить данные на тестовой выборке и сделать выводы.

Данные лежат в файле taxi.csv. Количество заказов находится в столбце num_orders (от англ. number of orders, «число заказов»).

Содержание

1 Подготовка
2 Анализ
3 Обучение
4 Тестирование
- 4.1 Проведение тестирования
- 4.2 Выводы из тестирования
5 Выводы из проекта
6 Чек-лист проверки

Подготовка¶

Подготовка тетради¶

In [1]:

# Базовые библиотеки
import pandas as pd # Датафреймы
import numpy as np # Математика для массивов
from math import factorial # Факториалы
from scipy import stats as st # Статистика
import os # Библиотека для оптимизации чтения данных из файла
import time # Расчет времени выполнения функций
# Pipeline (пайплайн)
from sklearn.pipeline import(
    Pipeline, # Pipeline с ручным вводом названий шагов.
    make_pipeline # Pipeline с автоматическим названием шагов.
)
# Функция для поддержки экспериментальной функции HavingGridSearchSV
from sklearn.experimental import enable_halving_search_cv
# Ускоренная автоматизация поиска лучших моделей и их параметров
from sklearn.model_selection import HalvingGridSearchCV
# Ускоренная автоматизация рандомного поиска лучших моделей и их параметров
from sklearn.model_selection import HalvingRandomSearchCV
# Автоматизация раздельного декодирования признаков
from sklearn.compose import(
    make_column_selector, 
    make_column_transformer, 
    ColumnTransformer
)

# Обработка данных для машинного обучения
# Стандартизация данных
import re
#! pip install sklearn.preprocessing 
from sklearn.preprocessing import(
    OneHotEncoder, # Создание отдельных столбцов для каждого категориального значения, drop='first' (удаление первого столбца против dummy-ловушки), sparse=False (?)
    OrdinalEncoder, # Кодирование порядковых категориальных признаков
    #TargetEncoder, # Кодирование категорий на основе таргетов (ошибка, модуль не найден) 
    LabelEncoder, 
    StandardScaler, 
    MinMaxScaler
)
# Кодирование категорий на основе таргетов 
#!pip install -pU category_encoders
#from category_encoders.target_encoder import TargetEncoder
# Другие функции предобработки данных
from sklearn.impute import KNNImputer # Заполнение пропусков в данных методом k-блжиайших соседей.
from sklearn.utils import shuffle # Перемешивание данных для уравновешивания их в разных выборках
from statsmodels.stats.outliers_influence import variance_inflation_factor # Коэффициент инфляции дисперсии (5 и более - признак коррелирует со всеми остальными, его можно удалить и выразить через другие признаки)
from sklearn.model_selection import(
    GridSearchCV, # Поиск гиперпараметров по сетке (GridSearch)
    train_test_split, # Разделение выборок с целевыми и нецелевыми признаками на обучающую и тестовую
    TimeSeriesSplit, # Разделение обучающей выборки при кросс-валидации (!) временных рядов
    validation_curve, 
    StratifiedKFold, # Кроссвалидация с указанием количества фолдов (частей, на которые будет разбита обучающая выборка, одна из которых будет участвовать в валидации)
    KFold, # Кроссвалидация 
    cross_val_score # Оценка качества модели на кроссвалидации
)
# Различные модели машинного обучения (в данном проекте требуется регрессия)
# (есть разбор на https://russianblogs.com/article/83691573909/)
# Линейная модель
from sklearn.linear_model import(
    #LogisticRegression, # Линейная классификация
    LinearRegression, # Линейная регрессия
    Ridge , # Линейная регрессия. "Хребтовая" регрессия (метод наименьших квадратов)
    BayesianRidge , # Линейная регрессия. Байесовская "хребтовая" регрессия (максимизации предельного логарифмического правдоподобия)
    SGDRegressor # Линейная регрессия. SGD - Стохастический градиентный спуск (минимизирует регуляризованные эмпирические потери за счет стохастического градиентного спуска)
)
# Решающее дерево
from sklearn.tree import(
    #DecisionTreeClassifier, # Решающее дерево. Классификация
    DecisionTreeRegressor # Решающее дерево. Регрессия
)
# Случайный лес
from sklearn.ensemble import(
    #RandomForestClassifier, # Случайный лес. Классификация
    RandomForestRegressor # Случайный лес. Регрессия
)
# Машина опорных векторов
from sklearn.svm import(
    SVR # # Линейная модель. Регрессия с использованием опорных векторов
)
# Нейронная сеть
from sklearn.neural_network import(
    MLPRegressor # Нейронная сеть. Регрессия
)
# CatBoost (made in Yandex)
from catboost import(
    CatBoostRegressor # CatBoost (Яндекс). Регрессия
)
# LightGBM
from lightgbm import(
    LGBMRegressor # LightGBM. Регрессия
)
# Метрики (Показатели качества моделей)
from sklearn.metrics import(
    # Метрики для моделей регрессии
    mean_absolute_error, # MAE, Средняя абсолютная ошибка (не чувствительная к выбросам)
    mean_absolute_percentage_error, # MAPE, Средняя абсолютная ошибка в % (универсальная в %)
    mean_squared_error, # MSE, Средняя квадратичная ошибка (дисперсия, чувствительная к выбросам), RMSE (сигма) = mean_squared_error(test_y, preds, squared=False)
    r2_score, # R^2, Коэффициент детерминации (универсальная в %, чувствительная к выбросам, может быть отрицательной и возвращать NaN)
    
    # Другое
    make_scorer, # Функция для использования собственных функций в параметре scoring функции HalvingGridSearchCV
    ConfusionMatrixDisplay
)

# Анализ сезонности и трендов
from statsmodels.tsa.seasonal import seasonal_decompose 

# Визуализация графиков
import seaborn as sns
import matplotlib
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import rcParams, rcParamsDefault
from pandas.plotting import scatter_matrix
# Для поиска совпадений 
# в названиях населённых пунктов
from fuzzywuzzy import fuzz
from fuzzywuzzy import process

/opt/conda/lib/python3.9/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')

In [2]:

# Отображение всех столбцов таблицы
pd.set_option('display.max_columns', None)
# Обязательно для нормального отображения графиков plt
rcParams['figure.figsize'] = 10, 6
%config InlineBackend.figure_format = 'svg'
# Дополнительно и не обязательно для декорирования графиков
factor = .8
default_dpi = rcParamsDefault['figure.dpi']
rcParams['figure.dpi'] = default_dpi * factor

# Глобальная переменная 
# для функций со случайными значениями
STATE = 42

Загрузка данных и их предварительный анализ¶

In [3]:

# Загрузка данных
def read_csv_file(path1, path2):
    if os.path.exists(path1):
        data = pd.read_csv(
            path1, 
            index_col=[0], 
            parse_dates=[0] 
        )
    elif os.path.exists(path12):
        data = pd.read_csv(
            path2, 
            index_col=[0], 
            parse_dates=[0])
    else:
        print('Файл не найден')
    return data

data = read_csv_file(
    '/datasets/taxi.csv', 
    'datasets/taxi.csv' 
)

In [4]:

# Анализ датафрейма
print(data.info()) 
data.head() 

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 26496 entries, 2018-03-01 00:00:00 to 2018-08-31 23:50:00
Data columns (total 1 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   num_orders  26496 non-null  int64
dtypes: int64(1)
memory usage: 414.0 KB
None

Out[4]:

	num_orders
datetime
2018-03-01 00:00:00	9
2018-03-01 00:10:00	14
2018-03-01 00:20:00	28
2018-03-01 00:30:00	20
2018-03-01 00:40:00	32

In [5]:

# Анализ значений датафрейма
data.hist() 
data.describe() 

Out[5]:

	num_orders
count	26496.000000
mean	14.070463
std	9.211330
min	0.000000
25%	8.000000
50%	13.000000
75%	19.000000
max	119.000000

Выводы из предварительного анализа

Датафрейм изначально содержал всего два признака: datetime (дата со временем) и num_orders (число заказов), а также 26496 объектов без пропусков.
Признак datetime использован в качестве индексов.
Признак num_orders имеет небольшие значения и может быть приведён к типу int32.
Согласно условию задачи требуется ресемплировать датафрейм до 1 часа.

Предварительная подготовка данных¶

In [7]:

# Ресемплирование данных до 1 часа
data = data.resample('1H').sum()

In [8]:

# Оптимизация типа данных 
# признака "num_orders" 
data['num_orders'] = data['num_orders'].astype('int32') 

In [9]:

# Анализ датафрейма после
# предварительной подготовки
data.info() 
data.head() 

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4416 entries, 2018-03-01 00:00:00 to 2018-08-31 23:00:00
Freq: H
Data columns (total 1 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   num_orders  4416 non-null   int32
dtypes: int32(1)
memory usage: 51.8 KB

Out[9]:

	num_orders
datetime
2018-03-01 00:00:00	124
2018-03-01 01:00:00	85
2018-03-01 02:00:00	71
2018-03-01 03:00:00	66
2018-03-01 04:00:00	43

In [10]:

# Анализ значений датафрейма после
# предварительной подготовки
data.rename(
    columns={'num_orders':'Количество заказов'} 
).hist() 
plt.title('Распределение количества заказов в час') 
data.describe() 

Out[10]:

	num_orders
count	4416.000000
mean	84.422781
std	45.023853
min	0.000000
25%	54.000000
50%	78.000000
75%	107.000000
max	462.000000

In [11]:

data.rename( 
    columns={'num_orders':'Количество заказов'} 
).plot(
    title='Количество заказов, распределенное по времени'
);

Результаты предварительной обработки данных

При загрузке датафрейма индексы заменены на значения признака datetime.
Данные ресемплированы до 1 часа.
Оптимизировать данных признака num_orders.
В результате предварительной обработки датафрейм имеет 1 признак и 4416 объектов, а индексы в виде даты и времени с 00:00 1 марта по 23:00 31 августа 2018 года. Каждый объект соответствует одному часу, следующему в хронологческом порядке.

Результаты подготовки¶

Изначально датафрейм содержал два признака: datetime (дата со временем) и num_orders (число заказов), а также 26496 объектов без пропусков. Признак datetime был использован в качестве индексов при загрузке датафрейма. Признак num_orders оптимизирован путем приведения к типу int32. Согласно условию задачи датафрейм ресемплирован до 1 часа.

В результате предварительной обработки датафрейм имеет 1 признак num_orders и 4416 объектов, а индексы в виде даты и времени.

Анализ¶

В рамках данного проекта будт проанализированы:

Распределение количества заказов в разных временных интервалах.
Сдвиги и скользящие средние.
Декомпозиция количества заказов на сезонную и трендовую составляющую с шагом в 1 день

Распределение количества заказов в разных временных интервалах¶

In [12]:

# Распределение заказов по часам в течение суток
data.groupby(data.index.hour).mean().rename(
    columns={'num_orders':'Количество заказов'}
).plot(
    title='Распределение заказов по часам в течение суток', 
    xlabel='Часы суток', 
    xlim=(0, 23), 
    grid=True
);

In [13]:

# Распределение заказов по дням в течение недели
data.groupby(data.index.weekday).mean().rename(
    columns={'num_orders':'Количество заказов'}, 
    index={0:'Понедельник', 1:'Вторник', 2:'Среда', 3:'Четверг', 4:'Пятница', 5:'Суббота', 6:'Воскресенье'}
).plot(
    title='Распределение заказов по дням в течение недели', 
    xlabel='Дни недели', 
    xlim=(0, 6), 
    grid=True
);

In [14]:

# Распределение заказов по дням в течение месяца
data.groupby(data.index.day).mean().rename(
    columns={'num_orders':'Количество заказов'}
).plot(
    title='Распределение заказов по дням в течение месяца', 
    xlabel='Числа месяца', 
    xlim=(1, 31), 
    grid=True
);

In [15]:

# Распределение заказов по месяцам за все время набюдений
data.groupby(data.index.month).mean().rename(
    columns={'num_orders':'Количество заказов'}, 
    index={3:'Март', 4:'Апрель', 5:'Май', 6:'Июнь', 7:'Июль', 8:'Август'}
).plot(
    title='Распределение заказов по месяцам за все время набюдений', 
    xlabel='Месяца', 
    xlim=(0, 5), 
    grid=True
);

Выводы из распределения количества заказов в разных временных интервалах

В среднем в течение суток минимум заказов бывает в 6 часов утра, а максимум в полночь. Между максимумом и минимумом набюдаются выраженные эстремумы. Так, максимальное количество заказов наблюдается в 10 утра и 16 часов дня, а минимальное в полдень и 18 часов вечера.
В среднем в течение недели наблюдается выраженное снижение активности во втроник и воскресенье, между которыми стабильный рост до максимумомов в понедельник и пятницу.
В среднем в течение месяца наблюдается рост от начала к концу месяца. Минимум заказов 5 числа, максимум 31 числа.
Наблюдается рост количества заказов от месяца к месяцу.

Анализ сдвигов и скользящих средних¶

In [16]:

# Создание аналитического датафрейма
data_analisys = data.copy()  
data_analisys.head() 

Out[16]:

	num_orders
datetime
2018-03-01 00:00:00	124
2018-03-01 01:00:00	85
2018-03-01 02:00:00	71
2018-03-01 03:00:00	66
2018-03-01 04:00:00	43

In [17]:

# Функция для создания новых признаков
# путем сдвига итерационно
def shift(data, target, start, finish, step): 
    for i in range(start, finish, step): 
        string = 'shift_' + str(i) 
        data[string] = data[target].shift(i)
    return data

In [18]:

# Функция для создания новых признаков
# со скользящими средними путем сдвига на 1 час итерационно
def rolling_mean_shift(data, target, start, finish, step): 
    for i in range(start, finish, step): 
        string = 'rolling_mean_' + str(i) + '_shift_1' 
        data[string] = data[target].shift().rolling(i).mean()
    return data

In [19]:

# Функция для создания новых ризнаков
# со скользящими средними итерационно
def rolling_mean(data, target, start, finish, step): 
    for i in range(start, finish, step): 
        string = 'rolling_mean_' + str(i) 
        data[string] = data[target].rolling(i).mean()
    return data

In [20]:

# Сдвиг с разным количеством часов
data_analisys = shift(data_analisys, 'num_orders', 1, 25, 1)
data_analisys = shift(data_analisys, 'num_orders', 48, 169, 24) 

In [21]:

# Скользящее среднее со сдвигом в 1 час
# и окном 168 часов, т.к. наблюдается
# "недельная сезонность". С таким окном 
# тренд будет более гладким
data_analisys = rolling_mean_shift(data_analisys, 'num_orders', 168, 168, 1) 

In [22]:

# Удаление объектов с пустыми значениями
data_analisys = data_analisys.dropna() 

In [23]:

# Вывод на печать RMSE сдвигов и скользящих средних
for i in data_analisys.columns:
    if i != 'num_orders':
        print('RMSE', i, '=', mean_squared_error(
            data_analisys[i], 
            data_analisys['num_orders'], 
            squared=False
        )) 

RMSE shift_1 = 39.30292142790866
RMSE shift_2 = 46.945798072729566
RMSE shift_3 = 52.952578009864375
RMSE shift_4 = 55.63490331656302
RMSE shift_5 = 56.233426099809954
RMSE shift_6 = 55.86740254089737
RMSE shift_7 = 53.87424107330057
RMSE shift_8 = 53.090801832062915
RMSE shift_9 = 54.62524483131728
RMSE shift_10 = 54.888993010186624
RMSE shift_11 = 52.785766072035706
RMSE shift_12 = 52.10447367572656
RMSE shift_13 = 52.879965251946615
RMSE shift_14 = 54.96986847972229
RMSE shift_15 = 55.78799702492222
RMSE shift_16 = 54.35478632659841
RMSE shift_17 = 55.052089697282746
RMSE shift_18 = 56.698811089014214
RMSE shift_19 = 57.2117354587293
RMSE shift_20 = 56.45780510312234
RMSE shift_21 = 54.76864110934485
RMSE shift_22 = 49.250063332241055
RMSE shift_23 = 42.425399798029
RMSE shift_24 = 35.399379297015024
RMSE shift_48 = 33.229463873243596
RMSE shift_72 = 34.112244793051836
RMSE shift_96 = 34.15574098427023
RMSE shift_120 = 33.298664881080796
RMSE shift_144 = 36.11251771851234
RMSE shift_168 = 27.573911972531693

In [24]:

# Удаление не востребованной далее переменной
del data_analisys

Выводы из анализа сдвигов и скользящих средних

Минимальную ошибку RMSE дает скользящее среднее с окном в 2 часа.
Минимальные ошибки RMSE среди скользящих и сдвигов, применимые для предсказания будущих периодов, наблюдаются при сдвиге на 168 часов (1 неделя). С большим отрывом, но тоже хороший результат показали сдвиги на 48, 72, 96 и 120 часов (2, 3, 4 и 5 суток).

Из полученных результатов можно сделать вывод о том, что почасовые значения аналогичного периода предыдущей недели дают наиболее точное предсказание текущих почасовых предсказываемых значений. Т.е. для предсказания количества заказов с 11:00 до 12:00 10.08.2023 требуется использовать количество заказов с 11:00 до 12:00 03.08.2023.

Декомпозиция количества заказов на сезонную и трендовую составляющую с шагом в 1 день¶

In [25]:

# Декомпозиция на сезонную 
# и трендовую составляющую
# с шагом в 1 день
decomposed = seasonal_decompose(data['num_orders'].resample('1H').sum())
#plt.figure(figsize=(10, 14))
#plt.subplot(311)
plt.show() 
# Чтобы график корректно отобразился, указываем его
# оси ax, равными plt.gca() (англ. get current axis,
# получить текущие оси)
decomposed.trend.plot(ax=plt.gca())
plt.title('Тренд')
#plt.subplot(312)
plt.show()
decomposed.seasonal.plot(ax=plt.gca())
plt.title('Сезонность')
#plt.subplot(313)
plt.show()
decomposed.resid.plot(ax=plt.gca())
plt.title('Остатки')
plt.tight_layout()

Выводы из анализа декомпозиции количества заказов на сезонную и трендовую составляющую с шагом в 1 день

Наблюдаются постепенно растущий тренд. Наиболее значимые корректировки тренда приходятся на начала апреля, мая, июня, августа.
Заметны 7-дневные (еженедельные) колебания.
Наиболее выраженные тренд и «сезонность» наблюдаются с середины марта по середину апреля, когда волатильность остатков минимальна. Минимально выраженны тренд и «сезонность» в начале марта и во второй половине августа, когда колебания остатков максимальны.
В августе наблюдаются существенные выбросы остатков. Данные этого периода попадут в тестовую выборку и могут ухудшить результаты предсказаний.

Выводы из анализа¶

В среднем в течение суток минимум заказов бывает в 6 часов утра, а максимум в полночь. Между максимумом и минимумом набюдаются выраженные эстремумы. Так, максимальное количество заказов наблюдается в 10 утра и 16 часов дня, а минимальное в полдень и 18 часов вечера.
В среднем в течение недели наблюдается выраженное снижение активности во втроник и воскресенье, между которыми стабильный рост до максимумомов в понедельник и пятницу.
В среднем в течение месяца наблюдается рост от начала к концу месяца. Минимум заказов 5 числа, максимум 31 числа.
Наблюдается рост количества заказов от месяца к месяцу.
Минимальные ошибки RMSE среди скользящих и сдвигов, применимые для предсказания будущих период, наблюдаются при сдвиге на 24 часа и при использовании скользящего среднего со сдвигом 1 час и размером окна в 24 часа. Т.е. для предсказания количества заказов с 11:00 до 12:00 05.08.2023 требуется использовать количество заказов с 11:00 до 12:00 04.08.2023. Либо использовать для этого среднее почасовое количество заказов с 10:00 04.08.2023 до 11:00 05.08.2023.
Наблюдаются постепенно растущий тренд. Наиболее значимые корректировки тренда приходятся на начала апреля, мая, июня, августа.
Заметны 7-дневные (еженедельные) колебания.
Наиболее выраженные тренд и «сезонность» наблюдаются с середины марта по середину апреля, когда волатильность остатков минимальна. Минимально выраженны тренд и «сезонность» в начале марта и во второй половине августа, когда колебания остатков максимальны.
В августе наблюдаются существенные выбросы остатков. Данные этого периода попадут в тестовую выборку и могут ухудшить результаты предсказаний.

Обучение¶

Полезные функции подготовки данных и подбора моделей и их параметров¶

In [26]:

# Функция подготовки данных перед подбором моделей и их параметров
def data_preprocessing(data, target_name):
    
    # Перемешивание объектов 
    # для их лучшего распределения в выборках
    # (нельзя исползовать во временных рядах)
    #data = shuffle(data, random_state=STATE)
    
    # Разделение датафреймов на целевую и нецелевую выборку
    features = data.drop([target_name], axis=1)
    target = data[target_name]

    # Разделение целевой и нецелевой выборки 
    # на обучающие и тестовые выборки
    features_train,  features_test, target_train, target_test = train_test_split(
        features, 
        target, 
        test_size=.25, 
        shuffle=False, 
        random_state=STATE
    )
    
    return features_train,  features_test, target_train, target_test

In [27]:

# Функция создания структуры пайплайна
def params_and_model_selection(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    model_params
):
    
    start_time = time.time()
    #funtion_time = %timeit
    
    # Стандартизация числовых значений
    numeric_transformer = make_pipeline(
        StandardScaler()
    )
    
    # Шаг препроцессинга в Пайплайне
    preprocessor = make_column_transformer(
        (numeric_transformer, features_train.columns)
    )
    
    # Pipeline
    pipe = Pipeline([
        ('preprocessor', preprocessor), 
        ('regressor', model_params[0]['regressor'][0])
    ])
    pipe.fit(features_train, target_train)
    #predict = pipe.predict(features_test) # спорно, т.к. это должно быть в результате, а здесь должна использоваться валидационная выборка
    
    # Передача функции ошибки через make_scorer в HalvingGridSearchCV
    smape_score = make_scorer(
        mean_squared_error, 
        squared=False # Для RMSE
    )
    
    # Кросс-валидация специально для временных рядов
    tscv = TimeSeriesSplit(n_splits=5) 
    
    # HalvingGridSearchCV
    # (о подборе оптимальных параметров:
    # https://scikit-learn.ru/3-2-tuning-the-hyper-parameters-of-an-estimator/)
    #grid = HalvingRandomSearchCV(
    grid = HalvingGridSearchCV(
        pipe, 
        model_params, 
        #cv=4, # параметр KFold для кроссвалидации (обучющая и валидационная выборки 75:25)
        cv=tscv, # реализация параметра KFold кроссвалидации для временных рядов
        n_jobs=-1, # количество параллельно выполняемых заданий (-1 - задействованы все процессоры)
        scoring=smape_score, # Передача функции ошибки через make_scorer в HalvingGridSearchCV
        error_score='raise', #0 , # 
        random_state=STATE
    )
    grid.fit(features_train, target_train)
    
    finish_time = time.time()
    funtion_time = finish_time - start_time
    
    return grid, funtion_time

In [28]:

# Вывод на печать результатов модели
def print_model_result(grids, data_times, model_name):
    print('Модель   :', model_name)
    print('RMSE     :', grids[-1].best_score_)
    print(f'Время    : {data_times[-1]} секунд')
    print('Параметры:\n', grids[-1].best_estimator_)
    print()
    print('-'*20)
    print()

Функции моделей¶

In [29]:

# LinearRegression
def grids_LinearRegression(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grid, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [LinearRegression()] # score: R^2
        }]
    )
    
    grids.append(grid)
    data_times.append(time_best)
    
    return grids, data_times

In [30]:

# DecisionTreeRegressor
def grids_DecisionTreeRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grids_this = 0
    grids_best = 0
    funtion_time = 0
    time_best = 0
    
    # Поиск "regressor__max_depth"
    
    range_min = 1
    range_max = 201
    range_step = 20
    
    for i in range(1, 5, 1):
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [DecisionTreeRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': range(
                    range_min, 
                    range_max, 
                    range_step
                )
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0:
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_:
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__max_depth = grids_this.best_params_['regressor__max_depth']
        if int(regressor__max_depth - range_step / 2) > 0:
            range_min = int(regressor__max_depth - range_step / 2)
        else:
            range_min = regressor__max_depth
        range_max = int(regressor__max_depth + range_step / 2) + 1
        range_step = int(range_step / 2)
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times

In [31]:

# RandomForestRegressor
def grids_RandomForestRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grids_this = 0
    grids_best = 0
    funtion_time = 0
    time_best = 0
    
    # Поиск "regressor__max_depth"
    
    range_min = 20
    range_max = 61
    range_step = 20
    
    for i in range(1, 5, 1):
        
        #print('regressor__max_depth =', range(range_min, range_max, range_step))
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': range(
                    range_min, 
                    range_max, 
                    range_step
                ), 
                'regressor__n_estimators': [1]
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0:
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__max_depth = grids_this.best_params_['regressor__max_depth']
        if int(regressor__max_depth - range_step / 2) > 0:
            range_min = int(regressor__max_depth - range_step / 2)
        else:
            range_min = regressor__max_depth
        range_max = int(regressor__max_depth + range_step / 2) + 1
        range_step = int(range_step / 2)
        if range_step == 0: range_step = 1
    
    # Поиск "regressor__n_estimators"
    
    range_min = 10
    range_max = 31
    range_step = 10
    
    for i in range(1, 5, 1):
        
        #print('regressor__n_estimators =', range(range_min, range_max, range_step))
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': [regressor__max_depth], 
                'regressor__n_estimators': range(
                    range_min, 
                    range_max, 
                    range_step
                )
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0: 
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__n_estimators = grids_this.best_params_['regressor__n_estimators']
        if int(regressor__n_estimators - range_step / 2) > 0:
            range_min = int(regressor__n_estimators - range_step / 2)
        else:
            range_min = regressor__n_estimators
        range_max = int(regressor__n_estimators + range_step / 2) + 1
        range_step = int(range_step / 10)
        if range_step == 0: range_step = 1
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times

In [32]:

# CatBoostRegressor
def grids_CatBoostRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    # Поиск лучших параметров
    
    grids_best, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [CatBoostRegressor()]
        }]
    )
    
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times

Применение функций¶

In [33]:

# Дополнение датафрейма признаками для предсказания

# Сдвиг с разным количеством часов
data = shift(data, 'num_orders', 1, 25, 1)
data = shift(data, 'num_orders', 48, 169, 24) 

# Удаление объектов с пропусками
data = data.dropna()

In [35]:

import matplotlib.pyplot as plt


# Код ревьюера
from statsmodels.graphics import tsaplots

taxi = pd.read_csv("/datasets/taxi.csv", index_col=[0], parse_dates=[0]).resample('1H').sum()
ts = taxi['num_orders']

fig = tsaplots.plot_acf(ts,lags= 168)
fig.set_size_inches(12, 4)
plt.ylim(-.1, 1.1)
plt.show() 

In [36]:

# Проверка на корреляцию 
# с учетом новых признаков
data.corr()

Out[36]:

	num_orders	shift_1	shift_2	shift_3	shift_4	shift_5	shift_6	shift_7	shift_8	shift_9	shift_10	shift_11	shift_12	shift_13	shift_14	shift_15	shift_16	shift_17	shift_18	shift_19	shift_20	shift_21	shift_22	shift_23	shift_24	shift_48	shift_72	shift_96	shift_120	shift_144	shift_168
num_orders	1.000000	0.620135	0.457495	0.309589	0.237669	0.221121	0.230579	0.283844	0.304086	0.263288	0.256139	0.311835	0.329491	0.309149	0.253096	0.230138	0.268893	0.250149	0.204702	0.190513	0.210192	0.256761	0.398424	0.553358	0.688453	0.721757	0.704936	0.703516	0.715268	0.662852	0.805569
shift_1	0.620135	1.000000	0.619400	0.457005	0.308968	0.237148	0.219818	0.229120	0.282670	0.303873	0.262809	0.255283	0.311336	0.328714	0.308038	0.251818	0.229030	0.269543	0.250961	0.204829	0.188482	0.210151	0.255151	0.397514	0.552344	0.565374	0.550262	0.555192	0.562925	0.526133	0.587533
shift_2	0.457495	0.619400	1.000000	0.619129	0.456444	0.308322	0.235709	0.217957	0.227518	0.282292	0.303217	0.261719	0.254477	0.310314	0.327265	0.306514	0.250393	0.229515	0.270267	0.250881	0.202438	0.188352	0.207948	0.253986	0.395758	0.412541	0.382758	0.401260	0.411728	0.375199	0.427841
shift_3	0.309589	0.457005	0.619129	1.000000	0.618861	0.456084	0.307669	0.234772	0.217100	0.227228	0.281862	0.302633	0.261225	0.253867	0.309518	0.326498	0.305776	0.250576	0.229812	0.270120	0.249841	0.202325	0.187191	0.207294	0.252997	0.268735	0.254242	0.272974	0.276470	0.235401	0.276096
shift_4	0.237669	0.308968	0.456444	0.618861	1.000000	0.618711	0.455610	0.307029	0.234138	0.216949	0.226962	0.281419	0.302365	0.260763	0.253275	0.308928	0.326001	0.306191	0.251090	0.229939	0.269270	0.249815	0.201492	0.186444	0.206243	0.193983	0.189115	0.187268	0.213590	0.167226	0.202079
shift_5	0.221121	0.237148	0.308322	0.456084	0.618711	1.000000	0.618513	0.455449	0.306755	0.234115	0.216867	0.226705	0.281354	0.302125	0.260503	0.252933	0.308706	0.326480	0.306751	0.251381	0.229377	0.269285	0.249486	0.200839	0.185665	0.200142	0.192737	0.185506	0.197632	0.170677	0.178847
shift_6	0.230579	0.219818	0.235709	0.307669	0.455610	0.618513	1.000000	0.617690	0.454482	0.306523	0.233578	0.215931	0.226097	0.280489	0.300952	0.259190	0.251805	0.309360	0.327329	0.306894	0.249467	0.229342	0.267628	0.248359	0.198738	0.196523	0.205320	0.183276	0.217891	0.166067	0.192922
shift_7	0.283844	0.229120	0.217957	0.234772	0.307029	0.455449	0.617690	1.000000	0.617115	0.454578	0.306270	0.232766	0.215577	0.225250	0.279489	0.299797	0.258256	0.252894	0.310676	0.327965	0.305015	0.249565	0.227821	0.266204	0.246102	0.242306	0.249564	0.230417	0.250664	0.222026	0.244383
shift_8	0.304086	0.282670	0.227518	0.217100	0.234138	0.306755	0.454482	0.617115	1.000000	0.617258	0.454454	0.305653	0.232489	0.214849	0.224363	0.278498	0.299067	0.259200	0.253988	0.311212	0.326480	0.305113	0.248364	0.226515	0.264368	0.253019	0.273134	0.249454	0.262986	0.241219	0.263164
shift_9	0.263288	0.303873	0.282292	0.227228	0.216949	0.234115	0.306523	0.454578	0.617258	1.000000	0.617266	0.454429	0.305673	0.232384	0.214782	0.224246	0.278455	0.299423	0.259614	0.254230	0.311209	0.326500	0.305145	0.247978	0.226249	0.220400	0.241174	0.219171	0.224227	0.194741	0.221463
shift_10	0.256139	0.262809	0.303217	0.281862	0.226962	0.216867	0.233578	0.306270	0.454454	0.617266	1.000000	0.617186	0.454412	0.305468	0.232172	0.214465	0.224041	0.278968	0.300007	0.259949	0.253774	0.311232	0.326335	0.304556	0.247373	0.216071	0.247371	0.225622	0.234992	0.208213	0.222351
shift_11	0.311835	0.255283	0.261719	0.302633	0.281419	0.226705	0.215931	0.232766	0.305653	0.454429	0.617186	1.000000	0.617090	0.454045	0.304905	0.231433	0.213879	0.224685	0.279738	0.300341	0.258857	0.253791	0.310497	0.325485	0.303443	0.265544	0.291495	0.263223	0.277438	0.245149	0.271616
shift_12	0.329491	0.311336	0.254477	0.261225	0.302365	0.281354	0.226097	0.215577	0.232489	0.305673	0.454412	0.617090	1.000000	0.616981	0.453952	0.304643	0.231239	0.214482	0.225356	0.280139	0.299914	0.258886	0.253554	0.309824	0.324953	0.275524	0.302211	0.276826	0.293052	0.264185	0.285856
shift_13	0.309149	0.328714	0.310314	0.253867	0.260763	0.302125	0.280489	0.225250	0.214849	0.232384	0.305468	0.454045	0.616981	1.000000	0.616657	0.453431	0.304101	0.231870	0.215224	0.225669	0.279071	0.299943	0.258045	0.252640	0.308712	0.256221	0.285916	0.261121	0.276818	0.246308	0.267079
shift_14	0.253096	0.308038	0.327265	0.309518	0.253275	0.260503	0.300952	0.279489	0.224363	0.214782	0.232172	0.304905	0.453952	0.616657	1.000000	0.616175	0.452933	0.305093	0.232980	0.215802	0.224028	0.279151	0.298967	0.256764	0.250839	0.223926	0.237397	0.219591	0.239061	0.202089	0.225588
shift_15	0.230138	0.251818	0.306514	0.326498	0.308928	0.252933	0.259190	0.299797	0.278498	0.224246	0.214465	0.231433	0.304643	0.453431	0.616175	1.000000	0.615748	0.454008	0.306252	0.233486	0.213905	0.224078	0.277889	0.297716	0.254792	0.213786	0.229401	0.214225	0.228231	0.200412	0.190575
shift_16	0.268893	0.229030	0.250393	0.305776	0.326001	0.308706	0.251805	0.258256	0.299067	0.278455	0.224041	0.213879	0.231239	0.304101	0.452933	0.615748	1.000000	0.616716	0.455085	0.306778	0.232028	0.213941	0.223090	0.276738	0.296217	0.250675	0.262880	0.246083	0.266385	0.240532	0.239505
shift_17	0.250149	0.269543	0.229515	0.250576	0.306191	0.326480	0.309360	0.252894	0.259200	0.299423	0.278968	0.224685	0.214482	0.231870	0.305093	0.454008	0.616716	1.000000	0.616835	0.455421	0.308261	0.232201	0.215278	0.223402	0.278043	0.227915	0.249851	0.231033	0.237987	0.230035	0.219059
shift_18	0.204702	0.250961	0.270267	0.229812	0.251090	0.306751	0.327329	0.310676	0.253988	0.259614	0.300007	0.279738	0.225356	0.215224	0.232980	0.306252	0.455085	0.616835	1.000000	0.617106	0.457435	0.308455	0.233742	0.215752	0.224871	0.187483	0.218592	0.176143	0.190961	0.184396	0.164577
shift_19	0.190513	0.204829	0.250881	0.270120	0.229939	0.251381	0.306894	0.327965	0.311212	0.254230	0.259949	0.300341	0.280139	0.225669	0.215802	0.233486	0.306778	0.455421	0.617106	1.000000	0.618372	0.457460	0.309330	0.233586	0.216173	0.185068	0.194705	0.172680	0.178141	0.179383	0.154198
shift_20	0.210192	0.188482	0.202438	0.249841	0.269270	0.229377	0.249467	0.305015	0.326480	0.311209	0.253774	0.258857	0.299914	0.279071	0.224028	0.213905	0.232028	0.308261	0.457435	0.618372	1.000000	0.619304	0.455788	0.307729	0.230399	0.178514	0.213873	0.175453	0.177832	0.183711	0.172041
shift_21	0.256761	0.210151	0.188352	0.202325	0.249815	0.269285	0.229342	0.249565	0.305113	0.326500	0.311232	0.253791	0.258886	0.299943	0.279151	0.224078	0.213941	0.232201	0.308455	0.457460	0.619304	1.000000	0.619829	0.455809	0.308040	0.247049	0.272047	0.258162	0.241553	0.264840	0.229088
shift_22	0.398424	0.255151	0.207948	0.187191	0.201492	0.249486	0.267628	0.227821	0.248364	0.305145	0.326335	0.310497	0.253554	0.258045	0.298967	0.277889	0.223090	0.215278	0.233742	0.309330	0.455788	0.619829	1.000000	0.618725	0.453872	0.389171	0.418439	0.394519	0.376572	0.406835	0.365971
shift_23	0.553358	0.397514	0.253986	0.207294	0.186444	0.200839	0.248359	0.266204	0.226515	0.247978	0.304556	0.325485	0.309824	0.252640	0.256764	0.297716	0.276738	0.223402	0.215752	0.233586	0.307729	0.455809	0.618725	1.000000	0.618042	0.540021	0.561861	0.543439	0.539196	0.554159	0.524784
shift_24	0.688453	0.552344	0.395758	0.252997	0.206243	0.185665	0.198738	0.246102	0.264368	0.226249	0.247373	0.303443	0.324953	0.308712	0.250839	0.254792	0.296217	0.278043	0.224871	0.216173	0.230399	0.308040	0.453872	0.618042	1.000000	0.682058	0.718080	0.703683	0.698514	0.714807	0.658818
shift_48	0.721757	0.565374	0.412541	0.268735	0.193983	0.200142	0.196523	0.242306	0.253019	0.220400	0.216071	0.265544	0.275524	0.256221	0.223926	0.213786	0.250675	0.227915	0.187483	0.185068	0.178514	0.247049	0.389171	0.540021	0.682058	1.000000	0.678322	0.716388	0.699773	0.696562	0.712505
shift_72	0.704936	0.550262	0.382758	0.254242	0.189115	0.192737	0.205320	0.249564	0.273134	0.241174	0.247371	0.291495	0.302211	0.285916	0.237397	0.229401	0.262880	0.249851	0.218592	0.194705	0.213873	0.272047	0.418439	0.561861	0.718080	0.678322	1.000000	0.675299	0.716536	0.696704	0.694499
shift_96	0.703516	0.555192	0.401260	0.272974	0.187268	0.185506	0.183276	0.230417	0.249454	0.219171	0.225622	0.263223	0.276826	0.261121	0.219591	0.214225	0.246083	0.231033	0.176143	0.172680	0.175453	0.258162	0.394519	0.543439	0.703683	0.716388	0.675299	1.000000	0.676018	0.714939	0.695163
shift_120	0.715268	0.562925	0.411728	0.276470	0.213590	0.197632	0.217891	0.250664	0.262986	0.224227	0.234992	0.277438	0.293052	0.276818	0.239061	0.228231	0.266385	0.237987	0.190961	0.178141	0.177832	0.241553	0.376572	0.539196	0.698514	0.699773	0.716536	0.676018	1.000000	0.675067	0.712939
shift_144	0.662852	0.526133	0.375199	0.235401	0.167226	0.170677	0.166067	0.222026	0.241219	0.194741	0.208213	0.245149	0.264185	0.246308	0.202089	0.200412	0.240532	0.230035	0.184396	0.179383	0.183711	0.264840	0.406835	0.554159	0.714807	0.696562	0.696704	0.714939	0.675067	1.000000	0.671957
shift_168	0.805569	0.587533	0.427841	0.276096	0.202079	0.178847	0.192922	0.244383	0.263164	0.221463	0.222351	0.271616	0.285856	0.267079	0.225588	0.190575	0.239505	0.219059	0.164577	0.154198	0.172041	0.229088	0.365971	0.524784	0.658818	0.712505	0.694499	0.695163	0.712939	0.671957	1.000000

Новые признаки предсказуемо имеют выше среднего корреляцию с целевым признаком, т.к. они созданы на его основе. При этом, корреляция не высокая и потому данный датафрейм может быть использован в машинном обучении.

Исключением является признак shift_168 со сдвигом целевого значения на 168 часов. Этот признак имеет высокую корреляцию с целевым признаком, поэтому требуется рассмотреть возможность исключён я его из датафрейма.

In [37]:

# Удаление высоко коррелирующего признака
data = data.drop('shift_168', axis=1)

In [38]:

# Проверка на корреляцию 
# с учетом новых признаков
data.corr()

Out[38]:

	num_orders	shift_1	shift_2	shift_3	shift_4	shift_5	shift_6	shift_7	shift_8	shift_9	shift_10	shift_11	shift_12	shift_13	shift_14	shift_15	shift_16	shift_17	shift_18	shift_19	shift_20	shift_21	shift_22	shift_23	shift_24	shift_48	shift_72	shift_96	shift_120	shift_144
num_orders	1.000000	0.620135	0.457495	0.309589	0.237669	0.221121	0.230579	0.283844	0.304086	0.263288	0.256139	0.311835	0.329491	0.309149	0.253096	0.230138	0.268893	0.250149	0.204702	0.190513	0.210192	0.256761	0.398424	0.553358	0.688453	0.721757	0.704936	0.703516	0.715268	0.662852
shift_1	0.620135	1.000000	0.619400	0.457005	0.308968	0.237148	0.219818	0.229120	0.282670	0.303873	0.262809	0.255283	0.311336	0.328714	0.308038	0.251818	0.229030	0.269543	0.250961	0.204829	0.188482	0.210151	0.255151	0.397514	0.552344	0.565374	0.550262	0.555192	0.562925	0.526133
shift_2	0.457495	0.619400	1.000000	0.619129	0.456444	0.308322	0.235709	0.217957	0.227518	0.282292	0.303217	0.261719	0.254477	0.310314	0.327265	0.306514	0.250393	0.229515	0.270267	0.250881	0.202438	0.188352	0.207948	0.253986	0.395758	0.412541	0.382758	0.401260	0.411728	0.375199
shift_3	0.309589	0.457005	0.619129	1.000000	0.618861	0.456084	0.307669	0.234772	0.217100	0.227228	0.281862	0.302633	0.261225	0.253867	0.309518	0.326498	0.305776	0.250576	0.229812	0.270120	0.249841	0.202325	0.187191	0.207294	0.252997	0.268735	0.254242	0.272974	0.276470	0.235401
shift_4	0.237669	0.308968	0.456444	0.618861	1.000000	0.618711	0.455610	0.307029	0.234138	0.216949	0.226962	0.281419	0.302365	0.260763	0.253275	0.308928	0.326001	0.306191	0.251090	0.229939	0.269270	0.249815	0.201492	0.186444	0.206243	0.193983	0.189115	0.187268	0.213590	0.167226
shift_5	0.221121	0.237148	0.308322	0.456084	0.618711	1.000000	0.618513	0.455449	0.306755	0.234115	0.216867	0.226705	0.281354	0.302125	0.260503	0.252933	0.308706	0.326480	0.306751	0.251381	0.229377	0.269285	0.249486	0.200839	0.185665	0.200142	0.192737	0.185506	0.197632	0.170677
shift_6	0.230579	0.219818	0.235709	0.307669	0.455610	0.618513	1.000000	0.617690	0.454482	0.306523	0.233578	0.215931	0.226097	0.280489	0.300952	0.259190	0.251805	0.309360	0.327329	0.306894	0.249467	0.229342	0.267628	0.248359	0.198738	0.196523	0.205320	0.183276	0.217891	0.166067
shift_7	0.283844	0.229120	0.217957	0.234772	0.307029	0.455449	0.617690	1.000000	0.617115	0.454578	0.306270	0.232766	0.215577	0.225250	0.279489	0.299797	0.258256	0.252894	0.310676	0.327965	0.305015	0.249565	0.227821	0.266204	0.246102	0.242306	0.249564	0.230417	0.250664	0.222026
shift_8	0.304086	0.282670	0.227518	0.217100	0.234138	0.306755	0.454482	0.617115	1.000000	0.617258	0.454454	0.305653	0.232489	0.214849	0.224363	0.278498	0.299067	0.259200	0.253988	0.311212	0.326480	0.305113	0.248364	0.226515	0.264368	0.253019	0.273134	0.249454	0.262986	0.241219
shift_9	0.263288	0.303873	0.282292	0.227228	0.216949	0.234115	0.306523	0.454578	0.617258	1.000000	0.617266	0.454429	0.305673	0.232384	0.214782	0.224246	0.278455	0.299423	0.259614	0.254230	0.311209	0.326500	0.305145	0.247978	0.226249	0.220400	0.241174	0.219171	0.224227	0.194741
shift_10	0.256139	0.262809	0.303217	0.281862	0.226962	0.216867	0.233578	0.306270	0.454454	0.617266	1.000000	0.617186	0.454412	0.305468	0.232172	0.214465	0.224041	0.278968	0.300007	0.259949	0.253774	0.311232	0.326335	0.304556	0.247373	0.216071	0.247371	0.225622	0.234992	0.208213
shift_11	0.311835	0.255283	0.261719	0.302633	0.281419	0.226705	0.215931	0.232766	0.305653	0.454429	0.617186	1.000000	0.617090	0.454045	0.304905	0.231433	0.213879	0.224685	0.279738	0.300341	0.258857	0.253791	0.310497	0.325485	0.303443	0.265544	0.291495	0.263223	0.277438	0.245149
shift_12	0.329491	0.311336	0.254477	0.261225	0.302365	0.281354	0.226097	0.215577	0.232489	0.305673	0.454412	0.617090	1.000000	0.616981	0.453952	0.304643	0.231239	0.214482	0.225356	0.280139	0.299914	0.258886	0.253554	0.309824	0.324953	0.275524	0.302211	0.276826	0.293052	0.264185
shift_13	0.309149	0.328714	0.310314	0.253867	0.260763	0.302125	0.280489	0.225250	0.214849	0.232384	0.305468	0.454045	0.616981	1.000000	0.616657	0.453431	0.304101	0.231870	0.215224	0.225669	0.279071	0.299943	0.258045	0.252640	0.308712	0.256221	0.285916	0.261121	0.276818	0.246308
shift_14	0.253096	0.308038	0.327265	0.309518	0.253275	0.260503	0.300952	0.279489	0.224363	0.214782	0.232172	0.304905	0.453952	0.616657	1.000000	0.616175	0.452933	0.305093	0.232980	0.215802	0.224028	0.279151	0.298967	0.256764	0.250839	0.223926	0.237397	0.219591	0.239061	0.202089
shift_15	0.230138	0.251818	0.306514	0.326498	0.308928	0.252933	0.259190	0.299797	0.278498	0.224246	0.214465	0.231433	0.304643	0.453431	0.616175	1.000000	0.615748	0.454008	0.306252	0.233486	0.213905	0.224078	0.277889	0.297716	0.254792	0.213786	0.229401	0.214225	0.228231	0.200412
shift_16	0.268893	0.229030	0.250393	0.305776	0.326001	0.308706	0.251805	0.258256	0.299067	0.278455	0.224041	0.213879	0.231239	0.304101	0.452933	0.615748	1.000000	0.616716	0.455085	0.306778	0.232028	0.213941	0.223090	0.276738	0.296217	0.250675	0.262880	0.246083	0.266385	0.240532
shift_17	0.250149	0.269543	0.229515	0.250576	0.306191	0.326480	0.309360	0.252894	0.259200	0.299423	0.278968	0.224685	0.214482	0.231870	0.305093	0.454008	0.616716	1.000000	0.616835	0.455421	0.308261	0.232201	0.215278	0.223402	0.278043	0.227915	0.249851	0.231033	0.237987	0.230035
shift_18	0.204702	0.250961	0.270267	0.229812	0.251090	0.306751	0.327329	0.310676	0.253988	0.259614	0.300007	0.279738	0.225356	0.215224	0.232980	0.306252	0.455085	0.616835	1.000000	0.617106	0.457435	0.308455	0.233742	0.215752	0.224871	0.187483	0.218592	0.176143	0.190961	0.184396
shift_19	0.190513	0.204829	0.250881	0.270120	0.229939	0.251381	0.306894	0.327965	0.311212	0.254230	0.259949	0.300341	0.280139	0.225669	0.215802	0.233486	0.306778	0.455421	0.617106	1.000000	0.618372	0.457460	0.309330	0.233586	0.216173	0.185068	0.194705	0.172680	0.178141	0.179383
shift_20	0.210192	0.188482	0.202438	0.249841	0.269270	0.229377	0.249467	0.305015	0.326480	0.311209	0.253774	0.258857	0.299914	0.279071	0.224028	0.213905	0.232028	0.308261	0.457435	0.618372	1.000000	0.619304	0.455788	0.307729	0.230399	0.178514	0.213873	0.175453	0.177832	0.183711
shift_21	0.256761	0.210151	0.188352	0.202325	0.249815	0.269285	0.229342	0.249565	0.305113	0.326500	0.311232	0.253791	0.258886	0.299943	0.279151	0.224078	0.213941	0.232201	0.308455	0.457460	0.619304	1.000000	0.619829	0.455809	0.308040	0.247049	0.272047	0.258162	0.241553	0.264840
shift_22	0.398424	0.255151	0.207948	0.187191	0.201492	0.249486	0.267628	0.227821	0.248364	0.305145	0.326335	0.310497	0.253554	0.258045	0.298967	0.277889	0.223090	0.215278	0.233742	0.309330	0.455788	0.619829	1.000000	0.618725	0.453872	0.389171	0.418439	0.394519	0.376572	0.406835
shift_23	0.553358	0.397514	0.253986	0.207294	0.186444	0.200839	0.248359	0.266204	0.226515	0.247978	0.304556	0.325485	0.309824	0.252640	0.256764	0.297716	0.276738	0.223402	0.215752	0.233586	0.307729	0.455809	0.618725	1.000000	0.618042	0.540021	0.561861	0.543439	0.539196	0.554159
shift_24	0.688453	0.552344	0.395758	0.252997	0.206243	0.185665	0.198738	0.246102	0.264368	0.226249	0.247373	0.303443	0.324953	0.308712	0.250839	0.254792	0.296217	0.278043	0.224871	0.216173	0.230399	0.308040	0.453872	0.618042	1.000000	0.682058	0.718080	0.703683	0.698514	0.714807
shift_48	0.721757	0.565374	0.412541	0.268735	0.193983	0.200142	0.196523	0.242306	0.253019	0.220400	0.216071	0.265544	0.275524	0.256221	0.223926	0.213786	0.250675	0.227915	0.187483	0.185068	0.178514	0.247049	0.389171	0.540021	0.682058	1.000000	0.678322	0.716388	0.699773	0.696562
shift_72	0.704936	0.550262	0.382758	0.254242	0.189115	0.192737	0.205320	0.249564	0.273134	0.241174	0.247371	0.291495	0.302211	0.285916	0.237397	0.229401	0.262880	0.249851	0.218592	0.194705	0.213873	0.272047	0.418439	0.561861	0.718080	0.678322	1.000000	0.675299	0.716536	0.696704
shift_96	0.703516	0.555192	0.401260	0.272974	0.187268	0.185506	0.183276	0.230417	0.249454	0.219171	0.225622	0.263223	0.276826	0.261121	0.219591	0.214225	0.246083	0.231033	0.176143	0.172680	0.175453	0.258162	0.394519	0.543439	0.703683	0.716388	0.675299	1.000000	0.676018	0.714939
shift_120	0.715268	0.562925	0.411728	0.276470	0.213590	0.197632	0.217891	0.250664	0.262986	0.224227	0.234992	0.277438	0.293052	0.276818	0.239061	0.228231	0.266385	0.237987	0.190961	0.178141	0.177832	0.241553	0.376572	0.539196	0.698514	0.699773	0.716536	0.676018	1.000000	0.675067
shift_144	0.662852	0.526133	0.375199	0.235401	0.167226	0.170677	0.166067	0.222026	0.241219	0.194741	0.208213	0.245149	0.264185	0.246308	0.202089	0.200412	0.240532	0.230035	0.184396	0.179383	0.183711	0.264840	0.406835	0.554159	0.714807	0.696562	0.696704	0.714939	0.675067	1.000000

In [39]:

# Подготовка выборок из датафрейма

# Разделение обучающего датафрейма на целевую и нецелевую выборку
features_train,  features_test, target_train, target_test = data_preprocessing(data, 'num_orders')

print(features_train.info())
features_train.head()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 3186 entries, 2018-03-08 00:00:00 to 2018-07-18 17:00:00
Freq: H
Data columns (total 29 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   shift_1    3186 non-null   float64
 1   shift_2    3186 non-null   float64
 2   shift_3    3186 non-null   float64
 3   shift_4    3186 non-null   float64
 4   shift_5    3186 non-null   float64
 5   shift_6    3186 non-null   float64
 6   shift_7    3186 non-null   float64
 7   shift_8    3186 non-null   float64
 8   shift_9    3186 non-null   float64
 9   shift_10   3186 non-null   float64
 10  shift_11   3186 non-null   float64
 11  shift_12   3186 non-null   float64
 12  shift_13   3186 non-null   float64
 13  shift_14   3186 non-null   float64
 14  shift_15   3186 non-null   float64
 15  shift_16   3186 non-null   float64
 16  shift_17   3186 non-null   float64
 17  shift_18   3186 non-null   float64
 18  shift_19   3186 non-null   float64
 19  shift_20   3186 non-null   float64
 20  shift_21   3186 non-null   float64
 21  shift_22   3186 non-null   float64
 22  shift_23   3186 non-null   float64
 23  shift_24   3186 non-null   float64
 24  shift_48   3186 non-null   float64
 25  shift_72   3186 non-null   float64
 26  shift_96   3186 non-null   float64
 27  shift_120  3186 non-null   float64
 28  shift_144  3186 non-null   float64
dtypes: float64(29)
memory usage: 746.7 KB
None

Out[39]:

	shift_1	shift_2	shift_3	shift_4	shift_5	shift_6	shift_7	shift_8	shift_9	shift_10	shift_11	shift_12	shift_13	shift_14	shift_15	shift_16	shift_17	shift_18	shift_19	shift_20	shift_21	shift_22	shift_23	shift_24	shift_48	shift_72	shift_96	shift_120	shift_144
datetime
2018-03-08 00:00:00	94.0	127.0	108.0	76.0	50.0	99.0	48.0	46.0	51.0	43.0	55.0	34.0	58.0	34.0	50.0	38.0	17.0	19.0	10.0	73.0	66.0	24.0	121.0	100.0	42.0	86.0	75.0	163.0	90.0
2018-03-08 01:00:00	143.0	94.0	127.0	108.0	76.0	50.0	99.0	48.0	46.0	51.0	43.0	55.0	34.0	58.0	34.0	50.0	38.0	17.0	19.0	10.0	73.0	66.0	24.0	121.0	75.0	176.0	60.0	108.0	120.0
2018-03-08 02:00:00	78.0	143.0	94.0	127.0	108.0	76.0	50.0	99.0	48.0	46.0	51.0	43.0	55.0	34.0	58.0	34.0	50.0	38.0	17.0	19.0	10.0	73.0	66.0	24.0	36.0	32.0	26.0	25.0	75.0
2018-03-08 03:00:00	65.0	78.0	143.0	94.0	127.0	108.0	76.0	50.0	99.0	48.0	46.0	51.0	43.0	55.0	34.0	58.0	34.0	50.0	38.0	17.0	19.0	10.0	73.0	66.0	49.0	51.0	42.0	64.0	64.0
2018-03-08 04:00:00	68.0	65.0	78.0	143.0	94.0	127.0	108.0	76.0	50.0	99.0	48.0	46.0	51.0	43.0	55.0	34.0	58.0	34.0	50.0	38.0	17.0	19.0	10.0	73.0	30.0	34.0	27.0	44.0	20.0

In [40]:

# Поиск лучших моделей и их параметров
data_grids = []
data_times = []

In [41]:

# LinearRegression (dummy-model)
data_grids, data_times = grids_LinearRegression(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'LinearRegression')

Модель   : LinearRegression
RMSE     : 21.963961830527317
Время    : 1.5816013813018799 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['shift_1', 'shift_2', 'shift_3', 'shift_4', 'shift_5', 'shift_6',
       'shift_7', 'shift_8', 'shift_9', 'shift_10', 'shift_11', 'shift_12',
       'shift_13', 'shift_14', 'shift_15', 'shift_16', 'shift_17', 'shift_18',
       'shift_19', 'shift_20', 'shift_21', 'shift_22', 'shift_23', 'shift_24',
       'shift_48', 'shift_72', 'shift_96', 'shift_120', 'shift_144'],
      dtype='object'))])),
                ('regressor', LinearRegression())])

--------------------

In [42]:

# DecisionTreeRegressor
data_grids, data_times = grids_DecisionTreeRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'DecisionTreeRegressor')

Модель   : DecisionTreeRegressor
RMSE     : 31.302573505395564
Время    : 2.1100189685821533 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['shift_1', 'shift_2', 'shift_3', 'shift_4', 'shift_5', 'shift_6',
       'shift_7', 'shift_8', 'shift_9', 'shift_10', 'shift_11', 'shift_12',
       'shift_13', 'shift_14', 'shift_15', 'shift_16', 'shift_17', 'shift_18',
       'shift_19', 'shift_20', 'shift_21', 'shift_22', 'shift_23', 'shift_24',
       'shift_48', 'shift_72', 'shift_96', 'shift_120', 'shift_144'],
      dtype='object'))])),
                ('regressor',
                 DecisionTreeRegressor(max_depth=161, random_state=42))])

--------------------

In [43]:

# RandomForestRegressor 
data_grids, data_times = grids_RandomForestRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'RandomForestRegressor')

Модель   : RandomForestRegressor
RMSE     : 32.970024310279314
Время    : 5.284083127975464 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['shift_1', 'shift_2', 'shift_3', 'shift_4', 'shift_5', 'shift_6',
       'shift_7', 'shift_8', 'shift_9', 'shift_10', 'shift_11', 'shift_12',
       'shift_13', 'shift_14', 'shift_15', 'shift_16', 'shift_17', 'shift_18',
       'shift_19', 'shift_20', 'shift_21', 'shift_22', 'shift_23', 'shift_24',
       'shift_48', 'shift_72', 'shift_96', 'shift_120', 'shift_144'],
      dtype='object'))])),
                ('regressor',
                 RandomForestRegressor(max_depth=60, n_estimators=1,
                                       random_state=42))])

--------------------

In [44]:

# CatBoostRegressor
data_grids, data_times = grids_CatBoostRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'CatBoostRegressor')

Learning rate set to 0.049169
0:	learn: 33.9824879	total: 60.9ms	remaining: 1m
1:	learn: 33.1848914	total: 73.7ms	remaining: 36.8s
2:	learn: 32.3944481	total: 84.2ms	remaining: 28s
3:	learn: 31.6594623	total: 93ms	remaining: 23.2s
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Модель   : CatBoostRegressor
RMSE     : 21.08639239601687
Время    : 55.57100772857666 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['shift_1', 'shift_2', 'shift_3', 'shift_4', 'shift_5', 'shift_6',
       'shift_7', 'shift_8', 'shift_9', 'shift_10', 'shift_11', 'shift_12',
       'shift_13', 'shift_14', 'shift_15', 'shift_16', 'shift_17', 'shift_18',
       'shift_19', 'shift_20', 'shift_21', 'shift_22', 'shift_23', 'shift_24',
       'shift_48', 'shift_72', 'shift_96', 'shift_120', 'shift_144'],
      dtype='object'))])),
                ('regressor',
                 <catboost.core.CatBoostRegressor object at 0x7ff27edc5760>)])

--------------------

Выбор лучшей модели¶

In [45]:

# Лучшая модель из расчета RMSE
data_grids_best = data_grids[0]
data_times_best = data_times[0]
n = 0
for i in range(0, len(data_grids)):
    if data_grids[i].best_score_ < data_grids_best.best_score_: 
    #if (data_grids[i].best_score_ < data_grids_best.best_score_) & (data_times[i] < data_times_best): 
        data_grids_best = data_grids[i]
        data_times_best = data_times[i]

print('Лучшее время : ', data_times_best)
print('Лучшее RMSE  : ', data_grids_best.best_score_)
print('Лучшая модель: ')
data_grids_best

Лучшее время :  55.57100772857666
Лучшее RMSE  :  21.08639239601687
Лучшая модель:

Out[45]:

HalvingGridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),
                    error_score='raise',
                    estimator=Pipeline(steps=[('preprocessor',
                                               ColumnTransformer(transformers=[('pipeline',
                                                                                Pipeline(steps=[('standardscaler',
                                                                                                 StandardScaler())]),
                                                                                Index(['shift_1', 'shift_2', 'shift_3', 'shift_4', 'shift_5', 'shift_6',
       'shift_7', 'shift_8', 'shift_9', '...
       'shift_48', 'shift_72', 'shift_96', 'shift_120', 'shift_144'],
      dtype='object'))])),
                                              ('regressor',
                                               <catboost.core.CatBoostRegressor object at 0x7ff27eddf340>)]),
                    n_jobs=-1,
                    param_grid=[{'regressor': [<catboost.core.CatBoostRegressor object at 0x7ff27eddf340>]}],
                    random_state=42,
                    refit=<function _refit_callable at 0x7ff290e17040>,
                    scoring=make_scorer(mean_squared_error, squared=False))

Выводы из обучения¶

Самой эффективной моделью с точки зрения RMSE, но и самой медленной оказалась модель CatBoostRegressor с параметрами по умолчанию. Данная модель показала RMSE более чем на половину меньше требуемого в техническом задании.

Тестирование¶

Проведение тестирования¶

In [46]:

start_time = time.time()

# Предсказание лучшей модели
predict = data_grids_best.predict(features_test)

finish_time = time.time()
funtion_time = finish_time - start_time

# Расчет RMSE и времени выполнения предсказания
print('RMSE              :', mean_squared_error(target_test, predict, squared=False))
print(f'Время предсказания: {funtion_time} секунд')

RMSE              : 40.03656030838836
Время предсказания: 0.009637832641601562 секунд

Выводы из тестирования¶

Модель CatBoostRegressor с параметрами по умолчанию при тестировании показала значение RMSE почти в 2 раза больше, чем с обучающей выборкой, но меньше требуемого техническим заданием. Важно отметить очень мальнькое время в 1/600 секунды, которое требуется модели для предсказания.

Выводы из проекта¶

Цель проекта достигнута. Для предсказания количества заказов на часовых интервалах выбрана модель CatBoostRegressor с параметрами по умолчанию. Ее значение RMSE на тестовых данных равно 40, что меньше требуемого в техническом задании.

Здесь важно заметить, что предсказание путем использования сдвига на 168 часов (1 неделю) даёт RMSE в размере 27.57, что лучше, чем лучший результат модели машинного обучения. Значит есть больше возможностей выбора эффектных инструментов для предсказания целевого количества заказов такси.

Для достижения цели было выполнено:

Подготовлена тетрадь.
Загружены и подготовлены данные.
Проанализрованы данные в датафрейме.
Выбрана и обучена модель.
Проведено тестирование модели

Чек-лист проверки¶

[x] Jupyter Notebook открыт
[x] Весь код выполняется без ошибок
[x] Ячейки с кодом расположены в порядке исполнения
[x] Данные загружены и подготовлены
[x] Данные проанализированы
[x] Модель обучена, гиперпараметры подобраны
[x] Качество моделей проверено, выводы сделаны
[x] Значение RMSE на тестовой выборке не больше 48