markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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В словарях отображать можно не только начала натурального ряда, а произвольные объекты. Представьте себе настоящий словарь или телефонную книжку. Имени человека соответствует номер телефона.Классическое использование словарей в анализе данных: хранить частоту слова в тексте.кот $\rightarrow$ 10и $\rightarrow$ 100Тейлор... | a = dict()
type(a)
a['chapter1'] = 'ghfdlksgrjkasgdjkagrdjksargs'
a
a[1] = 'hrjegrejk'
x = (3,4,5)
a[x] = 'hrjekwslerw'
x = (1,2,3,'str')
a[x] = 'fuidaslt'
a
a = dict()
a[(2,3)] = [2,3] # кортеж может быть ключом, потому что он неизменямый
a
b = dict()
b[[2,3]] = [2,3] # а список уже нет, получим ошибку
print(b) | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Создание словаряВ фигурных скобках (как множество), через двоеточие ключ:значение | d = dict()
d
d1 = {"кот": 10, "и": 100, "Тейлора": 2}
print(d1)
d1["кот"] | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Через функцию dict(). Обратите внимание, что тогда ключ-значение задаются не через двоеточие, а через знак присваивания. А строковые ключи пишем без кавычек - по сути мы создаем переменные с такими названиями и присваиваим им значения (а потом функция dict() уже превратит их в строки). | d2 = dict(кот=10, и=100, Тейлора=2)
print(d2) # получили тот же результат, что выше | {'кот': 10, 'и': 100, 'Тейлора': 2}
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
И третий способ - передаем функции dict() список списков или кортежей с парами ключ-значение. | d3 = dict([("кот", 10), ("и", 100), ("Тейлора", 2)]) # перечисление (например, список) tuple
print(d3) | {'кот': 10, 'и': 100, 'Тейлора': 2}
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Помните, когда мы говорили про списки, мы обсуждали проблему того, что важно создавать именно копию объекта, чтобы сохранять исходный список. Копию словаря можно сделать так | d4 = dict(d3) # фактически, копируем dict который строчкой выше
print(d4)
d1 == d2 == d3 == d4 # Содержание всех словарей одинаковое | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Пустой словарь можно создать двумя способами. | d2 = {} # это пустой словарь (но не пустое множество)
d4 = dict()
print(d2, d4)
x = {}
type(x) | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Операции со словарями Как мы уже говорили, словари неупорядоченные структуры и обратиться по индексу к объекту уже больше не удастся. | d1[1] # выдаст ошибку во всех случах кроме того, если в вашем словаре вдруг есть ключ 1 | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Но можно обращаться к значению по ключу. | d3 = dict([("кот", 10), ("и", 100), ("Тейлора", 2)])
print(d1['кот'])
d1[1] | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Можно создать новую пару ключ-значение. Для этого просто указываем в квадратных скобках название нового ключа. | d1[1] = 'test'
print(d1[1]) # теперь работает! | test
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Внимание: если элемент с указанным ключом уже существует, новый с таким же ключом не добавится! Ключ – это уникальный идентификатор элемента. Если мы добавим в словарь новый элемент с уже существующим ключом, мы просто изменим старый – словари являются изменяемыми объектами. | d3 = dict([("кот", 10), ("и", 100), ("Тейлора", 2)])
d1["кот"] = 11 # так же как в списке по индексу - можно присвоить новое значение по ключу
d1
d1["кот"] += 1 # или даже изменить его за счет арифметической операции
d1 | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
А вот одинаковые значения в словаре могут быть. | d1['собака'] = 13
print(d1) | {'кот': 13, 'и': 100, 'Тейлора': 2, 1: 'test', 'собака': 13}
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Кроме обращения по ключу, можно достать значение с помощью метода .get(). Отличие работы метода в том, что если ключа еще нет в словаре, он не генерирует ошибку, а возвращает объект типа None ("ничего"). Это очень полезно в решении некоторых задач. | d1
print(d1['кот'])
print(d1.get("ктоо")) # вернут None
| None
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Удобство метода .get() заключается в том, что мы сами можем установить, какое значение будет возвращено, в случае, если пары с выбранным ключом нет в словаре. Так, вместо None мы можем вернуть строку Not found, и ломаться ничего не будет: | print(d1.get("ктоо", 'Not found')) # передаем вторым аргументом, что возвращать
print(d1.get("ктоо", False)) # передаем вторым аргументом, что возвращать | Not found
False
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Также со словарями работают уже знакомые нам операции - проверка количества элементов, проверка на наличие объектов. | d1
print(d1)
print("кот" in d1) # проверка на наличие ключа
print("ктоо" not in d1) # проверка на отстуствие ключа
| {'кот': 13, 'и': 100, 'Тейлора': 2, 1: 'test', 'собака': 13}
True
True
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Удалить отдельный ключ или же очистить весь словарь можно специальными операциями. | del d1["кот"]
d1
d1.clear()
d1
del d1["кот"] # удалить ключ со своим значением
print(d1)
d1.clear() # удалить все
print(d1)
d1 = dict([("кот", 10), ("и", 10), ("Тейлора", 2)]) | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
У словарей есть три метода, с помощью которых мы можем сгенерировать список только ключей, только значений и список пар ключ-значения (на самом деле там несколько другая структура, но ведет себя она очень похоже на список). | print(d1.values()) # только значения
print(d1.keys()) # только ключи
print(d1.items()) # только ключ-значение | dict_values([10, 10, 2])
dict_keys(['кот', 'и', 'Тейлора'])
dict_items([('кот', 10), ('и', 10), ('Тейлора', 2)])
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Ну, и раз уж питоновские словари так похожи на обычные, давайте представим, что у нас есть словарь, где все слова многозначные. Ключом будет слово, а значением ‒ целый список. | my_dict = {'swear' : {'swear' : ['клясться', 'ругаться'], 'dream' : ['спать', 'мечтать']}, 'dream' : ['спать', 'мечтать']} | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
По ключу мы получим значение в виде списка: | my_dict['swear']['swear'][0] | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Так как значением является список, можем отдельно обращаться к его элементам: | my_dict['swear'][0] # первый элемент | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
Можем пойти дальше и создать словарь, где значениями являются словари! Например, представим, что в некотором сообществе проходят выборы, и каждый участник может проголосовать за любое число кандидатов. Данные сохраняются в виде словаря, где ключами являются имена пользователей, а значениями – пары *кандидат-голос*. | votes = {'user1': {'cand1': '+', 'cand2': '-'},
'user2' : {'cand1': 0, 'cand3' : '+'}} # '+' - за, '-' - против, 0 - воздержался
votes | _____no_output_____ | MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
По аналогии с вложенными списками по ключам мы сможем обратиться к значению в словаре, который сам является значением в `votes` (да, эту фразу нужно осмыслить): | votes['user1']['cand1'] # берем значение, соответствующее ключу user1, в нем – ключу cand1
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
dict3 = dict1.copy()
dict3.update(dict2)
print(dict3)
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
dict3 = {**dict1, **dict2}
print(dict3) | {'a': 1, 'b': 2, 'c': 3, 'd': 4}
| MIT | lect2/.ipynb_checkpoints/Set_Dict-checkpoint.ipynb | disimhot/DPO_Python-2021-2022 |
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
#import csv to pandas dataframe
income_data=pd.read_csv("income.csv",header=0, delimiter = ", ")... | 0.8258199238422799
White 27816
Black 3124
Asian-Pac-Islander 1039
Amer-Indian-Eskimo 311
Other 271
Name: race, dtype: int64
| MIT | income.ipynb | bibekuchiha/Predicting-Income-with-Random-Forests | |
Part 3 - Analyzing the Data Kiran TIRUMALE LAKSHMANA RAO Importing the Necessary Libraries | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns #install seaborn using 'pip intall seaborn'
%matplotlib inline
plt.style.use('seaborn-whitegrid') | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Loading the Cleaned Dataset | paris = pd.read_csv(r"C:\Users\ktirumalelakshmana\Desktop\Paris_Airbnb_Final.csv")
paris.head()
# Checking the data types etc
paris.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 280 entries, 0 to 279
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Hotel_Type_with_Location 280 non-null object
1 Hote_Type 280 non-null obj... | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Analyzing the Data The data from Airbnb for Paris location has been scraped for the dates `01-Dec-2020` to `31-Dec-2020` with 2 People or more Different locations where accommodations are available | Locations = paris['Location_in_Paris'].unique()
Locations.sort()
Locations
# Total number of locations in Paris
len(Locations) | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Visualizations Ratings and Discount | # Defining the x and y axis variables
x = paris['Discount']
y = paris['Ratings']
r_colors = paris['No_of_Reviews']
# Plotting using matplotlib
plt.scatter(x, y, alpha=0.2, c=r_colors, cmap='viridis')
# Adding Title
plt.title('Discount vs Ratings')
# Adding X-Label
plt.xlabel('Discount')
# Adding Y-Label
plt.ylabel(... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Price and Ratings | # Defining the x and y axis variables
x = paris['Previous_Price']
y = paris['Ratings']
r_colors = paris['Discount']
# Plotting using matplotlib
plt.scatter(x, y, alpha=0.2, c=r_colors, cmap='viridis')
# Adding Title
plt.title('Price vs Ratings')
# Adding X-Label
plt.xlabel('Previous Price')
# Adding Y-Label
plt.yla... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Ratings and No. of Reviews | # Defining the x and y axis variables
x = paris['No_of_Reviews']
y = paris['Ratings']
# Plotting using matplotlib
plt.scatter(x, y, alpha=0.2, cmap='viridis')
# Adding Title
plt.title('Ratings vs No. of Reviews')
# Adding X-Label
plt.xlabel('No. of Reviews')
# Adding Y-Label
plt.ylabel('Ratings'); | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Discount and Price Correlation between `Discounted_Price` and `Previous_Price` | #Same plot as above, but using matplotlib
x = paris['Discounted_Price']
y = paris['Previous_Price']
plt.scatter(x, y, alpha=0.2, cmap='viridis')
m, b = np.polyfit(x, y, 1)
plt.plot(x, m*x+b)
plt.title('Discount vs Previous Price - Trend')
plt.xlabel('Discounted Price')
plt.ylabel('Previous Price')
#Plotting using ... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Correlation between `Discount` and `Previous_Price` | #Plotting using seaborn scatterplot and hue
sns.scatterplot(x = 'Discount', y = 'Previous_Price',data=paris, hue='Type_Bed_Studio', alpha=0.5)
#Adding Trendline
m, b = np.polyfit(x, y, 1)
plt.plot(x, m*x+b)
# Set title
plt.title('Discount vs Previous Price - Trend')
# Set x-axis label
plt.xlabel('Discount')
# Set y... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Discount and Price Distributions | # Plot histogram
sns.distplot(paris['Discount'],kde = False, color='g')
# Set title
plt.title('Distribution of Discount')
plt.show()
# Plot histogram distribution
sns.distplot(paris['Previous_Price'],kde = False)
# Set title
plt.title('Previous Price Distribution')
plt.xlabel('Previous Price')
plt.show()
sns.distp... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Location wise information | location = paris.groupby('Location_in_Paris').mean()
location
# Location - lineplot for Discounted Price
sns.set_style("whitegrid", {'axes.grid' : False})
g1 = sns.lineplot(x = location.index.values, y = 'Discounted_Price', data = location,
palette = 'hls',
alpha = 0.5
)... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Would have loved to plot the Ratings and Discount on a Paris map. However, unable to do it! `Type_Bed_Studio` wise Information | Bed_Studio = paris[['Type_Bed_Studio', 'Ratings', 'No_of_Reviews', 'Previous_Price', 'Discount', 'No_of_Guests']].groupby('Type_Bed_Studio').mean()
Bed_Studio | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Studio vs Bedroom - Discount | # Plotting a barplot
sns.set_style("whitegrid", {'axes.grid' : False})
sns.barplot(x = Bed_Studio.index.values, y = 'Discount', data = Bed_Studio,
palette = 'hls',
order = ['Studio', 'Bedroom'],
alpha = 0.5
)
plt.title('Studio vs Bedroom - Discount'... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Studio vs Bedroom - Ratings | # Plotting a barplot
sns.set_style("whitegrid", {'axes.grid' : False})
sns.barplot(x = Bed_Studio.index.values, y = 'Ratings', data = Bed_Studio,
palette = 'hls',
order = ['Studio', 'Bedroom'],
alpha = 0.5
)
plt.title('Studio vs Bedroom - Ratings')
... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Studio vs Bedroom - Previous Price | # Plotting a barplot
sns.set_style("whitegrid", {'axes.grid' : False})
sns.barplot(x = Bed_Studio.index.values, y = 'Previous_Price', data = Bed_Studio,
palette = 'hls',
order = ['Studio', 'Bedroom'],
alpha = 0.5
)
plt.title('Studio vs Bedroom - Pri... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
`No_of_Guests` wise Information | guests = paris.groupby('No_of_Guests').mean()
guests | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Guests vs Ratings | # Plotting a lineplot for guests vs rating
sns.set_style("whitegrid", {'axes.grid' : False})
sns.barplot(x = guests.index.values, y = 'Ratings', data = guests,
palette = 'PuBu',
alpha = 0.5
)
plt.title('Guests vs Ratings')
plt.plot() | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Guests vs Previous Price | # Plotting a lineplot for guests vs previous price
sns.set_style("whitegrid", {'axes.grid' : False})
sns.lineplot(x = guests.index.values, y = 'Previous_Price', data = guests,
palette = 'PuBu',
alpha = 0.5
)
plt.title('Guests vs Previous Price')
plt.plot() | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Guests vs Discount | # Plotting a lineplot for guests vs discount
sns.set_style("whitegrid", {'axes.grid' : False})
sns.lineplot(x = guests.index.values, y = 'Discount', data = guests,
color = 'g',
alpha = 0.5
)
plt.title('Guests vs Discount')
plt.plot() | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Guests vs Discounted Price | # Plotting a lineplot for guests vs discounted price
sns.set_style("whitegrid", {'axes.grid' : False})
sns.lineplot(x = guests.index.values, y = 'Discounted_Price', data = guests,
color = 'r',
alpha = 0.5
)
plt.title('Guests vs Discounted Price')
plt.plot() | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
Overlaying above 3 graphs | # Plotting a lineplot of above graphs - overlaying them together
sns.set_style("whitegrid", {'axes.grid' : False})
sns.lineplot(x = guests.index.values, y = 'Previous_Price', data = guests,
palette = 'PuBu',
alpha = 0.5
)
sns.lineplot(x = guests.index.values, y = '... | _____no_output_____ | MIT | Part 3 - Analyzing the Data.ipynb | kirantl/Airbnb_WebScraping |
I. DATA Processinghttps://www.kaggle.com/henriqueyamahata/bank-marketing | def remove_duplicated_row(df):
df = df.drop(df[df.duplicated()].index).reset_index(drop=True)
return(df)
def remove_features(df,col_lst):
for col in col_lst:
df.pop(col)
return(df)
def replace_missing_by_value(df,column,replaced_value,missing_value='unknown'):
df[column] = df[column].apply... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
L O A D data | #### L O A D Data
file_path = "data/bank-additional-full.csv"
marketing_df = pd.read_csv(file_path,sep = ";") | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
P R O C E S S I N G data | process_mkt_df = marketing_df.copy()
test_size = 0.2
target = 'y'
## Processing data
process_mkt_df = data_processing_pipeline(process_mkt_df)
cat_cols = process_mkt_df.dtypes[process_mkt_df.dtypes == 'object'].index
num_cols = process_mkt_df.dtypes[process_mkt_df.dtypes != 'object'].index
## label encoding
process_... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
R U N with the whole data set | ## split train set and test_set
X_train, X_test, y_train, y_test = train_test_split(process_mkt_df.drop('y',axis=1), process_mkt_df['y'],
test_size=test_size, random_state = 101)
## standardize data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_t... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
U N D E R S A M P L E D Dataset | # main data set for training
under_mkt_df = under_resample_data(process_mkt_df, target = 'y')
# Showing ratio
print("Percentage of no clients: ", len(under_mkt_df[under_mkt_df[target] == 0])/len(under_mkt_df))
print("Percentage of yes clients: ", len(under_mkt_df[under_mkt_df[target] == 1])/len(under_mkt_df))
print("To... | Percentage of no clients: 0.5
Percentage of yes clients: 0.5
Total number of clients in resampled data: 9278
| MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
R U N with the under resampled data set | ## Split train and test undersampled dataset
X_under_train, X_under_test, y_under_train, y_under_test = train_test_split(under_mkt_df.drop(target,axis=1),under_mkt_df[target],
test_size=test_size, random_state = 101)
print("")
print("Number tra... |
Number transactions train dataset: 7422
Number transactions test dataset: 1856
Total number of transactions: 9278
[12:01:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' w... | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
C O M P A R E when training on the whole dataset and the under- resampled dataset | evaluations = pd.DataFrame()
evaluations = evalutation_df.append(under_evalutation_df, ignore_index = True)
evaluations | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
ROC curve in case of undersampled dataset Vì ta cần tỉ lệ bỏ sót khách hàng thành công của mô hình phải thấp nên mặc dù accuracy score chạy trên toàn tập data cao hơn, ta sẽ chọn cách train trên tập under sampled data vì trường hợp này cho độ Precision, Recall, F1 score trên nhãn successful tốt hơn và accuracy scor... | visualize_ROC_curves(y_under_test,y_under_test_pred_proba_df) | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
K F O L D validation to avoid improve the model's effectiveness on undersampled by models( LogisticClf, DecisionTree, Random Forest Classifier, XGBoost) | # # scaler on the whole dataset
# scaler = pickle.load(open("model/pkl_scaler.pkl", 'rb'))
## init kfold
target = 'y'
num_fold = 5
kfold = KFold(n_splits=num_fold, shuffle=True)
# fold_scaler = StandardScaler()
X = scaler.transform(under_mkt_df.drop('y',axis=1))
y = under_mkt_df['y']
fold_idx = 1
fold_evalutation_df =... | [12:02:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[12:03:16]... | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
E V A L U A T E to choose the optimal model with the best avarage metrics | # fold_evalutation_df.to_csv('fold_evalutation_df.csv')
# fold_evalutation_df
t = fold_evalutation_df.groupby(['Model']).mean().iloc[:,:-1].sort_values(['Recall_test','Accuracy_test'], ascending=False)
t
# Evaluation metrics for 4 models | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
O P T I M A L Model | ## EVALUATION METRICS of the optimal model = mean(EVALUATION METRICS) of k times folding the under_resampled dataset
# The optimal model:XGBoost Classifier
optimal_evaluation_df = t.head(1)
optimal_name =t.head(1).index.values[0]
optimal_model = models[names.index(optimal_name)]
print('The optimal model is '+str(optim... | The optimal model is XGBoost Classifier
| MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
O P T I M A L FIT : train on the whole under sampled dataset to get the last one | ## Train the optimal model on the whole under resampled dataset
target = 'y'
X_train = scaler.transform(under_mkt_df.drop(target, axis = 1))
y_train = under_mkt_df[target]
optimal_model.fit(X_train,y_train)
# ## ghi vào file pickle
# with open("model/pkl_model.pkl","wb") as f:
# pickle.dump(scaler,f) | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
O P T I M A L Feature Importances | ## Hệ số mô hình tối ưu
features = [i for i in under_mkt_df.columns.values.tolist() if i!= target]
opt_model_importances = pd.Series(data = optimal_model.feature_importances_, index = features, name = optimal_name)
plt.figure(figsize = (14,8))
sns.barplot(x = opt_model_importances.sort_values(ascending = False).values... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
O P T I M A L ROI on the whole dataset | ### Real R O I on the whole dataset
## ROI = roi_per_success * # of sales - cost_per_call * # of calls
cost_per_call = 10
roi_per_success = 20
target = 'y'
# X_opt_test = process_mkt_df.drop(targer,axis = 1)
X_opt_test = scaler.transform(process_mkt_df.drop(target,axis = 1))
y_opt_test = process_mkt_df[target]
y_pred ... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
R E S E A R C H Decision tree | ## init the research model
tree_model = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)
tree_name = 'Decision Tree Classifier'
X_tree_train, X_tree_test, y_tree_train, y_tree_test = train_test_split(under_mkt_df.drop(target,axis=1),under_mkt_df[target],
... | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
V I S U A L I Z E Decision Tree | # from sklearn import tree
plt.figure(figsize=(20,15))
tree.plot_tree(tree_model,feature_names = features,rounded=True, filled = True);
plt.title("Decision Tree on Banking Tele-marketing dataset"); | _____no_output_____ | MIT | assignment_7/.ipynb_checkpoints/Nguyen_Bank_Marketing_kFold-checkpoint.ipynb | ngttnguyen/atom-assignments |
Neural network hybrid recommendation system on Google Analytics data preprocessingThis notebook demonstrates how to implement a hybrid recommendation system using a neural network to combine content-based and collaborative filtering recommendation models using Google Analytics data. We are going to use the learned use... | # Import helpful libraries and setup our project, bucket, and region
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["... | Updated property [core/project].
Updated property [compute/region].
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Create ML dataset using Dataflow Let's use Cloud Dataflow to read in the BigQuery data, do some preprocessing, and write it out as CSV files.First, let's create our hybrid dataset query that we will use in our Cloud Dataflow pipeline. This will combine some content-based features and the user and item embeddings learn... | query_hybrid_dataset = """
WITH CTE_site_history AS (
SELECT
fullVisitorId as visitor_id,
(SELECT MAX(IF(index = 10, value, NULL)) FROM UNNEST(hits.customDimensions)) AS content_id,
(SELECT MAX(IF(index = 7, value, NULL)) FROM UNNEST(hits.customDimensions)) AS category,
(SELECT MAX(... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Let's pull a sample of our data into a dataframe to see what it looks like. | from google.cloud import bigquery
bq = bigquery.Client(project = PROJECT)
df_hybrid_dataset = bq.query(query_hybrid_dataset + "LIMIT 100").to_dataframe()
df_hybrid_dataset.head()
df_hybrid_dataset.describe()
import apache_beam as beam
import datetime, os
def to_csv(rowdict):
# Pull columns from BQ and create a lin... | Launching Dataflow job preprocess-hybrid-recommendation-features-190412-184419 ... hang on
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Let's check our files to make sure everything went as expected | %%bash
rm -rf features
mkdir features
!gsutil -m cp -r gs://{BUCKET}/hybrid_recommendation/preproc/features/*.csv* features/
!head -3 features/* | ==> features/eval.csv-00000-of-00001 <==
710535,951784927766849126,710535,News,"Haus aus Marmor und Grabsteinen",None,503,-0.00170100899413,0.00496714003384,0.0040482301265,0.000690933316946,3.52509268851e-05,-0.00172890012618,0.00153049221262,0.00100265210494,0.00228979066014,-0.00201142113656,-5.59943889043e-19,7.426... | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Create vocabularies using Dataflow Let's use Cloud Dataflow to read in the BigQuery data, do some preprocessing, and write it out as CSV files.Now we'll create our vocabulary files for our categorical features. | query_vocabularies = """
SELECT
CAST((SELECT MAX(IF(index = index_value, value, NULL)) FROM UNNEST(hits.customDimensions)) AS STRING) AS grouped_by
FROM `cloud-training-demos.GA360_test.ga_sessions_sample`,
UNNEST(hits) AS hits
WHERE
# only include hits on pages
hits.type = "PAGE"
AND (SELECT MAX(IF... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Also get vocab counts from the length of the vocabularies | import apache_beam as beam
import datetime, os
def count_to_txt(rowdict):
# Pull columns from BQ and create a line
# Write out count
return "{}".format(rowdict["count_number"])
def mean_to_txt(rowdict):
# Pull columns from BQ and create a line
# Write out mean
return "{}".format(rowdict["m... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Let's check our files to make sure everything went as expected | %%bash
rm -rf vocabs
mkdir vocabs
!gsutil -m cp -r gs://{BUCKET}/hybrid_recommendation/preproc/vocabs/*.txt* vocabs/
!head -3 vocabs/*
%%bash
rm -rf vocab_counts
mkdir vocab_counts
!gsutil -m cp -r gs://{BUCKET}/hybrid_recommendation/preproc/vocab_counts/*.txt* vocab_counts/
!head -3 vocab_counts/* | ==> vocab_counts/author_vocab_count.txt-00000-of-00001 <==
1103
==> vocab_counts/category_vocab_count.txt-00000-of-00001 <==
3
==> vocab_counts/content_id_vocab_count.txt-00000-of-00001 <==
15634
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/hybrid_recommendations/hybrid_recommendations_preproc.ipynb | Glairly/introduction_to_tensorflow |
Random VariableIn probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is described informally as a variable whose values depend on outcomes of a random phenomenon. In random variable we are considering a function whose domain is the set of possible outocmes and wh... | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
import seaborn as sns
from matplotlib.pyplot import figure | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Discrete Uniform Distribution or Discrete rectangular distributionThe Uniform Distribution can be easily derived from the Bernoulli Distribution. In this case, a possibly unlimited number of outcomes are allowed and all the events hold the same probability to take place. As an example, imagine the roll of a fair dice.... | # size = number of experiments
# loc = starting point
# scale = end point
uniform = stats.uniform.rvs(size=10000,loc =0, scale=6) # Means random variable from 0 to 6
ax = sns.distplot(uniform,
kde=True)
ax.set(xlabel='Uniform Distribution', ylabel='Frequency')
import numpy as np
x = [0,1,2,3,4,5]
coun... | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Mean:- E(X) = (a + b) /2 Variance:- Var = (((b -a +1)^2) - 1) /12 Median:- M = (a + b) /2 Mode:- Not Defined Skewness:- S = 0 Kurtosis:- K = -6(n^2 + 1) / 5(n^2 -1) Entropy:- Entropy = ln(n) Application:- 1. Success in an examination 2. Failure of an engine in an aircraft 3. Spinning of ... | # size = number of experiments
# p = probability of success
bern = stats.bernoulli.rvs(size=10000,p=0.5)
ax= sns.distplot(bern,
kde=False)
ax.set(xlabel='Bernoulli Distribution', ylabel='Frequency')
biased_bern = stats.bernoulli.rvs(size=1000,p=0.7)
ax= sns.distplot(biased_bern,
kde=Fa... | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Binomial DistributionThe repetition of multiple independent Bernoulli trials is called a Bernoulli process.The outcomes of a Bernoulli process will follow a Binomial distribution. As such, the Bernoulli distribution would be a Binomial distribution with a single trial.Some common examples of Bernoulli processes includ... | # example of simulating a binomial process and counting success
from numpy.random import binomial
# define the parameters of the distribution
p = 0.3
k = 100
# run a single simulation
success = binomial(k, p)
print('Total Success: %d' % success)
# calculate moments of a binomial distribution
from scipy.stats import bin... | P of 10 success: 0.000%
P of 20 success: 1.646%
P of 30 success: 54.912%
P of 40 success: 98.750%
P of 50 success: 99.999%
P of 60 success: 100.000%
P of 70 success: 100.000%
P of 80 success: 100.000%
P of 90 success: 100.000%
P of 100 success: 100.000%
| Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Poisson Distribution It is discovered by french mathematician and physicst simeon denis poisson (1781 - 1840) in 1837.It is a limiting case of binomial distribution because:- 1. n, the number of trial is indefinately large i.e n --> inf2. p, the constant probability of success for each trial is indefinately small i.e ... | from scipy.stats import poisson
mu = 0.6
mean, var, skew, kurt = poisson.stats(mu, moments='mvsk')
x = np.arange(poisson.ppf(0.01, mu),
poisson.ppf(0.99, mu)) #ppf = Percent point function.
print(x)
pmf = poisson.pmf(x, mu)
print('Mean=%.3f, Variance=%.3f, Skew=%.3f, Kurtosis=%.3f' % (mean, var,skew,kurt)... | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Negative Binomial DistributionIn poisson distribution mean and variance are equal and in binomial distribution mean is greater than variance.In negative binomial distribution variance is greater than mean. For ex.- Becterial clustering like death of insects, no of insect bite lead to negative binomial distribution and... | from scipy.stats import nbinom
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
n, p = 0.4, 0.4
mean, var, skew, kurt = nbinom.stats(n, p, moments='mvsk')
print('Mean=%.3f, Variance=%.3f, Skew=%.3f, Kurtosis=%.3f' % (mean, var,skew,kurt))
x = np.arange(nbinom.ppf(0.01, n, p), #0.01 is the probability
... | Mean=0.600, Variance=1.500, Skew=3.266, Kurtosis=15.667
| Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Geometric DistributionGeometric distribution is the distribution of the number of trial needed to get the first success in repeated bernoulli trial. For ex.- In a large population of adults, 30% have recieved CPR training. If adults from this population are randomly selected what is the probability that the 6th perso... | from scipy.stats import geom
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
p = 0.5
mean, var, skew, kurt = geom.stats(p, moments='mvsk')
x = np.arange(geom.ppf(0.01, p), # 0.01 is the probability
geom.ppf(0.99, p))
ax.plot(x, geom.pmf(x, p), 'bo', ms=8, label='geom pmf')
ax.vlines(x, 0, geo... | /home/himanshugoyal/.local/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for hi... | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Hypergeometric distributionThe hypergeometric distribution is used to calculate probabilities when sampling without replacement.Note that it is different from Binomial distribution as a binomial experiment requires that the probability of success be constant on every... | from scipy.stats import hypergeom
import matplotlib.pyplot as plt
[M, n, N] = [20, 7, 12]
rv = hypergeom(M, n, N)
x = np.arange(0, n+1)
pmf_dogs = rv.pmf(x)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, pmf_dogs, 'bo')
ax.vlines(x, 0, pmf_dogs, lw=2)
ax.set_xlabel('# of dogs in our group of chosen animals')
a... | /home/himanshugoyal/.local/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for hi... | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Multinomial DistributionMultinomial is the generalization of binomial distribution.A multinomial distribution is the probability distribution of the outcomes from a multinomial experiment.A multinomial experiment is a statistical experiment that has the following properties:1. The experiment consists of n repeated tri... | from scipy.stats import multinomial
rv = multinomial(8, [0.3, 0.2, 0.5]) # n:- (int)Number of trials, p:- (array_like)Probability of a trial falling into each category; should sum to 1
rv.pmf([1, 3, 4])
multinomial.pmf([[3, 4], [3, 5]], n=[7, 8], p=[.3, .7]) | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Uses:- A common example of the multinomial distribution is the occurrence counts of words in a text document, from the field of natural language processing. A multinomial distribution is summarized by a discrete random variable with K outcomes, a probability for each outcome from p1 to pK, and k successive trials. Co... | # sample a normal distribution
from numpy.random import normal
# define the distribution
mu = 50
sigma = 5
n = 10
# generate the sample
sample = normal(mu, sigma, n)
print(sample)
# pdf and cdf for a normal distribution
from scipy.stats import norm
from matplotlib import pyplot
# define distribution parameters
mu = 50
... | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Exponential Distribution Finally, the Exponential Distribution is used to model the time taken between the occurrence of different events. As an example, let's imagine we work at a restaurant and we want to predict what is going to be the time interval between different customers coming in the restaurant. Using an Exp... | expon = stats.expon.rvs(scale=1,loc=0,size=1000)
ax = sns.distplot(expon,
kde=True,
bins=100,
color='skyblue',
hist_kws={"linewidth": 15,'alpha':1})
ax.set(xlabel='Exponential Distribution', ylabel='Frequency')
figure(num=None, figsize=(8, 6), dpi=... | _____no_output_____ | Apache-2.0 | Statistics/Random Variable and distribution function..ipynb | himanshu-1205/Statistics |
Data types in Python ToC | S1 = 'abc'
S2 = 'DEF'
display(md('## Strings'))
display(md(f'S1 = {S1}; \nS2 = {S2}'))
display(md(f"Join strings: ''.join([S1, S2]) = {''.join([S1, S2])}"))
display(md(f'Change cases: \n* S2.lower(); S2 = {S2.lower()} \n* S1.upper(); S2 = {S1.upper()}'))
display(md("Right justify text with str.rjust(int))
display(m... | _____no_output_____ | MIT | DataTypes.ipynb | maufia/MyPyCourse |
ปฏิบัติการครั้งที่ 4 กระบวนวิชา 229351 Statistical Learning for Data Scienceคำชี้แจง1. ให้เริ่มทำปฏิบัติการจาก colab notebook ที่กำหนดให้ จากนั้นบันทึกเป็นไฟล์ *.ipynb (File -> Download .ipynb) Task 1 Empirical risk for virus testingในปัญหานี้เราจะทำการศึกษาการสร้างวิธีในการจำแนกคนที่เป็นโรคไวรัสจากการทดสอบชนิดหนึ่ง ก... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import norm
# Note: don't make any changes to this function
def generate_ground_truth(N, prevalence):
""" สร้างข้อมูลจำลอง"""
rs = np.random.RandomState(1)
reality = rs.binomial(1, prevalence, N)
... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
ฟังก์ชัน `alpha_threshold_decisions` ใช้ในการตัดสินใจว่าแต่ละคนเป็นหรือไม่เป็นพาหะ (`decisions`) | # Note: don't make any changes to this function, this is exatly the naive thresholding you completed in Lab 1
def alpha_threshold_decisions(p, alpha):
"""
Returns decisions on p using naive thresholding.
Inputs:
p: array p[i] คือความน่าจะเป็นที่ตัวอย่างที่ i มีเชื้อไวรัส
alpha: threshol... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
ฟังก์ชัน `report_results` ใช้ในการคำนวณว่าการตัดสินใจถูกหรือผิดอย่างไรบ้าง* TP (true positive): `ri`=1,`di`=1 * TN (true negative): `ri`=0,`di`=0 * FP (false positive): `ri`=0,`di`=1 * FN (false negative): `ri`=1,`di`=0 | # Note: don't make any changes to this function, this is the report_results function you completed in Lab 1
def report_results(decisions, reality):
"""
สร้าง dictionary ที่ประกอบไปด้วยจำนวนตัวอย่างในกลุ่ม true positives,
true negatives, false negatives, และ false positives
จากการตัดสินใจด้วย `alpha_th... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
`results_dictionary["FP_count"]` Exercise 1a: เติมฟังก์ชันเพื่อคำนวณ empirical risk จากค่าจำนวนความถูกต้องและจำนวนความผิดพลาด (TP, FP, TN, FN) ที่บันทึกใน `results_dictionary` โดยที่ `factor_k` คือค่า $k$ ที่ระบุในนิยามของ loss function ข้างบน Loss function คือ$$\begin{cases} \mathcal{l}(di=1,ri=0) = 1\\\mathcal{l}(d... | # TODO: fill in
def compute_empirical_risk(results_dictionary, factor_k):
""" คำนวณ empirical risk ด้วยค่า TP, FP, TN และ FN ใน results_dictionary
โดยที่ค่าของ false positive คือ 1
และค่าของ false negative คือ k
Inputs:
results_dictionary : dictionary ที่มีค่า TP, FP, TN... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
Exercise 1b: เติมฟังก์ชันเพื่อคำนวณ empirical risk โดยที่มี argument ดังนี้ * `reality` การเป็นพาหะจริง * `p` ความน่าจะเป็นที่ได้จากการทดสอบ * `alpha` ค่า threshold ในการตัดสินใจว่าแต่ละคนเป็นพาหะหรือไม่* `factor_k` คือค่า $k$ ที่ระบุในนิยามของ loss function ข้างบน | # TODO: complete the function
def compute_alpha_empirical_risk(p, reality, alpha, factor_k):
"""
คำนวณค่า empirical risk ที่ค่า threshold alpha
Inputs:
p: array of floats, p[i] คือความน่าจะเป็นที่ตัวอย่างที่ i มีเชื้อไวรัส
reality: array ที่มีค่า 0/1, reality[i] =1 ถ้าคนที่ i มีเชื้อไว... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
Exercise 1c: factor_k กับค่า $\alpha$ ที่ดีทีสุด (Optimal $\alpha$) มีความสัมพันธ์กันอย่างไร คำตอบ: Task 2 Sample points | np.random.seed(42)
N = 8
x = 10 ** np.linspace(-2, 0, N)
y = np.random.normal(loc = 10 - 1. / (x + 0.1), scale= 0.5)
plt.figure()
plt.scatter(x, y, c='k')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Sample points'); | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
Polynomial regression | p = np.polyfit(x, y, 2)
xfit = np.linspace(-0.2, 1.2, 1000)
yfit = np.polyval(p, xfit)
p
plt.figure()
plt.scatter(x, y, marker='x', c='k', s=50)
plt.plot(xfit, yfit, '-b')
plt.xlabel('x')
plt.ylabel('y')
plt.title('d = 2')
xfit = np.linspace(-0.2, 1.2, 1000)
titles = ['d = 1 (under-fit)', 'd = 2', 'd = 6 (over-fit)... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
ในกรณีนี้ เราใช้ squared-loss ดังนั้น empirical risk เท่ากับ MSE (mean-squared error) | def empirical_risk(y, yfit):
return np.mean((y - yfit) ** 2) | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
Exercise 2: 1. ทำการสร้าง polynomial regression ที่มีค่า degree ตั้งแต่ 1-10 โดยใช้ training set ข้างบน 2. หลังจากสร้างโมเดลแต่ละตัวเสร็จแล้ว ให้คำนวณค่า empirical risk ของการทำนายบน test set เก็บค่าที่ได้ไว้ใน list ที่ชื่อว่า `empirical_risks` (เพราะฉะนั้น list นี้จะมีสมาชิก 10 ตัว)3. สร้าง plot โดยให้แกนนอนคือค่า de... | max_degree = 10
empirical_risks = [0]*max_degree
for d in range(1,max_degree+1):
#TODO: fill code here
p = np.polyfit(xtrain, ytrain, d)
ypred = np.polyval(p, xtest)
r = empirical_risk(ytest, ypred)
p = np.polyfit(xtrain, ytrain, 6)
plt.figure()
plt.scatter(xtest, ytest)
xfit2 = np.linspace(0, 1.1,... | _____no_output_____ | MIT | Labs/229351-LAB04.ipynb | donlapark/ds351.github.io |
Detecting Payment Card FraudIn this section, we'll look at a credit card fraud detection dataset, and build a binary classification model that can identify transactions as either fraudulent or valid, based on provided, *historical* data. In a [2016 study](https://nilsonreport.com/upload/content_promo/The_Nilson_Report... | import io
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
%matplotlib inline
import boto3
import sagemaker
from sagemaker import get_execution_role | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
I'm storing my **SageMaker variables** in the next cell:* sagemaker_session: The SageMaker session we'll use for training models.* bucket: The name of the default S3 bucket that we'll use for data storage.* role: The IAM role that defines our data and model permissions. | # sagemaker session, role
sagemaker_session = sagemaker.Session()
role = sagemaker.get_execution_role()
# S3 bucket name
bucket = sagemaker_session.default_bucket()
| _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
Loading and Exploring the DataNext, I am loading the data and unzipping the data in the file `creditcardfraud.zip`. This directory will hold one csv file of all the transaction data, `creditcard.csv`.As in previous notebooks, it's important to look at the distribution of data since this will inform how we develop a fr... | # only have to run once
!wget https://s3.amazonaws.com/video.udacity-data.com/topher/2019/January/5c534768_creditcardfraud/creditcardfraud.zip
!unzip creditcardfraud
# read in the csv file
local_data = 'creditcard.csv'
# print out some data
transaction_df = pd.read_csv(local_data)
print('Data shape (rows, cols): ', tr... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
EXERCISE: Calculate the percentage of fraudulent dataTake a look at the distribution of this transaction data over the classes, valid and fraudulent. Complete the function `fraudulent_percentage`, below. Count up the number of data points in each class and calculate the *percentage* of the data points that are fraudul... | # Calculate the fraction of data points that are fraudulent
def fraudulent_percentage(transaction_df):
'''Calculate the fraction of all data points that have a 'Class' label of 1; fraudulent.
:param transaction_df: Dataframe of all transaction data points; has a column 'Class'
:return: A fractional pe... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
Test out your code by calling your function and printing the result. | # call the function to calculate the fraud percentage
fraud_percentage = fraudulent_percentage(transaction_df)
print('Fraudulent percentage = ', fraud_percentage)
print('Total # of fraudulent pts: ', fraud_percentage*transaction_df.shape[0])
print('Out of (total) pts: ', transaction_df.shape[0])
| _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
EXERCISE: Split into train/test datasetsIn this example, we'll want to evaluate the performance of a fraud classifier; training it on some training data and testing it on *test data* that it did not see during the training process. So, we'll need to split the data into separate training and test sets.Complete the `tra... | # split into train/test
def split_data(transaction_df, test_size= 0.3, random_state=1):
'''
Shuffle the data and randomly split into train and test sets;
separate the class labels (the column in transaction_df) from the features.
:param df: Dataframe of all credit card transaction data
:param train_frac:... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
Test CellIn the cells below, I'm creating the train/test data and checking to see that result makes sense. The tests below test that the above function splits the data into the expected number of points and that the labels are indeed, class labels (0, 1). | # get train/test data
train_features, test_features, train_labels, test_labels = split_data(transaction_df, test_size=0.3, random_state=1)
# manual test
# for a split of 0.7:0.3 there should be ~2.33x as many training as test pts
print('Training data pts: ', len(train_features))
print('Test data pts: ', len(test_feat... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
--- ModelingNow that you've uploaded your training data, it's time to define and train a model!In this notebook, you'll define and train the SageMaker, built-in algorithm, [LinearLearner](https://sagemaker.readthedocs.io/en/stable/linear_learner.html). A LinearLearner has two main applications:1. For regression tasks i... | # import LinearLearner
from sagemaker import LinearLearner
# specify an output path
prefix = 'creditcard'
output_path = 's3://{}/{}'.format(bucket, prefix)
# instantiate LinearLearner
linear = LinearLearner(role=role,
train_instance_count=1,
train_instance_type='ml.c4.xl... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
EXERCISE: Convert data into a RecordSet formatNext, prepare the data for a built-in model by converting the train features and labels into numpy array's of float values. Then you can use the [record_set function](https://sagemaker.readthedocs.io/en/stable/linear_learner.htmlsagemaker.LinearLearner.record_set) to forma... | # convert features/labels to numpy
train_x_np = train_features.astype('float32')
train_y_np = train_labels.astype('float32')
# create RecordSet
formatted_train_data = linear.record_set(train_x_np, labels=train_y_np) | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
EXERCISE: Train the EstimatorAfter instantiating your estimator, train it with a call to `.fit()`, passing in the formatted training data. | %%time
# train the estimator on formatted training data
linear.fit(formatted_train_data) | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
EXERCISE: Deploy the trained modelDeploy your model to create a predictor. We'll use this to make predictions on our test data and evaluate the model. | %%time
# deploy and create a predictor
linear_predictor = linear.deploy(initial_instance_count=1, instance_type='ml.t2.medium') | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
--- Evaluating Your ModelOnce your model is deployed, you can see how it performs when applied to the test data.According to the deployed [predictor documentation](https://sagemaker.readthedocs.io/en/stable/linear_learner.htmlsagemaker.LinearLearnerPredictor), this predictor expects an `ndarray` of input features and r... | # test one prediction
test_x_np = test_features.astype('float32')
result = linear_predictor.predict(test_x_np[0])
print(result) | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
Helper function for evaluationThe provided function below, takes in a deployed predictor, some test features and labels, and returns a dictionary of metrics; calculating false negatives and positives as well as recall, precision, and accuracy. | # code to evaluate the endpoint on test data
# returns a variety of model metrics
def evaluate(predictor, test_features, test_labels, verbose=True):
"""
Evaluate a model on a test set given the prediction endpoint.
Return binary classification metrics.
:param predictor: A prediction endpoint
:para... | _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
Test ResultsThe cell below runs the `evaluate` function. The code assumes that you have a defined `predictor` and `test_features` and `test_labels` from previously-run cells. | print('Metrics for simple, LinearLearner.\n')
# get metrics for linear predictor
metrics = evaluate(linear_predictor,
test_features.astype('float32'),
test_labels,
verbose=True) # verbose means we'll print out the metrics
| _____no_output_____ | MIT | notebooks/Payment_Fraud_Detection/Fraud_Detection_Solution.ipynb | LourensWalters/deep-learning-udacity |
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