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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Accuracy, precision and recallClassification accuracy for each class: | for i,j in enumerate(cm.diagonal()/cm.sum(axis=1)): print("%d: %.4f" % (i,j)) | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
Precision and recall for each class: | print(classification_report(y_test, pred_nn_fast, labels=labels)) | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
Failure analysisWe can also inspect the results in more detail. Let's use the `show_failures()` helper function (defined in `pml_utils.py`) to show the wrongly classified test digits.The helper function is defined as:```show_failures(predictions, y_test, X_test, trueclass=None, predictedclass=None, maxtoshow=10)```whe... | show_failures(pred_nn_fast, y_test, X_test) | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
We can use `show_failures()` to inspect failures in more detail. For example:* show failures in which the true class was "5": | show_failures(pred_nn_fast, y_test, X_test, trueclass='5') | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
* show failures in which the prediction was "0": | show_failures(pred_nn_fast, y_test, X_test, predictedclass='0') | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
* show failures in which the true class was "0" and the prediction was "2": | show_failures(pred_nn_fast, y_test, X_test, trueclass='0', predictedclass='2') | _____no_output_____ | MIT | notebooks/sklearn-mnist-nn.ipynb | CSCfi/machine-learning-scripts |
"Sequence classification using Recurrent Neural Networks"> "PyTorch implementation for sequence classification using RNNs"- toc: false- branch: master- badges: true- comments: true- categories: [PyTorch, classification, RNN]- image: images/- hide: false- search_exclude: true- metadata_key1: metadata_value1- metadata_k... | from sequential_tasks import TemporalOrderExp6aSequence as QRSU
# Create a data generator. Predefined generator is implemented in file sequential_tasks.
example_generator = QRSU.get_predefined_generator(
difficulty_level=QRSU.DifficultyLevel.EASY,
batch_size=32,
)
example_batch = example_generator[1... | The sequence is: BbXcXcbE
The class label is: Q
| Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
We can see that our sequence contain 8 elements starting with B and ending with E. This sequence belong to class Q as per the rule defined earlier. Each element is one-hot encoded. Thus, we can represent our first sequence (BbXcXcbE) with a sequence of rows of one-hot encoded vectors (as shown above). . Similarly, clas... | import torch
import torch.nn as nn
# Set the random seed for reproducible results
torch.manual_seed(1)
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
# This just calls the base class constructor
super().__init__()
# Neural network layers assigned as a... | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
3. Defining the Training Loop | def train(model, train_data_gen, criterion, optimizer, device):
# Set the model to training mode. This will turn on layers that would
# otherwise behave differently during evaluation, such as dropout.
model.train()
# Store the number of sequences that were classified correctly
num_correct = 0
... | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
4. Defining the Testing Loop | def test(model, test_data_gen, criterion, device):
# Set the model to evaluation mode. This will turn off layers that would
# otherwise behave differently during training, such as dropout.
model.eval()
# Store the number of sequences that were classified correctly
num_correct = 0
# A context m... | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
5. Putting it All Together | import matplotlib.pyplot as plt
from plot_lib import set_default, plot_state, print_colourbar
set_default()
def train_and_test(model, train_data_gen, test_data_gen, criterion, optimizer, max_epochs, verbose=True):
# Automatically determine the device that PyTorch should use for computation
device = torch.device... | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
5. Simple RNN: 10 EpochsLet's create a simple recurrent network and train for 10 epochs. | # Setup the training and test data generators
difficulty = QRSU.DifficultyLevel.EASY
batch_size = 32
train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)
test_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)
# Setup the RNN and training settings
input_size = train_data_gen.n... | torch.Size([4, 8])
torch.Size([4, 4])
torch.Size([4])
torch.Size([4])
torch.Size([4, 4])
torch.Size([4])
| Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
6. RNN: Increasing Epoch to 100 | # Setup the training and test data generators
difficulty = QRSU.DifficultyLevel.EASY
batch_size = 32
train_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)
test_data_gen = QRSU.get_predefined_generator(difficulty, batch_size)
# Setup the RNN and training settings
input_size = train_data_gen.n... | [Epoch 100/100] loss: 0.0081, acc: 100.00% - test_loss: 0.0069, test_acc: 100.00%
| Apache-2.0 | _notebooks/2021-01-07-seq-classification.ipynb | aizardar/blogs |
Make RACs from initial structure | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pickle
from collections import defaultdict
from molSimplify.Informatics.autocorrelation import*
def make_rac(xyz_file, m_depth, l_depth, is_oct):
properties = ['electronegativity', 'size', 'polarizability', 'nuclear_charge']
this_mol... | _____no_output_____ | MIT | make_racs.ipynb | craigerboi/oer_active_learning |
Now we define different racs with differing feature depths so we can perform the gridsearch in rac_depth_search.ipynb | mc_depths = [2, 3, 4]
lc_depths = [0, 1]
oer_desc_data = pickle.load(open("racs_and_desc/oer_desc_data.p", "rb"),)
name2oer_desc_and_rac = defaultdict()
for mc_d in mc_depths:
for lc_d in lc_depths:
racs = []
oer_desc_for_ml = []
cat_names_for_ml = []
for name in oer_desc_data:
... | _____no_output_____ | MIT | make_racs.ipynb | craigerboi/oer_active_learning |
使用线性回归预测波士顿房价**作者:** [PaddlePaddle](https://github.com/PaddlePaddle) **日期:** 2021.05 **摘要:** 本示例教程将会演示如何使用线性回归完成波士顿房价预测。 一、简要介绍经典的线性回归模型主要用来预测一些存在着线性关系的数据集。回归模型可以理解为：存在一个点集，用一条曲线去拟合它分布的过程。如果拟合曲线是一条直线，则称为线性回归。如果是一条二次曲线，则被称为二次回归。线性回归是回归模型中最简单的一种。 本示例简要介绍如何用飞桨开源框架，实现波士顿房价预测。其思路是，假设uci-housing数据集中的房子属性和房价之间的关系可以被属性间的线性组合... | import paddle
import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
print(paddle.__version__) | 2.1.0
| Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
三、数据集介绍本示例采用uci-housing数据集，这是经典线性回归的数据集。数据集共7084条数据，可以拆分成506行,每行14列。前13列用来描述房屋的各种信息，最后一列为该类房屋价格中位数。 前13列用来描述房屋的各种信息 3.1 数据处理 | #下载数据
!wget https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data -O housing.data
# 从文件导入数据
datafile = './housing.data'
housing_data = np.fromfile(datafile, sep=' ')
feature_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE','DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
feature... | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
3.2 数据归一化处理下图展示各属性的取值范围分布： | sns.boxplot(data=df.iloc[:, 0:13]) | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
从上图看出，各属性的数值范围差异太大，甚至不能够在一个画布上充分的展示各属性具体的最大、最小值以及异常值等。下面进行归一化。 做归一化（或 Feature scaling）至少有以下2个理由：* 过大或过小的数值范围会导致计算时的浮点上溢或下溢。* 不同的数值范围会导致不同属性对模型的重要性不同（至少在训练的初始阶段如此），而这个隐含的假设常常是不合理的。这会对优化的过程造成困难，使训练时间大大的加长. | features_max = housing_data.max(axis=0)
features_min = housing_data.min(axis=0)
features_avg = housing_data.sum(axis=0) / housing_data.shape[0]
BATCH_SIZE = 20
def feature_norm(input):
f_size = input.shape
output_features = np.zeros(f_size, np.float32)
for batch_id in range(f_size[0]):
for index in ... | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
四、模型组网线性回归就是一个从输入到输出的简单的全连接层。对于波士顿房价数据集，假设属性和房价之间的关系可以被属性间的线性组合描述。 | class Regressor(paddle.nn.Layer):
def __init__(self):
super(Regressor, self).__init__()
self.fc = paddle.nn.Linear(13, 1,)
def forward(self, inputs):
pred = self.fc(inputs)
return pred | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
定义绘制训练过程的损失值变化趋势的方法 `draw_train_process` . | train_nums = []
train_costs = []
def draw_train_process(iters, train_costs):
plt.title("training cost", fontsize=24)
plt.xlabel("iter", fontsize=14)
plt.ylabel("cost", fontsize=14)
plt.plot(iters, train_costs, color='red', label='training cost')
plt.show() | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
五、方式1：使用基础API完成模型训练&预测 5.1 模型训练下面展示模型训练的代码。这里用到的是线性回归模型最常用的损失函数--均方误差（MSE），用来衡量模型预测的房价和真实房价的差异。对损失函数进行优化所采用的方法是梯度下降法. | import paddle.nn.functional as F
y_preds = []
labels_list = []
def train(model):
print('start training ... ')
# 开启模型训练模式
model.train()
EPOCH_NUM = 500
train_num = 0
optimizer = paddle.optimizer.SGD(learning_rate=0.001, parameters=model.parameters())
for epoch_id in range(EPOCH_NUM):
... | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
可以从上图看出，随着训练轮次的增加，损失在呈降低趋势。但由于每次仅基于少量样本更新参数和计算损失，所以损失下降曲线会出现震荡。 5.2 模型预测 | # 获取预测数据
INFER_BATCH_SIZE = 100
infer_features_np = np.array([data[:13] for data in test_data]).astype("float32")
infer_labels_np = np.array([data[-1] for data in test_data]).astype("float32")
infer_features = paddle.to_tensor(infer_features_np)
infer_labels = paddle.to_tensor(infer_labels_np)
fetch_list = model(infe... | _____no_output_____ | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
上图可以看出，训练出来的模型的预测结果与真实结果是较为接近的。 六、方式2：使用高层API完成模型训练&预测也可以用飞桨的高层API来做线性回归训练，高层API相较于底层API更加的简洁方便。 | import paddle
paddle.set_default_dtype("float64")
# step1:用高层API定义数据集，无需进行数据处理等，高层API为你一条龙搞定
train_dataset = paddle.text.datasets.UCIHousing(mode='train')
eval_dataset = paddle.text.datasets.UCIHousing(mode='test')
# step2:定义模型
class UCIHousing(paddle.nn.Layer):
def __init__(self):
super(UCIHousing, self)... | The loss value printed in the log is the current step, and the metric is the average value of previous steps.
Epoch 1/5
step 51/51 [==============================] - loss: 624.0728 - 2ms/step
Eval begin...
step 13/13 [==============================] - loss: 397.2567 - 878us/step
Eval samples: 102
Epoc... | Apache-2.0 | docs/practices/linear_regression/linear_regression.ipynb | Liu-xiandong/docs |
Exercícios Aula 02 - Thainá Mariane Souza Silva 816118386 Exercicios de Lista1 Crie um programa que recebe uma lista de números e - retorne o maior elemento - retorne a soma dos elementos - retorne o número de ocorrências do primeiro elemento da lista - retorne a média dos elementos - retorne o valor mais próximo d... | import math
lista = [input("Digite uma lista") for i in range(5)]
maior = max(lista)
soma = sum(lista)
ocorrencia = lista.count(lista[0])
negativo = sum(i for i in list if i < 0)
media = sum(lista)/ len(lista)
print("O maior elemento da lista é: {} " .formart(maior))
print("A soma dos elementos da lista é: {} " .fo... | _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
2 Faça um programa que receba duas listas e retorne True se são iguais ou False caso contrario. Duas listas são iguais se possuem os mesmos valores e na mesma ordem. |
#Para cada x no input ..... split quebrar a string de acordo com o que foi definido
lista = [input("Digite um valor para incluir na lista ") for i in range(3)]
lista2 = [input("Digite um valor para incluir na lista 2 ") for i in range(3)]
if lista == lista2:
print("True")
else:
print("False")
| _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
3 Faça um programa que receba duas listas e retorne True se têm os mesmos elementos ou False caso contrário Duas listas possuem os mesmos elementos quando são compostas pelos mesmos valores, mas não obrigatoriamente na mesma ordem |
#Para cada x no input ..... split quebrar a string de acordo com o que foi definido
lista = [input("Digite um valor para incluir na lista: ") for i in range(3)]
lista = [input("Digite um valor para incluir na lista: ") for i in range(3)]
result = lista
if lista == lista2:
print("true")
else:
i=0
for i in ... | _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
4 Faça um programa que percorre uma lista com o seguinte formato: [['Brasil', 'Italia', [10, 9]], ['Brasil', 'Espanha', [5, 7]], ['Italia', 'Espanha', [7,8]]]. Essa lista indica o número de faltas que cada time fez em cada jogo. Na lista acima, no jogo entre Brasil e Itália, o Brasil fez 10 faltas e a Itália fez 9. O ... | import operator
lista = [['Brasil', 'Italia', [10, 9]], ['Brasil', 'Espanha', [5, 7]], ['Italia', 'Espanha', [7,8]]]
dicionario = {"Brasil": 0, "Italia": 0, "Espanha": 0}
total_faltas = 0
for item in lista:
total_faltas += sum(item[2])
dicionario[item[0]] += item[2][0]
dicionario[item[1]] += item[2][1... | _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
Exercicios Dicionario 5 Escreva um programa que conta a quantidade de vogais em uma string e armazena tal quantidade em um dicionário, onde a chave é a vogal considerada. | import string
palavra = input("Digite uma palavra: ")
vogal = ['a', 'e', 'i', 'o', 'u']
dicionario = {'a': 0, 'e': 0, 'i':0, 'o': 0, 'u': 0}
for letra in palavra:
if letra in vogal:
dicionario[letra] = dicionario[letra] + 1
print(dicionario)
| _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
6 Escreva um programa que lê̂ duas notas de vários alunos e armazena tais notas em um dicionário, onde a chave é o nome do aluno. A entrada de dados deve terminar quando for lida uma string vazia como nome. Escreva uma função que retorna a média do aluno, dado seu nome. | texto = input("Digite o nome do aluno é duas notas, Nome, nota1, nota2 separando por ponto e virgula")
texto = texto.split(";")
notas = {}
count = 0
for n in texto:
nota = n.split(",")
notas[nota[0]] = {"nota1": nota[1], "nota2": nota[2]}
for n in notas:
media = (int(notas[n]['nota1']) + int(notas[... | _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
7 Uma pista de Kart permite 10 voltas para cada um de 6 corredores. Escreva um programa que leia todos os tempos em segundos e os guarde em um dicionário, onde a chave é o nome do corredor. Ao fnal diga de quem foi a melhor volta da prova e em que volta; e ainda a classifcação fnal em ordem (1o o campeão). O campeão é... | i = 0
while(i <= 6):
voltas = input("Digite os valores de voltas: 'Piloto':[2,6,8,1] ")
dic = dict(x.split() for x in voltas.splitlines())
print(dic) | _____no_output_____ | MIT | Exercicio02.ipynb | thainamariianr/LingProg |
Self Study 3 In this self study we perform character recognition using SVM classifiers. We use the MNIST dataset, which consists of 70000 handwritten digits 0..9 at a resolution of 28x28 pixels. Stuff we need: | import matplotlib.pyplot as plt
import numpy as np
import time
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix,accuracy_score
from sklearn.datasets import fetch_openml ##couldn't run with the ... | _____no_output_____ | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
Now we get the MNIST data. Using the fetch_mldata function, this will be downloaded from the web, and stored in the directory you specify as data_home (replace my path in the following cell): | from sklearn.datasets import fetch_openml
mnist = fetch_openml(name='mnist_784', data_home='/home/starksultana/Documentos/Mestrado_4o ano/2o sem AAU/ML/ML_selfstudy3')
| _____no_output_____ | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
The data has .data and .target attributes. The following gives us some basic information on the data: | print("Number of datapoints: {}\n".format(mnist.data.shape[0]))
print("Number of features: {}\n".format(mnist.data.shape[1]))
print("features: ", mnist.data[0].reshape(196,4))
print("List of labels: {}\n".format(np.unique(mnist.target))) | Number of datapoints: 70000
Number of features: 784
features: [[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[... | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
We can plot individual datapoints as follows: | index = 9
print("Value of datapoint no. {}:\n{}\n".format(index,mnist.data[index]))
print("As image:\n")
plt.imshow(mnist.data[index].reshape(28,28),cmap=plt.cm.gray_r)
#plt.show() | Value of datapoint no. 9:
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.... | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
To make things a little bit simpler (and faster!), we can extract from the data binary subsets, that only contain the data for two selected digits: | digit0='4'
digit1='5'
mnist_bin_data=mnist.data[np.logical_or(mnist.target==digit0,mnist.target==digit1)]
mnist_bin_target=mnist.target[np.logical_or(mnist.target==digit0,mnist.target==digit1)]
print("The first datapoint now is: \n")
plt.imshow(mnist_bin_data[0].reshape(28,28),cmap=plt.cm.gray_r)
plt.show()
print(mnist... | The first datapoint now is:
| MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
**Exercise 1 [SVM]:** Split the mnist_bin data into training and test set. Learn different SVM models by varying the kernel functions (SVM). For each configuration, determine the time it takes to learn the model, and the accuracy on the test data. You can get the current time using:`import time` `now = time.time()`*Cau... | ###Exercise 1
''' Completely dies with 7 and 9, cant make it work :(
In the rest of the tasks it performed quite well with really high accuracies, for example 1 and 0 it ran with 99 % accuracy with a test size of 30 %,
and 7 misclassifications, ran in 1,72 secs. 6 and 3 it only has 23 misclassification but runs in 4 t... | _____no_output_____ | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
---Exercise 21st approach try to reshape the data?normalize the pixels? | ##exercise 2
from sklearn.preprocessing import StandardScaler
now = time.time()
#x: np.ndarray = mnist_bin_data
#y: np.ndarray = mnist_bin_target
print("don't worry i've just started")
scaler = StandardScaler()
trnX, tstX, trnY, tstY = train_test_split(mnist.data, mnist.target, test_size=0.3,random_state=20)
pri... | _____no_output_____ | MIT | ML_selfstudy3/MLSelfStudy3-F20.ipynb | Jbarata98/ML_AAU1920 |
Object classification | from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
import numpy as np
import os
import ast
from glob import glob
import random
import traceback
from tabulate import tabulate
import pickle
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Stan... | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Parameters | new_data=True
load_old_params=True
save_params=False
selected_space=True
from google.colab import drive
drive.mount('/content/drive') | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Utils functions | def translate(name):
translate_dict={"apple":"mela",
"ball":"palla",
"bell pepper":"peperone",
"binder":"raccoglitore",
"bowl":"ciotola",
"calculator":"calcolatrice",
"camera":"fotocamera",
... | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Data | obj_dir = "/content/drive/My Drive/Tesi/Code/Object_classification"
#obj_dir = "/Users/marco/Google Drive/Tesi/Code/Object_classification"
data_dir = obj_dir+"/Data"
model_filename = obj_dir+"/model.pkl"
exclusion_list=["binder","camera","cell phone","dry battery"]
test_folder=["apple_3",
"bell_pepper_1",
... | 0.07%
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0.... | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Save input data | if selected_space:
new_y_train=[]
for i in y_train:
new_label=dictionary[i][1]
#new_label=new_label.split("-")[0]
new_y_train.append(new_label)
new_y_test=[]
for i in y_test:
new_label=dictionary[i][1]
#new_label=new_label.split("-")[0]
new_y_test.... | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Classifier fitting | if load_old_params and False:
with open(model_filename, 'rb') as file:
clf = pickle.load(file)
else:
clf = RandomForestClassifier(n_jobs=-1, n_estimators=30)
clf.fit(X_train,y_train)
print(clf.score(X_test,y_test)) | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Saving parameters | if save_params:
with open(model_filename, 'wb') as file:
pickle.dump(clf, file) | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Score | def classify_prediction(prediction):
sure=[]
unsure=[]
dubious=[]
cannot_answer=[]
for pred in prediction:
o,p=pred
values=list(p.values())
keys=list(p.keys())
# sure
if values[0]>0.8:
sure.append(pred)
# unsure
elif values... | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
Test | clf.score(X_test,y_test)
plt.plot(clf.feature_importances_)
clf.feature_importances_
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
import pandas as pd
def classification_report(y_true, y_pred):
print(f"Accuracy: {accuracy_score(y_true, y_pred)}.")
print(f"... | _____no_output_____ | MIT | Test/Object_classification/Object_classification_Random_Forest.ipynb | marcolamartina/LamIra |
You are given two strings as input. You want to find out if these **two strings** are **at most one edit away** from each other.An edit is defined as either- **inserting a character**: length increased by 1- **removing a character**: length decreased by 1- **replacing a character**: length doesn't change*this edit dist... | # method 1: brutal force
# O(N)
# N is the length of the **shorter** string
def oneEdit(s1, s2):
l1 = len(s1)
l2 = len(s2)
if (l1 == l2):
return checkReplace(s1, s2)
elif abs(l1-l2) == 1:
return checkInsRem(s1, s2)
else:
return False
def checkReplace(s1, s2):
foundDiff ... | _____no_output_____ | MIT | notebooks/ch1_arrays_and_strings/1.5 One Away.ipynb | Julyzzzzzz/Practice-on-data-structures-and-algorithms |
Differential Methylated Genes - Pairwise | import pandas as pd
import anndata
import xarray as xr
from ALLCools.plot import *
from ALLCools.mcds import MCDS
from ALLCools.clustering import PairwiseDMG, cluster_enriched_features
import pathlib | _____no_output_____ | MIT | docs/allcools/cell_level/dmg/04-PairwiseDMG.ipynb | mukamel-lab/ALLCools |
Parameters | adata_path = '../step_by_step/100kb/adata.with_coords.h5ad'
cluster_col = 'L1'
# change this to the paths to your MCDS files
gene_fraction_dir = 'gene_frac/'
obs_dim = 'cell'
var_dim = 'gene'
# DMG
mc_type = 'CHN'
top_n = 1000
adj_p_cutoff = 1e-3
delta_rate_cutoff = 0.3
auroc_cutoff = 0.9
random_state = 0
n_jobs = 30 | _____no_output_____ | MIT | docs/allcools/cell_level/dmg/04-PairwiseDMG.ipynb | mukamel-lab/ALLCools |
Load | adata = anndata.read_h5ad(adata_path)
cell_meta = adata.obs.copy()
cell_meta.index.name = obs_dim
gene_meta = pd.read_csv(f'{gene_fraction_dir}/GeneMetadata.csv.gz', index_col=0)
gene_mcds = MCDS.open(f'{gene_fraction_dir}/*_da_frac.mcds', use_obs=cell_meta.index)
gene_mcds | _____no_output_____ | MIT | docs/allcools/cell_level/dmg/04-PairwiseDMG.ipynb | mukamel-lab/ALLCools |
Pairwise DMG | pwdmg = PairwiseDMG(max_cell_per_group=1000,
top_n=top_n,
adj_p_cutoff=adj_p_cutoff,
delta_rate_cutoff=delta_rate_cutoff,
auroc_cutoff=auroc_cutoff,
random_state=random_state,
n_jobs=n_jobs)
pwdmg.fit... | _____no_output_____ | MIT | docs/allcools/cell_level/dmg/04-PairwiseDMG.ipynb | mukamel-lab/ALLCools |
Aggregating Cluster DMGWeighted total AUROC aggregated from the pairwise comparisons Aggregate Pairwise Comparisons | cluster_dmgs = pwdmg.aggregate_pairwise_dmg(adata, groupby=cluster_col)
# save all the DMGs
with pd.HDFStore(f'{cluster_col}.ClusterRankedPWDMG.{mc_type}.hdf') as hdf:
for cluster, dmgs in cluster_dmgs.items():
hdf[cluster] = dmgs[dmgs > 0.0001] | _____no_output_____ | MIT | docs/allcools/cell_level/dmg/04-PairwiseDMG.ipynb | mukamel-lab/ALLCools |
Import libraries | import os
import warnings
warnings.filterwarnings('ignore')
#Packages related to data importing, manipulation, exploratory data #analysis, data understanding
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from termcolor import colored as cl # text customization
#Packages related to data vi... | _____no_output_____ | MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Importing data This dataset contains the real bank transactions made by European cardholders in the year 2013, the dataset can be downlaoded here: https://www.kaggle.com/mlg-ulb/creditcardfraud | data=pd.read_csv("creditcard.csv") | _____no_output_____ | MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Checking transactions we can see that only 17% are fraud transactions | Total_transactions = len(data)
normal = len(data[data.Class == 0])
fraudulent = len(data[data.Class == 1])
fraud_percentage = round(fraudulent/normal*100, 2)
print(cl('Total number of Trnsactions are {}'.format(Total_transactions), attrs = ['bold']))
print(cl('Number of Normal Transactions are {}'.format(normal), attrs... | [1mTotal number of Trnsactions are 284807[0m
[1mNumber of Normal Transactions are 284315[0m
[1mNumber of fraudulent Transactions are 492[0m
[1mPercentage of fraud Transactions is 0.17[0m
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Feature Scaling | sc = StandardScaler()
amount = data['Amount'].values
data['Amount'] = sc.fit_transform(amount.reshape(-1, 1)) | _____no_output_____ | MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Dropping columns and other features | data.drop(['Time'], axis=1, inplace=True)
data.drop_duplicates(inplace=True)
X = data.drop('Class', axis = 1).values
y = data['Class'].values | _____no_output_____ | MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Training the model | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 1) | _____no_output_____ | MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Decision Trees | DT = DecisionTreeClassifier(max_depth = 4, criterion = 'entropy')
DT.fit(X_train, y_train)
dt_yhat = DT.predict(X_test)
print('Accuracy score of the Decision Tree model is {}'.format(accuracy_score(y_test, dt_yhat)))
print('F1 score of the Decision Tree model is {}'.format(f1_score(y_test, dt_yhat))) | F1 score of the Decision Tree model is 0.7521367521367521
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
K nearest neighbor | n = 7
KNN = KNeighborsClassifier(n_neighbors = n)
KNN.fit(X_train, y_train)
knn_yhat = KNN.predict(X_test)
print('Accuracy score of the K-Nearest Neighbors model is {}'.format(accuracy_score(y_test, knn_yhat)))
print('F1 score of the K-Nearest Neighbors model is {}'.format(f1_score(y_test, knn_yhat))) | Accuracy score of the K-Nearest Neighbors model is 0.999288989494457
F1 score of the K-Nearest Neighbors model is 0.7949790794979079
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Logistic Regression | lr = LogisticRegression()
lr.fit(X_train, y_train)
lr_yhat = lr.predict(X_test)
print('Accuracy score of the Logistic Regression model is {}'.format(accuracy_score(y_test, lr_yhat)))
print('F1 score of the Logistic Regression model is {}'.format(f1_score(y_test, lr_yhat))) | F1 score of the Logistic Regression model is 0.6666666666666666
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
SVM classifier | svm = SVC()
svm.fit(X_train, y_train)
svm_yhat = svm.predict(X_test)
print('Accuracy score of the Support Vector Machines model is {}'.format(accuracy_score(y_test, svm_yhat)))
print('F1 score of the Support Vector Machines model is {}'.format(f1_score(y_test, svm_yhat))) | F1 score of the Support Vector Machines model is 0.7813953488372093
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
Random Forest | rf = RandomForestClassifier(max_depth = 4)
rf.fit(X_train, y_train)
rf_yhat = rf.predict(X_test)
print('Accuracy score of the Random Forest model is {}'.format(accuracy_score(y_test, rf_yhat)))
print('F1 score of the Random Forest model is {}'.format(f1_score(y_test, rf_yhat))) | F1 score of the Random Forest model is 0.7397260273972602
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
XGBClassifier | xgb = XGBClassifier(max_depth = 4)
xgb.fit(X_train, y_train)
xgb_yhat = xgb.predict(X_test)
print('Accuracy score of the XGBoost model is {}'.format(accuracy_score(y_test, xgb_yhat)))
print('F1 score of the XGBoost model is {}'.format(f1_score(y_test, xgb_yhat))) | F1 score of the XGBoost model is 0.8495575221238937
| MIT | Credit Card Fraud Detection.ipynb | mouhamadibrahim/Credit-Card-Fraud-Detection |
 Serving Spark NLP with API: Synapse ML SynapseML Installation | import json
import os
from google.colab import files
license_keys = files.upload()
with open(list(license_keys.keys())[0]) as f:
license_keys = json.load(f)
# Defining license key-value pairs as local variables
locals().update(license_keys)
# Adding license key-value pairs to environment variables
os.environ.up... | _____no_output_____ | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Imports and Spark Session | import pandas as pd
import pyspark
import sparknlp
import sparknlp_jsl
from pyspark.sql import SparkSession
from pyspark.ml import Pipeline, PipelineModel
import pyspark.sql.functions as F
from pyspark.sql.types import *
from sparknlp.base import *
from sparknlp.annotator import *
from sparknlp_jsl.annotator import *
f... | _____no_output_____ | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Preparing a pipeline with Entity Resolution | # Annotator that transforms a text column from dataframe into an Annotation ready for NLP
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
# Sentence Detector DL annotator, processes various sentences per line
sentenceDetectorDL = SentenceDetectorDLModel.pretrained(... | sentence_detector_dl_healthcare download started this may take some time.
Approximate size to download 367.3 KB
[OK!]
embeddings_clinical download started this may take some time.
Approximate size to download 1.6 GB
[OK!]
ner_clinical download started this may take some time.
Approximate size to download 13.9 MB
[OK!]
... | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Adding a clinical note as a text example | clinical_note = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years
prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior
episode of HTG-induced pancreatitis three years prior to presentation, associated
with an acute hepatitis, and ob... | _____no_output_____ | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Creating a JSON file with the clinical noteSince SynapseML runs a webservice that accepts HTTP calls with json format | data_json = {"text": clinical_note } | _____no_output_____ | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Running a Synapse server | serving_input = spark.readStream.server() \
.address("localhost", 9999, "benchmark_api") \
.option("name", "benchmark_api") \
.load() \
.parseRequest("benchmark_api", data.schema)
serving_output = resolver_p_model.transform(serving_input) \
.makeReply("icd10cm_code")
server = serving_output.writeS... | _____no_output_____ | Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
Checking Results | for i in range (0, len(response_list[0].json())):
print(response_list[0].json()[i]['result']) | O2441
O2411
E11
K8520
B15
E669
Z6841
R35
R631
R630
R111
J988
E11
G600
K130
R52
M6283
R4689
O046
E785
E872
E639
H5330
R799
R829
E785
A832
G600
J988
| Apache-2.0 | tutorials/RestAPI/Serving_SparkNLP_with_Synapse.ipynb | iamvarol/spark-nlp-workshop |
5.1 - Introduction to convnetsThis notebook contains the code sample found in Chapter 5, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: i... | from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64... | _____no_output_____ | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
Let's display the architecture of our convnet so far: | model.summary() | Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
__________________________________... | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
You can see above that the output of every `Conv2D` and `MaxPooling2D` layer is a 3D tensor of shape `(height, width, channels)`. The width and height dimensions tend to shrink as we go deeper in the network. The number of channels is controlled by the first argument passed to the `Conv2D` layers (e.g. 32 or 64).The ne... | model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) | _____no_output_____ | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
We are going to do 10-way classification, so we use a final layer with 10 outputs and a softmax activation. Now here's what our network looks like: | model.summary() | Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
__________________________________... | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
As you can see, our `(3, 3, 64)` outputs were flattened into vectors of shape `(576,)`, before going through two `Dense` layers.Now, let's train our convnet on the MNIST digits. We will reuse a lot of the code we have already covered in the MNIST example from Chapter 2. | from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_i... | Epoch 1/5
60000/60000 [==============================] - 10s 173us/step - loss: 0.1661 - accuracy: 0.9479
Epoch 2/5
60000/60000 [==============================] - 10s 165us/step - loss: 0.0454 - accuracy: 0.9857
Epoch 3/5
60000/60000 [==============================] - 10s 164us/step - loss: 0.0314 - accuracy: 0.9900
Ep... | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
Let's evaluate the model on the test data: | test_loss, test_acc = model.evaluate(test_images, test_labels)
test_acc | _____no_output_____ | MIT | .ipynb_checkpoints/5.1-introduction-to-convnets-checkpoint.ipynb | zhangdongwl/deep-learning-with-python-notebooks |
A short video on how bagging works https://www.youtube.com/watch?v=2Mg8QD0F1dQ | def bootstrap(X,Y, n=None):
#Bootstrap function
if n == None:
n = len(X)
resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)
X_resample = X[resample_i]
Y_resample = Y[resample_i]
return X_resample, Y_resample
def bagging(n_sample,n_bag):
#Perform bagging procedure. Bootstrap and o... | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
First, we need to perform the splitting procedure as we did in the CNN notebook to get train and test sets Now lets perform bagging of an ensamble of 50 models with each model containing 3800 bootstrapped samples from X_train | bagModel = bagging(3800,50) | Model fitting on the 1th bootstrapped set
(28, 300, 1)
Epoch 1/20
3800/3800 [==============================] - 201s 53ms/step - loss: 0.1491 - acc: 0.4853 - mean_squared_error: 0.1491
Epoch 2/20
3800/3800 [==============================] - 7s 2ms/step - loss: 0.1416 - acc: 0.5047 - mean_squared_error: 0.1416
Epoch 3/20... | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
Bagging different numbers of models in an ensamble to test accuracy change | bagModel2 = bagging(3800,10)
bagged_predict.keys()
bagged_predict = predict(bagModel)
Accuracy= baggedAccuracy(bagged_predict,Y_test)
print("Bagged Accuracy(50 models): %.2f%% "%Accuracy) | Bagged Accuracy(50 models): 62.48%
| MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
Improved accuracy! Variance reduction helps! | bagModel2.keys()
bag_pred2 = predict(bagModel2)
Accuracy2= baggedAccuracy(bag_pred2,Y_test)
print("Bagged Accuracy(10 models): %.2f%% "%Accuracy2) | Bagged Accuracy(10 models): 62.29%
| MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
The results for 10-model and 50-model ensamble are only slightly different | n_sample = int(len(X_train)*0.6)
n_sample
model_num_list = [10,20,30,40,50]
def accuracy_bag(n_sample,model_num_list):
model_bags = []
accuracy_bags = []
for i in model_num_list:
print('Bagging {} models'.format(i))
bagmodel = bagging(n_sample,i)
bag_pred = predict(bagmodel)
... | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
I tried to get the accuracy of an ensamble from 1 to 50 models but my machine broke down overnight.. I guess this is where GCP becomes handy. Next up: perform bagging for LSTM model. You can see from the accuracy plot: 3/5 of the bagging accuracy is better than a single model accuracy without bagging(Single model accu... | accuracybags = accuracy_bag(n_sample,model_num_list)
accuracybags
accuracybags_array = np.asarray(accuracybags)
from matplotlib import pyplot as plt
plt.figure(figsize=(10,10),dpi=80)
plt.scatter(model_num_list,accuracybags)
plt.xlabel('Ensamble model quantity',fontsize=20)
plt.ylabel('Bagging accuracy',fontsize=16) | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
The accuracy doesn't show an ascending trend as the ensamble contains more models, which is weird. Then I ran the model model for 3800 samples out of 4522 observations in the train set instead of 2639 samples with the same test train split. | n_sample = 3800
accuracybag2 = accuracy_bag(n_sample,[20,30])
accuracybag3 = accuracy_bag(n_sample,[40,50])
accuracybag4 = accuracy_bag(n_sample,[10])
accuracybag2
accuracybag3 | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
I did the bagging separately because I was afraid of my machine breaking down.. | accuracy_3800sample = accuracybag4 +accuracybag2 + accuracybag3
accuracy_2640sample = accuracybags
accuracy_3800sample | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
Combine the accuracy results for 2640 and 3800 samples for (10,20,30,40,50) models bagging | plt.figure(figsize=(10,10),dpi=80)
scatter_3800sample = plt.scatter([10,20,30,40,50],accuracy_3800sample,color = 'Blue')
scatter_2640sample = plt.scatter([10,20,30,40,50],accuracy_2640sample,color = 'Green')
plt.xlabel('Bagging Ensamble Model Quantity',fontsize=20)
plt.ylabel('Bagging Accuracy(%)',fontsize=16)
CNN_accu... | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
I tried to get the accuracy bags from 1 to 50 models but my machine broke down overnight.. I guess this is where GCP becomes handy. Next up: perform bagging for LSTM model. You can see from the accuracy plot: 3/5 of the bagging accuracy is better than a single model accuracy without bagging(Single model accuracy 60.97... | from collections import Counter
bagModel.keys()
lists = []
for i in range(30):
model_number = "model%s" % (i+1)
pred_list = conversion(prediction[model_number])
lists.append(pred_list)
Ytest_list=conversion(Y_test)
pred_list = []
for i in range(1522):
for j in range(30):
new_list = []
ne... | _____no_output_____ | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
The result is worse! | from keras.layers import Dropout, Convolution2D, MaxPooling2D
top_words = 1000
max_words = 150
filters = 32 #filter = 1 x KERNEL
inpurt_shape = (X_train.shape[1:])
print(inpurt_shape)
# create the model
model = Sequential()
model.add(Convolution2D(16, kernel_size=3, activation='elu', padding='same',
... | (28, 300, 1)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_305 (Conv2D) (None, 28, 300, 16) 160
___________________________________________... | MIT | examples/CNN Bagging.ipynb | sarahalamdari/DIRECT_capstone |
A basic machine learning problem: image classification . A basic machine learning problem: image classification```{admonition} Can a machine (function) tell the difference ? Mathematically, gray-scale image can be just taken as matrix in $R^{n_0\times n_0}$. The next figure shows different result from: human vision an... | from IPython.display import HTML
HTML('<iframe id="kaltura_player" src="https://cdnapisec.kaltura.com/p/2356971/sp/235697100/embedIframeJs/uiconf_id/41416911/partner_id/2356971?iframeembed=true&playerId=kaltura_player&entry_id=1_b5pq3bnx&flashvars[streamerType]=auto&flashvars[localizationCode]=en&flashvars[lead... | /anaconda3/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning: Consider using IPython.display.IFrame instead
warnings.warn("Consider using IPython.display.IFrame instead")
| MIT | ch01/Untitled.ipynb | liuzhengqi1996/math452 |
Calculating Pagerank on Wikidata | import numpy as np
import pandas as pd
import os
%env MY=/Users/pedroszekely/data/wikidata-20200504
%env WD=/Volumes/GoogleDrive/Shared drives/KGTK/datasets/wikidata-20200504 | env: MY=/Users/pedroszekely/data/wikidata-20200504
env: WD=/Volumes/GoogleDrive/Shared drives/KGTK/datasets/wikidata-20200504
| MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
We need to filter the wikidata edge file to remove all edges where `node2` is a literal. We can do this by running `ifexists` to keep edges where `node2` also appears in `node1`.This takes 2-3 hours on a laptop. | !time gzcat "$WD/wikidata_edges_20200504.tsv.gz" \
| kgtk ifexists --filter-on "$WD/wikidata_edges_20200504.tsv.gz" --input-keys node2 --filter-keys node1 \
| gzip > "$MY/wikidata-item-edges.tsv.gz"
!gzcat $MY/wikidata-item-edges.tsv.gz | wc | 460763981 3225347876 32869769062
| MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
We have 460 million edges that connect items to other items, let's make sure this is what we want before spending a lot of time computing pagerank | !gzcat $MY/wikidata-item-edges.tsv.gz | head | id node1 label node2 rank node2;magnitude node2;unit node2;date node2;item node2;lower node2;upper node2;latitude node2;longitude node2;precision node2;calendar node2;entity-type
Q8-P31-1 Q8 P31 Q331769 normal Q331769 item
Q8-P31-2 Q8 P31 Q60539479 normal Q60539479 item
Q8-P31-3 Q8 P31 Q9415 normal ... | MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
Let's do a sanity check to make sure that we have the edges that we want.We can do this by counting how many edges of each `entity-type`. Good news, we only have items and properties. | !time gzcat $MY/wikidata-item-edges.tsv.gz | kgtk unique $MY/wikidata-item-edges.tsv.gz --column 'node2;entity-type' | node1 label node2
item count 460737401
property count 26579
gzcat: error writing to output: Broken pipe
gzcat: /Users/pedroszekely/data/wikidata-20200504/wikidata-item-edges.tsv.gz: uncompress failed
real 21m44.450s
user 21m29.078s
sys 0m7.958s
| MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
We only needd `node`, `label` and `node2`, so let's remove the other columns | !time gzcat $MY/wikidata-item-edges.tsv.gz | kgtk remove-columns -c 'id,rank,node2;magnitude,node2;unit,node2;date,node2;item,node2;lower,node2;upper,node2;latitude,node2;longitude,node2;precision,node2;calendar,node2;entity-type' \
| gzip > $MY/wikidata-item-edges-only.tsv.gz
!gzcat $MY/wikidata-item-edges-only.tsv.... | _____no_output_____ | MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
The `kgtk graph-statistics` command will compute pagerank. It will run out of memory on a laptop with 16GB of memory. | !time kgtk graph_statistics --directed --degrees --pagerank --log $MY/log.txt -i $MY/wikidata-item-edges-only.tsv > $MY/wikidata-pagerank-degrees.tsv | /bin/sh: line 1: 89795 Killed: 9 kgtk graph-statistics --directed --degrees --pagerank --log $MY/log.txt -i $MY/wikidata-item-edges-only.tsv > $MY/wikidata-pagerank-degrees.tsv
real 32m57.832s
user 19m47.624s
sys 8m58.352s
| MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
We ran it on a server with 256GM of memory. It used 50GB and produced the following files: | !exa -l "$WD"/*sorted*
!gzcat "$WD/wikidata-pagerank-only-sorted.tsv.gz" | head | node1 property node2 id
Q13442814 vertex_pagerank 0.02422254325848587 Q13442814-vertex_pagerank-881612
Q1860 vertex_pagerank 0.00842243515354162 Q1860-vertex_pagerank-140
Q5 vertex_pagerank 0.0073505352600377934 Q5-vertex_pagerank-188
Q5633421 vertex_pagerank 0.005898322426631837 Q5633421-vertex_pagerank-101732
Q215024... | MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
Oh, the `graph_statistics` command is not using standard column naming, using `property` instead of `label`.This will be fixed, for now, let's rename the columns. | !kgtk rename-col -i "$WD/wikidata-pagerank-only-sorted.tsv.gz" --mode NONE --output-columns node1 label node2 id | gzip > $MY/wikidata-pagerank-only-sorted.tsv.gz
!gzcat $MY/wikidata-pagerank-only-sorted.tsv.gz | head | node1 label node2 id
Q13442814 vertex_pagerank 0.02422254325848587 Q13442814-vertex_pagerank-881612
Q1860 vertex_pagerank 0.00842243515354162 Q1860-vertex_pagerank-140
Q5 vertex_pagerank 0.0073505352600377934 Q5-vertex_pagerank-188
Q5633421 vertex_pagerank 0.005898322426631837 Q5633421-vertex_pagerank-101732
Q21502402 ... | MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
Let's put the labels on the entity labels as columns so that we can read what is what. To do that, we concatenate the pagerank file with the labels file, and then ask kgtk to lift the labels into new columns. | !time kgtk cat -i "$MY/wikidata_labels.tsv" $MY/pagerank.tsv | gzip > $MY/pagerank-and-labels.tsv.gz
!time kgtk lift -i $MY/pagerank-and-labels.tsv.gz | gzip > "$WD/wikidata-pagerank-en.tsv.gz" |
real 32m37.811s
user 11m5.594s
sys 10m30.283s
| MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
Now we can look at the labels. Here are the top 20 pagerank items in Wikidata: | !gzcat "$WD/wikidata-pagerank-en.tsv.gz" | head -20 | node1 label node2 id node1;label label;label node2;label
Q13442814 vertex_pagerank 0.02422254325848587 Q13442814-vertex_pagerank-881612 'scholarly article'@en
Q1860 vertex_pagerank 0.00842243515354162 Q1860-vertex_pagerank-140 'English'@en
Q5 vertex_pagerank 0.0073505352600377934 Q5-vertex_pagerank-188 'human'@en ... | MIT | examples/Example4 - Wikidata Pagerank.ipynb | robuso/kgtk |
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