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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Hacer variables dummies | n=-10
final=general.MWh.tail(-n)
onlyMWh=pd.DataFrame(general.MWh)
general['Month'] = general.index.month
general['Weekday_Name'] = general.index.weekday_name
dates=general.index
dummies = pd.get_dummies(general['Weekday_Name']).astype(int)
dummies2 = pd.get_dummies(general['Month']).astype(int)
Dum=pd.DataFrame(dummie... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Observar descomposición | part=general.MWh.tail(100)
result=seasonal_decompose(part, model='multiplicative')
fig = result.seasonal.plot(figsize=(20,5)) | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Al ver la decomposición, se puede ver por la forma que fourier debe estblecerse en senos y cosenos absolutos, para que se parezca a la estacionalidad de la serie. Se agrega a las variables dummies esta estacionalidad semanal, que parece ser fundamental en los datos Detectar efecto de las variables dummies | t=np.arange(1,len(onlyMWh)+1)
Tiempo=pd.DataFrame(t)
Tiempo["one"]=np.ones(len(onlyMWh))
Tiempo['sen']=np.abs(np.sin(((2*np.pi)/14)*t))
Tiempo['cos']=np.abs(np.cos(((2*np.pi)/14)*t))
Combinacion=kronecker(Dum,Tiempo)
model = LinearRegression()
prediction=regresion_linear(Combinacion[:n],general.MWh.values[:n])
plt.figu... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Obtener error de datos con variables dummies vs datos reales | Tabla=pd.DataFrame(columns=['regresion','datos','resta'])
Tabla["regresion"]=prediction
Tabla["datos"]=onlyMWh.MWh.values[:n]
Tabla["resta"]=Tabla.datos-Tabla.regresion
plt.plot(Tabla.resta)
plt.show() | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Establecer las frecuencias que se debe considerar en la serie de fourier | f, Pxx_den = signal.periodogram(Tabla.resta, 1)
plt.plot(1/f, Pxx_den)
plt.xlabel('periodo')
plt.ylabel('PSD')
plt.show()
top_50_periods = {}
# get indices for 3 highest Pxx values
top50_freq_indices = np.flip(np.argsort(Pxx_den), 0)[2:12]
freqs = f[top50_freq_indices]
power = Pxx_den[top50_freq_indices]
periods = 1 /... | power periods
0 8.749328e+09 1911.333333
1 5.225123e+09 45.507937
2 4.883263e+09 819.142857
3 4.626260e+09 1146.800000
4 4.413181e+09 36.522293
5 4.324008e+09 955.666667
6 4.069662e+09 573.400000
7 4.064267e+09 521.272727
8 3.968931e+09 61.000000
9 3.914966e+09 91.015873
| MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Hacer la regresión del efecto cruzado de variables dummies y senos/cosenos absolutos de frecuencia de error | sencos = pd.DataFrame()
sencos["t"]=np.arange(1,len(onlyMWh)+1)
for i in matrix.periods:
sencos["{}_sen".format(i)] = np.abs(np.sin(((2*np.pi)/i)*t))
sencos["{}_cos".format(i)] = np.abs(np.cos(((2*np.pi)/i)*t))
sencos["unos"] = 1
sencos['sen']=np.abs(np.sin(((2*np.pi)/14)*t))
sencos['cos']=np.abs(np.c... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
MAPE de la regresion | plt.figure(figsize=(10,5))
plt.plot(onlyMWh.MWh[0:n].values,label ="Datos")
plt.plot(prediction,label="Predicción")
plt.ylabel("demanda en MWh")
plt.xlabel("días")
plt.legend()
plt.show()
#%%obtener mape de regresión
comp=comparacion(onlyMWh.MWh.values[:n],prediction)
MAPE=comp.error.mean()
print("MAPE = ",round(MAPE,4... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Graficar residuales de la regresión | Tabla=pd.DataFrame(columns=['regresion','datos','resta'])
Tabla["regresion"]=prediction
Tabla["datos"]=onlyMWh.MWh[0:n].values
Tabla["resta"]=Tabla.datos-Tabla.regresion
plt.plot(Tabla.resta)
plt.show()
plt.hist(Tabla["resta"],bins=50)
plt.show()
resta=pd.DataFrame(Tabla["resta"])
from statsmodels.tsa.arima_model impor... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
Gráfica de manera dinámica | extra=results.predict(len(onlyMWh.MWh[0:n]),len(onlyMWh.MWh[0:n])-n)
extra=extra.iloc[1:]
from sklearn.linear_model import Lasso
Combinaciontest=kronecker(Dum_test,sencos_test)
#Initializing the Lasso Regressor with Normalization Factor as True
lasso_reg = Lasso(normalize=True)
#Fitting the Training data to the Lasso r... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
todo para pronóstico | comp_pronostico=comparacion(final,y_pred_lass+extra.values)
#comp_pronostico=comparacion(final,hubpredict+extra.values)
MAPE=comp_pronostico.error.mean()
plt.figure(figsize=(10,5))
plt.plot(final,label="Real")
plt.plot(comp_pronostico.prediccion,label="Pronóstico")
plt.ylabel("demanda en MWh")
plt.xlabel("días")
plt.le... | _____no_output_____ | MIT | Efecto Arima en final.ipynb | ramirezdiana/Forecast-with-fourier |
МАДМО Физтех-Школа Прикладной математики и информатики МФТИ Лаборатория нейронных сетей и глубокого обучения (DeepHackLab) Домашнее задание необходимо загрузить в общий репозиторий с именной папкой Домашнее задание 1 Основы Python и пакет NumPy--- | import numpy as np
import random
import scipy.stats as sps | _____no_output_____ | MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Задача 1В первой задаче вам предлагается перемножить две квадратные матрицы двумя способами -- без использования пакета ***numpy*** и с ним. | # Для генерации матриц используем фукнцию random -- она используется для генерации случайных объектов
# функция sample создает случайную выборку. В качестве аргумента ей передается кортеж (i,j), здесь i -- число строк,
# j -- число столбцов.
a = np.random.sample((1000,1000))
b = np.random.sample((1000,1000))
# выведит... | Wall time: 46.8 ms
| MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Задача 2Напишите функцию, которая по данной последовательности $\{A_i\}_{i=1}^n$ строит последовательность $S_n$, где $S_k = \frac{A_1 + ... + A_k}{k}$. Аналогично -- с помощью библиотеки **NumPy** и без нее. Сравните скорость, объясните результат. |
# функция, решающая задачу с помощью NumPy
def sec_av(A):
return np.cumsum(A)/list(range(1,len(A)+1))
pass
# функция без NumPy
def stupid_sec_av(A):
S = [0 for i in range(len(A))]
S[0] = A[0]
for i in range(len(A)-1):
S[i+1] = A[i+1] + S[i]
numb = list(range(1,len(A)+1))... | Wall time: 1.39 s
Wall time: 10.7 s
| MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Задача 3Пусть задан некоторый массив $X$. Надо построить новый массив, где все элементы с нечетными индексами требуется заменить на число $a$ (если оно не указано, то на 1). Все элементы с четными индексами исходного массива нужно возвести в куб и записать в обратном порядке относительно позиций этих элементов. Массив... | # функция, решающая задачу с помощью NumPy
def transformation(X, a = 1):
X[1::2] = a
X[::2] **= 3
X[::2] = X[::2][::-1]
return X
# функция, решающая задачу без NumPy
def stupid_transformation(X):
t_odd = []
t_ev = []
t_ev_inv = []
Y = []
t_odd = int(round... | Wall time: 172 ms
| MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Почему методы ***numpy*** оказываются эффективнее? | # Написаны на C | _____no_output_____ | MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Дополнительные задачи Дополнительные задачи подразумевают, что Вы самостоятельно разберётесь в некоторых функциях ***numpy***, чтобы их сделать. Эти задачи не являются обязательными, но могут повлиять на Ваш рейтинг в лучшую сторону (точные правила учёта доп. задач будут оглашены позже). Задача 4* Дана функция двух ... | from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
def f(x, y):
'''Функция двух переменных'''
return np.sin(x) * np.cos(y)
def draw_f(grid_x, grid_y):
'''Функция отрисовки функции f(x, y)'''
fig = plt.figure(figsize=(10, 8))
ax = Axes3D(fig)
ax.plot... | _____no_output_____ | MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Задача 5* Выберите любую картинку и загрузите ее в папку с кодом. При загрузке её размерность равна 3: **(w, h, num_channels)**, где **w** - ширина картинки в пикселях, **h** - высота картинки в пикселях, **num_channels** - количество каналов *(R, G, B, alpha)*.Вам нужно "развернуть" картинку в одномерный массив разме... | from matplotlib import pyplot as plt
%matplotlib inline
path_to_image = './image.png'
image_array = plt.imread(path_to_image)
plt.imshow(image_array);
flat_image_array = = image_array.flatten()
print(len(flat_image_array)) | _____no_output_____ | MIT | Students/Tunitskaya/Hometask_1(1).ipynb | Alken1/Sberbank_ML |
Colab-pytorch-image-classification
Original repo: [bentrevett/pytorch-image-classification](https://github.com/bentrevett/pytorch-image-classification)
[SqueezeNet code](https://github.com/pytorch/vision/blob/master/torchvision/models/squeezenet.py): [pytorch/vision](https://github.com/pytorch/vision)
My fork: [styl... | !nvidia-smi | _____no_output_____ | MIT | 9_SqueezeNet.ipynb | styler00dollar/Colab-image-classification |
DATASET CREATION | #@title Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')
print('Google Drive connected.')
# copy data somehow
!mkdir '/content/classification'
!mkdir '/content/classification/images'
!cp "/content/drive/MyDrive/classification_v2.7z" "/content/classification/images/classification.7z"... | _____no_output_____ | MIT | 9_SqueezeNet.ipynb | styler00dollar/Colab-image-classification |
CALC MEANS & STDS | #@title print means and stds
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from tqdm import tqdm
train_data = datasets.ImageFolder(root = train_dir,
transform = transforms.ToTensor())
means = torch.zeros(3)
stds = torch.zeros(3)
for... | _____no_output_____ | MIT | 9_SqueezeNet.ipynb | styler00dollar/Colab-image-classification |
TRAIN | #@title import, seed, transforms, dataloader, functions, plot, model, parameter
%cd /content/
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.optim.lr_scheduler import _LRScheduler
import torc... | _____no_output_____ | MIT | 9_SqueezeNet.ipynb | styler00dollar/Colab-image-classification |
TESTING | #@title Calc test loss
model.load_state_dict(torch.load('best-validation-accuracy.pt'))
print("best-validation-accuracy.pt")
test_loss, test_acc_1, test_acc_5 = evaluate(model, test_iterator, criterion, device)
print("-----------------------------")
model.load_state_dict(torch.load('best-validation-loss.pt'))
print("be... | _____no_output_____ | MIT | 9_SqueezeNet.ipynb | styler00dollar/Colab-image-classification |
From Variables to Classes A short Introduction Python - as any programming language - has many extensions and libraries at its disposal. Basically, there are libraries for everything. But what are **libraries**? Basically, **libraries** are a collection of methods (_small pieces of code where you put sth in and g... | x = 4.2
print(type(x))
y = 'Hello World!'
print(type(y))
z = True
print(type(z)) | <class 'float'>
<class 'str'>
<class 'bool'>
| MIT | 00_Variables_to_Classes.ipynb | darius74/geothermics |
We can use operations (normal arithmetic operations) to use variables for getting results we want. With numbers, you can add, substract, multiply, divide, basically taking the values from the memory assigned to the variable name and performing calculations. Let's have a look at operations with numbers and strings. We... | n1 = 7
n2 = 42
s1 = 'Looking good, '
s2 = 'you are.'
first_sum = n1 + n2
print(first_sum)
first_conc = s1 + s2
print(first_conc) | 49
Looking good, you are.
| MIT | 00_Variables_to_Classes.ipynb | darius74/geothermics |
Variables can be more than just a number. If you think of an Excel-Spreadsheet, a variable can be the content of a single cell, or multiple cells can be combined in one variable (e.g. one column of an Excel table). So let's create a list -_a collection of variables_ - from `x`, `n1`, and `n2`. Lists in python are creat... | first_list = [x, n1, n2]
second_sum = first_list[0] + first_list[1] + first_list[2]
print('manual sum {}'.format(second_sum))
# This can also be done with a function
print('sum function {}'.format(sum(first_list))) | manual sum 53.2
sum function 53.2
| MIT | 00_Variables_to_Classes.ipynb | darius74/geothermics |
Functions The `sum()` method we used above is a **function**. Functions (later we will call them methods) are pieces of code, which take an input, perform some kind of operation, and (_optionally_) return an output. In Python, functions are written like: ```pythondef func(input): """ Description of the funct... | def sumup(inp):
"""
input: inp - list/array with floating point or integer numbers
return: sumd - scalar value of the summed up list
"""
val = 0
for i in inp:
val = val + i
return val
# let's compare the implemented standard sum function with the new sumup function
sum1 = sum(first_... | the sum of the array is: 5050
| MIT | 00_Variables_to_Classes.ipynb | darius74/geothermics |
As we see above, functions are quite practical and save a lot of time. Further, they help structuring your code. Some functions are directly available in python without any libraries or other external software. In the example above however, you might have noticed, that we `import`ed a library called `numpy`. In those ... | # here we just create the data for clustering
from sklearn.datasets.samples_generator import make_blobs
import matplotlib.pyplot as plt
%matplotlib inline
X, y = make_blobs(n_samples=100, centers=3, cluster_std= 0.5,
random_state=0)
plt.scatter(X[:,0], X[:,1], s=70)
# now we create an instance of the... | _____no_output_____ | MIT | 00_Variables_to_Classes.ipynb | darius74/geothermics |
Implementing LSH algorithm 0. Dataset 0.1 Import 20-news dataset | newsgroup_dataset = datasets.fetch_20newsgroups(data_home='./dataset/', subset='train',
remove=('headers', 'footers', 'quotes'), download_if_missing=True)
raw_documents = newsgroup_dataset['data'][:]
len(raw_documents)
raw_documents[0] | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
0.2 Preprocess the documents | K = 5 # number of word tokens to shinlge
def preprocess(documents):
processed_words = defaultdict(list)
cnt = 0
for doc in documents:
# first, filter out some uncesseary symbols like punctuations
doc = re.sub('\/|\-|\'|\@|\%|\$|\#|\,|\(|\)|\}|\"|\{|\?|\.|\!|\;|\:', '', doc)
... | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
1. Shingling | ########################################################################################################################
# Programming 1 [15pt] #
# In this section, you will implement the shingling algorithm to convert the ... | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
1.1 Get Shingles from the documents | def get_shingles(documents):
######################################################################################
# Programming 1.1 [10pt] #
# Implement 'get_shingles' function to get 1-singles from the preprocessed documents #
# You shoul... | 1.0299334526062012
| MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
1.2 Build document to shingles dictionary | def build_doc_to_shingle_dictionary(documents, shingles):
################################################################################################################################
# Programming 1.2 [5pt] ... | Test passed
| MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
2. Min-Hashing | ############################################################################################################################
# Programming 2 [25pt] #
# In this section, you will implement the min-hashing algorithm to co... | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
2.1 Generate Prime numbers for Universal Hashing | def is_prime(n):
for i in range(2,int(np.sqrt(n))+1):
if not n % i:
return False
return True
def generate_prime_numbers(M, N):
# this function generate the M prime numbers where each prime number is greater than N
primes = []
cnt = 0
n = N + 1
while cnt < M:
... | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
2.2 Jaccard Similarity | def jaccard_similarity(s1, s2):
##################################################################################
# Programming 2.2 [5pt] #
# Implement the jaccard similarity algorithm to get the similarity of tw... | Test passed
| MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
2.3 Min Hash | M = 100 # Number of Hash functions to use
N = len(shingles)
# First we will create M universal hashing functions
# You can also modify or implement your own hash functions for implementing min_hash function
class Hash():
def __init__(self, M, N):
self.M = M
self.N = N
self.p = generate_p... | 100%|██████████| 10000/10000 [00:04<00:00, 2437.12it/s] | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
2.4 Qualitive Analysis | print('Document 3542')
print(documents[3542])
print('-------------')
print('Document 8033')
print(documents[8033])
print('-------------')
print('true jaccard similarity:' ,jaccard_similarity(set(doc_to_shingles[3542]), set(doc_to_shingles[8033])))
print('approx jaccard similarity:',sum(np.equal(signatures[:,3542], si... | Document 3542
i have one complaint for the cameramen doing the jerseypitt series show the shots not the hits on more than one occassion the camera zoomed in on a check along the boards while the puck was in the slot they panned back to show the rebound maybe moms camera people were a little more experienced
-----------... | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
3. Locality Sensitive Hashing | ########################################################################################################################
# Programming 3 [35pt] #
# In this section, you will implement the Min-Hash based Locality Sensitive H... | _____no_output_____ | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
3.1 Min-Hash based LSH | def lsh(signatures, b, r):
#########################################################################################################
# Programming 3.1 [20pt] #
# Implement the min-hash based LSH algorithm to find the candidate ... | b=10
# of candidate pairs = 162
0th sample pair: (1658, 5780)
Jaccard similarity: 0.8108108108108109
-------------
from paynecrldeccom andrew payne messageid organization dec cambridge research lab date tue apr gmt does anyone know if a source for the modem chips as used in the baycom and my pmp modems ideally somethin... | MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
3.2 Compute the precision, recall, and F1-score | # Compute the number of condition positives, which is the number of every document pair whose Jaccard similarity is greater than or equal to the threshold
s = 0.8 # similarity threshold for checking condition positives
numConditionPositives = 151 # This is the computed result when s=0.8, but I gave it to you to save ... | -----Test results-----
[Test 1.1 (1)]: Passed
[Test 1.1 (2)]: Passed
[Test 1.2]: Passed
[Test 2.1]: Passed
[Test 2.2]: Passed
----------------------
| MIT | AI506/hw1/hw1-20194331/hw1_problem.ipynb | sungnyun/AI-assignments |
Plotting functions | from recnn.preprocessing import sequentialize_by_pt
def load_tf(filename_train, preprocess=None, n_events_train=-1):
# Make training data
print("Loading training data...")
fd = open(filename_train, "rb")
X, y = pickle.load(fd)
fd.close()
y = np.array(y)
if n_events_train > 0:
... | _____no_output_____ | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Count parameters | def count(params):
def _count(thing):
if isinstance(thing, list):
c = 0
for stuff in thing:
c += _count(stuff)
return c
elif isinstance(thing, np.ndarray):
return np.prod(thing.shape)
c = 0
for k, v in params.items():
... | ('Simple =', 10081)
('Gated =', 50361)
| BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Embedding visualization | prefix_train = "antikt-kt"
prefix_test = prefix_train
tf = load_tf("../data/w-vs-qcd/final/%s-train.pickle" % prefix_train)
X, y, w = load_test(tf, "../data/w-vs-qcd/final/%s-test.pickle" % prefix_test)
fd = open("../models/jet-study-2/model-w-s-antikt-kt-1.pickle", "rb")
params = pickle.load(fd)
fd.close()
Xt = grnn_... | _____no_output_____ | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Generate all ROCs | for pattern, gated in [
# Simple
## Particles
("antikt-kt", False),
("antikt-cambridge", False),
("antikt-antikt", False),
("antikt-random", False),
("antikt-seqpt", False),
("antikt-seqpt-reversed", False),
## Towers
("antikt-kt-delphes", ... | _____no_output_____ | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Table | for pattern, gated, label in [
# Simple
## Particles
("antikt-kt", False, "RNN $k_t$"),
("antikt-cambridge", False, "RNN C/A"),
("antikt-antikt", False, "RNN anti-$k_t$"),
("antikt-random", False, "RNN random"),
("antikt-seqpt", False, "RNN asc-$p_T$"),
("... | particles & RNN $k_t$ & 0.9178 $\pm$ 0.0008 & 67.0 $\pm$ 2.6 \\
towers & RNN $k_t$ & 0.8801 $\pm$ 0.0012 & 24.1 $\pm$ 0.6 \\
towers & RNN $k_t$ & 0.8332 $\pm$ 0.0029 & 12.6 $\pm$ 0.4 \\
particles & RNN $k_t$ (gated) & 0.918... | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Plots | # Simple vs gated
for pattern, gated, label, color in [
("antikt-kt", False, "RNN $k_t$ (simple)", "r"),
("antikt-kt", True, "RNN $k_t$ (gated)", "b")
]:
fd = open("../models/jet-study-2/rocs/rocs-%s-%s.pickle" % ("s" if not gated else "g", pattern), "rb")
r, f, t = pickle.load(fd)
... | particles ROC AUC=0.9195+-0.00 1/FPR@TPR=0.5=74.34+-2.40
towers ROC AUC=0.8822+-0.00 1/FPR@TPR=0.5=25.41+-0.36
images ROC AUC=0.8277+-0.00 1/FPR@TPR=0.5=12.36+-0.30
| BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Trimming | for pattern_train, pattern_test, gated in [
("antikt-kt", "antikt-kt", False),
("antikt-kt", "antikt-kt-trimmed", False),
("antikt-kt-trimmed", "antikt-kt-trimmed", False),
("antikt-kt-trimmed", "antikt-kt", False),
]:
r, f, t = build_rocs(pattern_train, pattern_test, pattern_tra... | $k_t$ on $k_t$ ROC AUC=0.9185+-0.00 1/FPR@TPR=0.5=68.32+-1.80
$k_t$ on $k_t$-trimmed ROC AUC=0.8952+-0.00 1/FPR@TPR=0.5=28.70+-1.30
$k_t$-trimmed on $k_t$-trimmed ROC AUC=0.9034+-0.00 1/FPR@TPR=0.5=33.05+-1.85
$k_t$-trimmed on $k_t$ ROC AUC=0.8893+-0.01 1/FPR@TPR=0.5=36.78+-5.88
| BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Colinear splits | from functools import partial
from recnn.preprocessing import sequentialize_by_pt
preprocess_seqpt = partial(sequentialize_by_pt, reverse=False)
preprocess_seqpt_rev = partial(sequentialize_by_pt, reverse=True)
for pattern_train, pattern_test, gated, preprocess in [
# kt
("antikt-kt", "antikt-kt-colin... | $k_t$ colinear1 & 0.9183 $\pm$ 0.0006 & 68.7 $\pm$ 2.0 \\
$k_t$ colinear10 & 0.9174 $\pm$ 0.0006 & 67.5 $\pm$ 2.6 \\
$k_t$ colinear1-max & 0.9184 $\pm$ 0.0006 & 68.5 $\pm$ 2.8 \\
$k_t$ colinear10-max & 0.9159 $\pm$ 0.0009 & 65.7 $\pm$ 2.7 \\
asc-$p_T$... | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Soft particles | from functools import partial
from recnn.preprocessing import sequentialize_by_pt
preprocess_seqpt = partial(sequentialize_by_pt, reverse=False)
preprocess_seqpt_rev = partial(sequentialize_by_pt, reverse=True)
for pattern_train, pattern_test, gated, preprocess in [
("antikt-kt", "antikt-kt-soft", False, None... | $k_t$ soft & 0.9179 $\pm$ 0.0006 & 68.2 $\pm$ 2.3 \\
asc-$p_T$ soft & 0.9121 $\pm$ 0.0032 & 51.3 $\pm$ 6.0 \\
desc-$p_T$ soft & 0.9188 $\pm$ 0.0009 & 70.2 $\pm$ 3.7 \\
| BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Learning curve | for pattern, gated, n_events in [
# ("antikt-kt", False, 6000),
# ("antikt-seqpt-reversed", False, 6000),
("antikt-kt", True, 6000),
("antikt-seqpt-reversed", True, 6000),
# ("antikt-kt", False, 15000),
# ("antikt-seqpt-reversed", False, 15000),
("antikt-kt", True... | $k_t$ 100k ROC AUC=0.9195+-0.00 1/FPR@TPR=0.5=74.34+-2.40
$k_t$ 10k ROC AUC=0.9042+-0.00 1/FPR@TPR=0.5=46.42+-5.09
$k_t$ 1k ROC AUC=0.8726+-0.01 1/FPR@TPR=0.5=22.63+-2.97
desc-$p_T$ 100k ROC AUC=0.9212+-0.00 1/FPR@TPR=0.5=83.27+-3.08
... | BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
Tau21 | import h5py
f = h5py.File("../data/w-vs-qcd/h5/w_100000_j1p0_sj0p30_delphes_jets_images.h5", "r")["auxvars"]
tau1 = f["tau_1"]
tau2 = f["tau_2"]
tau21 = np.true_divide(tau2, tau1)
pt = f["pt_trimmed"]
mass = f["mass_trimmed"]
mask = (f["mass_trimmed"] < 110) & (f["mass_trimmed"] > 50) & (f["pt_trimmed"] < 300) & (f["pt... | tau21+mass ROC AUC=0.8207+-0.00 1/FPR@TPR=0.5=11.06+-0.00
| BSD-3-Clause | notebooks/04-jet-study.ipynb | glouppe/recnn |
RandomizedSearchCV | big_param_grid = {
'C' : np.arange(0, 10, 0.01),
'tol': np.arange(0, 0.001, 1e-5),
}
big_param_grid
# Create the RandomizedSearch estimator along with a parameter object containing the values to adjust
from sklearn.model_selection import RandomizedSearchCV
random_clf = RandomizedSearchCV(model, big_param_grid, ... | precision recall f1-score support
blue 0.83 1.00 0.91 10
red 1.00 0.87 0.93 15
accuracy 0.92 25
macro avg 0.92 0.93 0.92 25
weighted avg 0.93 0.92 0.92 ... | ADSL | 01-Lesson-Plans/19-Supervised-Machine-Learning/1/Extra-Activities/01-Ins_Hyperparameters/Solved/Ins_Hyperparameters.ipynb | anirudhmungre/sneaky-lessons |
AveragePooling3D **[pooling.AveragePooling3D.0] input 4x4x4x2, pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(290)
data_in... |
in shape: (4, 4, 4, 2)
in: [-0.453175, -0.475078, 0.486234, -0.949643, -0.349099, -0.837108, 0.933439, 0.167853, -0.995191, 0.459466, -0.788337, -0.120985, 0.06215, 0.138625, -0.102201, 0.976605, -0.591051, -0.592066, 0.469334, -0.435067, 0.621416, 0.817698, 0.790015, 0.485862, 0.469679, -0.611443, 0.582845, -0.503885... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.1] input 4x4x4x2, pool_size=(2, 2, 2), strides=(1, 1, 1), padding='valid', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(291)
da... |
in shape: (4, 4, 4, 2)
in: [-0.803361, 0.348731, 0.30124, -0.168638, 0.516406, -0.258765, 0.297839, 0.993235, 0.958465, -0.273175, -0.704992, 0.261477, -0.301255, 0.263104, 0.678631, -0.644936, -0.029034, -0.320266, 0.307733, -0.479016, 0.608177, 0.034951, 0.456908, -0.929353, 0.594982, -0.243058, -0.524918, 0.455339,... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.2] input 4x5x2x3, pool_size=(2, 2, 2), strides=(2, 1, 1), padding='valid', data_format='channels_last'** | data_in_shape = (4, 5, 2, 3)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=(2, 1, 1), padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(282)
da... |
in shape: (4, 5, 2, 3)
in: [-0.263147, -0.216555, -0.75766, -0.396007, 0.85243, 0.98415, -0.230197, -0.979579, 0.117628, -0.66833, 0.714058, -0.907302, -0.574249, 0.299573, 0.101165, 0.655872, -0.104788, 0.242064, -0.409262, -0.124059, 0.105687, -0.969325, -0.167941, 0.382377, 0.710487, 0.793042, 0.180663, -0.80231, 0... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.3] input 4x4x4x2, pool_size=(3, 3, 3), strides=None, padding='valid', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(283)
data_in... |
in shape: (4, 4, 4, 2)
in: [0.19483, -0.346754, 0.281648, -0.656271, 0.588328, 0.864284, -0.661556, 0.344578, 0.534692, 0.187914, -0.172976, 0.100575, 0.287857, 0.151936, 0.679748, 0.137527, 0.726773, -0.503042, -0.902524, -0.895315, 0.870645, 0.792427, -0.102238, -0.748643, -0.048728, -0.025835, 0.358631, 0.804295, -... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.4] input 4x4x4x2, pool_size=(3, 3, 3), strides=(3, 3, 3), padding='valid', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=(3, 3, 3), padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(284)
da... |
in shape: (4, 4, 4, 2)
in: [0.691755, -0.79282, -0.953135, 0.756956, -0.736874, 0.171061, -0.801845, 0.588236, -0.884749, 0.06721, -0.585121, -0.546211, -0.605281, -0.998989, 0.309413, -0.260604, -0.123585, 0.168908, -0.179496, 0.657412, -0.973664, 0.146258, -0.851615, -0.320588, 0.375102, -0.048494, 0.822789, 0.06357... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.5] input 4x4x4x2, pool_size=(2, 2, 2), strides=None, padding='same', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=None, padding='same', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(285)
data_in ... |
in shape: (4, 4, 4, 2)
in: [-0.495196, -0.886872, 0.220815, 0.126844, 0.168234, -0.640849, 0.457897, -0.375014, 0.001134, -0.486501, -0.819617, -0.468351, 0.15859, 0.39238, -0.590545, -0.402922, 0.821619, -0.208255, -0.512219, -0.586151, -0.365648, -0.195611, -0.280978, -0.08818, -0.449229, 0.169082, 0.075074, -0.7197... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.6] input 4x4x4x2, pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(286)
dat... |
in shape: (4, 4, 4, 2)
in: [-0.709952, -0.532913, -0.169956, -0.391538, 0.729034, -0.2004, -0.67324, -0.973672, 0.879975, -0.981827, -0.4828, -0.887985, 0.843364, 0.710745, -0.260613, 0.20082, 0.309563, 0.721671, -0.967848, -0.976471, -0.13058, 0.052684, 0.666494, -0.319759, -0.060338, 0.359151, -0.795562, 0.70488, 0.... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.7] input 4x5x4x2, pool_size=(2, 2, 2), strides=(1, 2, 1), padding='same', data_format='channels_last'** | data_in_shape = (4, 5, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=(1, 2, 1), padding='same', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(287)
dat... |
in shape: (4, 5, 4, 2)
in: [-0.71103, 0.421506, 0.752321, 0.542455, -0.557162, -0.963774, 0.910303, -0.933284, 0.67521, 0.588709, -0.782848, -0.108964, -0.767069, 0.338318, -0.660374, -0.967294, -0.501079, -0.917532, -0.087991, -0.160473, 0.520493, 0.612007, -0.955448, -0.809749, -0.627003, 0.494441, 0.985405, 0.99813... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.8] input 4x4x4x2, pool_size=(3, 3, 3), strides=None, padding='same', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=None, padding='same', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(288)
data_in ... |
in shape: (4, 4, 4, 2)
in: [0.106539, 0.430065, 0.625063, -0.956042, 0.681684, 0.345995, -0.589061, 0.186737, 0.535452, -0.125905, -0.396262, -0.44893, 0.39021, 0.253402, -0.238515, 0.337141, 0.178107, 0.244331, -0.93179, -0.081267, 0.895223, 0.820023, 0.365435, -0.738456, 0.893031, -0.787916, -0.518813, 0.661518, -0.... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.9] input 4x4x4x2, pool_size=(3, 3, 3), strides=(3, 3, 3), padding='same', data_format='channels_last'** | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=(3, 3, 3), padding='same', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(289)
dat... |
in shape: (4, 4, 4, 2)
in: [0.454263, 0.047178, -0.644362, 0.432654, 0.776147, -0.088086, -0.16527, -0.152361, -0.723283, 0.119471, -0.020663, 0.230897, 0.249349, -0.825224, 0.809245, 0.37136, 0.649976, 0.690981, -0.5766, 0.750394, -0.777363, -0.359006, 0.398419, -0.851015, -0.479232, -0.924962, -0.898893, 0.135445, 0... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.10] input 2x3x3x4, pool_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_first'** | data_in_shape = (2, 3, 3, 4)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_first')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(290)
d... |
in shape: (2, 3, 3, 4)
in: [-0.453175, -0.475078, 0.486234, -0.949643, -0.349099, -0.837108, 0.933439, 0.167853, -0.995191, 0.459466, -0.788337, -0.120985, 0.06215, 0.138625, -0.102201, 0.976605, -0.591051, -0.592066, 0.469334, -0.435067, 0.621416, 0.817698, 0.790015, 0.485862, 0.469679, -0.611443, 0.582845, -0.503885... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.11] input 2x3x3x4, pool_size=(3, 3, 3), strides=(1, 1, 1), padding='same', data_format='channels_first'** | data_in_shape = (2, 3, 3, 4)
L = AveragePooling3D(pool_size=(3, 3, 3), strides=(1, 1, 1), padding='same', data_format='channels_first')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(291)
da... |
in shape: (2, 3, 3, 4)
in: [-0.803361, 0.348731, 0.30124, -0.168638, 0.516406, -0.258765, 0.297839, 0.993235, 0.958465, -0.273175, -0.704992, 0.261477, -0.301255, 0.263104, 0.678631, -0.644936, -0.029034, -0.320266, 0.307733, -0.479016, 0.608177, 0.034951, 0.456908, -0.929353, 0.594982, -0.243058, -0.524918, 0.455339,... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
**[pooling.AveragePooling3D.12] input 3x4x4x3, pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_first'** | data_in_shape = (3, 4, 4, 3)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_first')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(292)
data_i... |
in shape: (3, 4, 4, 3)
in: [-0.497409, -0.250345, 0.196124, -0.044334, -0.324906, 0.560065, 0.220435, -0.167776, -0.923771, 0.77337, -0.862909, -0.584756, -0.70451, 0.870272, 0.841773, -0.312016, 0.599915, 0.073955, 0.944336, -0.4175, 0.865698, 0.609184, 0.033839, -0.72494, -0.239473, 0.514968, -0.318523, -0.244443, 0... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
export for Keras.js tests | print(json.dumps(DATA)) | {"pooling.AveragePooling3D.0": {"input": {"data": [-0.453175, -0.475078, 0.486234, -0.949643, -0.349099, -0.837108, 0.933439, 0.167853, -0.995191, 0.459466, -0.788337, -0.120985, 0.06215, 0.138625, -0.102201, 0.976605, -0.591051, -0.592066, 0.469334, -0.435067, 0.621416, 0.817698, 0.790015, 0.485862, 0.469679, -0.61144... | MIT | notebooks/layers/pooling/AveragePooling3D.ipynb | GTDev87/tpt-hackathon |
Debugging strategiesIn this notebook, we'll talk about what happens when you get an error message (it will happen often!) and some steps you can take to resolve them.Run the code in the next cell. | x = 10
if x > 20
print(f'{x} is greater than 20!') | _____no_output_____ | MIT | reference/Debugging strategies.ipynb | ireapps/cfj-2018 |
The "traceback" message shows you a couple of useful things:- What line the error is on: `line 3`- The class of error: `SyntaxError` (v common)- Exactly _where_ the error occured -- see where the `^` symbol is pointing?What's the problem? GooglingIf it's not immediately clear what's wrong -- if you're not even sure wha... | staff = [
{'name': 'Fran', 'years_of_service': 2, 'job': 'Reporter'},
{'name': 'Graham', 'years_of_service': 7, 'job': 'Reporter'},
{'name': 'Pat', 'years_of_service': 4, 'job': 'Web Producer'},
{'name': 'John', 'years_of_service': '26', 'job': 'Managing Editor'},
{'name': 'Sue', 'years_of_service':... | _____no_output_____ | MIT | reference/Debugging strategies.ipynb | ireapps/cfj-2018 |
We didn't get an exception, but something is _clearly_ wrong with John's bonus. What's going on?Maybe you spot the error already. If not, we might Google something like ["python multiply numbers repeating"](https://www.google.com/search?q=python+multiply+numbers+repeating) -- which leads us to [this StackOverflow answe... | for person in staff:
name = person['name']
bonus = person['years_of_service'] * 50
print(name, type(person['years_of_service']))
print(f'{name} is getting a bonus of {bonus}') | _____no_output_____ | MIT | reference/Debugging strategies.ipynb | ireapps/cfj-2018 |
Aha! John's value for `years_of_service` has been stored as a string, not an integer. Let's fix that by using the [`int()`](https://docs.python.org/3/library/functions.htmlint) function to coerce the value to an integer. | for person in staff:
name = person['name']
bonus = int(person['years_of_service']) ** 2
print(f'{name} is getting a bonus of {bonus}') | _____no_output_____ | MIT | reference/Debugging strategies.ipynb | ireapps/cfj-2018 |
Winner winner, chicken dinner.Here are some more debugging exercises for you to work through. See if you can figure out what's wrong and fix them. | print(Hello, Pittsburgh!)
desk = {
'wood': 'fir',
'color': 'black',
'height_in': 36,
'width_in': 48,
'length_in': 68
}
print(desk['drawer_count'])
students = ['Kelly', 'Larry', 'José', 'Frank', 'Sarah', 'Sue']
for student in students:
if student = 'Kelly':
print('It's Kelly!')
elif stu... | _____no_output_____ | MIT | reference/Debugging strategies.ipynb | ireapps/cfj-2018 |
Make the Dataset | CA_x, CA_y = [], []
KS_x, KS_y = [], []
MT_x, MT_y = [], []
TX_x, TX_y = [], []
OH_x, OH_y = [], []
states = {"CA" : [CA_x, CA_y, "Roi_1"],
"KS" : [KS_x, KS_y, "Roi_2"],
"MT" : [MT_x, MT_y, "Roi_3"],
"TX" : [TX_x, TX_y, "Roi_4"],
"OH" : [OH_x, OH_y, "Roi_5"]} | _____no_output_____ | MIT | Notebooks/GCN_cuda_Implement_speed.ipynb | oqbrady/Sentinel2_Traffic |
Load into RAM | master_df = pd.read_csv("Sentinel2_Traffic/Traffic_Data/5_state_traffic.csv")
master_df = master_df.set_index("Unnamed: 0")
CA_x, CA_y = [], []
KS_x, KS_y = [], []
MT_x, MT_y = [], []
TX_x, TX_y = [], []
OH_x, OH_y = [], []
states = {"Cali" : [CA_x, CA_y, "Roi_1"],
"KS" : [KS_x, KS_y, "Roi_2"],
"MT" :... | _____no_output_____ | MIT | Notebooks/GCN_cuda_Implement_speed.ipynb | oqbrady/Sentinel2_Traffic |
Train the Model | # X = dataset[:][0]
# y = dataset[:][1]
print(dataset.state[0:37])
print(dataset.state[37:64])
print(dataset.state[64:88])
print(dataset.state[88:119])
print(dataset.state[119:124])
from dgl.dataloading import GraphDataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import DataLoad... | _____no_output_____ | MIT | Notebooks/GCN_cuda_Implement_speed.ipynb | oqbrady/Sentinel2_Traffic |
Bi directional GRU encoder | class EncoderRNN(nn.Module):
def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
super(EncoderRNN, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.embedding = embedding
self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
... | _____no_output_____ | Apache-2.0 | ChatBot.ipynb | DEK11/Chatbot-RNN |
Attention module | class Attn(torch.nn.Module):
def __init__(self, method, hidden_size):
super(Attn, self).__init__()
self.method = method
if self.method not in ['dot', 'general', 'concat']:
raise ValueError(self.method, "is not an appropriate attention method.")
self.hidden_size = hidden_s... | > Hi
Bot: hi .
> How are you?
Bot: i m fine .
> Are you in love?
Bot: i m not .
> What are you thinking?
Bot: i m thinking of a little .
> Do you watch movies?
Bot: sure .
> which is your favourite?
Bot: the big one .
> Okay, bye now!
Bot: okay .
> q
| Apache-2.0 | ChatBot.ipynb | DEK11/Chatbot-RNN |
When can we start watching?---Henry Rachootin - December 2018MIT License: https://opensource.org/licenses/MIT---BitTorrent allows people to download movies without staying strictly within the confines of the law, but because of the peer to peer nature of the download, the file will not download sequentially. The VLC p... | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
import numpy as np
from scipy.stats import poisson
from math import ceil,exp,floor
from thinkbayes2 impor... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
First we will just define some values.Let's define $T$ to be the runtime of the movie in seconds and $N$ to be the number of 4 MB pieces in the movie. From these, we can define $t_p$, the runtime of a single 4 MB piece as $\frac{T}{N}$. | T = 149*60 #movie runtime in seconds
N = ceil(12.91*1000/4) #number of 4MB pieces in the whole movie
t_p = T/N #runtime of a single 4MB piece
print(f"The runtime of a single piece is {t_p:.2f} seconds") | The runtime of a single piece is 2.77 seconds
| MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
Let's now consider where we are going with this calculation. When watching the movie, we need to have the next piece every 2.77 seconds. If we assume that each piece is equally likely to be downloaded, we can define a function $P_p(t)$ which tells us the probability of having a specific piece after $t$ seconds, and tha... | #wireshark dump
data = pd.read_csv('torrent pieces.csv')
#this finds the piece packets
data = data[data.Info=="Piece[Malformed Packet]"]
#extract the time each piece arrived at
times = np.array(data.Time)
#dump the initial times, they don't represent the long term behavior
times = times[45:]
interTimes = np.diff... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
And we can implement all the functions we defined above: | def P_n(n,t,lam):
"""probability of having exactly n pieces at time t for rate lambda"""
return poisson.pmf(n,lam*t)
def P_p(t,n_0,lam):
"""probability of having a specific piece at time t for rate lambda"""
#all the numbers of pieces there could be
ns = np.array(range(n_0,N+1))
#the pro... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
With those done, we can make our final $P(t,x)$ function, which will give us the probability of getting to time $t$ if we start at $x$ percent downloaded with our derived distribution for $\lambda$. | def PWatch(t,x):
"""Probability of getting to time t with initial download percentage x"""
#intial piece number approximation
n0 = floor(x*N/100)
Ptot = 0
#law of total probability
for l,p in lam.Items():
Ptot += p*P(t,n0,l)
return Ptot | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
Unfortunately that function is prohibitively slow. We can speed it up quite a lot by improving our $P_p$ function to be less accurate but much faster. We will approximate it by$$P_p(t)=\frac{\min(\lambda t+n_0,N)}{N}$$which is just assuming that we get one piece every $\lambda$ seconds. This ignores the uncertanty of t... | def P_p_fast(t,n_0,lam):
return min(lam*t+n_0,N)/N
testLam = lam.Mean()
ts = np.linspace(0,4000)
ps = np.array([P_p(t,0,testLam) for t in ts])
psFast = np.array([P_p_fast(t,0,testLam) for t in ts])
thinkplot.plot(ts,ps,label='Correct')
thinkplot.plot(ts,psFast,label='Fast')
thinkplot.decorate(title='Probability of... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
From the graph we can see that this is an ok approximation.With that done, we can start making graphs and answering the original question. | P_p = P_p_fast #use the fast function from now on
ts = np.linspace(0,500)
xs = [50,90,95,99]
for x in xs:
ps = [PWatch(t,x) for t in ts]
thinkplot.plot(ts,ps,label=f'start at {x}%')
thinkplot.decorate(title='Probability of getting to different times in the movie',
xlabel='Time (s)',
... | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
That graph is zoomed in near the start of the movie, but here's what it looks like over the whole runtime: | ts = np.linspace(0,T)
xs = [50,90,95,99]
for x in xs:
ps = [PWatch(t,x) for t in ts]
thinkplot.plot(ts,ps,label=f'start at {x}%')
thinkplot.decorate(title='Probability of getting to different times in the movie',
xlabel='Time (s)',
ylabel='Probability') | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
So we can see there is a definite falling off period, and after that we will probably finish the movie. With that in mind, we can ask what the probability of finishing the movie will be for different starting percentages. | xs = np.linspace(0,100)
ps = [PWatch(T,x) for x in xs]
thinkplot.plot(xs,ps)
thinkplot.decorate(title='Probability of finishing movie',
xlabel='Starting percent downloaded',
ylabel='Probability of finishing movie') | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
Here's the nonzero portion of that graph: | xs = np.linspace(90,100)
ps = [PWatch(T,x) for x in xs]
thinkplot.plot(xs,ps)
thinkplot.decorate(title='Probability of finishing movie',
xlabel='Starting percent downloaded',
ylabel='Probability of finishing movie') | _____no_output_____ | MIT | examples/.ipynb_checkpoints/Greedy pirates-checkpoint.ipynb | sportsracer48/ThinkBayes2 |
Notebook para o PAN - Atribuição Autoral - 2018 | %matplotlib inline
#python basic libs
from __future__ import print_function
from tempfile import mkdtemp
from shutil import rmtree
import os;
from os.path import join as pathjoin;
import re;
import glob;
import json;
import codecs;
from collections import defaultdict;
import pprint;
from pprint import pprint
from t... | Darwin-17.5.0-x86_64-i386-64bit
NumPy 1.14.2
SciPy 1.0.1
Scikit-Learn 0.19.1
seaborn 0.8.1
| Apache-2.0 | 2018/PAN_AA_2018-char-simplified.ipynb | jeleandro/PANAA2018 |
paths configuration | baseDir = '/Users/joseeleandrocustodio/Dropbox/mestrado/02 - Pesquisa/code';
inputDir= pathjoin(baseDir,'pan18aa');
outputDir= pathjoin(baseDir,'out',"oficial");
if not os.path.exists(outputDir):
os.mkdir(outputDir); | _____no_output_____ | Apache-2.0 | 2018/PAN_AA_2018-char-simplified.ipynb | jeleandro/PANAA2018 |
loading the dataset | def readCollectionsOfProblems(path):
# Reading information about the collection
infocollection = path+os.sep+'collection-info.json'
with open(infocollection, 'r') as f:
problems = [
{
'problem': attrib['problem-name'],
'language': attrib['language'],
... | _____no_output_____ | Apache-2.0 | 2018/PAN_AA_2018-char-simplified.ipynb | jeleandro/PANAA2018 |
examinando o parametro min_df isoladamente | def runML(problem):
print ("\nProblem: %s, language: %s, " %(problem['problem'],problem['language']))
train_docs, train_labels, _ = zip(*problem['candidates'])
problem['training_docs_size'] = len(train_docs);
test_docs, _, test_filename = zip(*problem['unknown'])
pipeline = Pipeline([... | _____no_output_____ | Apache-2.0 | 2018/PAN_AA_2018-char-simplified.ipynb | jeleandro/PANAA2018 |
analisando os demais parametros | def runML(problem):
print ("\nProblem: %s, language: %s, " %(problem['problem'],problem['language']))
train_docs, train_labels, _ = zip(*problem['candidates'])
problem['training_docs_size'] = len(train_docs);
test_docs, _, test_filename = zip(*problem['unknown'])
pipeline = Pipeline([... | \begin{table}[h]
\centering
\caption{Medida F1 para os parâmetros }
\begin{tabular}{llllrrrr}
\toprule
& & & ngram\_range & (2, 3) & (2, 4) & (2, 5) & (3, 5) \\
problem & language & sublinear\_tf & norm & & & & \\
\midrule
1 & inglesa & False & l1 & 0.680 & 0.787 & 0.804 & 0.803 \\
& & & l2 & 0.670 & 0.760 & 0.715 & ... | Apache-2.0 | 2018/PAN_AA_2018-char-simplified.ipynb | jeleandro/PANAA2018 |
Example usageTo use `webtoon_data` in a project: | import webtoon_data
print(webtoon_data.__version__) | _____no_output_____ | MIT | docs/example.ipynb | ekimz/webtoons_data |
Copyright 2018 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/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Save and restore models View on TensorFlow.org Run in Google Colab View source on GitHub Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your wo... | !pip install h5py pyyaml | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Get an example datasetWe'll use the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) to train our model to demonstrate saving weights. To speed up these demonstration runs, only use the first 1000 examples: | from __future__ import absolute_import, division, print_function
import os
!pip install tf-nightly-2.0-preview
import tensorflow as tf
keras = tf.keras
tf.__version__
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
train_labels = train_labels[:1000]
test_labels = test_... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Define a model Let's build a simple model we'll use to demonstrate saving and loading weights. | # Returns a short sequential model
def create_model():
model = tf.keras.models.Sequential([
keras.layers.Dense(512, activation=tf.keras.activations.relu, input_shape=(784,)),
keras.layers.Dropout(0.2),
keras.layers.Dense(10, activation=tf.keras.activations.softmax)
])
model.compile(optimizer='adam'... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Save checkpoints during training The primary use case is to automatically save checkpoints *during* and at *the end* of training. This way you can use a trained model without having to retrain it, or pick-up training where you left of—in case the training process was interrupted.`tf.keras.callbacks.ModelCheckpoint` is... | checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1)
... | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
This creates a single collection of TensorFlow checkpoint files that are updated at the end of each epoch: | !ls {checkpoint_dir} | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Create a new, untrained model. When restoring a model from only weights, you must have a model with the same architecture as the original model. Since it's the same model architecture, we can share weights despite that it's a different *instance* of the model.Now rebuild a fresh, untrained model, and evaluate it on the... | model = create_model()
loss, acc = model.evaluate(test_images, test_labels)
print("Untrained model, accuracy: {:5.2f}%".format(100*acc)) | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
Then load the weights from the checkpoint, and re-evaluate: | model.load_weights(checkpoint_path)
loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc)) | _____no_output_____ | Apache-2.0 | site/en/2/tutorials/keras/save_and_restore_models.ipynb | allenlavoie/docs |
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