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 |
|---|---|---|---|---|---|
We can inspect the pipeline definition in JSON format: | import json
definition = json.loads(pipeline.definition())
definition | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
After upserting its definition, we can start the pipeline with the `Pipeline` object's `start` method: | pipeline.upsert(role_arn=role)
execution = pipeline.start() | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
We can now confirm that the pipeline is executing. In the log output below, confirm that `PipelineExecutionStatus` is `Executing`. | execution.describe() | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Typically this pipeline should take about 10 minutes to complete. We can wait for completion by invoking `wait()`. After execution is complete, we can list the status of the pipeline steps. | execution.wait()
execution.list_steps() | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Check the score reportAfter the batch scoring job in the pipeline is complete, the batch scoring report is uploaded to S3. For simplicity, this report simply states the test MSE, but in general reports can include as much detail as desired. Reports such as these also can be formatted for use in conditional approval ... | report_path = f"{step_batch.outputs[0].destination}/score-report.txt"
!aws s3 cp {report_path} ./score-report.txt && cat score-report.txt | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
ML Lineage Tracking SageMaker ML Lineage Tracking creates and stores information about the steps of a ML workflow from data preparation to model deployment. With the tracking information you can reproduce the workflow steps, track model and dataset lineage, and establish model governance and audit standards.Let's now ... | from sagemaker.lineage.visualizer import LineageTableVisualizer
viz = LineageTableVisualizer(sagemaker.session.Session())
for execution_step in reversed(execution.list_steps()):
if execution_step['StepName'] == 'TF2WorkflowTrain':
display(viz.show(pipeline_execution_step=execution_step)) | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
link: https://www.kaggle.com/jindongwang92/crossposition-activity-recognitionhttps://archive.ics.uci.edu/ml/datasets/pamap2+physical+activity+monitoring DSADSColumns 1~405 are features, listed in the order of 'Torso', 'Right Arm', 'Left Arm', 'Right Leg', and 'Left Leg'. Each position contains 81 columns of fea... | import scipy.io
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
filename = "dsads"
mat = scipy.io.loadmat('../Dataset/DASDS/'+filename+".mat")
mat
raw = pd.DataFrame(mat["data_dsads"])
raw.head()
columns = ["Feat"+str(i) for... | ['sitting' 'standing' 'lying on back side' 'lying on right side'
'ascending stairs' 'descending stairs' 'standing in an elevator still'
'moving around in an elevator' 'walking in a parking lot'
'walking on a treadmill1' 'walking on a treadmill2'
'running on a treadmill3' 'exercising on a stepper'
'exercising on a ... | MIT | Reports/v0/DSADS Dataset.ipynb | hillshadow/continual-learning-for-HAR |
#@title Calculation of density of gases
#@markdown Demonstration of ideal gas law and equations of state. An introduction to equations of state can be seen in the [EoS Wikipedia pages](https://en.wikipedia.org/wiki/Equation_of_state).
#@markdown <br><br>This document is part of the module ["Introduction to Gas Processi... | _____no_output_____ | Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim | |
Comparison of ideal and real gas behaviourIn the following example we use the ideal gas law and the PR/SRK-EOS to calculate the density of a pure component gas. At low pressure we see that the ideal gas and the real density are the same, at higher pressures the real gas density is higher, while at very high pressures... | #@title Select component and equation of state. Set temperature [K] and pressure range [bara]. { run: "auto" }
componentName = "CO2" #@param ["methane", "ethane", "propane", "CO2", "nitrogen"]
temperature = 323.0 #@param {type:"number"}
minPressure = 1.0 #@param {type:"number"}
maxPressure = 350.0 #@param {type:"nu... | molar mass of CO2 is 44.01 kg/mol
| Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim |
Pressure of gas as function of volume1 m3 methane at 1 bar and 25 C is compressed to 200 bar and cooled to 25 C. What isthe volume of the gas? What is the density of the compressed gas? | componentName = "nitrogen" #@param ["methane", "ethane", "propane", "CO2", "nitrogen"]
temperature = 298.15 #@param {type:"number"}
initialVolume = 1.0 #@param {type:"number"}
initialPressure = 1.0 #@param {type:"number"}
endPressure = 10.0 #@param {type:"number"}
R = 8.314 # J/mol/K
initialMoles = initialPressur... | initialVolume 1.0000083601119607 m3
initial gas density 1.1301476964655142 kg/m3
initial gas compressiility 0.9999527817885948 [-]
end volume 0.09997979623211148 m3
volume ratio 0.09997896039680884 m3/m3
end gas density 11.30767486281327 kg/m3
end gas compressibility 0.9997423956912075 [-]
| Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim |
Calculation of density of LNGThe density of liquified methane at the boiling point at atomspheric pressure can be calcuated as demonstrated in the following example. In this case we use the SRK EoS and the PR-EoS. | # Creating a fluid in neqsim
eos = 'srk' #@param ["srk", "pr"]
pressure = 1.01325 #@param {type:"number"}
temperature = -162.0 #@param {type:"number"}
fluid1 = fluid(eos) #create a fluid using the SRK-EoS
fluid1.addComponent('methane', 1.0)
fluid1.setTemperature(temperature)
fluid1.setPressure(pressure)
bubt(fluid1)
fl... | temperature at boiling point -161.1441471093413 C
LNG density 428.1719693971862 kg/m3
| Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim |
Accuracy of EoS for calculating the densityThe density calculated with any equation of state will have an uncertainty. The GERG-2008 is a reference equation of state with high accuracy in prediction of thermodynamic properties. In the following example we compare the gas density calculations of SRK/PR with the GERG-(2... | #@title Select component and equation of state. Set temperature [K] and pressure range [bara]. { run: "auto" }
componentName = "methane" #@param ["methane", "ethane", "propane", "CO2", "nitrogen"]
temperature = 298.0 #@param {type:"number"}
minPressure = 1.0 #@param {type:"number"}
maxPressure = 500.0 #@param {type... | _____no_output_____ | Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim |
Calculation of density and compressibility factor for a natual gas mixtureIn the following example we calculate the density of a multicomponent gas mixture. | #@title Select equation of state and set temperature [C] and pressure [bara] { run: "auto" }
temperature = 15.0 #@param {type:"number"}
pressure = 100.0 #@param {type:"number"}
eosname = "srk" #@param ["srk", "pr"]
fluid1 = fluid(eosname)
fluid1.addComponent('nitrogen', 1.2)
fluid1.addComponent('CO2', 2.6)
fluid1... | gas compressibility 0.7735308707131694 -
gas density 102.2871090151433 kg/m3
| Apache-2.0 | notebooks/thermodynamics/density_of_gas.ipynb | EvenSol/testneqsim |
 | import torch
import h5py
import numpy as np
import csv | _____no_output_____ | MIT | intro_PyTorch/3.1.ipynb | caffeflow/intro_pytorch |
加载数据, 创建tensor | wine_path = "./data/chapter3/winequality-white.csv"
wine_data = np.loadtxt(fname=wine_path,delimiter=';',skiprows=1) # 第一行是标签
wine_data.shape
wine_label = next(csv.reader(open(wine_path),delimiter=';'))
wine_label = np.array(wine_label)
wine_label.shape
# ndarray转为tensor
wine_data = torch.from_numpy(wine_data) | _____no_output_____ | MIT | intro_PyTorch/3.1.ipynb | caffeflow/intro_pytorch |
预处理张量 | # 划分出评分做为ground_truth
wine_content = wine_data[:,:-1]
wine_score = wine_data[:,-1]
wine_content.shape,wine_score.shape
wine_score | _____no_output_____ | MIT | intro_PyTorch/3.1.ipynb | caffeflow/intro_pytorch |
特征缩放 | # 标准化
content_mean = wine_content.mean(dim=0)
content_var = wine_content.var(dim=0)
content_normalized = (wine_content - content_mean)/torch.sqrt(content_var) | _____no_output_____ | MIT | intro_PyTorch/3.1.ipynb | caffeflow/intro_pytorch |
数据审查 | # 酒分3个等级
content_bad = wine_content[torch.lt(wine_score,6)]
content_mid = wine_content[torch.ge(wine_score,6) & torch.lt(wine_score,8)]
content_good = wine_content[torch.gt(wine_score,8)]
content_bad.shape
# 对酒中的化学含量做平均值
content_bad = content_bad.mean(dim=0)
content_mid = content_mid.mean(dim=0)
content_good = content... | 0 fixed acidity 6.96 6.81 7.42
1 volatile acidity 0.31 0.26 0.30
2 citric acid 0.33 0.33 0.39
3 residual sugar 7.05 6.08 4.12
4 chlorides 0.05 0.04 0.03
5 free sulfur dioxide 35.34 35.21 33.40
6 total sulfur dioxide 148.60 133.64 116.00
7 de... | MIT | intro_PyTorch/3.1.ipynb | caffeflow/intro_pytorch |
**[Python Micro-Course Home Page](https://www.kaggle.com/learn/python)**--- These exercises accompany the tutorial on [functions and getting help](https://www.kaggle.com/colinmorris/functions-and-getting-help).As before, don't forget to run the setup code below before jumping into question 1. | # SETUP. You don't need to worry for now about what this code does or how it works.
from learntools.core import binder; binder.bind(globals())
from learntools.python.ex2 import *
print('Setup complete.') | Setup complete.
| MIT | Project Notes/Kaggle Learn/01 Python/exercise02 functions and getting help.ipynb | JoaoAnt/Projects |
Exercises 1.Complete the body of the following function according to its docstring.HINT: Python has a builtin function `round` | def round_to_two_places(num):
"""Return the given number rounded to two decimal places.
>>> round_to_two_places(3.14159)
3.14
"""
return round(num,2)
pass
round_to_two_places(3.14)
q1.check()
# Uncomment the following for a hint
#q1.hint()
# Or uncomment the following to peek at the soluti... | _____no_output_____ | MIT | Project Notes/Kaggle Learn/01 Python/exercise02 functions and getting help.ipynb | JoaoAnt/Projects |
2.The help for `round` says that `ndigits` (the second argument) may be negative.What do you think will happen when it is? Try some examples in the following cell?Can you think of a case where this would be useful? | # Put your test code here
round(105.8555, -1)
print('Yes')
#q2.solution() | _____no_output_____ | MIT | Project Notes/Kaggle Learn/01 Python/exercise02 functions and getting help.ipynb | JoaoAnt/Projects |
3.In a previous programming problem, the candy-sharing friends Alice, Bob and Carol tried to split candies evenly. For the sake of their friendship, any candies left over would be smashed. For example, if they collectively bring home 91 candies, they'll take 30 each and smash 1.Below is a simple function that will cal... | def to_smash(total_candies, number_friends=3):
"""Return the number of leftover candies that must be smashed after distributing
the given number of candies evenly between 3 friends.
>>> to_smash(91)
1
"""
return total_candies % number_friends
q3.check()
#q3.hint()
#q3.solution() | _____no_output_____ | MIT | Project Notes/Kaggle Learn/01 Python/exercise02 functions and getting help.ipynb | JoaoAnt/Projects |
4.It may not be fun, but reading and understanding error messages will be an important part of your Python career.Each code cell below contains some commented-out buggy code. For each cell...1. Read the code and predict what you think will happen when it's run.2. Then uncomment the code and run it to see what happens.... | round_to_two_places(9.9999)
x = -10
y = 5
# Which of the two variables above has the smallest absolute value?
smallest_abs = min(abs(x),abs(y))
def f(x):
y = abs(x)
return y
print(f(5)) | 5
| MIT | Project Notes/Kaggle Learn/01 Python/exercise02 functions and getting help.ipynb | JoaoAnt/Projects |
Channel Flow Example | # Written for JHTDB by German G Saltar Rivera (2019)
# To use K3D capabilities, use Firefox or Chrome browser.
# Safari has trouble with K3D generated objects
#
import pyJHTDB
from pyJHTDB import libJHTDB
import time as tt
import numpy as np
import k3d #https://github.com/K3D-tools/K3D-jupyter
import ipywidgets as wid... | _____no_output_____ | Apache-2.0 | examples/JHTDB_visualization_with_K3D.ipynb | lento234/pyJHTDB |
Forced Isotropic Turbulence Example | #Set domain to be queried
#Generates a 3D plot of Q iso-surface with overlayed kinetic energy volume
#rendering in a [0,0.5]^3 subcube in isotropic turbulence
time1 = 3.00
nx1=80
ny1=80
nz1=80
xmin1, xmax1 = 0, 0.5
ymin1, ymax1 = 0, 0.5
zmin1, zmax1 = 0, 0.5
#Creates query points and arranges their coordinates int... | _____no_output_____ | Apache-2.0 | examples/JHTDB_visualization_with_K3D.ipynb | lento234/pyJHTDB |
Kobart tokenizer sampleTokenizer를 간단하게 살펴봅니다. | from kobart import get_kobart_tokenizer | _____no_output_____ | Apache-2.0 | src/summarization/2. tokenizer_sample.ipynb | youngerous/kobart-voice-summarization |
tokenize | tok = get_kobart_tokenizer()
# only tokenize
tokenized = tok.tokenize('비정형데이터분석 팀 식사과정입니다. 무야호!')
tokenized
# convert to indice
tok.convert_tokens_to_ids(tokenized)
# encode = tokenize + convert_tokens_to_ids
tok.encode('비정형데이터분석 팀 식사과정입니다. 무야호!') | _____no_output_____ | Apache-2.0 | src/summarization/2. tokenizer_sample.ipynb | youngerous/kobart-voice-summarization |
check vocab | vocab = dict(sorted(tok.vocab.items(), key=lambda item: item[1]))
len(vocab)
vocab | _____no_output_____ | Apache-2.0 | src/summarization/2. tokenizer_sample.ipynb | youngerous/kobart-voice-summarization |
DraftKings NFL Constraint Satisfaction===This is the companion code to a [blog post](https://zwlevonian.medium.com/integer-linear-programming-with-pulp-optimizing-a-draftkings-nfl-lineup-5e7524dd42d3) I wrote on Medium. | import pandas as pd
import pulp | _____no_output_____ | MIT | _notebook/DraftKingsNFLConstraintSatisfaction.ipynb | levon003/ml-visualized |
Load in the weekly data | df = pd.read_csv('DKSalaries.csv')
len(df)
df.sample(n=5)
# trim any postponed games, since those can't be included in a lineup
df = df[df['Game Info'] != 'Postponed']
len(df)
exclude_list = ['Dak Prescott']
df = df[~df['Name'].isin(exclude_list)]
len(df)
# this is equivalent to an extra constraint that requires playin... | _____no_output_____ | MIT | _notebook/DraftKingsNFLConstraintSatisfaction.ipynb | levon003/ml-visualized |
Create the constraint problemGoal: maximize AvgPointsPerGame - TotalPlayers = 9 - TotalSalary <= 50000 - TotalPosition_WR = 3 - TotalPosition_RB = 2 - TotalPosition_TE = 1 - TotalPosition_QB = 1 - TotalPosition_FLEX = 1 - TotalPosition_DST = 1 - Each player in only one position (relevant only for FLEX) | prob = pulp.LpProblem('DK_NFL_weekly', pulp.LpMaximize)
player_vars = [pulp.LpVariable(f'player_{row.ID}', cat='Binary') for row in df.itertuples()]
# total assigned players constraint
prob += pulp.lpSum(player_var for player_var in player_vars) == 9
# position constraints
# TODO fix this, currently won't work
# as it ... | RB/FLEX Dalvin Cook MIN 8200 28.65
QB Russell Wilson SEA 7600 32.01
WR/FLEX Tyler Lockett SEA 6800 22.07
WR/FLEX Corey Davis TEN 5900 17.98
RB/FLEX Melvin Gordon III DEN 5300 15.72
WR/FLEX CeeDee Lamb DAL 4900 14.21
TE/FLEX Hunter Henry LAC 4000 9.63
WR/FLEX Keelan Cole JAX 4000 12.37
DST Colts IND 3300 11.71
| MIT | _notebook/DraftKingsNFLConstraintSatisfaction.ipynb | levon003/ml-visualized |
Python Crash CourseMaster in Data Science - Sapienza UniversityHomework 2: Python ChallengesA.A. 2017/18Tutor: Francesco Fabbri InstructionsSo guys, here we are! **Finally** you're facing your first **REAL** homework. Are you ready to fight?We're going to apply all the P... | n=12
x=1
while n>1:
x=x*n
n=n-1
print(x) | 479001600
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
2. More math...Write a program which will find all such numbers which are divisible by 7 but are not a multiple of 5, between 0 and 1000 (both included). The numbers obtained should be printed in a comma-separated sequence on a single line. (range and CFS) | ris=[]
for x in range(0,1001):
if x%7==0 and x%5!=0:
ris.append(str(x))
r2=','.join(ris)
print(r2) | 7,14,21,28,42,49,56,63,77,84,91,98,112,119,126,133,147,154,161,168,182,189,196,203,217,224,231,238,252,259,266,273,287,294,301,308,322,329,336,343,357,364,371,378,392,399,406,413,427,434,441,448,462,469,476,483,497,504,511,518,532,539,546,553,567,574,581,588,602,609,616,623,637,644,651,658,672,679,686,693,707,714,721,7... | MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
2. Count capital lettersIn this exercises you're going to deal with YOUR DATA. Indeed, in the list below there are stored your Favorite Tv Series. But, as you can see, there is something weird. There are too much CaPITal LeTTErs. Your task is to count the capital letters in all the strings and then print the total num... | tv_series = ['Game of THRroneS',
'big bang tHeOrY',
'MR robot',
'WesTWorlD',
'fIRefLy',
"i haven't",
'HOW I MET your mothER',
'friENds',
'bRon broen',
'gossip girl',
'prISon break',
... | _____no_output_____ | MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
3. A remarkUsing the list above, create a dictionary where the keys are Unique IDs and values the TV Series.You have to do the exercise keeping in mind these 2 constraints: 1. The order of the IDs has to be **dependent on the alphabetical order of the titles**, i.e. 0: first_title_in_alphabetical_order and so on...2. ... |
lista=[]
for i in tv_series:
lista.append(i.title())
idx=list(range(0+1,12+1))
dic_one=dict(zip(sorted(lista),idx))
print(dic_one)
| {'Big Bang Theory': 1, 'Breaking Bad': 2, 'Bron Broen': 3, 'Firefly': 4, 'Friends': 5, 'Game Of Thrrones': 6, 'Gossip Girl': 7, 'How I Met Your Mother': 8, "I Haven'T": 9, 'Mr Robot': 10, 'Prison Break': 11, 'Westworld': 12}
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
4. Dictionary to its maximumInvert the keys with the values in the dictionary built before. | inv_dic={v: k for k, v in dic_one.items()}
inv_dic | _____no_output_____ | MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
Have you done in **one line of code**? If not, try now! 4. Other boring mathLet's talk about our beloved exams. Starting from the exams and CFU below, are you able to compute the weighted mean of them?Let's do it and print the result.Description of the data:exams[1] = $(title_1, grade_1)$cfu[1] = $CFU_1$ | exams = [('BIOINFORMATICS', 29),
('DATA MANAGEMENT FOR DATA SCIENCE', 30),
('DIGITAL EPIDEMIOLOGY', 26),
('NETWORKING FOR BIG DATA AND LABORATORY',28),
('QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT','30 e lode'),
('DATA MINING TECHNOLOGY FOR BUSINESS AND SOCIETY... | 29.095238095238095
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
5. Palindromic numbersWrite a script which finds all the Palindromic numbers, in the range [0,**N**] (bounds included). The numbers obtained should be printed in a comma-separated sequence on a single line.What is **N**?Looking at the exercise before:**N** = (Total number of CFU) x (Sum of all the grades)(details: htt... | top=cfu*sum(voti)
tot_num=list(range(1,top))
def palindo(s):
return str(s)==str(s)[::-1]
tt=[]
for i in tot_num:
c=palindo(i)
if c==True:
tt.append(str(i))
r6=','.join(tt)
print(r6) | 1,2,3,4,5,6,7,8,9,11,22,33,44,55,66,77,88,99,101,111,121,131,141,151,161,171,181,191,202,212,222,232,242,252,262,272,282,292,303,313,323,333,343,353,363,373,383,393,404,414,424,434,444,454,464,474,484,494,505,515,525,535,545,555,565,575,585,595,606,616,626,636,646,656,666,676,686,696,707,717,727,737,747,757,767,777,787... | MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
6. StackOverflow Let's start using your new best friend. Now I'm going to give other task, slightly more difficult BUT this time, just googling, you will find easily the answer on the www.stackoverflow.com. You can use the code there for solving the exercise BUT you have to understand the solution there **COMMENTING**... | # you start with a try statement: if python can do this statement you will find "Hello".
try:
print("HELLO")
# in the other case will be execute the except statement. In this case it will be execute only if there is an ImportError
except ImportError:
print ("NO module found") | HELLO
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
6. BGiving this list of words below, after copying in a variable, explain and provide me a code for obtaining a **Bag of Words** from them.(Hint: use dictionaries and loops) ['theory', 'of', 'bron', 'firefly', 'thrones', 'break', 'bad', 'mother', 'firefly', "haven't", 'prison', 'big', 'friends', 'girl', 'westworld', '... | list_6=['theory', 'of', 'bron', 'firefly', 'thrones', 'break', 'bad', 'mother', 'firefly', "haven't", 'prison', 'big', 'friends', 'girl', 'westworld', 'bad', "haven't", 'gossip', 'thrones', 'your', 'big', 'how', 'friends', 'theory', 'your', 'bron', 'bad', 'bad', 'breaking', 'met', 'breaking', 'breaking', 'game', 'bron'... | {'theory': 79, 'of': 74, 'bron': 34, 'firefly': 99, 'thrones': 77, 'break': 88, 'bad': 98, 'mother': 83, "haven't": 46, 'prison': 70, 'big': 90, 'friends': 80, 'girl': 14, 'westworld': 15, 'gossip': 91, 'your': 100, 'how': 76, 'breaking': 36, 'met': 97, 'game': 95, 'bang': 89, 'i': 94, 'robot': 92, 'broen': 84}
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
6. CAnd now, write down a code which computes the first 10 Fibonacci numbers(details: https://en.wikipedia.org/wiki/Fibonacci_number) | y=0
z=1
rr=[]
for count in range(1,11):
v=0
v=z
z=y+z
y=v
count=count+1
rr.append(z)
print(rr) | [1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
| MIT | 02/homework_day2.ipynb | Py101/py101-assignments-andremarco |
SVM |
# evaluate a logistic regression model using k-fold cross-validation
from numpy import mean
from numpy import std
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import ShuffleSplit
from sklearn.linear_model import LogisticRegression
# create ... | _____no_output_____ | MIT | Diabetics Prediction (ML) CV=5.ipynb | AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance |
LR | from numpy import mean
from numpy import std
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import ShuffleSplit
from sklearn.linear_model import LogisticRegression
# create dataset
#X, y = make_classification(n_samples=1000, n_features=20, n_i... | _____no_output_____ | MIT | Diabetics Prediction (ML) CV=5.ipynb | AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance |
RF | from numpy import mean
from numpy import std
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import ShuffleSplit
# create dataset
#X, y = make_classification(n_samples=1000, n_features=20, n_i... | _____no_output_____ | MIT | Diabetics Prediction (ML) CV=5.ipynb | AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance |
DT | from numpy import mean
from numpy import std
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import ShuffleSplit
from sklearn.tree import DecisionTreeClassifier
# create dataset
#X, y = make_classification(n_samples=1000, n_features=20, n_info... | _____no_output_____ | MIT | Diabetics Prediction (ML) CV=5.ipynb | AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance |
ANN | import keras
from keras.models import Sequential
from keras.layers import Dense,Dropout
classifier=Sequential()
classifier.add(Dense(units=256, kernel_initializer='uniform',activation='relu',input_dim=24))
classifier.add(Dense(units=128, kernel_initializer='uniform',activation='relu'))
classifier.add(Dropout(p=0.1))
cl... | Accuracy: 0.8886 (+/- 0.0027) [Ensemble]
| MIT | Diabetics Prediction (ML) CV=5.ipynb | AmitHasanShuvo/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance |
Define data convert functions | def peek(iterable):
try:
first = next(iterable)
except StopIteration:
return None
return first, itertools.chain([first], iterable)
def json_to_feather(filename, new_filename_base, records_per_file = 1000000, pipe_func = None):
records = map(json.loads, open(filename))
records_p... | _____no_output_____ | MIT | capstone.ipynb | jasonbossert/TDI_Capstone |
Convert Data to Feather | filename = "yelp_academic_dataset_business.json"
new_filename_base = "yelp_business"
business_drop = partial(pipeable_drop, labels = ["address", "is_open", "attributes", "hours"])
json_to_feather(filename, new_filename_base, pipe_func = business_drop)
filename = "yelp_academic_dataset_user.json"
new_filename_base = "... | _____no_output_____ | MIT | capstone.ipynb | jasonbossert/TDI_Capstone |
GBDT | regr = GradientBoostingRegressor(max_depth=20, random_state=0,max_features=2,n_estimators=333)
regr.fit(X_train, Y_trainmin/90000)
regr2 = GradientBoostingRegressor(max_depth=20, random_state=0,max_features=2,n_estimators=333)
regr2.fit(X_train, Y_trainmax/100000)
from sklearn.metrics import mean_squared_error
from s... | _____no_output_____ | MIT | src/Prediction/salary/presalary.ipynb | chenshihang/Analysis-of-College-Graduates-Employment-Orientation |
T1053.002 - Scheduled Task/Job: At (Windows)Adversaries may abuse the at.exe utility to perform task scheduling for initial or recurring execution of malicious code. The [at](https://attack.mitre.org/software/S0110) utility exists as an executable within Windows for scheduling tasks at a specified time and date. Using... | #Import the Module before running the tests.
# Checkout Jupyter Notebook at https://github.com/cyb3rbuff/TheAtomicPlaybook to run PS scripts.
Import-Module /Users/0x6c/AtomicRedTeam/atomics/invoke-atomicredteam/Invoke-AtomicRedTeam.psd1 - Force | _____no_output_____ | MIT | playbook/tactics/privilege-escalation/T1053.002.ipynb | haresudhan/The-AtomicPlaybook |
Atomic Test 1 - At.exe Scheduled taskExecutes cmd.exeNote: deprecated in Windows 8+Upon successful execution, cmd.exe will spawn at.exe and create a scheduled task that will spawn cmd at a specific time.**Supported Platforms:** windows Attack Commands: Run with `command_prompt````command_promptat 13:20 /interactive cm... | Invoke-AtomicTest T1053.002 -TestNumbers 1 | _____no_output_____ | MIT | playbook/tactics/privilege-escalation/T1053.002.ipynb | haresudhan/The-AtomicPlaybook |
Image ClassificationIn this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be norm... | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... | All files found!
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Explore the DataThe dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:* airplane* automobile* bird* cat* deer* dog* frog* hor... | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 1
sample_id = 5
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id) |
Stats of batch 1:
Samples: 10000
Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}
First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]
Example of Image 5:
Image - Min Value: 0 Max Value: 252
Image - Shape: (32, 32, 3)
Label - Label Id: 1 Nam... | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Implement Preprocess Functions NormalizeIn the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`. | def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
x_norm = x.reshape(x.size)
x_norm = (x_norm - min(x_norm))/(max(x_norm)-min(x_norm))
return x_n... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
One-hot encodeJust like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are... | def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
one_hot_array = np.zeros((len(x), 10))
for index in range(len(x)):
val_index = x[index]
... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Randomize DataAs you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset. Preprocess all the data and save itRunning the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also... | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode) | _____no_output_____ | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Check PointThis is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb')) | _____no_output_____ | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Build the networkFor the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittest... | import tensorflow as tf
def neural_net_image_input(image_shape):
"""
Return a Tensor for a batch of image input
: image_shape: Shape of the images
: return: Tensor for image input.
"""
return tf.placeholder(tf.float32, shape=(None, image_shape[0], image_shape[1], image_shape[2]), name='x')
d... | Image Input Tests Passed.
Label Input Tests Passed.
Keep Prob Tests Passed.
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Convolution and Max Pooling LayerConvolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.* Apply a convolution to `x... | def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple fo... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Flatten LayerImplement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [Tens... | def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
shaped = x_tensor.get_shape().as_list()
reshaped = tf.resha... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Fully-Connected LayerImplement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tens... | def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_out... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Output LayerImplement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/ap... | def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Create Convolutional ModelImplement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:* Apply 1, 2, or 3 Convolution and Max Pool layers* Apply a Flatten Layer* Apply 1, 2, or ... | def conv_net(x, keep_prob):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# Play around with different number of outputs, kernel... | Neural Network Built!
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Train the Neural Network Single OptimizationImplement the function `train_neural_network` to do a single optimization. The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:* `x` for image input* `y` for labels* `keep_prob` for keep probability for dropoutThis function w... | def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Num... | Tests Passed
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Show StatsImplement the function `print_stats` to print loss and validation accuracy. Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy. Use a keep probability of `1.0` to calculate the loss and validation accuracy. | def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy... | _____no_output_____ | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
HyperparametersTune the following parameters:* Set `epochs` to the number of iterations until the network stops learning or start overfitting* Set `batch_size` to the highest number that your machine has memory for. Most people set them to common sizes of memory: * 64 * 128 * 256 * ...* Set `keep_probability` to the ... | # TODO: Tune Parameters
epochs = 20
batch_size = 128
keep_probability = 0.5 | _____no_output_____ | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Train on a Single CIFAR-10 BatchInstead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section. | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_label... | Checking the Training on a Single Batch...
Epoch 1, CIFAR-10 Batch 1: Loss: 2.09 Valid Accuracy: 0.321
Epoch 2, CIFAR-10 Batch 1: Loss: 1.93 Valid Accuracy: 0.397
Epoch 3, CIFAR-10 Batch 1: Loss: 1.81 Valid Accuracy: 0.432
Epoch 4, CIFAR-10 Batch 1: Loss: 1.68 Valid Accuracy: 0.458
Epoch 5, CIF... | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Fully Train the ModelNow that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches. | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batc... | Training...
Epoch 1, CIFAR-10 Batch 1: Loss: 2.1 Valid Accuracy: 0.31
Epoch 1, CIFAR-10 Batch 2: Loss: 1.8 Valid Accuracy: 0.391
Epoch 1, CIFAR-10 Batch 3: Loss: 1.64 Valid Accuracy: 0.411
Epoch 1, CIFAR-10 Batch 4: Loss: 1.61 Valid Accuracy: 0.438
Epoch 1, CIFAR-10 Batch 5: Loss: 1.65 ... | MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
CheckpointThe model has been saved to disk. Test ModelTest your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters. | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_clas... | INFO:tensorflow:Restoring parameters from ./image_classification
Testing Accuracy: 0.6132318037974683
| MIT | image-classification/dlnd_image_classification.ipynb | cfcdavidchan/Deep-Learning-Foundation-Nanodegree |
Learn the standard library to at least know what's there itertools and collections have very useful features - chain - product - permutations - combinations - izip | %matplotlib inline
%config InlineBackend.figure_format='retina'
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('talk')
sns.set_style('darkgrid')
plt.rcParams['figure.figsize'] = 12, 8 # plotsize
import numpy as np
import pandas as pd
# plot residuals
from itertools import groupby # NOT REGUL... | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
Challenge (Easy)Write a function to return the total number of digits in a given string, and those digits. | test_string = """de3456yghj87654edfghuio908ujhgyuY^YHJUi8ytgh gtyujnh y7"""
count = 0
digits = []
for x in test_string:
try:
int(x)
count += 1
digits.append(int(x))
except:
pass
print("Number of digits:", str(count) + ";")
print("They are:", digits) | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
Challenge (Tricky)Same as above -- but were consecutive digits are available, return as a single number. Ex. "2a78b123" returns "3 numbers, they are: 2, 78, 123" | test_string
groups = []
uniquekeys = []
for k, g in groupby(test_string, lambda x: x.isdigit()):
groups.append(list(g))
uniquekeys.append(k)
print(groups)
print(uniquekeys)
numbers = []
for x, y in izip(groups, uniquekeys):
if y:
numbers.append(int(''.join([j for j in x])))
print("Number:", np.sum(... | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
Challenge (Tricky)Same as above, but do it a second way. | def solution_3(test_string):
"""Regular expressions can be a very powerful and useful tool."""
groups = [int(j) for j in re.findall(r'\d+', test_string)]
return len(groups), groups
solution_3(test_string) | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
Challenge (Hard)Same as above, but all valid numbers expressed in digits, commas, and decimal points. Ex. "a23.42dx9,331nm87,55" -> 4; 23.42, 9331, 87, 55Left as an exercise :) Don't spend much time on this one. Generators | def ex1(num):
"""A stupid example generator to prove a point."""
while num > 1:
num += 1
yield num
hey = ex1(5)
hey.next()
hey.next() | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
GotchasModifying a dictionary's keys while iterating over it. ```pythonfor key in dictionary: if key == "bat": del dictionary[key]```If you have to do someeven_better_name like this: ```pythonlist_of_keys = dictionary.keys()for key in list_of_keys: if key == "bat": del dictionary[key]``` | even_better_name = 5
even_better_name = 5
even_better_name = 5
even_better_name = 5
even_better_name = 5
even_better_name = 5 | _____no_output_____ | MIT | notebooks/09-Extras.ipynb | jbwhit/WSP-312-Tips-and-Tricks |
1. Store .csv files into dataframe individually | # import .csv into dataframe of big cities health data
# organized the big cities health data in the excel sheet
big_cities_csv_file = "data/Big_Cities_Health_Data_Inventory.csv"
big_cities_health_df = pd.read_csv(big_cities_csv_file)
big_cities_health_df.head()
# import 2010 hospital beds by ownership types; test res... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
2. Extract the data sources A. cleaning the Big Cities Health Data for year and state; plus, any information that needs to be cleaned out before loading into the sql database | # for references of created dataframe for big cities health data:
# big_cities_csv_file = "data/Big_Cities_Health_Data_Inventory.csv"
# big_cities_health_df = pd.read_csv(big_cities_csv_file)
# big_cities_health_df.head()
# Extract information of the cities data by year, category, indicator, gender, race/ethnicity, ... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
B. cleaning the hospital beds data for year and state; plus any necessary data that needs to be cleaned, such as null values | # show hospital bed rate in dataframe structure that we made earlier
hosp_df.head()
hosp_df.tail()
# check the rows if it's matching same as earlier after joining them together
hosp_df.shape
# rename each columns for hospital bed data for each year and state, state local gov, non-profit, for-profit, and total
new_hosp... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
C. Cleaning the data before joining | # look into the data where the columns type
sort_city_data.info()
# convert the year object into integers
sort_city_data['year'] = sort_city_data['year'].astype(int)
# check if the year turned into integer
sort_city_data.info()
# check hospital bed data column type
hospital_df.info()
# convert the column type into st... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Loading the extracted data into SQL database | # import dependencies
from pin import username, password
# make a connection string for the database on localhost, and create engine for the database we made
rds_connection_string = (f"{username}:{password}@localhost:5432/healthcities_db")
engine = create_engine(f'postgresql://{rds_connection_string}') | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Check the table names | engine.table_names() | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Use Pandas to load csv converted DataFrame into SQL database | cleaned_city.to_sql(name='health_cities', con=engine, if_exists='append', index=False)
cleaned_city.shape
hospital_df.to_sql(name='hospital_data', con=engine, if_exists='append', index=False)
hospital_df.shape
# this is where I previously loaded each converted DataFrame into SQL database, and tried to join them using S... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Confirm if the data has been added into SQL database query successfully for health_cities database. | cities_df = pd.read_sql_query('SELECT * FROM health_cities', con=engine)
cities_df.head()
cities_df.shape
cities_df.tail()
cleaned_hospital_df = pd.read_sql_query('SELECT * FROM hospital_data', con=engine)
cleaned_hospital_df.head()
cleaned_hospital_df.tail()
cleaned_hospital_df.shape
# This is just individual query th... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Merging the data together using SQL database | merged_health_data = pd.read_sql_query('SELECT hc.category, hc.cause_of_death, hc.gender, hc.race_ethnicity, hc.death_rate, ho.state, hc.year, ho.state_local_gov, ho.non_profit, ho.profit, ho.total FROM health_cities hc INNER JOIN hospital_data ho ON hc.year = ho.year AND hc.state = ho.state ORDER BY hc.year ASC',
... | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
Source we used | # Data Source 1- Health Status across US urban cities
# https://data.world/health/big-cities-health
# Data Source 2 - Hospital Data
# https://www.kff.org/other/state-indicator/beds-by-ownership/?currentTimeframe=10&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D | _____no_output_____ | MIT | ETL_Cities_Health_Data_Project.ipynb | erikayi/Project-2-ETL-Cities-Health-Data |
1. Inspecting transfusion.data fileBlood transfusion saves lives - from replacing lost blood during major surgery or a serious injury to treating various illnesses and blood disorders. Ensuring that there's enough blood in supply whenever needed is a serious challenge for the health professionals. According to WebMD, ... | # Print out the first 5 lines from the transfusion.data file
!head -n 5 datasets/transfusion.data | Recency (months),Frequency (times),Monetary (c.c. blood),Time (months),"whether he/she donated blood in March 2007"
2 ,50,12500,98 ,1
0 ,13,3250,28 ,1
1 ,16,4000,35 ,1
2 ,20,5000,45 ,1
| MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
2. Loading the blood donations dataWe now know that we are working with a typical CSV file (i.e., the delimiter is ,, etc.). We proceed to loading the data into memory. | # Import pandas
import pandas as pd
# Read in dataset
transfusion = pd.read_csv('datasets/transfusion.data')
# Print out the first rows of our dataset
transfusion.head() | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
3. Inspecting transfusion DataFrameLet's briefly return to our discussion of RFM model. RFM stands for Recency, Frequency and Monetary Value and it is commonly used in marketing for identifying your best customers. In our case, our customers are blood donors.RFMTC is a variation of the RFM model. Below is a descriptio... | # Print a concise summary of transfusion DataFrame
transfusion.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 748 entries, 0 to 747
Data columns (total 5 columns):
Recency (months) 748 non-null int64
Frequency (times) 748 non-null int64
Monetary (c.c. blood) 748 non-null int64
Time (months) ... | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
4. Creating target columnWe are aiming to predict the value in whether he/she donated blood in March 2007 column. Let's rename this it to target so that it's more convenient to work with. | # Rename target column as 'target' for brevity
transfusion.rename(
columns={'whether he/she donated blood in March 2007': 'target'},
inplace=True
)
# Print out the first 2 rows
transfusion.head(2) | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
5. Checking target incidenceWe want to predict whether or not the same donor will give blood the next time the vehicle comes to campus. The model for this is a binary classifier, meaning that there are only 2 possible outcomes:0 - the donor will not give blood1 - the donor will give bloodTarget incidence is defined as... | # Print target incidence proportions, rounding output to 3 decimal places
transfusion.target.value_counts(normalize=True).round(3) | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
6. Splitting transfusion into train and test datasetsWe'll now use train_test_split() method to split transfusion DataFrame.Target incidence informed us that in our dataset 0s appear 76% of the time. We want to keep the same structure in train and test datasets, i.e., both datasets must have 0 target incidence of 76%.... | # Import train_test_split method
from sklearn.model_selection import train_test_split
# Split transfusion DataFrame into
# X_train, X_test, y_train and y_test datasets,
# stratifying on the `target` column
X_train, X_test, y_train, y_test = train_test_split(
transfusion.drop(columns='target'),
transfusion.targ... | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
7. Selecting model using TPOTTPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automatically explore hundreds of possible pipelines to find the best one for our dataset. Note, the outcome of this search will be a scikit-learn pipeline, meanin... | # Import TPOTClassifier and roc_auc_score
from tpot import TPOTClassifier
from sklearn.metrics import roc_auc_score
# Instantiate TPOTClassifier
tpot = TPOTClassifier(
generations=5,
population_size=20,
verbosity=2,
scoring='roc_auc',
random_state=42,
disable_update_check=True,
config_dict=... | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
8. Checking the varianceTPOT picked LogisticRegression as the best model for our dataset with no pre-processing steps, giving us the AUC score of 0.7850. This is a great starting point. Let's see if we can make it better.One of the assumptions for linear regression models is that the data and the features we are givin... | # X_train's variance, rounding the output to 3 decimal places
pd.DataFrame.var(X_train).round(3) | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
9. Log normalizationMonetary (c.c. blood)'s variance is very high in comparison to any other column in the dataset. This means that, unless accounted for, this feature may get more weight by the model (i.e., be seen as more important) than any other feature.One way to correct for high variance is to use log normalizat... | # Import numpy
import numpy as np
# Copy X_train and X_test into X_train_normed and X_test_normed
X_train_normed, X_test_normed = X_train.copy(), X_test.copy()
# Specify which column to normalize
col_to_normalize = 'Monetary (c.c. blood)'
# Log normalization
for df_ in [X_train_normed, X_test_normed]:
# Add log ... | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
10. Training the linear regression modelThe variance looks much better now. Notice that now Time (months) has the largest variance, but it's not the orders of magnitude higher than the rest of the variables, so we'll leave it as is.We are now ready to train the linear regression model. | # Importing modules
from sklearn import linear_model
# Instantiate LogisticRegression
logreg = linear_model.LogisticRegression(
solver='liblinear',
random_state=42
)
# Train the model
logreg.fit(X_train_normed, y_train)
# AUC score for tpot model
logreg_auc_score = roc_auc_score(y_test, logreg.predict_proba(... |
AUC score: 0.7891
| MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
11. ConclusionThe demand for blood fluctuates throughout the year. As one prominent example, blood donations slow down during busy holiday seasons. An accurate forecast for the future supply of blood allows for an appropriate action to be taken ahead of time and therefore saving more lives.In this notebook, we explore... | # Importing itemgetter
from operator import itemgetter
# Sort models based on their AUC score from highest to lowest
sorted(
[('tpot', tpot_auc_score), ('logreg', logreg_auc_score)],
key=itemgetter(1),
reverse=True,
) | _____no_output_____ | MIT | DataCamp/Give Life: Predict Blood Donations/notebook.ipynb | lukzmu/data-courses |
IntroductionIn this chapter, we will use Game of Thrones as a case study to practice our newly learnt skills of network analysis.It is suprising right? What is the relationship between a fatansy TV show/novel and network science or Python(not dragons).If you haven't heard of Game of Thrones, then you must be really go... | from nams import load_data as cf
books = cf.load_game_of_thrones_data() | _____no_output_____ | MIT | notebooks/05-casestudies/01-gameofthrones.ipynb | khanin-th/Network-Analysis-Made-Simple |
The resulting DataFrame books has 5 columns: Source, Target, Type, weight, and book. Source and target are the two nodes that are linked by an edge. As we know a network can have directed or undirected edges and in this network all the edges are undirected. The weight attribute of every edge tells us the number of inte... | # We also add this weight_inv to our dataset.
# Why? we will discuss it in a later section.
books['weight_inv'] = 1/books.weight
books.head() | _____no_output_____ | MIT | notebooks/05-casestudies/01-gameofthrones.ipynb | khanin-th/Network-Analysis-Made-Simple |
From the above data we can see that the characters Addam Marbrand and Tywin Lannister have interacted 6 times in the first book.We can investigate this data by using the pandas DataFrame. Let's find all the interactions of Robb Stark in the third book. | robbstark = (
books.query("book == 3")
.query("Source == 'Robb-Stark' or Target == 'Robb-Stark'")
)
robbstark.head() | _____no_output_____ | MIT | notebooks/05-casestudies/01-gameofthrones.ipynb | khanin-th/Network-Analysis-Made-Simple |
As you can see this data easily translates to a network problem. Now it's time to create a network.We create a graph for each book. It's possible to create one `MultiGraph`(Graph with multiple edges between nodes) instead of 5 graphs, but it is easier to analyse and manipulate individual `Graph` objects rather than a `... | # example of creating a MultiGraph
# all_books_multigraph = nx.from_pandas_edgelist(
# books, source='Source', target='Target',
# edge_attr=['weight', 'book'],
# create_using=nx.MultiGraph)
# we create a list of graph objects using
# nx.from_pandas_edgelist and specifying
# the edge at... | _____no_output_____ | MIT | notebooks/05-casestudies/01-gameofthrones.ipynb | khanin-th/Network-Analysis-Made-Simple |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.