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 |
|---|---|---|---|---|---|
**Option 3:** Draw once before loop | np.random.seed(1917)
x = np.random.normal(0,1,size=100)
print(f'var(x) = {np.var(x):.3f}')
y_ = np.random.normal(0,1,size=x.size)
for sigma in [0.5,1.0,0.5]:
y = sigma*y_
print(f'sigma = {sigma:2f}: f = {f(x,y):.4f}') | var(x) = 0.951
sigma = 0.500000: f = 0.5522
sigma = 1.000000: f = 0.0143
sigma = 0.500000: f = 0.5522
| MIT | web/06/Examples_and_overview.ipynb | Jovansam/lectures-2021 |
Image Combination Joint Single Dish and Interferometer Image Reconstruction The SDINT imaging algorithm allows joint reconstruction of wideband single dish and interferometer data. This algorithm is available in the task [sdintimaging](../api/casatasks.rstimaging) and described in [Rau, Naik & Braun (2019)](https://... | _____no_output_____ | Apache-2.0 | docs/notebooks/image_combination.ipynb | yohei99/casadocs | |
IPL Dataset Analysis Problem StatementWe want to know as to what happens during an IPL match which raises several questions in our mind with our limited knowledge about the game called cricket on which it is based. This analysis is done to know as which factors led one of the team to win and how does it matter. Abou... | import numpy as np
# Not every data format will be in csv there are other file formats also.
# This exercise will help you deal with other file formats and how toa read it.
path = './ipl_matches_small.csv'
data_ipl = np.genfromtxt(path, delimiter=',', skip_header=1, dtype=str)
print(data_ipl) | [['392203' '2009-05-01' 'East London' ... '' '' '']
['392203' '2009-05-01' 'East London' ... '' '' '']
['392203' '2009-05-01' 'East London' ... '' '' '']
...
['335987' '2008-04-21' 'Jaipur' ... '' '' '']
['335987' '2008-04-21' 'Jaipur' ... '' '' '']
['335987' '2008-04-21' 'Jaipur' ... '' '' '']]
| MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
Calculate the unique no. of matches in the provided dataset ? | # How many matches were held in total we need to know so that we can analyze further statistics keeping that in mind.im
import numpy as np
unique_match_code=np.unique(data_ipl[:,0])
print(unique_match_code) | ['335987' '392197' '392203' '392212' '501226' '729297']
| MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
Find the set of all unique teams that played in the matches in the data set. | # this exercise deals with you getting to know that which are all those six teams that played in the tournament.
import numpy as np
unique_match_team3=np.unique(data_ipl[:,3])
print(unique_match_team3)
unique_match_team4=np.unique(data_ipl[:,4])
print(unique_match_team4)
union=np.union1d(unique_match_team3,unique_matc... | _____no_output_____ | MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
Find sum of all extras in all deliveries in all matches in the dataset | # An exercise to make you familiar with indexing and slicing up within data.
import numpy as np
extras=data_ipl[:,17]
data=extras.astype(np.int)
print(sum(data))
| 88
| MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
Get the array of all delivery numbers when a given player got out. Also mention the wicket type. | import numpy as np
deliveries=[]
wicket_type=[]
for i in data_ipl:
if(i[20]!=""):
a=i[11]
b=i[21]
deliveries.append(a)
wicket_type.append(b)
print(deliveries)
print(wicket_type)
| _____no_output_____ | MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
How many matches the team `Mumbai Indians` has won the toss? | data_arr=[]
for i in data_ipl:
if(i[5]=="Mumbai Indians"):
data_arr.append(i[0])
unique_match_id=np.unique(data_arr)
print(unique_match_id)
print(len(unique_match_id))
| ['392197' '392203']
2
| MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
Create a filter that filters only those records where the batsman scored 6 runs. Also who has scored the maximum no. of sixes overall ? | # An exercise to know who is the most aggresive player or maybe the scoring player
import numpy as np
counter=0
run_dict={}
arr=[]
for i in data_ipl:
#print(i[13])
#current_run = i[16]
#prev_run = run_dict[batsman_nm]
#batsman_nm = i[13]
#if prev_run == None:
#run_dict[batsman_nm] = current_run
#else:
... | _____no_output_____ | MIT | Manipulating_Data_with_NumPy_Code_Along.ipynb | vidSanas/greyatom-python-for-data-science |
读取数据 | import torchvision.transforms as T
img_shape = (3, 224, 224)
def read_raw_img(path, resize, L=False):
img = Image.open(path)
if resize:
img = img.resize(resize)
if L:
img = img.convert('L')
return np.asarray(img)
class DogCat(data.Dataset):
def __init__(self,path, img_shape):
# ... | _____no_output_____ | Apache-2.0 | Pytorch/Task4.ipynb | asd55667/DateWhale |
构建模型 | import math
class Vgg16(nn.Module):
def __init__(self, features, num_classes=1, init_weights=True):
super(Vgg16, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
... | _____no_output_____ | Apache-2.0 | Pytorch/Task4.ipynb | asd55667/DateWhale |
损失函数与优化器 | vgg = Vgg16(make_layers(cfg, 'RGB')).cuda()
print(vgg)
criterion = nn.BCELoss()
optimizer = t.optim.Adam(vgg.parameters(),lr=0.001) | Vgg16(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5):... | Apache-2.0 | Pytorch/Task4.ipynb | asd55667/DateWhale |
模型训练 | use_cuda = t.cuda.is_available()
device = t.device("cuda:0" if use_cuda else "cpu")
# cudnn.benchmark = True
# Parameters
params = {'batch_size': 64,
'shuffle': True,
'num_workers': 6}
max_epochs = 1
# train = DogCat(path+'train', img_shape)
training_generator = data.DataLoader(train, **params... | /home/wcw/anaconda3/envs/tf/lib/python3.6/site-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([64])) that is different to the input size (torch.Size([64, 1])) is deprecated. Please ensure they have the same size.
"Please ensure they have the same size.".format(target.size(), input.... | Apache-2.0 | Pytorch/Task4.ipynb | asd55667/DateWhale |
模型评估 | accs = []
test = DogCat(path+'test', img_shape=img_shape)
test_loader = data.DataLoader(test, **params)
with t.set_grad_enabled(False):
for x, y_ in test_loader:
x, y_ = x.float().to(device), y_.float().to(device)
y = vgg(x)
acc = y.eq(y_).sum().item()/y.shape[0]
# ... | _____no_output_____ | Apache-2.0 | Pytorch/Task4.ipynb | asd55667/DateWhale |
<imgsrc="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"><imgsrc="https://img.shields.io/badge/GitHub-100000?logo=github&logoColor=white" alt="GitHub"> Text Annotation Import* This notebook will provide examples of each supported annotation type for text assets. It will cover the foll... | !pip install -q 'labelbox[data]' | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Imports | from labelbox.schema.ontology import OntologyBuilder, Tool, Classification, Option
from labelbox import Client, LabelingFrontend, LabelImport, MALPredictionImport
from labelbox.data.annotation_types import (
Label, TextData, Checklist, Radio, ObjectAnnotation, TextEntity,
ClassificationAnnotation, Classificatio... | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
API Key and ClientProvide a valid api key below in order to properly connect to the Labelbox Client. | # Add your api key
API_KEY = None
client = Client(api_key=API_KEY) | INFO:labelbox.client:Initializing Labelbox client at 'https://api.labelbox.com/graphql'
| Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
---- Steps1. Make sure project is setup2. Collect annotations3. Upload Project setup We will be creating two projects, one for model-assisted labeling, and one for label imports | ontology_builder = OntologyBuilder(
tools=[
Tool(tool=Tool.Type.NER, name="named_entity")
],
classifications=[
Classification(class_type=Classification.Type.CHECKLIST, instructions="checklist", options=[
Option(value="first_checklist_answer"),
Option(value="second... | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Create Label using Annotation Type Objects* It is recommended to use the Python SDK's annotation types for importing into Labelbox. Object Annotations | def create_objects():
named_enity = TextEntity(start=10,end=20)
named_enity_annotation = ObjectAnnotation(value=named_enity, name="named_entity")
return named_enity_annotation | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Classification Annotations | def create_classifications():
checklist = Checklist(answer=[ClassificationAnswer(name="first_checklist_answer"),ClassificationAnswer(name="second_checklist_answer")])
checklist_annotation = ClassificationAnnotation(value=checklist, name="checklist")
radio = Radio(answer = ClassificationAnswer(name = "second_radio... | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Create a Label object with all of our annotations | image_data = TextData(uid=data_row.uid)
named_enity_annotation = create_objects()
checklist_annotation, radio_annotation = create_classifications()
label = Label(
data=image_data,
annotations = [
named_enity_annotation, checklist_annotation, radio_annotation
]
)
label.__dict__ | _____no_output_____ | Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Model Assisted Labeling To do model-assisted labeling, we need to convert a Label object into an NDJSON. This is easily done with using the NDJSONConverter classWe will create a Label called mal_label which has the same original structure as the label aboveNotes:* Each label requires a valid feature schema id. We wil... | mal_label = Label(
data=image_data,
annotations = [
named_enity_annotation, checklist_annotation, radio_annotation
]
)
mal_label.assign_feature_schema_ids(ontology_builder.from_project(mal_project))
ndjson_labels = list(NDJsonConverter.serialize([mal_label]))
ndjson_labels
upload_job = MALPredict... | INFO:labelbox.schema.annotation_import:Sleeping for 10 seconds...
| Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Label Import Label import is very similar to model-assisted labeling. We will need to re-assign the feature schema before continuing, but we can continue to use our NDJSonConverterWe will create a Label called li_label which has the same original structure as the label above | #for the purpose of this notebook, we will need to reset the schema ids of our checklist and radio answers
image_data = TextData(uid=data_row.uid)
named_enity_annotation = create_objects()
checklist_annotation, radio_annotation = create_classifications()
li_label = Label(
data=image_data,
annotations = [
... | INFO:labelbox.schema.annotation_import:Sleeping for 10 seconds...
| Apache-2.0 | examples/model_assisted_labeling/ner_mal.ipynb | Cyniikal/labelbox-python |
Hough Lines Import resources and display the image | import numpy as np
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
# Read in the image
image = cv2.imread('images/phone.jpg')
# Change color to RGB (from BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image) | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/.ipynb_checkpoints/6_1. Hough lines-checkpoint.ipynb | sxtien/CVND_Exercises |
Perform edge detection | # Convert image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Define our parameters for Canny
low_threshold = 50
high_threshold = 100
edges = cv2.Canny(gray, low_threshold, high_threshold)
plt.imshow(edges, cmap='gray') | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/.ipynb_checkpoints/6_1. Hough lines-checkpoint.ipynb | sxtien/CVND_Exercises |
Find lines using a Hough transform | # Define the Hough transform parameters
# Make a blank the same size as our image to draw on
rho = 1
theta = np.pi/180
threshold = 60
min_line_length = 50
max_line_gap = 5
line_image = np.copy(image) #creating an image copy to draw lines on
# Run Hough on the edge-detected image
lines = cv2.HoughLinesP(edges, rho, th... | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/.ipynb_checkpoints/6_1. Hough lines-checkpoint.ipynb | sxtien/CVND_Exercises |
import torch
x = torch.arange(18).view(3,2,3)
print(x)
print(x[0,0,0])
print(x[1,0,0])
print(x[1,1,1])
x[1,0:2,0:2] | _____no_output_____ | MIT | Chapter3_Slicing_3D_Tensors.ipynb | SokichiFujita/PyTorch-for-Deep-Learning-and-Computer-Vision | |
Using the same code as before, please solve the following exercises 1. Change the number of observations to 100,000 and see what happens. 2. Play around with the learning rate. Values like 0.0001, 0.001, 0.1, 1 are all interesting to observe. 3. Change the loss function. An alternative loss for regressions i... | # We must always import the relevant libraries for our problem at hand. NumPy and TensorFlow are required for this example.
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf | _____no_output_____ | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Data generationWe generate data using the exact same logic and code as the example from the previous notebook. The only difference now is that we save it to an npz file. Npz is numpy's file type which allows you to save numpy arrays into a single .npz file. We introduce this change because in machine learning most oft... | # First, we should declare a variable containing the size of the training set we want to generate.
observations = 1000
# We will work with two variables as inputs. You can think about them as x1 and x2 in our previous examples.
# We have picked x and z, since it is easier to differentiate them.
# We generate them rand... | _____no_output_____ | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Solving with TensorFlowNote: This intro is just the basics of TensorFlow which has way more capabilities and depth than that. | # Load the training data from the NPZ
training_data = np.load('TF_intro.npz')
# Declare a variable where we will store the input size of our model
# It should be equal to the number of variables you have
input_size = 2
# Declare the output size of the model
# It should be equal to the number of outputs you've got (for ... | Epoch 1/100
1000/1000 - 0s - loss: 24.5755
Epoch 2/100
1000/1000 - 0s - loss: 1.1773
Epoch 3/100
1000/1000 - 0s - loss: 0.4253
Epoch 4/100
1000/1000 - 0s - loss: 0.3853
Epoch 5/100
1000/1000 - 0s - loss: 0.3727
Epoch 6/100
1000/1000 - 0s - loss: 0.3932
Epoch 7/100
1000/1000 - 0s - loss: 0.3817
Epoch 8/100
1000/1000 - 0... | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Extract the weights and biasExtracting the weight(s) and bias(es) of a model is not an essential step for the machine learning process. In fact, usually they would not tell us much in a deep learning context. However, this simple example was set up in a way, which allows us to verify if the answers we get are correct. | # Extracting the weights and biases is achieved quite easily
model.layers[0].get_weights()
# We can save the weights and biases in separate variables for easier examination
# Note that there can be hundreds or thousands of them!
weights = model.layers[0].get_weights()[0]
weights
# We can save the weights and biases in ... | _____no_output_____ | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Extract the outputs (make predictions)Once more, this is not an essential step, however, we usually want to be able to make predictions. | # We can predict new values in order to actually make use of the model
# Sometimes it is useful to round the values to be able to read the output
# Usually we use this method on NEW DATA, rather than our original training data
model.predict_on_batch(training_data['inputs']).round(1)
# If we display our targets (actual ... | _____no_output_____ | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Plotting the data | # The model is optimized, so the outputs are calculated based on the last form of the model
# We have to np.squeeze the arrays in order to fit them to what the plot function expects.
# Doesn't change anything as we cut dimensions of size 1 - just a technicality.
plt.plot(np.squeeze(model.predict_on_batch(training_data... | _____no_output_____ | Apache-2.0 | 17 - Deep Learning with TensorFlow 2.0/5_Introduction to TensorFlow 2/8_Exercises/TensorFlow_Minimal_example_All_exercises.ipynb | olayinka04/365-data-science-courses |
Loan Default Risk - Exploratory Data Analysis This notebook is focused on data exploration. The key objective is to familiarise myself with the data and to identify any issues. This could lead to data cleaning or feature engineering. Contents 1. Importing Relevant Libraries, Reading In Data 2. Anomly Detection and... | #Importing data wrangling library
import pandas as pd #Data Wrangling/Cleaning package for mixed data
import numpy as np #Data wrangling & manipulation for numerical data
import os
#Importing visulization libraries
from matplotlib i... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
1.2 Reading In Data | rawfilepath = 'C:/Users/chara.geru/OneDrive - Avanade/DataScienceProject/HomeCreditModel/data/raw/'
filename = 'application_train.csv'
interimfilepath1 = 'C:/Users/chara.geru/OneDrive - Avanade/DataScienceProject/HomeCreditModel/data/interim/'
filename1 = 'df1.csv'
filename2 = 'df2.csv'
application_train = pd.read_c... | Size of application_train data: (307511, 122)
| MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
This dataset has: - 122 columns (features)- 307511 rows | application_train.columns.values #Printing all column names
pd.set_option('display.max_columns', None) #Display all columns
application_train.describe() #Get summary statistics for all columns
application_train.head() ... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
Generally the data looks good based on the statistics shown from the describe method. Potentional issues - Values in DAYS_BIRTH column are negative. They represent number of days a person was before they applied for a loan. For a better representation, I will convert them to positive values and convert days to years, ... | (application_train['DAYS_BIRTH']).describe()
(application_train['DAYS_EMPLOYED']).describe() | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
3.1 Check for Nulls | # "This function creates a table to summarize the null values"
def nulltable(df):
"""
This function creates a table to summarize the null values
"""
total = df.isnull().sum().sort_values(ascending = False)
percent = (df.isnull().sum()/df.isnull().count()*100).sort_values(ascending = False)
... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
- There are a large number of columns(features) with more than 50% NULLS.- I've deicided to drop these columns as they will not provide much information for training the model.- If a feaure has less than 50% NULLS, these maybe filled up using an appropriate calcualtion such as mean, median or mode 3.2 Data balanced or... | #Data balanced or imbalanced
temp = application_train["TARGET"].value_counts()
fig1, ax1 = plt.subplots()
ax1.pie(temp, labels=['Loan Repayed','Loan Not Repayed'], autopct='%1.1f%%',wedgeprops={'edgecolor':'black'})
ax1.axis('equal')
plt.title('Loan Repayed or Not')
plt.show() | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
Data is higly imbalanced - This emphasises the importance of assessing the precision/recall to evaluate results. For example, predicting all rows as not defaulted would lead to an accuracy of 91.9%.- Consider rebalancing the training data 3.3 Number of each type of column | # Number of each type of column
application_train.dtypes.value_counts() | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
- There are 16 object columns.- These will need to be encoded when building the model (using label encoder or one hot encoder) 3.4 Number of unique classes in each object column | # Number of unique classes in each object column
application_train.select_dtypes('object').apply(pd.Series.nunique, axis = 0) | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
- Gender has 3 values. This needs investigation and correction. 4. SummaryBased on the analysis so far we have identified the need to handle: - skewed data - removing rows with large number of NULLS - remove rows with third gender This led to the development of 2 new datasets, as we can't be sure whic... | # Set the style of plots
plt.style.use('fivethirtyeight')
plt.figure(figsize = (10, 12))
plt.subplot(2, 1, 1)
plt.title("Distribution of AMT_CREDIT")
plt.hist(df1["AMT_CREDIT"], bins =20)
plt.xlabel("AMT_CREDIT")
plt.subplot(2, 1, 2)
plt.title(" Log Distribution of AMT_CREDIT")
plt.his... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
- It is not resonable to have such high years of employment (> 40-60 years).- As there are amny rows, I would try and repalce the values with average years of employment- I would plot the distribution of the reasonable values to get a clearer picture of the disrtibution.- Based on the ditribution, I would decide to tak... | less_years = df1[df1.YEARS_EMPLOYED <= 80]
more_years = df1[df1.YEARS_EMPLOYED >80]
plt.hist(less_years['YEARS_EMPLOYED'])
plt.xlabel('Distribution of Lesser Years of Employment') | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
log this data and change it in df1 | less_years['YEARS_EMPLOYED'].mean()
df1['YEARS_EMPLOYED'] = np.where(df1['YEARS_EMPLOYED'] > 80, 7, df1['YEARS_EMPLOYED'])
plt.hist(df1['YEARS_EMPLOYED'])
plt.xlabel('Years of Employment')
plt.hist(df2['YEARS_EMPLOYED'])
plt.xlabel('Years of Employment')
##Defaul = df1[df1['TARGET'] == 1]
Not_defaul = df1[df1['TARGET']... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
DAYS_BIRTH is positively correlated with EXT_SOURCE_1 indicating that maybe one of the factors in this score is the client age.so try build model with EXT_SOURCE_1 and/or DAYS_BIRTH | plt.figure(figsize = (10, 12))
# iterate through the sources
for i, source in enumerate(['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']):
# create a new subplot for each source
#
plt.subplot(3, 1, i+1)
# plot repaid loans
sns.kdeplot(df1.loc[df1['TARGET'] == 0, source], label = 'target == 0')... | _____no_output_____ | MIT | notebooks/ExploratoryDataAnalysis.ipynb | geracharu/DataScienceProject |
import numpy as np
A=([1,2,-1],[4,6,-2],[-1,3,3])
print(A)
print(round(np.linalg.det(A)))
| _____no_output_____ | Apache-2.0 | Determinant_of_Matrix.ipynb | jnrtnan/Linear-Algebra-58020 | |
Copyright 2020 DeepMind Technologies Limited. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); Full license text | # 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
#
# http://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 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
A (very) basic GAN for MNIST in JAX/HaikuBased on a TensorFlow tutorial written by Mihaela Rosca.Original GAN paper: https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf Imports | # Uncomment the line below if running on colab.research.google.com.
# !pip install dm-haiku
import functools
from typing import Any, NamedTuple
import haiku as hk
import jax
from jax.experimental import optix
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tenso... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
Define the dataset | # Download the data once.
mnist = tfds.load("mnist")
def make_dataset(batch_size, seed=1):
def _preprocess(sample):
# Convert to floats in [0, 1].
image = tf.image.convert_image_dtype(sample["image"], tf.float32)
# Scale the data to [-1, 1] to stabilize training.
return 2.0 * image - 1.0
ds = mni... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
Define the model | class Generator(hk.Module):
"""Generator network."""
def __init__(self, output_channels=(32, 1), name=None):
super().__init__(name=name)
self.output_channels = output_channels
def __call__(self, x):
"""Maps noise latents to images."""
x = hk.Linear(7 * 7 * 64)(x)
x = jnp.reshape(x, x.shape[:... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
Train the model | #@title {vertical-output: true}
num_steps = 20001
log_every = num_steps // 100
# Let's see what hardware we're working with. The training takes a few
# minutes on a GPU, a bit longer on CPU.
print(f"Number of devices: {jax.device_count()}")
print("Device:", jax.devices()[0].device_kind)
print("")
# Make the dataset.... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
Visualize the lossesUnlike losses for classifiers or VAEs, GAN losses do not decrease steadily, instead going up and down depending on the training dynamics. | sns.set_style("whitegrid")
fig, axes = plt.subplots(1, 2, figsize=(20, 6))
# Plot the discriminator loss.
axes[0].plot(steps, disc_losses, "-")
axes[0].plot(steps, np.log(2) * np.ones_like(steps), "r--",
label="Discriminator is being fooled")
axes[0].legend(fontsize=20)
axes[0].set_title("Discriminator ... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
Visualize samples | #@title {vertical-output: true}
def make_grid(samples, num_cols=8, rescale=True):
batch_size, height, width = samples.shape
assert batch_size % num_cols == 0
num_rows = batch_size // num_cols
# We want samples.shape == (height * num_rows, width * num_cols).
samples = samples.reshape(num_rows, num_cols, heig... | _____no_output_____ | Apache-2.0 | examples/mnist_gan.ipynb | tirkarthi/dm-haiku |
¿Cómo podemos calcular con las funciones de Python los autovectores y los autovalores? | # Importamos las bibliotecas
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# Creamos una matriz
X = np.array([[3, 2], [4, 1]])
print(X)
# Vemos la biblioteca para calcular los autovectores y autovalores de Numpy
print(np.linalg.eig(X))
# Pedimos que muestre los autovalores
autovalores, autovec... | _____no_output_____ | MIT | Code/4.-Cómo calcular los autovalores y autovectores.ipynb | DataEngel/Linear-algebra-applied-to-ML-with-Python |
Alert Statistics | plt.figure(figsize=(base_width, base_height), dpi=dpi)
ax1 = plt.subplot(111)
reasons = [x if x != "Poor Signalness and Localisation" else "Poor Signalness \n and Localisation" for x in non["Rejection reason"]]
reasons = [x if x != "Separation from Galactic Plane" else "Separation from \n Galactic Plane" for x in reas... | ['Observed', 'Alert Retraction', 'Low Altitude', 'Poor Signalness \n and Localisation', 'Proximity to Sun', 'Separation from \n Galactic Plane', 'Southern Sky', 'Telescope Maintenance']
| MIT | notebooks/stats_alerts.ipynb | robertdstein/nuztfpaper |
Observed alerts (Table 1) | text = r"""
\begin{table*}
\centering
\begin{tabular}{||c | c c c c c c ||}
\hline
\textbf{Event} & \textbf{R.A. (J2000)} & \textbf{Dec (J2000)} & \textbf{90\% area} & \textbf{ZTF obs} &~ \textbf{Signalness}& \textbf{Refs}\\
& \textbf{[deg]}&\textbf{[deg]}& \textbf{[sq. deg.]}& \textbf{[... |
\begin{table*}
\centering
\begin{tabular}{||c | c c c c c c ||}
\hline
\textbf{Event} & \textbf{R.A. (J2000)} & \textbf{Dec (J2000)} & \textbf{90\% area} & \textbf{ZTF obs} &~ \textbf{Signalness}& \textbf{Refs}\\
& \textbf{[deg]}&\textbf{[deg]}& \textbf{[sq. deg.]}& \textbf{[sq. deg.]} ... | MIT | notebooks/stats_alerts.ipynb | robertdstein/nuztfpaper |
Not observed | reasons = ["Alert Retraction", "Proximity to Sun", "Low Altitude", "Southern Sky", "Separation from Galactic Plane", "Poor Signalness and Localisation", "Telescope Maintenance"]
seps = [1, 0, 0, 0, 1, 1, 1]
full_mask = np.array([float(x[2:6]) > 1802 for x in non["Event"]])
text = r"""
\begin{table*}
\centering
... |
\begin{table*}
\centering
\begin{tabular}{||c c ||}
\hline
\textbf{Cause} & \textbf{Events} \\
\hline
Alert Retraction & IC180423A \citep{ic180423a}, IC181031A \citep{ic181031a} \\
& IC190205A \citep{ic190205a}, IC190529A \citep{ic190529a} \\
& IC200120A \citep{ic200120a}, IC... | MIT | notebooks/stats_alerts.ipynb | robertdstein/nuztfpaper |
Full Neutrino List | text = fr"""
\begin{{longtable}}[c]{{||c c c c c c ||}}
\caption{{Summary of all {len(joint)} neutrino alerts issued since under the IceCube Realtime Program. Directions are not indicated for retracted events.}} \label{{tab:all_nu_alerts}} \\
\hline
\textbf{{Event}} & \textbf{{R.A. (J2000)}} & \textbf{{Dec (J2000)}} ... | 1 V1 alerts, 23 V2 alerts
| MIT | notebooks/stats_alerts.ipynb | robertdstein/nuztfpaper |
Exploratory Data Analysis of Stringer Dataset @authors: Simone Azeglio, Chetan Dhulipalla , Khalid Saifullah Part of the code here has been taken from [Neuromatch Academy's Computational Neuroscience Course](https://compneuro.neuromatch.io/projects/neurons/README.html), and specifically from [this notebook](https://co... | #@title Data retrieval
import os, requests
fname = "stringer_spontaneous.npy"
url = "https://osf.io/dpqaj/download"
if not os.path.isfile(fname):
try:
r = requests.get(url)
except requests.ConnectionError:
print("!!! Failed to download data !!!")
else:
if r.status_code != requests.codes.ok:
pr... | _____no_output_____ | MIT | src/NeuronBlock.ipynb | sazio/NMAs |
Exploratory Data Analysis (EDA) | #@title Data loading
import numpy as np
dat = np.load('stringer_spontaneous.npy', allow_pickle=True).item()
print(dat.keys())
# functions
def moving_avg(array, factor = 5):
"""Reducing the number of compontents by averaging of N = factor
subsequent elements of array"""
zeros_ = np.zeros((array.shape[0], 2))
a... | _____no_output_____ | MIT | src/NeuronBlock.ipynb | sazio/NMAs |
Extracting Data for RNN (or LFADS)The first problem to address is that for each layer we don't have the exact same number of neurons. We'd like to have a single RNN encoding all the different layers activities, to make it easier we can take the number of neurons ($N_{neurons} = 1131$ of the least represented class (la... | # Extract labels from z - coordinate
from sklearn import preprocessing
x, y, z = dat['xyz']
le = preprocessing.LabelEncoder()
labels = le.fit_transform(z)
### least represented class (layer with less neurons)
n_samples = np.histogram(labels, bins=9)[0][-1]
resp = np.array(dat['sresp'])
xyz = np.array(dat['xyz'])
prin... | _____no_output_____ | MIT | src/NeuronBlock.ipynb | sazio/NMAs |
issue: does the individual scaling by layer introduce bias that may artificially increase performance of the network? Data Loader | import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# set the seed
np.random.seed(42)
# number of neurons
NN = dataRNN.shape[0]
# let's use 270 latent components
ncomp = 10
# swapping the axes to maintain consistency with seq2seq no... | _____no_output_____ | MIT | src/NeuronBlock.ipynb | sazio/NMAs |
Training | from tqdm.notebook import tqdm
# you can keep re-running this cell if you think the cost might decrease further
cost = nn.MSELoss()
niter = 100000
for k in tqdm(range(niter)):
# the network outputs the single-neuron prediction and the latents
z, y = net(x1)
# our cost
loss = cost(z, x2)
# train the networ... | _____no_output_____ | MIT | src/NeuronBlock.ipynb | sazio/NMAs |
Import CSV log | data = readfile("ver3.txt")
## normal discogan
dloss = []
gloss = []
time= []
epouch = []
for i in range(len(data)):
try:
t = data[i][data[i].find('time: ')+5: data[i].find(', [d_loss:') - len(data[i])].replace(" ","")
if len(t)<3: continue
time.append(t)
glossA = (float(data[i][d... | _____no_output_____ | MIT | MylogReader.ipynb | Telexine/colorizeSketch |
L'objectif de ce notebook sera de réaliser une ébauche de solution ETL en python | #Import des packages necessaires à la réalisation du projet
import pyodbc
import pyspark
import pandas as pd
import pandasql as ps
import sqlalchemy
#creation des variables depuis le fichier de configuration qui doit être placé dans le même dossier que le notebook
from configparser import ConfigParser
#recuperation de... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Utilisation de pyodbc pour la connexion à la base de donnée SQL SERVER | #String de connexion procare
connection_string_procare ='DRIVER={SQL Server Native Client 11.0};SERVER='+sqlsUrl+';DATABASE='+database_procare+';UID='+username+';PWD='+password+';Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;Authentication=ActiveDirectoryIntegrated'
#String de connexion date
connection_st... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Utilisation des packages pandas et pandasql pour respecter les règles de gestion * On cree la query qui sert à faire la jointure entre les deux dataframes precedemment crees* On fait une jointure glissante sur les dates de début et de fin de contrats* Le filtre nous permet de récupérer les données cohérentes (le premi... | psql_join_query ='''
SELECT * FROM data_procare pro
INNER JOIN data_date DAT
ON DAT.DT_DATE BETWEEN PRO.StartAppliesTo AND PRO.EndAppliesTo
WHERE (PRO.DayNumber = 1 AND DAT.LB_DAY_NAME ='LUNDI')
OR (PRO.DayNumber = 2 AND DAT.LB_DAY_NAME ='MARDI')
OR (PRO.DayNumber = 3 AND DAT.LB_DAY_NAME ='MERCREDI')
OR (PRO.DayNumber ... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
* Execution de la query via pandasql pui affichage des 5 premières lignes | df_psql = ps.sqldf(psql_join_query)
df_psql.head(5) | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
On recupere les donnees etp dans un dataframe pour les rajouter dans le dataframe final | df_etp = pd.read_csv('etp.csv',sep=';') | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
* Conversion des donnees en decimal | df_etp_int = df_etp.astype('float64')
sql_etp_query = '''
SELECT
PRO.*,
ETP.[Correspondance ETP]
FROM df_psql PRO
INNER JOIN df_etp ETP
ON PRO.HoursWorked BETWEEN ETP.MinDailyHour AND ETP.MaxDailyHour
'''
df_join_etp = ps.sqldf(sql_etp_query)
df_join_etp.head(2) | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Creation de la table dans SQL Server via Pyodbc | #on établit une nouvelle connexion avec la base DW_BABILOU
connexion_dwh = pyodbc.connect(connection_string_date)
dwh_crusor = connexion_dwh.cursor()
#Table déjà installée
# dwh_crusor.execute('''
# CREATE TABLE [dbo].PROCARE_ETP(
# Jour DATE,
# DayNumber INT,
# [Database] VARCHAR(40),
# PersonID INT,
#... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
on efface les données de la table dont l'extractdate est à la date du jour | dwh_crusor.execute('''DELETE FROM PROCARE_ETP WHERE ExtractDate = CONVERT (date, GETDATE())''')
connexion_dwh.commit() | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Insertion des donnees dans la table PROCATE_ETP nouvellement creee | # On créé un nouveau DataFrame à l'image de la table finale
df_insert_procareETP = ps.sqldf('''SELECT
date(DT_DATE) AS jour,
DayNumber,
[Database],
PersonID,
SchoolID,
[Correspondance ETP] AS ETP,
date(ExtractDate) as ExtractDate
FROM df_join_etp''')
df_insert_procareETP.head(2) | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
* quelques tests de connexion infructeux | for index,row in df_insert_procareETP.iterrows():
dwh_crusor.execute('''INSERT INTO PROCATE_ETP(
[jour],
[DayNumber],
[Database],
PersonID,
SchoolID,
ETP,
ExtractDate)
val... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Script d'insertion dans la table, pandas.to_sql et sqlachemy * On cree les informations de connexion à la table en utilisant le connexion string utilise precedemment dans pyodbc | from sqlalchemy.engine import URL,create_engine
connection_url = URL.create("mssql+pyodbc", query={"odbc_connect": connection_string_date})
engine = create_engine(connection_url,fast_executemany=True)
#https://docs.sqlalchemy.org/en/14/dialects/mssql.html#module-sqlalchemy.dialects.mssql.pyodbc
#https://pandas.pydata... | _____no_output_____ | CNRI-Python | Poc_ETL.ipynb | charlesLavignasse/POC-Python-ETL |
Classfication report* A Classification report is used to measure the quality of predictions from a classification algorithm. ... The report shows the main classification metrics precision, recall and f1-score on a per-class basis. The metrics are calculated by using true and false positives, true and false negatives.`... | import numpy as np
from sklearn.metrics import classification_report
from matplotlib import pyplot as plt
y_true = np.array([1., 0., 1, 1, 0, 0, 1])
y_pred = np.array([1., 1., 1., 0., 0. ,1, 0]) | _____no_output_____ | MIT | 04_Evaluation_Methods/06_Classification_Report/.ipynb_checkpoints/Classification_Report-checkpoint.ipynb | CrispenGari/keras-api |
Using `scikit-learn` to generate the classification report for our predictions | labels = np.array([0., 1])
report = classification_report(y_true, y_pred, labels=labels)
print(report) | precision recall f1-score support
0.0 0.33 0.33 0.33 3
1.0 0.50 0.50 0.50 4
accuracy 0.43 7
macro avg 0.42 0.42 0.42 7
weighted avg 0.43 0.43 0.43 ... | MIT | 04_Evaluation_Methods/06_Classification_Report/.ipynb_checkpoints/Classification_Report-checkpoint.ipynb | CrispenGari/keras-api |
Ploting the ``classification_report`` | import numpy as np
import seaborn as sns
from sklearn.metrics import classification_report
import pandas as pd
report = classification_report(y_true, y_pred, labels=labels, output_dict=True)
sns.heatmap(pd.DataFrame(report).iloc[:-1, :].T, annot=True)
plt.title("Classification Report")
plt.show() | _____no_output_____ | MIT | 04_Evaluation_Methods/06_Classification_Report/.ipynb_checkpoints/Classification_Report-checkpoint.ipynb | CrispenGari/keras-api |
Синхронизация потоков Спасибо Сове Глебу, Голяр Димитрису и Николаю Васильеву за участие в написании текста Сегодня в программе:* Мьютексы MUTEX ~ MUTual EXclusion* Spinlock'и и атомики [Атомики в С на cppreference](https://ru.cppreference.com/w/c/atomic) Atomic в C и как с этим жить (раздел от Николая Васильев... | %%cpp mutex.c
%# Санитайзер отслеживает небезопасный доступ
%# к одному и тому же участку в памяти из разных потоков
%# (а так же другие небезопасные вещи).
%# В таких задачах советую всегда использовать
%run gcc -fsanitize=thread mutex.c -lpthread -o mutex.exe # вспоминаем про санитайзеры
%run ./mutex.exe
#define _... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Spinlock[spinlock в стандартной библиотеке](https://linux.die.net/man/3/pthread_spin_init) | %%cpp spinlock.c
%run gcc -fsanitize=thread -std=c11 spinlock.c -lpthread -o spinlock.exe
%run ./spinlock.exe
#define _GNU_SOURCE
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/syscall.h>
#include <sys/types.h>
#include <sys/time.h>
#include <pthread.h>
#include <stdatomic.h> //! Этот заголов... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Condition variable | %%cpp condvar.c
%run gcc -fsanitize=thread condvar.c -lpthread -o condvar.exe
%run ./condvar.exe > out.txt
//%run cat out.txt
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/syscall.h>
#include <sys/time.h>
#include <pthread.h>
#include <stdatomic.h>
const char* log_pre... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Способ достичь успеха без боли: все изменения данных делаем под mutex. Операции с condvar тоже делаем только под заблокированным mutex. Пример thread-safe очереди | %%cpp condvar_queue.c
%run gcc -fsanitize=thread condvar_queue.c -lpthread -o condvar_queue.exe
%run (for i in $(seq 0 100000); do echo -n "$i " ; done) | ./condvar_queue.exe > out.txt
//%run cat out.txt
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <sys/types.h>
#include <sys/syscall.h>
#include... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Atomic в C и как с этим житьВ C++ атомарные переменные реализованы через `std::atomic` в силу объектной ориентированности языка. В C же к объявлению переменной приписывается _Atomic или _Atomic(). Лучше использовать второй вариант (почему, будет ниже). Ситуация усложняется отсуствием документации. Про атомарные функц... | %%cpp atomic_example1.c
%run gcc -fsanitize=thread atomic_example1.c -lpthread -o atomic_example1.exe
%run ./atomic_example1.exe > out.txt
%run cat out.txt
#include <stdatomic.h>
#include <stdint.h>
#include <stdio.h>
// _Atomic навешивается на `int`
_Atomic int x;
int main(int argc, char* argv[]) {
atomic_store... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Казалось бы все хорошо, но давайте попробуем с указателями | %%cpp atomic_example2.c
%run gcc -fsanitize=thread atomic_example2.c -lpthread -o atomic_example2.exe
%run ./atomic_example2.exe > out.txt
%run cat out.txt
#include <stdatomic.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
// ПЛОХОЙ КОД!!!
_Atomic int* x;
int main(int argc, char* argv[]) {
int dat... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Получаем ад из warning/error от компилятора(все в зависимости от компилятора и платформы: `gcc 7.4.0 Ubuntu 18.04.1` - warning, `clang 11.0.0 macOS` - error).Может появиться желание написать костыль, явно прикастовав типы: | %%cpp atomic_example3.c
%run gcc -fsanitize=thread atomic_example3.c -lpthread -o atomic_example3.exe
%run ./atomic_example3.exe > out.txt
%run cat out.txt
#include <stdatomic.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
// ПЛОХОЙ КОД!!!
_Atomic int* x;
int main(int argc, char* argv[]) {
int dat... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
Теперь gcc перестает кидать warnings (в clang до сих пор error). Но код может превратиться в ад из кастов. Но! Этот код идейно полностью некорректен.Посмотрим на `_Atomic int* x;`В данном случае это работает как `(_Atomic int)* x`, а не как `_Atomic (int*) x` что легко подумать!То есть получается неатомарный указатель ... | %%cpp atomic_example4.c
%run gcc -fsanitize=thread atomic_example4.c -lpthread -o atomic_example4.exe
%run ./atomic_example4.exe > out.txt
%run cat out.txt
#include <stdatomic.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
// Теперь именно атомарный указатель. Как и должно было быть.
_Atomic (int*) x;
... | _____no_output_____ | MIT | sem20-synchronizing/synchronizing.ipynb | Disadvantaged/caos_2019-2020 |
reference for calulating quartile [here](http://web.mnstate.edu/peil/MDEV102/U4/S36/S363.html:~:text=The%20third%20quartile%2C%20denoted%20by,25%25%20lie%20above%20Q3%20) | mean(case_duration_dic.values())
# quartile calculation
import statistics
def calc_third_quartile(lis):
lis.sort()
size = len(lis)
lis_upper_half = lis[size//2:-1]
third_quartile = statistics.median(lis_upper_half)
return third_quartile
case_durations = list(case_duration_dic.values())
third_qu... | _____no_output_____ | MIT | src/dataset_div/dataset_div_bpi_12_w.ipynb | avani17101/goal-oriented-next-best-activity-recomendation |
Filter dataset for RL model | cases_gs = []
cases_gv = []
for k,v in case_duration_dic.items():
if v <= third_quartile:
cases_gs.append(k)
else:
cases_gv.append(k)
len(cases_gs), len(cases_gv)
tot = len(cases_gs)+ len(cases_gv)
percent_gs_cases = len(cases_gs) / tot
print(percent_gs_cases)
cases_train = cases_gs
cases_test =... | _____no_output_____ | MIT | src/dataset_div/dataset_div_bpi_12_w.ipynb | avani17101/goal-oriented-next-best-activity-recomendation |
Analysing unique events | a = get_unique_act(data_train)
len(a)
tot = get_unique_act(df)
len(tot)
lis = []
for act in tot:
if act not in a:
lis.append(act)
lis
for act in lis:
df_sub = df[df["class"] == act]
caseid_lis = list(df_sub["CaseID"])
l = len(caseid_lis)
caseid_sel = caseid_lis[:l//2]
if len(case... | _____no_output_____ | MIT | src/dataset_div/dataset_div_bpi_12_w.ipynb | avani17101/goal-oriented-next-best-activity-recomendation |
Implementing the Gradient Descent AlgorithmIn this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data. | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#Some helper functions for plotting and drawing lines
def plot_points(X, y):
admitted = X[np.argwhere(y==1)]
rejected = X[np.argwhere(y==0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', ... | _____no_output_____ | MIT | intro-neural-networks/gradient-descent/GradientDescent.ipynb | Abhinav2604/deep-learning-v2-pytorch |
Reading and plotting the data | data = pd.read_csv('data.csv', header=None)
X = np.array(data[[0,1]])
y = np.array(data[2])
plot_points(X,y)
plt.show() | _____no_output_____ | MIT | intro-neural-networks/gradient-descent/GradientDescent.ipynb | Abhinav2604/deep-learning-v2-pytorch |
TODO: Implementing the basic functionsHere is your turn to shine. Implement the following formulas, as explained in the text.- Sigmoid activation function$$\sigma(x) = \frac{1}{1+e^{-x}}$$- Output (prediction) formula$$\hat{y} = \sigma(w_1 x_1 + w_2 x_2 + b)$$- Error function$$Error(y, \hat{y}) = - y \log(\hat{y}) - (... | # Implement the following functions
# Activation (sigmoid) function
def sigmoid(x):
y=1/(1+np.exp(-x))
return y
# Output (prediction) formula
def output_formula(features, weights, bias):
y_hat=sigmoid(np.dot(features,weights)+bias)
return y_hat
# Error (log-loss) formula
def error_formula(y,... | _____no_output_____ | MIT | intro-neural-networks/gradient-descent/GradientDescent.ipynb | Abhinav2604/deep-learning-v2-pytorch |
Training functionThis function will help us iterate the gradient descent algorithm through all the data, for a number of epochs. It will also plot the data, and some of the boundary lines obtained as we run the algorithm. | np.random.seed(44)
epochs = 100
learnrate = 0.01
def train(features, targets, epochs, learnrate, graph_lines=False):
errors = []
n_records, n_features = features.shape
last_loss = None
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
bias = 0
for e in range(epochs):
... | _____no_output_____ | MIT | intro-neural-networks/gradient-descent/GradientDescent.ipynb | Abhinav2604/deep-learning-v2-pytorch |
Time to train the algorithm!When we run the function, we'll obtain the following:- 10 updates with the current training loss and accuracy- A plot of the data and some of the boundary lines obtained. The final one is in black. Notice how the lines get closer and closer to the best fit, as we go through more epochs.- A ... | train(X, y, epochs, learnrate, True) |
========== Epoch 0 ==========
Train loss: 0.7135845195381634
Accuracy: 0.4
========== Epoch 10 ==========
Train loss: 0.6225835210454962
Accuracy: 0.59
========== Epoch 20 ==========
Train loss: 0.5548744083669508
Accuracy: 0.74
========== Epoch 30 ==========
Train loss: 0.501606141872473
Accuracy: 0.84
==... | MIT | intro-neural-networks/gradient-descent/GradientDescent.ipynb | Abhinav2604/deep-learning-v2-pytorch |
ElasticNet with RobustScaler & Power Transformer This Code template is for regression analysis using the ElasticNet regressor where rescaling method used is RobustScaler and feature transformation is done via Power Transformer. Required Packages | import numpy as np
import pandas as pd
import seaborn as se
import warnings
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from imblearn.over_sampling import RandomOverSampler
from sklearn.prepro... | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
InitializationFilepath of CSV file | #filepath
file_path= "" | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
List of features which are required for model training. | #x_values
features=[] | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
Target feature for prediction. | #y_value
target='' | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
Data FetchingPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry. | df=pd.read_csv(file_path) #reading file
df.head() | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
Feature SelectionsIt is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.We will assign all the required input features to... | X=df[features]
Y=df[target] | _____no_output_____ | Apache-2.0 | Regression/Linear Models/ElasticNet_RobustScaler_PowerTransformer.ipynb | shreepad-nade/ds-seed |
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