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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**Question 3.4.** Companies pay executives in a variety of ways: in cash, by granting stock or other equity in the company, or with ancillary benefits (like private jets). Compute the proportion of each CEO's pay that was cash. (Your answer should be an array of numbers, one for each CEO in the dataset.)*Note:* When ... | cash_proportion = ...
cash_proportion
grader.check("q34") | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
Check out the `% Change` column in `compensation`. It shows the percentage increase in the CEO's pay from the previous year. For CEOs with no previous year on record, it instead says "(No previous year)". The values in this column are *strings*, not numbers, so like the `Total Pay` column, it's not usable without a ... | # Definition to turn percent to number
def percent_string_to_num(percent_string):
"""Converts a percentage string to a number."""
return ...
# Compensation table where there is a previous year
having_previous_year = ...
# Get the percent changes as numbers instead of strings
# We're still working off the tabl... | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
**Question 3.6.** What was the average pay of these CEOs in 2014?<!--BEGIN QUESTIONname: q36--> | average_pay_2014 = np.average(with_previous_compensation.column("2014 Total Pay ($)"))
average_pay_2014
grader.check("q36") | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
**Why is `apply` useful?**For operations like arithmetic, or the functions in the NumPy library, you don't need to use `apply`, because they automatically work on each element of an array. But there are many things that don't. The string manipulation we did in today's lab is one example. Since you can write any code... | ... | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
**Question 4.2.** How many CEOs made more than $30 million in total pay? Find the value using code, then check that the value you found is consistent with what you see in the histogram.*Hint:* Use the table method `where` and the property `num_rows`.<!--BEGIN QUESTIONname: q42--> | num_ceos_more_than_30_million_2 = compensation.where("Total Pay ($)", are.above(30000000)).num_rows
num_ceos_more_than_30_million_2
grader.check("q42") | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
5. Project 1 Partner FormProject 1 will be released this Friday! You have the option of working with a partner that is enrolled in your lab. Your GSI will be sending out a form to match you up with a partner for this project. You may also indicate if you're working alone or have already found a partner and do not need... | submitted = ...
grader.check("q5") | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
Great job! You're finished with lab 4! Be sure to...* **run all the tests** (the next cell has a shortcut for that),* **Save and Checkpoint** from the File menu,* **run the last cell to submit your work**,* and **ask one of the staff members to check you off**. ---To double-check your work, the cell below will rerun al... | grader.check_all() | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
SubmissionMake sure you have run all cells in your notebook in order before running the cell below, so that all images/graphs appear in the output. The cell below will generate a zip file for you to submit. **Please save before exporting!** | # Save your notebook first, then run this cell to export your submission.
grader.export() | _____no_output_____ | BSD-3-Clause | lab/lab04/.ipynb_checkpoints/lab04-checkpoint.ipynb | ds-modules/Colab-demo |
Assign value of B = 1000 where C = 1 & D = -1 & | df2 = df
df2.loc[df2.D.eq(-1), 'B'] = 1000
df2.head(5)
df3 = df
df3.loc[df3.D.eq(-1) & df3.C.eq(1), 'B'] = 1000
df3.head(5) | _____no_output_____ | MIT | notebooks/pandas/df_assign_col_values.ipynb | codenamewei/pydata-science-playground |
this works, but with warnings | df1 = df
df1[df1.D == -1].B = 1000
df1.head(5)
| C:\Users\codenamewei\.conda\envs\data-science-playground\lib\site-packages\pandas\core\generic.py:5494: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pan... | MIT | notebooks/pandas/df_assign_col_values.ipynb | codenamewei/pydata-science-playground |
Use .loc[row_indexer,col_indexer] = value instead This works, but with warning | df_ = df
df_.iloc[df_.loc[df_.D == -1].index[0]].B = 1000
df_.head(5) | C:\Users\codenamewei\.conda\envs\data-science-playground\lib\site-packages\pandas\core\generic.py:5494: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view... | MIT | notebooks/pandas/df_assign_col_values.ipynb | codenamewei/pydata-science-playground |
Aux Functions | def generateDates(start=date(2019, 1, 1), end=date(2019, 8, 31), delta=timedelta(days=1), strFormat=""):
curr = start
dates = []
while curr < end:
if strFormat == "":
dates.append(str(curr))
else:
dates.append(curr.strftime(strFormat))
curr += delta
return... | Url-Base: https://elpais.com/hemeroteca/elpais/{date}/{partOfDay}/portada.html
-> urlsPerDay length: 3
-> urlsPerDay length: 6
-> urlsPerDay length: 9
-> urlsPerDay length: 12
-> urlsPerDay length: 15
-> urlsPerDay length: 18
-> urlsPerDay length: 21
-> urlsPerDay length: 24
-> urlsPerDay ... | Apache-2.0 | resources/notebooks/OldScrapper/ElPais-getUrlsFromHemeroteca.ipynb | cfespinoza/comments-retriever |
Getting urls | url="https://www.elpais.com/"
renderUrl = render(url)
renderedPage = htmlRenderer.fromstring(renderUrl)
auxLinks = renderedPage.xpath("//a/@href")
# obtener links, cuidado que alguno ya empieza por http...
auxFinalLinks = list(dict.fromkeys([link for link in auxLinks
if not link.end... | _____no_output_____ | Apache-2.0 | resources/notebooks/OldScrapper/ElPais-getUrlsFromHemeroteca.ipynb | cfespinoza/comments-retriever |
!pip install geopandas
# Read in solar image
# Import library functions to download and read images
import requests
from io import BytesIO
# Dataset: Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification
# California Aerial Imagery SolarArrayPolygons.geojson
url... | _____no_output_____ | MIT | ShowSolarArrayLocations.ipynb | ccai-course/Module2-Mitigation | |
Wasserstein 1D with PyTorchIn this small example, we consider the following minization problem:\begin{align}\mu^* = \min_\mu W(\mu,\nu)\end{align}where $\nu$ is a reference 1D measure. The problem is handledby a projected gradient descent method, where the gradient is computedby pyTorch automatic differentiation. The ... | # Author: Nicolas Courty <ncourty@irisa.fr>
# Rémi Flamary <remi.flamary@polytechnique.edu>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import matplotlib as mpl
import torch
from ot.lp import wasserstein_1d
from ot.datasets import make_1D_gauss as gauss
from ot.utils import proj_... | _____no_output_____ | MIT | master/_downloads/cc2d1e2d3ec6e3b42bceea0b50c4db77/plot_wass1d_torch.ipynb | PythonOT/pythonot.github.io |
Wasserstein barycenterIn this example, we consider the following Wasserstein barycenter problem$$ \\eta^* = \\min_\\eta\;\;\; (1-t)W(\\mu,\\eta) + tW(\\eta,\\nu)$$where $\\mu$ and $\\nu$ are reference 1D measures, and $t$is a parameter $\in [0,1]$. The problem is handled by a project gradientdescent method, where the ... | device = "cuda" if torch.cuda.is_available() else "cpu"
# use pyTorch for our data
x_torch = torch.tensor(x).to(device=device)
a_torch = torch.tensor(a).to(device=device)
b_torch = torch.tensor(b).to(device=device)
bary_torch = torch.tensor((a + b).copy() / 2).to(device=device).requires_grad_(True)
lr = 1e-6
nb_iter... | _____no_output_____ | MIT | master/_downloads/cc2d1e2d3ec6e3b42bceea0b50c4db77/plot_wass1d_torch.ipynb | PythonOT/pythonot.github.io |
Remove empty folders | import shutil
from os import listdir
for repo in hw4:
try:
l = listdir(repo.name)
except FileNotFoundError:
pass
if len(l) < 2:
# has .git folder
shutil.rmtree(repo.name)
| _____no_output_____ | CC0-1.0 | Download_Repos.ipynb | rajathkmp/COMS4995-s18 |
Convert notebooks to python files |
from glob import glob
notebooks = glob("*/*.ipynb")
import nbconvert
import shlex
for notebook in tqdm.tqdm(notebooks):
#print(notebook)
# fixme: whitespace in names?
if not os.path.exists(notebook.replace("ipynb", "py")):
os.system("jupyter-nbconvert {} --to script".format(shlex.quote(notebook)))... | Report Url: http://moss.stanford.edu/results/374137986
| CC0-1.0 | Download_Repos.ipynb | rajathkmp/COMS4995-s18 |
http://moss.stanford.edu/results/374137986 | # Save report file
m.saveWebPage(url, "report2.html")
# Download whole report locally including code diff links
mosspy.download_report(url, "submission2/", connections=8, log_level=20)
import logging
logging.DEBUG
logging.WARNING
# TODO: use absolute paths everywhere...
def clone_repos(pattern="homework-1", store_at="... | _____no_output_____ | CC0-1.0 | Download_Repos.ipynb | rajathkmp/COMS4995-s18 |
Tidy Data with Pandas |
# Reading the csv files into a pandas data frame
temperature = pd.read_csv("../../datasets/temperature.csv")
humidity = pd.read_csv("../../datasets/humidity.csv")
wind_speed = pd.read_csv("../../datasets/wind_speed.csv")
temperature.head()
# Importing the libraries
import pandas as pd
import numpy as np# Displaying ... | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Data ManipulationLet us unpivot, or melt: convert from wide format to long format, as tidy-data thinking recommends.Tidy data essentially says: - Each row should be an observation - Each column should be a variable. Roughly, each column that is not an identifier or dimension should be a measure. - A dataframe... | tidy_temperature = pd.melt(temperature,
id_vars="datetime",
var_name="city", value_name="temperature")
tidy_temperature.describe(include='all')
tidy_temperature.head()
tidy_temperature.sample(20)
tidy_humidity = pd.melt(humidity,
id_var... | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Let's cleanup the data There are many strategies to deal with NaN data. Here, since it is weather, perhaps a reasonable way would be interpolating the temperature, humidity and wind_speed. In other words, the tempeture today is reasonable between that of yesterday and tomorrow, as a good approximation. | raw_weather.describe()
# The amount of missing values
raw_weather.isna().sum()
weather = raw_weather.interpolate()
weather.isna().sum() | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
This is because we could not interpolate into the first row! Therefore, let us omit it. | weather = weather.dropna()
weather.isna().sum()
# Filter down to only San Francisco weather
sf_weather = weather[weather['city'] == 'San Francisco']
sf_weather.sample(10)
# Project down to only temperature and humidity
data = weather[['datetime', 'city','temperature', 'humidity']]
data.sample(10)
# The average weather ... | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Note the two-levels of the columns above. Let us now flatten the data: | means = means.reset_index()
means.head() | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Now, let's join it back with the original data | means.sample(10)
df = means.set_index('city') | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Therefore, now we can remove the unnecessary column city. | means_data = means.drop(['city'], axis=1)
means_data.sample(10)
cor = means_data.corr()
cor
data = weather.merge(means, left_on='city', right_on='city')
data.sample(10) | _____no_output_____ | MIT | notebook/python/tidy-data-with-pandas.ipynb | praveenhm/machine-learning-tutorials |
Germany: LK Wesermarsch (Niedersachsen)* Homepage of project: https://oscovida.github.io* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Niedersachsen-LK-Wesermarsch.ipynb) | import datetime
import time
start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="LK Wesermarsch");
# load the data
cases, deaths, ... | _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Niedersachsen-LK-Wesermarsch.ipynb | RobertRosca/oscovida.github.io |
Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Niedersachsen-LK-Wesermarsch.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyte... | print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
f"deaths at {fetch_deaths_last_execution()}.")
# to force a fresh download of data, run "clear_cache()"
print(f"Notebook execution took: {datetime.datetime.now()-start}")
| _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Niedersachsen-LK-Wesermarsch.ipynb | RobertRosca/oscovida.github.io |
Центр непрерывного образования Программа «Python для автоматизации и анализа данных»Неделя 3 - 1*Ян Пиле, НИУ ВШЭ* Цикл for. Применение циклов к строкам, спискам, кортежам и словарям. Циклы мы используем в тех случаях, когда нужно повторить что-нибудь n-ное количество раз. Например, у нас уже был цикл **While** | ss = {1,2,3}
ss.pop()
ss.pop()
i = 1
while i<=10:
print(i)
i+=1 | 1
2
3
4
5
6
7
8
9
10
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Здесь мы проверяем условие *i <= 10* (оно выполнено, *i = 1*), заходим в цикл с *i = 1*, печатаем значение *i*, добавляем к нему 1 иииии... \Снова проверяем условие *i <= 10* (оно выполнено, *i = 2*), заходим в цикл с *i = 2*, печатаем значение *i*, добавляем к нему 1 иииии... \Делаем такие же действия, пока *i* не ста... | i = 1
while True:
print(i)
i+=1
if i==11:
break | 1
2
3
4
5
6
7
8
9
10
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
FOR В Python цикл начинается с ключевого слова **for**, за которым следует произвольное имя переменной, которое будет хранить значения следующего объекта последовательности. Общий синтаксис **for...in** в python выглядит следующим образом:**for** **in** : **else:** Элементы “последовательности” перебир... | languages = ["C", "C++", "Perl", "Python"]
for x in languages:
print(x) | C
C++
Perl
Python
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Элементы “последовательности” перебираются один за другим “переменной” цикла; \если быть точным, переменная указывает на элементы. Для каждого элемента выполняется “действие”.\Здесь в роли "последовательности" у нас список Итерируемый объект **Итерация** - это общий термин, который описывает процедуру взятия элементов... | s = {1,2,3,4,5}
print(type(s))
print(type(iter(s)))
for i in iter(s):
print(i) | 1
2
3
4
5
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Посмотрим на встроенную функцию next(). Она должна отдавать следующий элемент итератора. | s = {1,2,3,4,5}
s_iter = iter(s)
print(next(s_iter))
print(next(s_iter))
print(next(s_iter)) | 1
2
3
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Отлично! мы по одному научились перебирать элементы из итерируемого объекта. Стоит отдельно остановиться на том, что цикл **for**, в Python, устроен несколько иначе, чем в большинстве других языков. Он больше похож на **for...each**, или же **for...of**. Например, для Javascript проход по списку с выводом на печать все... | %%js
let numbers = [10, 12, 15, 18, 20];
for (let i = 0; i < numbers.length; i += 1) {
console.log(numbers[i])
}
l = [1,2,3,4,5]
list(map(str,l)) | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Если же, мы перепишем цикл **for** с помощью цикла **while**, используя индексы, то работать такой подход будет только с последовательностями: | list_of_numbers = [1,2,3]
index = 0
while index < len(list_of_numbers):
print(list_of_numbers[index])
index += 1 | 1
2
3
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
А с итерируемыми объектами, последовательностями не являющимися, не будет (потому что в множестве к элементу по индексу не обращаются!): | set_of_numbers = {1,2,3}
index = 0
while index < len(set_of_numbers):
print(set_of_numbers[index])
index += 1 | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Ну если уж прям совсем никак без индексации, то к любому итерируемому объекту можно применить функцию enumerate(), \которая, как следует из названия, коллекцию занумерует. Здесь мы наделали кортежей вида (индекс, элемент) | set_of_numbers = {1,2,3,4,5,6}
for i in enumerate(set_of_numbers):
print(i) | (0, 1)
(1, 2)
(2, 3)
(3, 4)
(4, 5)
(5, 6)
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Чтобы выдавать это в человеческом виде, можно прямо после for сказать, что мы "итерируемся" по индексам и объектам. \Выглядит это следующим образом: | set_of_numbers = [1,2,3]
for index, element in enumerate(set_of_numbers):
print(index, element) | 0 1
1 2
2 3
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Немного умных слов об итераторах**Протокол итератора**Теперь формализуем протокол итератора целиком:* Чтобы получить итератор мы должны передать функции iter итерируемый объект.* Далее мы передаём итератор функции next.* Когда элементы в итераторе закончились, порождается исключение StopIteration. (Пока представим себ... | x = 'Take a look around'
for i in x:
print(i)
# Здесь мы прошлись по элементам списка и посчитали их сумму
# В цикл вошли с total = 0 и на каждом элементе добавляли к total значение элемента
x = [1,2,3,4]
total = 0
for i in x:
total+=i
print(total)
# Здесь мы прошлись по элементам кортежа и посчитали сумму тех... | 34
{1, 2, 3, 4, 7, 49, 23, 28, 63}
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
СловариВ случае словарей итерация (по умолчанию) происходит по ключам | d = {'foo': 1, 'bar': 2, 'baz': 3}
for k in d:
print(k) | foo
bar
baz
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Но по ключам можно вынимать и соответствующие значения | for k in d:
print(d[k]) | 1
2
3
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Также можно напрямую указать, по чему мы итерируемся: по ключам, по значениям или по кортежам ключ-значение\Помните методы **.values()** , **.keys()** и **.items()** ? | print(d)
for v in d.values():
print(v)
print(d)
for v in d.keys():
print(v)
print(d)
for v in d.items():
print(v) | {'foo': 1, 'bar': 2, 'baz': 3}
('foo', 1)
('bar', 2)
('baz', 3)
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
А еще можно "распаковать" эти кортежи-ключ значения (примерно так же, как мы сделали для **enumerate**) | d = {'foo': 1, 'bar': 2, 'baz': 3}
for k, v in d.items():
print('k =', k, ', v =', v) | k = foo , v = 1
k = bar , v = 2
k = baz , v = 3
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Перед тем, как начать решать какие-то задачи, остается упомянуть крайне полезную функцию **range()**. \Простыми словами, **range()** позволяет вам генерировать ряд чисел в рамках заданного диапазона. В зависимости от того, как много аргументов вы передаете функции, вы можете решить, где этот ряд чисел начнется и законч... | # Это как раз первый случай (мы вывели все целые числа ДО трех)
for i in range(3):
print(i)
# Это второй случай (мы вывели все целые числа от 0 до 10 не включая правый конец)
for i in range(0, 10):
print(i)
# Ну а это третий случай (мы вывели все целые числа от 0 до 10 не включая правый конец с шагом 2)
# То ес... | 0
2
4
6
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| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Шаг здесь может быть положительным или отрицательным числом, но не может быть нулем! Отрицательное число будет означать уменьшение аргумента, то есть: | for i in range(10, 0, -1):
print(i) | 10
9
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1
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
А теперь отличие от итераторов! У range можно обратиться к элементу или даже срезу (как в списках) | print(range(3)[1])
print(range(10)[2:5]) | range(2, 5)
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Немного истории: в Python 2 были функции **range** и **xrange**. Первая создавала список (прям настоящий список), а вторая - \именно то, что теперь в Python 3 называется **range** Задача 1Считайте с клавиатуры несколько чисел через пробел и выведите сумму их кубов**Вход:** 1 2 3 \**Выход:** 36 | # Решение
numbers = map(int,input().split())
x = 0
for i in numbers:
x += i**2
print(x) | 1 2 3 4 5 6
91
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Задача 2Считайте с клавиатуры две последовательности чисел через пробел и выведите список уникальных общих элементов этих двух последовательностей. Сделать это можно с помощью вложенного цикла for и, например, множеств.**Вход:** Последовательность 1: 1 2 3 Последовательность 2: 2,2,4,7,... | common = set()
list1 = list(map(int,input().split()))
list2 = list(map(int,input().split()))
for elem1 in list1:
for elem2 in list2:
if elem1 == elem2:
common.add(elem1)
break
print(common) | 1 2 3 4 5
2 2 3 3
{2, 3}
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Но можно было и без этого. Решать можно было в несколько строк с использованием функционала множеств | set1 = set(map(int,input().split()))
set2 = set(map(int,input().split()))
set1.intersection(set2) | 1 2 2 2 2 3 4 6 4
3 7 6 3 2
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Задача 3Дан список, содержащий строки, целые числа и числа с плавающей точкой. Разбить его на три списка так, чтобы в одном остались только строки, в другом - только целые числа, а в третьем - только числа с плавающей точкой. Заметьте, что при проверке типов название типа пишется без кавычек, например **int**.**Вход:*... | #Решение
list1 = [1, 2, 5.6, 7.5, 'Boo', 1, 'RocknRoll']
ints, floats, strings = [], [], []
for i in list1:
if type(i)==int:
ints.append(i)
elif type(i)==float:
floats.append(i)
else:
strings.append(i)
print(ints)
print(floats)
print(strings) | [1, 2, 1]
[5.6, 7.5]
['Boo', 'RocknRoll']
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Генераторы списков и списковые включения. aka List ComprehensionsЭтот элемент языка считается его "визитной карточкой". Это своего рода метод быстро создать новый список, не применяя цикл for. Пусть мы, к примеру, хотим создать список с числами от 0 до 20 | a = []
for i in range(20):
a.append(i)
a | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Это же выражение можно записать с помощью спискового включения | a = [i for i in range(20)]
print(type(a))
print(a) | <class 'list'>
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Что мы видим? Во-первых, на выходе такой конструкцими мы получили лист (конечно, это же СПИСКОВОЕ включение). А во-вторых, все написано в одну строчку и , кажется следует вот такой конструкции:**new_list** = [**expression** for **member** in **iterable**]1. **expression** какое либо вычисление, вызов метода или любое д... | lst = [1,2,3,4,5,45,67,8,765,854,76]
x = [str(i) for i in lst]
x | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Но и это еще не все. В списковое включение можно добавить какое нибудь условие (как мы это делали с **if**). Выглядеть это будет так: new_list = [expression for member in iterable (if conditional)]Разберем на примере: | lst = [1,2,3,4,5,45,67,8,765,854,76]
x = [i for i in lst if i%2 == 0] #Здесь я взял и включил в новый список только четные элементы
x | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Более того - не зря в условии написано iterable, а не list. Значит можно попробовать проделать что-то подобное с любыми другими итерируемыми объектами. с кортежами все точно должно получиться: | # Предложение
sentence = '''The rocket, who was named Ted, came back
from Mars because he missed his friends.'''
# Гласные английского языка и пробел
vowels = 'aeiou '
# достанем в список все символы строки, которые не являются гласными и пробелом.
consonants = [i for i in sentence if i not in vowels]
c... | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
А еще вот так можно было... Не зря же регулярные выражения проходили. | import re
re.findall(r'[^aeiou ]',sentence) | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Мы уже поняли, что можно поместить условие в конец оператора для простой фильтрации, но что, если хочется изменить значение элемента вместо его фильтрации? В этом случае полезно поместить условное выражение в начале выражения. Выглядит это вот так: new_list = [expression (if conditional) for member in iterable]С пом... | original_prices = [1.25, -9.45, 10.22, 3.78, -5.92, 1.16]
prices = [i if i > 0 else 0 for i in original_prices]
prices | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Здесь, наше выражение **i** содержит условный оператор, **if i> 0** else **0**. Это говорит Python выводить значение **i**, если число положительное, но менять **i** на **0**, если число отрицательное. Включения для множеств и словарей Хотя **list comprehension** в Python является распространенным инструментом, вы так... | quote = "life, uh, finds a way"
unique_vowels = {i for i in quote if i in 'aeiou'}
unique_vowels | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Здесь мы вывели все уникальные гласные, которые встретились в строке **Dictionary comprehensions** , по сути, работает так же, но с дополнительным требованием определения ключа. Ключ отделяется двоеточием. | squares = {i: i * i for i in range(10)}
squares | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Генераторы списков По сути, это то же самое, что списковое включение, но только возвращает оно не сам список, а генератор. | type((i * i for i in range(10))) | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Проверим: | x = (i * i for i in range(10))
next(x)
next(x) | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Так-так, функция next работает. | x[4] | _____no_output_____ | MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
К элементам обращаться нельзя | x = (i * i for i in range(10))
while True:
print(next(x)) | 0
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| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
**StopIteration!** Опять что-то знакомое) Получается, что генератор, это , на самом деле, какой-то вид итератора. Так оно и есть. Генератор это итератор, который можно получить с помощью генераторного выражения, например, (i * i for i in range(10)) или с помощью функции-генератора (но об этом в следующей серии. Так а з... | %time
sum([i * i for i in range(1000000)])
%time
sum(i * i for i in range(1000000)) | CPU times: user 2 µs, sys: 0 ns, total: 2 µs
Wall time: 3.81 µs
| MIT | lect04_for/.ipynb_checkpoints/for-checkpoint.ipynb | pileyan/DPO_python_2022 |
Class 14: Prescott's Real Business Cycle Model IIn this notebook, we'll consider a centralized version of the model from pages 11-17 in Edward Prescott's article "Theory Ahead of Business Cycle Measurement in the Fall 1986 of the Federal Reserve Bank of Minneapolis' *Quarterly Review* (link to article: https://www.min... | # Create a variable called 'parameters' that stores the model parameter values in a Pandas Series
parameters = pd.Series(dtype=float)
parameters['rho'] = .75
parameters['beta'] = 0.99
parameters['phi'] = 1.7317
parameters['alpha'] = 0.35
parameters['delta'] = 0.025
# Print the model's parameters
print(parameters)
# Cr... | _____no_output_____ | MIT | Lecture Notebooks/Econ126_Class_14.ipynb | letsgoexploring/econ126 |
Next, initialize the model using `ls.model` which takes the following required arguments:* `equations`* `n_states`* `var_names`* `shock_names`* `parameters` | # Initialize the model into a variable named 'rbc_model'
rbc_model = ls.model(equations = equilibrium_equations,
n_states=2,
var_names=var_names,
shock_names=shock_names,
parameters=parameters)
# Compute the steady state numerically using .compute_ss()... | _____no_output_____ | MIT | Lecture Notebooks/Econ126_Class_14.ipynb | letsgoexploring/econ126 |
Impulse ResponsesCompute a 26 period impulse responses of the model's variables to a 0.01 unit shock to TFP in period 5. | # Compute impulse responses
rbc_model.impulse(T=26,t0=5,shocks=[0.01,0])
# Print the first 10 rows of the computed impulse responses to the TFP shock
print(rbc_model.irs['e_a'].head(10)) | e_a a k y c i l
0 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
1 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
2 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
3 0.00 0.000000 0.000000 0.000000 0.000000 ... | MIT | Lecture Notebooks/Econ126_Class_14.ipynb | letsgoexploring/econ126 |
Construct a $2\times3$ grid of plots of simulated TFP, output, labor, consumption, investment, and capital. Be sure to multiply simulated values by 100 so that vertical axis units are in "percent deviation from steady state." | # Create figure. PROVIDED
fig = plt.figure(figsize=(18,8))
# Create upper-left axis. PROVIDED
ax = fig.add_subplot(2,3,1)
ax.plot(rbc_model.irs['e_a']['a']*100,'b',lw=5,alpha=0.75)
ax.set_title('TFP')
ax.set_ylabel('% dev from steady state')
ax.set_ylim([-0.5,2])
ax.grid()
# Create upper-center axis. PROVIDED
ax = fi... | _____no_output_____ | MIT | Lecture Notebooks/Econ126_Class_14.ipynb | letsgoexploring/econ126 |
The thermodynamics of ideal solutions*Authors: Enze Chen (University of California, Berkeley)*This animation will show how the Gibbs free energy curves correspond to a lens phase diagram. Python imports | # General libraries
import io
import os
# Scientific computing libraries
import numpy as np
from scipy.misc import derivative
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.animation as animation
from PIL import Image
import cv2
from moviepy.editor import * | _____no_output_____ | CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Helper functions | # analytical function for the solid free energy curve
def curve_s(x, T, beta=0):
"""This function plots the Gibbs free energy curve for the solid solution.
Args:
x (numpy.ndarray): An array of atomic fractions of B.
T (float): The temperature in Kelvin.
beta (float): The interaction... | _____no_output_____ | CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Animations using `FuncAnimation`Finally!! VLC/Windows has buggy glitches, but the embedded HTML version looks fine.Also, **extremely high quality and low memory footprint**!! 🎉 | # Initialize quantities
n = int(1e4)
xmin, xmax = (0.001, 0.999)
x = np.linspace(xmin, xmax, n)
liquidus = []
solidus = []
Ts = np.arange(1300, 2301, 5)
# Plot settings
plt.rcParams.update({'figure.figsize':(7,9.5), 'font.size':16})
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
# Initialize plot settings
ymin... | 1300
1400
1500
1600
1700
1800
1900
2000
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2200
2300
| CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Other (sub-par) methods that I've tried... | # Accumulate images in a list for post-processing
n = int(1e4)
xmin, xmax = (0.001, 0.999)
x = np.linspace(xmin, xmax, n)
liquidus = []
solidus = []
Ts = np.arange(1300, 1450, 10)
plt.rcParams.update({'figure.figsize':(7,9)})
fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True)
fig.tight_layout()
ymin, ymax = -39, 19... | _____no_output_____ | CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Convert PIL images to mp4 using [OpenCV](https://docs.opencv.org/master/d6/d00/tutorial_py_root.html)OK, this works! Quality could be improved... this is where FuncAnimation native support would probably be better. | # This movie is very large in size!!
opencv_images = [cv2.cvtColor(np.array(i), cv2.COLOR_RGB2BGR) for i in images]
height, width, channels = opencv_images[0].shape
fourcc = cv2.VideoWriter_fourcc(*'MP4V') # can also be 'MJPG' or 'MP4V'
video = cv2.VideoWriter(filename='C:/Users/Enze/Desktop/test_opencv.mp4',
... | _____no_output_____ | CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Convert figure files using [`moviepy`](https://moviepy.readthedocs.io/en/latest/index.html)Quality seems a little worse than OpenCV. Also takes a longggg time lol, but the file size is very small! | datadir = 'C:/Users/Enze/Desktop/plots/'
clips = [ImageClip(os.path.join(datadir, m)).set_duration(0.2) for m in os.listdir(datadir)]
concat = concatenate_videoclips(clips, method='compose')
concat.write_videofile('C:/Users/Enze/Desktop/test_moviepy.mp4', fps=10) | _____no_output_____ | CC0-1.0 | assets/fig/scripts/thermo_animation.ipynb | LuisG-R/mi-book |
Character level language model - Dinosaurus IslandWelcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to giv... | import numpy as np
from utils import *
import random
import pprint | _____no_output_____ | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
1 - Problem Statement 1.1 - Dataset and PreprocessingRun the following cell to read the dataset of dinosaur names, create a list of unique characters (such as a-z), and compute the dataset and vocabulary size. | data = open('dinos.txt', 'r').read()
data= data.lower()
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
print('There are %d total characters and %d unique characters in your data.' % (data_size, vocab_size)) | There are 19909 total characters and 27 unique characters in your data.
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
* The characters are a-z (26 characters) plus the "\n" (or newline character).* In this assignment, the newline character "\n" plays a role similar to the `` (or "End of sentence") token we had discussed in lecture. - Here, "\n" indicates the end of the dinosaur name rather than the end of a sentence. * `char_to_i... | chars = sorted(chars)
print(chars)
char_to_ix = { ch:i for i,ch in enumerate(chars) }
ix_to_char = { i:ch for i,ch in enumerate(chars) }
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(ix_to_char) | { 0: '\n',
1: 'a',
2: 'b',
3: 'c',
4: 'd',
5: 'e',
6: 'f',
7: 'g',
8: 'h',
9: 'i',
10: 'j',
11: 'k',
12: 'l',
13: 'm',
14: 'n',
15: 'o',
16: 'p',
17: 'q',
18: 'r',
19: 's',
20: 't',
21: 'u',
22: 'v',
23: 'w',
24: 'x',
... | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
1.2 - Overview of the modelYour model will have the following structure: - Initialize parameters - Run the optimization loop - Forward propagation to compute the loss function - Backward propagation to compute the gradients with respect to the loss function - Clip the gradients to avoid exploding gradients ... | ### GRADED FUNCTION: clip
def clip(gradients, maxValue):
'''
Clips the gradients' values between minimum and maximum.
Arguments:
gradients -- a dictionary containing the gradients "dWaa", "dWax", "dWya", "db", "dby"
maxValue -- everything above this number is set to this number, and everything... | gradients["dWaa"][1][2] = 10.0
gradients["dWax"][3][1] = -10.0
gradients["dWya"][1][2] = 0.29713815361
gradients["db"][4] = [ 10.]
gradients["dby"][1] = [ 8.45833407]
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
** Expected output:**```Pythongradients["dWaa"][1][2] = 10.0gradients["dWax"][3][1] = -10.0gradients["dWya"][1][2] = 0.29713815361gradients["db"][4] = [ 10.]gradients["dby"][1] = [ 8.45833407]``` | # Test with a maxValue of 5
mValue = 5
np.random.seed(3)
dWax = np.random.randn(5,3)*10
dWaa = np.random.randn(5,5)*10
dWya = np.random.randn(2,5)*10
db = np.random.randn(5,1)*10
dby = np.random.randn(2,1)*10
gradients = {"dWax": dWax, "dWaa": dWaa, "dWya": dWya, "db": db, "dby": dby}
gradients = clip(gradients, mValue... | gradients["dWaa"][1][2] = 5.0
gradients["dWax"][3][1] = -5.0
gradients["dWya"][1][2] = 0.29713815361
gradients["db"][4] = [ 5.]
gradients["dby"][1] = [ 5.]
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
** Expected Output: **```Pythongradients["dWaa"][1][2] = 5.0gradients["dWax"][3][1] = -5.0gradients["dWya"][1][2] = 0.29713815361gradients["db"][4] = [ 5.]gradients["dby"][1] = [ 5.]``` 2.2 - SamplingNow assume that your model is trained. You would like to generate new text (characters). The process of generation is e... | matrix1 = np.array([[1,1],[2,2],[3,3]]) # (3,2)
matrix2 = np.array([[0],[0],[0]]) # (3,1)
vector1D = np.array([1,1]) # (2,)
vector2D = np.array([[1],[1]]) # (2,1)
print("matrix1 \n", matrix1,"\n")
print("matrix2 \n", matrix2,"\n")
print("vector1D \n", vector1D,"\n")
print("vector2D \n", vector2D)
print("Multiply 2D a... | Adding a (3,) vector to a (3 x 1) vector
broadcasts the 1D array across the second dimension
Not what we want here!
[[2 4 6]
[2 4 6]
[2 4 6]]
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
- **Step 3**: Sampling: - Now that we have $y^{\langle t+1 \rangle}$, we want to select the next letter in the dinosaur name. If we select the most probable, the model will always generate the same result given a starting letter. To make the results more interesting, we will use np.random.choice to select a next le... | # GRADED FUNCTION: sample
def sample(parameters, char_to_ix, seed):
"""
Sample a sequence of characters according to a sequence of probability distributions output of the RNN
Arguments:
parameters -- python dictionary containing the parameters Waa, Wax, Wya, by, and b.
char_to_ix -- python dictio... | Sampling:
list of sampled indices:
[12, 17, 24, 14, 13, 9, 10, 22, 24, 6, 13, 11, 12, 6, 21, 15, 21, 14, 3, 2, 1, 21, 18, 24, 7, 25, 6, 25, 18, 10, 16, 2, 3, 8, 15, 12, 11, 7, 1, 12, 10, 2, 7, 7, 11, 17, 24, 12, 13, 24, 0]
list of sampled characters:
['l', 'q', 'x', 'n', 'm', 'i', 'j', 'v', 'x', 'f', 'm', 'k', 'l', '... | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
** Expected output:**```PythonSampling:list of sampled indices: [12, 17, 24, 14, 13, 9, 10, 22, 24, 6, 13, 11, 12, 6, 21, 15, 21, 14, 3, 2, 1, 21, 18, 24, 7, 25, 6, 25, 18, 10, 16, 2, 3, 8, 15, 12, 11, 7, 1, 12, 10, 2, 7, 7, 11, 17, 24, 12, 13, 24, 0]list of sampled characters: ['l', 'q', 'x', 'n', 'm', 'i', 'j', 'v', ... | # GRADED FUNCTION: optimize
def optimize(X, Y, a_prev, parameters, learning_rate = 0.01):
"""
Execute one step of the optimization to train the model.
Arguments:
X -- list of integers, where each integer is a number that maps to a character in the vocabulary.
Y -- list of integers, exactly the... | Loss = 126.503975722
gradients["dWaa"][1][2] = 0.194709315347
np.argmax(gradients["dWax"]) = 93
gradients["dWya"][1][2] = -0.007773876032
gradients["db"][4] = [-0.06809825]
gradients["dby"][1] = [ 0.01538192]
a_last[4] = [-1.]
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
** Expected output:**```PythonLoss = 126.503975722gradients["dWaa"][1][2] = 0.194709315347np.argmax(gradients["dWax"]) = 93gradients["dWya"][1][2] = -0.007773876032gradients["db"][4] = [-0.06809825]gradients["dby"][1] = [ 0.01538192]a_last[4] = [-1.]``` 3.2 - Training the model * Given the dataset of dinosaur names, ... | # GRADED FUNCTION: model
def model(data, ix_to_char, char_to_ix, num_iterations = 35000, n_a = 50, dino_names = 7, vocab_size = 27, verbose = False):
"""
Trains the model and generates dinosaur names.
Arguments:
data -- text corpus
ix_to_char -- dictionary that maps the index to a character
... | _____no_output_____ | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
Run the following cell, you should observe your model outputting random-looking characters at the first iteration. After a few thousand iterations, your model should learn to generate reasonable-looking names. | parameters = model(data, ix_to_char, char_to_ix, verbose = True) | j = 0 idx = 0
single_example = turiasaurus
single_example_chars ['t', 'u', 'r', 'i', 'a', 's', 'a', 'u', 'r', 'u', 's']
single_example_ix [20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19]
X = [None, 20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19]
Y = [20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19, 0]
Iteration: 0, Loss: 23.0... | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
** Expected Output**```Pythonj = 0 idx = 0single_example = turiasaurussingle_example_chars ['t', 'u', 'r', 'i', 'a', 's', 'a', 'u', 'r', 'u', 's']single_example_ix [20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19] X = [None, 20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19] Y = [20, 21, 18, 9, 1, 19, 1, 21, 18, 21, 19, 0] It... | from __future__ import print_function
from keras.callbacks import LambdaCallback
from keras.models import Model, load_model, Sequential
from keras.layers import Dense, Activation, Dropout, Input, Masking
from keras.layers import LSTM
from keras.utils.data_utils import get_file
from keras.preprocessing.sequence import p... | Using TensorFlow backend.
| MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
To save you some time, we have already trained a model for ~1000 epochs on a collection of Shakespearian poems called [*"The Sonnets"*](shakespeare.txt). Let's train the model for one more epoch. When it finishes training for an epoch---this will also take a few minutes---you can run `generate_output`, which will prom... | print_callback = LambdaCallback(on_epoch_end=on_epoch_end)
model.fit(x, y, batch_size=128, epochs=1, callbacks=[print_callback])
# Run this cell to try with different inputs without having to re-train the model
generate_output() | _____no_output_____ | MIT | notebooks/Dinosaurus_Island_Character_level_language_model_final_v3b.ipynb | tfburns/deep-learning-specialization |
Central Limit Theorem - Part IIWork through the questions and use the created variables to answer the questions that follow below the notebook.Run the below cell to get started. | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
pop_data = np.random.gamma(1,100,3000)
plt.hist(pop_data); | _____no_output_____ | MIT | Part 3 Descriptive Statistics/Study materials/08-sampling-distributions/08_Sampling Distributions/Sampling Distributions - 19 - Central Limit Theorem - Part II.ipynb | bimarsha7/Data-analysis |
`1.` In order to create the sampling distribution for the average of 100 draws of this distribution, follow these steps:`a.` Use numpy's **random.choice** to simulate 100 draws from the `pop_data` array. `b.` Compute the mean of these 100 draws. `c.` Write a loop to simulate this process 10,000 times, and store each ... | means_size_100 = []
for _ in range(10000):
sample = np.random.choice(pop_data, 100)
means_size_100.append(sample.mean())
plt.hist(means_size_100); | _____no_output_____ | MIT | Part 3 Descriptive Statistics/Study materials/08-sampling-distributions/08_Sampling Distributions/Sampling Distributions - 19 - Central Limit Theorem - Part II.ipynb | bimarsha7/Data-analysis |
Copyright 2019 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Classification on imbalanced data View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work... | import tensorflow as tf
from tensorflow import keras
import os
import tempfile
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sk... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Data processing and exploration Download the Kaggle Credit Card Fraud data setPandas is a Python library with many helpful utilities for loading and working with structured data. It can be used to download CSVs into a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.htmlpandas.DataFram... | file = tf.keras.utils
raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')
raw_df.head()
raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe() | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Examine the class label imbalanceLet's look at the dataset imbalance: | neg, pos = np.bincount(raw_df['Class'])
total = neg + pos
print('Examples:\n Total: {}\n Positive: {} ({:.2f}% of total)\n'.format(
total, pos, 100 * pos / total)) | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
This shows the small fraction of positive samples. Clean, split and normalize the dataThe raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range. | cleaned_df = raw_df.copy()
# You don't want the `Time` column.
cleaned_df.pop('Time')
# The `Amount` column covers a huge range. Convert to log-space.
eps = 0.001 # 0 => 0.1¢
cleaned_df['Log Ammount'] = np.log(cleaned_df.pop('Amount')+eps) | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generaliz... | # Use a utility from sklearn to split and shuffle your dataset.
train_df, test_df = train_test_split(cleaned_df, test_size=0.2)
train_df, val_df = train_test_split(train_df, test_size=0.2)
# Form np arrays of labels and features.
train_labels = np.array(train_df.pop('Class'))
bool_train_labels = train_labels != 0
val_... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Normalize the input features using the sklearn StandardScaler.This will set the mean to 0 and standard deviation to 1.Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. | scaler = StandardScaler()
train_features = scaler.fit_transform(train_features)
val_features = scaler.transform(val_features)
test_features = scaler.transform(test_features)
train_features = np.clip(train_features, -5, 5)
val_features = np.clip(val_features, -5, 5)
test_features = np.clip(test_features, -5, 5)
prin... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export. Look at the data distributionNext compare the distributions of the positive and negative examples over a few features. Good q... | pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)
neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)
sns.jointplot(pos_df['V5'], pos_df['V6'],
kind='hex', xlim=(-5,5), ylim=(-5,5))
plt.suptitle("Positive distribution")
sns.jointplot(neg_... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Define the model and metricsDefine a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction bei... | METRICS = [
keras.metrics.TruePositives(name='tp'),
keras.metrics.FalsePositives(name='fp'),
keras.metrics.TrueNegatives(name='tn'),
keras.metrics.FalseNegatives(name='fn'),
keras.metrics.BinaryAccuracy(name='accuracy'),
keras.metrics.Precision(name='precision'),
keras.metrics... | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Understanding useful metricsNotice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.* **False** negatives and **false** positives are samples that were **incorrectly** classified* **True** negatives and **true** positives are samples ... | EPOCHS = 100
BATCH_SIZE = 2048
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor='val_prc',
verbose=1,
patience=10,
mode='max',
restore_best_weights=True)
model = make_model()
model.summary() | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Test run the model: | model.predict(train_features[:10]) | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
Optional: Set the correct initial bias. These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: "init well"](http://karpathy.github.io/2019/04/25/recipe/2-set-up-the-end-to-end-trainingevaluation-skeleton--get-du... | results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)
print("Loss: {:0.4f}".format(results[0])) | _____no_output_____ | Apache-2.0 | site/en/tutorials/structured_data/imbalanced_data.ipynb | arshPratap/docs |
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