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Hyperparameter Tuning in KNN Manually finding the optimal value of n_neighbors parameter | # Find the optimal value of the n_neighbors parameter
models={f'KNN_{i}':KNeighborsClassifier(n_neighbors=i) for i in range(2,31)}
# run the model only for fold number 4 ie the 5th fold
accuracy,confusion_matrices,classification_report=run(fold=4,df=df_optimal_KNN,models=models,print_details=True)
x=[i for i in range... | _____no_output_____ | MIT | Diabetes.ipynb | AryanMethil/Diabetes-KNN-vs-Naive-Bayes |
Using Grid Search to find optimal values of n_neighbors and p | from sklearn import model_selection
from sklearn import metrics
def hyperparameter_tune_and_run(df,num_folds,models,target_name,param_grid,evaluation_metric,print_details=False):
X=df.drop(labels=[target_name,'kfolds'],axis=1).values
y=df[target_name]
model_name,model_constructor=list(models.items())[0]
mode... | Fitting 5 folds for each of 58 candidates, totalling 290 fits
[CV] n_neighbors=2, p=2 ..............................................
[CV] .................. n_neighbors=2, p=2, score=0.770, total= 0.0s
[CV] n_neighbors=2, p=2 ..............................................
[CV] .................. n_neighbors=2, p=2, s... | MIT | Diabetes.ipynb | AryanMethil/Diabetes-KNN-vs-Naive-Bayes |
Comparison between KNN and NB1. Dataset when KNN was considered for feature selection2. Dataset when NB was considered for feature selection | # Compare between KNN and Naive Bayes
models={
'KNN': KNeighborsClassifier(n_neighbors=12,p=3),
'Gaussian Naive Bayes': GaussianNB(),
}
# accuracies => list of 5 lists. Each list will contain 3 values ie KNN accuracy, Gaussian Naive Bayes
accuracies,confusion_matrices,classification_reports=[]... | _____no_output_____ | MIT | Diabetes.ipynb | AryanMethil/Diabetes-KNN-vs-Naive-Bayes |
Getting info on Priming experiment dataset that's needed for modeling Info:* __Which gradient(s) to simulate?__* For each gradient to simulate: * Infer total richness of starting community * Get distribution of total OTU abundances per fraction * Number of sequences per sample * Infer total abundance of each ta... | baseDir = '/home/nick/notebook/SIPSim/dev/priming_exp/'
workDir = os.path.join(baseDir, 'exp_info')
otuTableFile = '/var/seq_data/priming_exp/data/otu_table.txt'
otuTableSumFile = '/var/seq_data/priming_exp/data/otu_table_summary.txt'
metaDataFile = '/var/seq_data/priming_exp/data/allsample_metadata_nomock.txt'
#otu... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Init | import glob
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(fitdistrplus)
if not os.path.isdir(workDir):
os.makedirs(workDir) | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Loading OTU table (filter to just bulk samples) | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(ends_with('.NA'))
tbl %>% ncol %>% print
tbl[1:4,1:4]
%%R
tbl.h = tbl %>%
gather('sample', 'count', 1:ncol(tbl)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'), sep='\\.', remove=F)
tbl.h %>... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Which gradient(s) to simulate? | %%R -w 900 -h 400
tbl.h.s = tbl.h %>%
group_by(sample) %>%
summarize(total_count = sum(count)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'), sep='\\.', remove=F)
ggplot(tbl.h.s, aes(day, total_count, color=rep %>% as.character)) +
geom_point() +
facet_grid(isotope ~ treatme... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
NotesSamples to simulate* Isotope: * 12C vs 13C* Treatment: * 700* Days: * 14 * 28 * 45 | %%R
# bulk soil samples for gradients to simulate
samples.to.use = c(
"X12C.700.14.05.NA",
"X12C.700.28.03.NA",
"X12C.700.45.01.NA",
"X13C.700.14.08.NA",
"X13C.700.28.06.NA",
"X13C.700.45.01.NA"
) | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Total richness of starting (bulk-soil) communityMethod:* Total number of OTUs in OTU table (i.e., gamma richness)* Just looking at bulk soil samples Loading just bulk soil | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(ends_with('.NA'))
tbl$OTUId = rownames(tbl)
tbl %>% ncol %>% print
tbl[1:4,1:4]
%%R
tbl.h = tbl %>%
gather('sample', 'count', 1:(ncol(tbl)-1)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'),... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Number of taxa in all fractions corresponding to each bulk soil sample* Trying to see the difference between richness of bulk vs gradients (veil line effect) | %%R -i otuTableFile
# loading OTU table
tbl = read.delim(otuTableFile, sep='\t') %>%
select(-ends_with('.NA'))
tbl.h = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'), sep='\\.', remove=F)
tbl.h %>% head
%%R
# basename of fractions
samp... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Distribution of total sequences per fraction * Number of sequences per sample* Using all samples to assess this one* Just fraction samples__Method:__* Total number of sequences (total abundance) per sample Loading OTU table | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(-ends_with('.NA'))
tbl %>% ncol %>% print
tbl[1:4,1:4]
%%R
tbl.h = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'), sep='\\.', remove=F)
tbl.h %... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Distribution fitting | %%R -w 700 -h 350
plotdist(tbl.h.s$total_seqs)
%%R -w 450 -h 400
descdist(tbl.h.s$total_seqs, boot=1000)
%%R
f.n = fitdist(tbl.h.s$total_seqs, 'norm')
f.ln = fitdist(tbl.h.s$total_seqs, 'lnorm')
f.ll = fitdist(tbl.h.s$total_seqs, 'logis')
#f.c = fitdist(tbl.s$count, 'cauchy')
f.list = list(f.n, f.ln, f.ll)
plot.legend... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Notes:* best fit: * lognormal * mean = 10.113 * sd = 1.192 Does sample size correlate to buoyant density? Loading OTU table | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(-ends_with('.NA')) %>%
select(-starts_with('X0MC'))
tbl = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
mutate(sample = gsub('^X', '', sample))
tbl %>% head
%%R
# summarize
tbl.s = tbl %>%
group_by(sa... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Loading metadata | %%R -i metaDataFile
tbl.meta = read.delim(metaDataFile, sep='\t')
tbl.meta %>% head(n=3) | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Determining association | %%R -w 700
tbl.j = inner_join(tbl.s, tbl.meta, c('sample' = 'Sample'))
ggplot(tbl.j, aes(Density, total_count, color=rep)) +
geom_point() +
facet_grid(Treatment ~ Day)
%%R -w 600 -h 350
ggplot(tbl.j, aes(Density, total_count)) +
geom_point(aes(color=Treatment)) +
geom_smooth(method='lm') +
labs(... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Number of taxa along the gradient | %%R
tbl.s = tbl %>%
filter(count > 0) %>%
group_by(sample) %>%
summarize(n_taxa = sum(count > 0))
tbl.j = inner_join(tbl.s, tbl.meta, c('sample' = 'Sample'))
tbl.j %>% head(n=3)
%%R -w 900 -h 600
ggplot(tbl.j, aes(Density, n_taxa, fill=rep, color=rep)) +
#geom_area(stat='identity', alpha=0.5, posit... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Notes:* Many taxa out to the tails of the gradient.* It seems that the DNA fragments were quite diffuse in the gradients. Total abundance of each target taxon: bulk soil approach* Getting relative abundances from bulk soil samples * This has the caveat of likely undersampling richness vs using all gradient fraction ... | %%R -i otuTableFile
# loading OTU table
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(matches('OTUId'), ends_with('.NA'))
tbl %>% ncol %>% print
tbl[1:4,1:4]
%%R
# long table format w/ selecting samples of interest
tbl.h = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
separa... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
For each sample, writing a table of OTU_ID and count | %%R -i workDir
setwd(workDir)
samps = tbl.h$sample %>% unique %>% as.vector
for(samp in samps){
outFile = paste(c(samp, 'OTU.txt'), collapse='_')
tbl.p = tbl.h %>%
filter(sample == samp, count > 0)
write.table(tbl.p, outFile, sep='\t', quote=F, row.names=F)
message('Table writt... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Making directories for simulations | p = os.path.join(workDir, '*_OTU.txt')
files = glob.glob(p)
baseDir = os.path.split(workDir)[0]
newDirs = [os.path.split(x)[1].rstrip('.NA_OTU.txt') for x in files]
newDirs = [os.path.join(baseDir, x) for x in newDirs]
for newDir,f in zip(newDirs, files):
if not os.path.isdir(newDir):
print 'Making new di... | Directory exists: /home/nick/notebook/SIPSim/dev/priming_exp/X13C.700.28.06
Directory exists: /home/nick/notebook/SIPSim/dev/priming_exp/X12C.700.28.03
Directory exists: /home/nick/notebook/SIPSim/dev/priming_exp/X13C.700.14.08
Directory exists: /home/nick/notebook/SIPSim/dev/priming_exp/X13C.700.45.01
Directory exists... | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Rank-abundance distribution for each sample | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(matches('OTUId'), ends_with('.NA'))
tbl %>% ncol %>% print
tbl[1:4,1:4]
%%R
# long table format w/ selecting samples of interest
tbl.h = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
separate(sample, c('isoto... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Taxon abundance range for each sample-fraction | %%R -i otuTableFile
tbl = read.delim(otuTableFile, sep='\t')
# filter
tbl = tbl %>%
select(-ends_with('.NA')) %>%
select(-starts_with('X0MC'))
tbl = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
mutate(sample = gsub('^X', '', sample))
tbl %>% head
%%R
tbl.ar = tbl %>%
#mutate(fraction = gsu... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Total abundance of each target taxon: all fraction samples approach* Getting relative abundances from all fraction samples for the gradient * I will need to calculate (mean|max?) relative abundances for each taxon and then re-scale so that cumsum = 1 | %%R -i otuTableFile
# loading OTU table
tbl = read.delim(otuTableFile, sep='\t') %>%
select(-ends_with('.NA'))
tbl.h = tbl %>%
gather('sample', 'count', 2:ncol(tbl)) %>%
separate(sample, c('isotope','treatment','day','rep','fraction'), sep='\\.', remove=F)
tbl.h %>% head
%%R
# basename of fractions
samp... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
For each sample, writing a table of OTU_ID and count | %%R -i workDir
setwd(workDir)
# each sample is a file
samps = otu.rel.abund.l$sample %>% unique %>% as.vector
for(samp in samps){
outFile = paste(c(samp, 'frac_OTU.txt'), collapse='_')
tbl.p = otu.rel.abund %>%
filter(sample == samp, mean_perc_abund > 0)
write.table(tbl.p, outFile, se... | _____no_output_____ | MIT | ipynb/bac_genome/priming_exp/priming_exp_info.ipynb | arischwartz/test |
Here Z ∼ binomial(1, 0.5) is the protected attribute. Features related to the protected attribute are sampled from X ∼ N(µ, I) with µ = 1 when Z = 0 and µ = 2 when Z = 1. Other features not related to the protected attribute Z are generated with µ = 0. First 4 features are correlated with z. The first 10 features are c... | z = np.zeros(1000)
for j in range(1000):
z[j] = np.random.binomial(1,0.5)
x_correlated = np.zeros((1000,4))
x_uncorrelated = np.zeros((1000,16))
for j in range(16):
for i in range (1000):
if j < 4:
x_correlated[i][j] = np.random.normal((z[i]*2 + 10), 1, 1)
x_uncorrelated[i][j] = np.r... | 809 191 104 87 498 502
| MIT | benchmark/synthetic.ipynb | DebolinaHalder/599 |
 Annif tutorial with Jupyter notebook [Annif](https://annif.org/) is an open source subject indexing tool for new documents and aims to improve the discoverability of vast amount of electronic documents. In order to accomplish automatic subject indexing task, annif uses ML/... | import requests
import json
from pandas import json_normalize
headers = {'Accept': 'application/json'}
base_url='https://annif.rahtiapp.fi/v1/projects' # Annif webserver hosted by CSC
#base_url='https://api.annif.org/v1/projects' # Annif webserver by NatLibFi
response = requests.get(base_url, headers=headers)
d=resp... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
Perform subject indexing with AnnifThere are mainly six types of projects in a test case example of [Annif](https://annif.rahtiapp.fi) hosted at CSC and it runson Rahti container cloud. Let's see how to get subject indexing with each of these projects here 1. Perform subject indexing for your own text using YSO TFIDF ... | projectid='yso-tfidf-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = {'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = requests.pos... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
2. Perform subject indexing with YSO ensemble project (projectid:'yso-ensemble-en') This can be accomplished using swagger API POST call> **Note**: curl command - curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' -d 'text=frequently occurring or otherwise sal... | projectid='yso-ensemble-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = {'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = requests.... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
3. Perform subject indexing with for your own text using project 'yso-maui-en' This can be accomplished using swagger API POST call> **curl command**:curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' -d 'text=frequently occurring or otherwise salient terms in ... | projectid='yso-maui-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = { 'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = requests.post... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
4. Perform subject indexing for your own text using project 'yso-omikuji-parabel-en' This can be accomplished using swagger API POST call>**curl command**: curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' -d 'text=frequently occurring or otherwise salient ter... | projectid='yso-omikuji-parabel-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = {'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = req... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
5. Perform subject indexing for your own text using project 'yso-omikuji-bonsai-en' This can be accomplished using swagger API POST call>**curl command**:curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' -d 'text=frequently occurring or otherwise salient terms... | projectid='yso-omikuji-bonsai-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = {'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = req... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
6. Perform subject indexing for your own text using project 'yso-nn-ensemble-en' This can be accomplished using swagger API POST call>**curl command**: curl -X POST --header 'Content-Type: application/x-www-form-urlencoded' --header 'Accept: application/json' -d 'text=frequently occurring or otherwise salient terms ... | projectid='yso-nn-ensemble-en'
text='frequently occurring or otherwise salient terms in the document are matched with terms in the vocabulary'
url= base_url+ '/' + projectid +'/suggest'
data = {'text': text}
headers = { 'Content-Type': 'application/x-www-form-urlencoded','Accept': 'application/json'}
response = reques... | _____no_output_____ | Apache-2.0 | annif.ipynb | CSCfi/annif-utils |
Several exercises: Jupyter Notebook, iPython and ipyparallel, and HPC MPICHThe content of this notebook is borrowed extensively from Daan Van Hauwermeiren, from his tutotial of ipyparallel, stored on github https://github.com/DaanVanHauwermeiren/ipyparallel-tutorial/blob/master/02-ipyparallel-tutorial-direct-interface... | # import the IPython ipyparallel module and create a Client instance
# In this demonstration, an MPI-oriented client is created, referenced by the 'mpi' profile
# There are 4 mpi engines that have been configured and running on 4 separate HPC compute nodes
import ipyparallel as ipp
rc = ipp.Client(profile='mpi')
# Sho... | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Python’s builtin map() functions allows a function to be applied to a sequence element-by-element. This type of code is typically trivial to parallelize. In fact, since IPython’s interface is all about functions anyway, you can just use the builtin map() with a RemoteFunction, or a vobject’s map() method.do an arbitrar... | %%time
serial_result = list(map(lambda x:x**2**2, range(30))) | CPU times: user 18 µs, sys: 3 µs, total: 21 µs
Wall time: 25.5 µs
| CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Now do the same computation using the MPI compute nodes HPC cluster, ... and show how long it takes | %%time
parallel_result = vobject.map_sync(lambda x:x**2**2, range(30))
serial_result==parallel_result | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Remote function decoratorsRemote functions are just like normal functions, but when they are called, they execute on one or more engines, rather than locally. Here we will demonstrate the @parallel function decorator, which creates parallel functions that break up an element-wise operations and distribute them to rem... | # First, we'll enable blocking, which will be explored more throughly later.
# In short, blocking will ensure that each task won't proceed until all the remotely distributed work is complete.
@vobject.remote(block=True)
# Define a function called "getpid" that ... well, you can see the description
def getpid():
... | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
We'll use numpy to create some complicated (random) arrays, then use those arrays for some big computations that should benefit by some distributed HPC compute resources. | import numpy as np
A = np.random.random((64,48))
# Create a little function that can do the calculations as a distribution among multiple, parallel compute nodes
@vobject.parallel(block=True)
def pmul(A,B):
return A*B | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
We want to be able to compare the amount of time it takes to do the calulation locally on the HPC headand the amount of time it takes to do the calculation among the distributed compute nodesFirst, do the calculation locally, then do it remotely | %%time
C_local = A*A
%%time
C_remote = pmul(A,A)
(C_local == C_remote).all() | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Create a simple, new function that can be called locally but that will execute remotely, in parallel.It's just a simple instruction that will "echo" the output of what is run on the remote worker | @vobject.parallel(block=True)
def echo(x):
return str(x)
echo(range(5))
echo.map(range(5)) | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Blocking executionIn blocking mode, the iPython ipyparallel object (called vobject in these examples; defined at the beginning of this notebook) submits the command to the controller, which places the command in the engines’ queues for execution. The apply() call then blocks until the engines are done executing the co... | # Show function names (on the remote worker) that beging with the string "apply"
[x for x in dir(vobject) if x.startswith('apply')]
vobject.block = True
vobject['a'] = 5
vobject['b'] = 10
vobject.apply(lambda x: a+b+x, 27)
vobject.block = False
vobject.apply_sync(lambda x: a+b+x, 27) | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Python commands can be executed as strings on specific engines by using a vobject’s execute method: | rc[::2].execute('c=a+b')
rc[1::2].execute('c=a-b')
vobject['c'] # shorthand for vobject.pull('c', block=True) | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Non-blocking executionIn non-blocking mode, apply() submits the command to be executed and then returns a AsyncResult object immediately. The AsyncResult object gives you a way of getting a result at a later time through its get() method.More info on the AsyncResult object: http://ipyparallel.readthedocs.io/en/6.0.2/a... | # define our function
def wait(t):
import time
tic = time.time()
time.sleep(t)
return time.time()-tic
# In non-blocking mode
ar = vobject.apply_async(wait, 3)
# Now block for the result, and the output won't disply until after 3 seconds
ar.get()
# Again in non-blocking mode, with longer wait (10 seconds... | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Often, it is desirable to wait until a set of AsyncResult objects are done. For this, there is the method wait(). This method takes a tuple of AsyncResult objects (or msg_ids or indices to the client’s History), and blocks until all of the associated results are ready.In proper Jupyter Notebook fashion, the step progre... | vobject.block=False
# A trivial list of AsyncResults objects
pr_list = [vobject.apply_async(wait, 3) for i in range(10)]
# Wait until all of the clients have completed the instruction
vobject.wait(pr_list)
# Then, their results are ready using get() or the `.r` attribute
pr_list[0].get() | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
Scatter and gatherSometimes it is useful to partition a sequence and push the partitions to different engines. In MPI language, this is know as scatter/gather and we follow that terminology. However, it is important to remember that in IPython’s Client class, scatter() is from the interactive IPython session to the en... | vobject.scatter('a',range(16))
vobject['a']
vobject.gather('a') | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
parallel list comprehensionsIn many cases list comprehensions are nicer than using the map function. While we don’t have fully parallel list comprehensions, it is simple to get the basic effect using scatter() and gather():The %px magic executes a single Python command on the engines specified by the targets attribute... | vobject.scatter('x', range(64))
#Parallel execution on engines: [0, 1, 2, 3]
%px y = [i**10 for i in x]
y = vobject.gather('y')
print(y.get()[-10:]) | [210832519264920576, 253295162119140625, 303305489096114176, 362033331456891249, 430804206899405824, 511116753300641401, 604661760000000000, 713342911662882601, 839299365868340224, 984930291881790849]
| CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
example: monte carlo approximation of piA simple toy problem to get a handle on multiple engines is a Monte Carlo approximation of π.Let’s say we have a dartboard with a round target inscribed on a square board. If you threw darts randomly, and they land evenly distributed on the square board, how many darts would you... | from random import random
from math import pi
vobject['random'] = random
def mcpi(nsamples):
s = 0
for i in range(nsamples):
x = random()
y = random()
if x*x + y*y <= 1:
s+=1
return 4.*s/nsamples
def multi_mcpi(view, nsamples):
p = len(view.targets)
if nsampl... | _____no_output_____ | CC0-1.0 | mpi.ipynb | craiggardner/jupyter_notebooks |
def what_is_installed():
import pycaret
from pycaret import show_versions
show_versions()
try:
what_is_installed()
except:
!pip install pycaret-ts-alpha
what_is_installed()
import numpy as np
import pandas as pd
from pycaret.datasets import get_data
from pycaret.time_series import TSForecasting... | _____no_output_____ | MIT | time_series/pycaret/pycaret_ts_ccf.ipynb | ngupta23/medium_articles | |
**Not much to go by in terms of forecasting y just by itself (without exogenous variables)** | exp.plot_model(plot="ccf") | _____no_output_____ | MIT | time_series/pycaret/pycaret_ts_ccf.ipynb | ngupta23/medium_articles |
Q-Learning> Off-Policy Temporal Difference Learning. | import gym
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import clear_output
from time import sleep
env_name = "Taxi-v3"
epsilon = 1
decay_rate = 0.001
min_epsilon = 0.01
max_episodes = 2500
print_interval = 100
test_episodes = 3
lr = 0.4
gamma = 0.99
env = gym.make(env_name)
env = gym.wrapper... | _____no_output_____ | MIT | Q_Learning/Taxi_env.ipynb | alirezakazemipour/Q-Table-Numpy |
Pseudocode> | running_reward = []
for episode in range(1, 1 + max_episodes):
state = env.reset()
done = False
episode_reward = 0
while not done:
action = choose_action(state)
next_state, reward, done, _ = env.step(action)
update_table(state, action, reward, done, next_state)
... | Ep:3| Ep_reward:4|
| MIT | Q_Learning/Taxi_env.ipynb | alirezakazemipour/Q-Table-Numpy |
Stationary freatic flow between two water courses above a semi-pervious layer (Wesseling) Constant precipitation N on a strip of land between two parallel water courses with water level hs causes a rise h(x) of the groundwater level that induces in a groundwater flow towards the water courses. The phreatic groundwate... | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
def hx(x,L,hs,p,H,c,T):
"""Return phreatic groundwater level h(x) between two water courses
Parameters:
x : numpy array
Distance from centre of the water course (m)
L : float
D... | 320.0 3.7037037037037033
2800.0000000000005 32.40740740740741
-308.63433088123435 -3.5721566074216935
-2628.53571800518 -30.422867106541435
181.26361767539473 2.097958537909661
-2138.637769448551 -24.752751961210077
| MIT | Watercourse/Stationary freatic flow between two water courses above a semi-pervious layer (Wesseling).ipynb | tdmeij/GWF |
Text Classification with PySpark - Multiclass Text ClassificationTask- Predict the subject category given a course title or text | import pyspark
from pyspark import SparkContext
sc = SparkContext(master='local[2]')
# lunch UI
sc
# create spark seassion
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("Text Classifier").getOrCreate()
# read the dataset and load
df = spark.read.csv('udemy.csv',header=True, inferSchema=True... | +--------------------+----------------+
| course_title| subject|
+--------------------+----------------+
|Ultimate Investme...|Business Finance|
|Complete GST Cour...|Business Finance|
|Financial Modelin...|Business Finance|
|Beginner to Pro -...|Business Finance|
|How To Maximize Y...|Business Finance|
... | Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
Feature Extractionbuild features + count vectorizer+ tfIDF+ wordEmbeddings+ hashingTF+ etc...We have 2 things in Pipeline stages- Transformer- Estimator**Transformer** (Data to Data)Function that takes data and fit, transform them into augmented data or featuresi.e Extractors, Vectorizer, Scalers (Tokenizer, StopwordR... | from pyspark.ml.feature import Tokenizer, StopWordsRemover, CountVectorizer, IDF, StringIndexer
# dir(pyspark.ml.feature)
# Stages for the pipeline
tokenizer = Tokenizer(inputCol='course_title', outputCol='mytokens')
stopwordRemover = StopWordsRemover(inputCol='mytokens',outputCol='filtered_tokens')
vectorizer = CountV... | _____no_output_____ | Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
Building the pipeline | from pyspark.ml import Pipeline
pipeline = Pipeline(
stages=[tokenizer, stopwordRemover, vectorizer, idf, lr]
)
pipeline.stages
# model building
lr_model = pipeline.fit(train_df)
lr_model
# get predicction on test data
predictions = lr_model.transform(test_df)
# predictions.show()
predictions.columns
predictions.se... | +--------------------+--------------------+-------------------+-----+----------+
| rawPrediction| probability| subject|label|prediction|
+--------------------+--------------------+-------------------+-----+----------+
|[8.30964874634511...|[0.87877993991729...|Musical Instruments| 2.0| 0... | Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
model evaluation+ Accuracy+ Precision+ F1Score+ etc | from pyspark.ml.evaluation import MulticlassClassificationEvaluator
evaluator = MulticlassClassificationEvaluator(predictionCol='prediction',labelCol='label')
accuracy = evaluator.evaluate(predictions)
accuracy*100
"""
Method 2:
precision, f1score classification report
"""
from pyspark.mllib.evaluation import Multicla... | Accuracy 0.9182509505703422
precision 0.9544159544159544
f1Score 0.9178082191780822
recall 0.8839050131926122
| Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
Confusion matrix- convert to pandas- sklearn | y_true = predictions.select('label')
y_true = y_true.toPandas()
y_predict = predictions.select('prediction')
y_predict = y_predict.toPandas()
from sklearn.metrics import confusion_matrix, classification_report
cm = confusion_matrix(y_true, y_predict)
cm | _____no_output_____ | Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
making prediction on one sample+ sample as df+ apply pipeline | from pyspark.sql.types import StringType
exl = spark.createDataFrame([
("Building Machine Learning Apps with Python and PySpark", StringType())
],
#column name
['course_title']
)
exl.show()
# show fill
exl.show(truncate=False)
# making prediction
prediction_ex1 = lr_model.transform(exl)
prediction_ex1.show(trunca... | +--------------------+--------------------+--------------------+----------+
| course_title| rawPrediction| probability|prediction|
+--------------------+--------------------+--------------------+----------+
|Building Machine ...|[14.7174498131555...|[0.99999814636182...| 0.0|
+---------------... | Apache-2.0 | getStarted/TextClassification/textClassification.ipynb | iamhimanshu0/Spark |
______Copyright by Pierian Data Inc.For more information, visit us at www.pieriandata.com Text Methods A normal Python string has a variety of method calls available: | mystring = 'hello'
mystring.capitalize()
mystring.isdigit()
help(str) | Help on class str in module builtins:
class str(object)
| str(object='') -> str
| str(bytes_or_buffer[, encoding[, errors]]) -> str
|
| Create a new string object from the given object. If encoding or
| errors is specified, then the object must expose a data buffer
| that will be decoded using the given e... | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Pandas and TextPandas can do a lot more than what we show here. Full online documentation on things like advanced string indexing and regular expressions with pandas can be found here: https://pandas.pydata.org/docs/user_guide/text.html Text Methods on Pandas String Column | import pandas as pd
names = pd.Series(['andrew','bobo','claire','david','4'])
names
names.str.capitalize()
names.str.isdigit() | _____no_output_____ | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Splitting , Grabbing, and Expanding | tech_finance = ['GOOG,APPL,AMZN','JPM,BAC,GS']
len(tech_finance)
tickers = pd.Series(tech_finance)
tickers
tickers.str.split(',')
tickers.str.split(',').str[0]
tickers.str.split(',',expand=True) | _____no_output_____ | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Cleaning or Editing Strings | messy_names = pd.Series(["andrew ","bo;bo"," claire "])
# Notice the "mis-alignment" on the right hand side due to spacing in "andrew " and " claire "
messy_names
messy_names.str.replace(";","")
messy_names.str.strip()
messy_names.str.replace(";","").str.strip()
messy_names.str.replace(";","").str.strip().str.cap... | _____no_output_____ | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Alternative with Custom apply() call | def cleanup(name):
name = name.replace(";","")
name = name.strip()
name = name.capitalize()
return name
messy_names
messy_names.apply(cleanup) | _____no_output_____ | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Which one is more efficient? | import timeit
# code snippet to be executed only once
setup = '''
import pandas as pd
import numpy as np
messy_names = pd.Series(["andrew ","bo;bo"," claire "])
def cleanup(name):
name = name.replace(";","")
name = name.strip()
name = name.capitalize()
return name
'''
# code snippet whose exe... | _____no_output_____ | Apache-2.0 | 07-Text-Methods.ipynb | srijikabanerjee/demo2 |
Texas updates their data daily at noon CDT | from selenium import webdriver
import time
import pandas as pd
import pendulum
import re
import yaml
from selenium.webdriver.chrome.options import Options
chrome_options = Options()
#chrome_options.add_argument("--disable-extensions")
#chrome_options.add_argument("--disable-gpu")
#chrome_options.add_argument("--no-sand... | _____no_output_____ | MIT | TX by county.ipynb | kirbs-/covid-19-dataset |
Convert Dataset FormatsThis recipe demonstrates how to use FiftyOne to convert datasets on disk between common formats. Setup If you haven't already, install FiftyOne: | !pip install fiftyone
import fiftyone as fo | _____no_output_____ | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
If the above import fails due to a `cv2` error, it is an issue with OpenCV in Colab environments. [Follow these instructions to resolve it.](https://github.com/voxel51/fiftyone/issues/1494issuecomment-1003148448). This notebook contains bash commands. To run it as a notebook, you must install the [Jupyter bash kernel]... | pip install bash_kernel
python -m bash_kernel.install | _____no_output_____ | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
In this recipe we'll use the [FiftyOne Dataset Zoo](https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/zoo_datasets.html) to download some open source datasets to work with.Specifically, we'll need [TensorFlow](https://www.tensorflow.org/) and [TensorFlow Datasets](https://www.tensorflow.org/datasets) instal... | pip install tensorflow tensorflow-datasets | _____no_output_____ | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Download datasets Download the test split of the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html) from the [FiftyOne Dataset Zoo](https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/zoo_datasets.html) using the command below: | # Download the test split of CIFAR-10
fiftyone zoo datasets download cifar10 --split test | Downloading split 'test' to '~/fiftyone/cifar10/test'
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ~/fiftyone/cifar10/tmp-download/cifar-10-python.tar.gz
170500096it [00:04, 35887670.65it/s]
Extracting ~/fiftyone/cifar10/tmp-download/cifar-10-python.... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Download the validation split of the [KITTI dataset]( http://www.cvlibs.net/datasets/kitti) from the [FiftyOne Dataset Zoo](https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/zoo_datasets.html) using the command below: | # Download the validation split of KITTI
fiftyone zoo datasets download kitti --split validation | Split 'validation' already downloaded
| Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
The fiftyone convert command The [FiftyOne CLI](https://voxel51.com/docs/fiftyone/cli/index.html) provides a number of utilities for importing and exporting datasets in a variety of common (or custom) formats.Specifically, the `fiftyone convert` command provides a convenient way to convert datasets on disk between for... | fiftyone convert -h | usage: fiftyone convert [-h] [--input-dir INPUT_DIR] [--input-type INPUT_TYPE]
[--output-dir OUTPUT_DIR] [--output-type OUTPUT_TYPE]
Convert datasets on disk between supported formats.
Examples::
# Convert an image classification directory tree to TFRecords format
fiftyone... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Convert CIFAR-10 dataset When you downloaded the test split of the CIFAR-10 dataset above, it was written to disk as a dataset in [fiftyone.types.FiftyOneImageClassificationDataset](https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/datasets.htmlfiftyoneimageclassificationdataset) format.You can verify this... | fiftyone zoo datasets info cifar10 | ***** Dataset description *****
The CIFAR-10 dataset consists of 60000 32 x 32 color images in 10
classes, with 6000 images per class. There are 50000 training images and
10000 test images.
Dataset size:
132.40 MiB
Source:
https://www.cs.toronto.edu/~kriz/cifar.html
***** Supporte... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
The snippet below uses `fiftyone convert` to convert the test split of the CIFAR-10 dataset to [fiftyone.types.ImageClassificationDirectoryTree](https://voxel51.com/docs/fiftyone/user_guide/export_datasets.htmlimageclassificationdirectorytree) format, which stores classification datasets on disk in a directory tree str... | INPUT_DIR=$(fiftyone zoo datasets find cifar10 --split test)
OUTPUT_DIR=/tmp/fiftyone/cifar10-dir-tree
fiftyone convert \
--input-dir ${INPUT_DIR} --input-type fiftyone.types.FiftyOneImageClassificationDataset \
--output-dir ${OUTPUT_DIR} --output-type fiftyone.types.ImageClassificationDirectoryTree | Loading dataset from '~/fiftyone/cifar10/test'
Input format 'fiftyone.types.dataset_types.FiftyOneImageClassificationDataset'
100% |███| 10000/10000 [4.2s elapsed, 0s remaining, 2.4K samples/s]
Import complete
Exporting dataset to '/tmp/fiftyone/cifar10-dir-tree'
Export format 'fiftyone.types.dataset_types.Image... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Let's verify that the conversion happened as expected: | ls -lah /tmp/fiftyone/cifar10-dir-tree/
ls -lah /tmp/fiftyone/cifar10-dir-tree/airplane/ | head | total 8000
drwxr-xr-x 1002 voxel51 wheel 31K Jul 14 11:08 .
drwxr-xr-x 12 voxel51 wheel 384B Jul 14 11:08 ..
-rw-r--r-- 1 voxel51 wheel 1.2K Jul 14 11:23 000004.jpg
-rw-r--r-- 1 voxel51 wheel 1.1K Jul 14 11:23 000011.jpg
-rw-r--r-- 1 voxel51 wheel 1.1K Jul 14 11:23 000022.jpg
-rw-r--r-- ... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Now let's convert the classification directory tree to [TFRecords](https://voxel51.com/docs/fiftyone/user_guide/export_datasets.htmltfimageclassificationdataset) format! | INPUT_DIR=/tmp/fiftyone/cifar10-dir-tree
OUTPUT_DIR=/tmp/fiftyone/cifar10-tfrecords
fiftyone convert \
--input-dir ${INPUT_DIR} --input-type fiftyone.types.ImageClassificationDirectoryTree \
--output-dir ${OUTPUT_DIR} --output-type fiftyone.types.TFImageClassificationDataset | Loading dataset from '/tmp/fiftyone/cifar10-dir-tree'
Input format 'fiftyone.types.dataset_types.ImageClassificationDirectoryTree'
100% |███| 10000/10000 [4.0s elapsed, 0s remaining, 2.5K samples/s]
Import complete
Exporting dataset to '/tmp/fiftyone/cifar10-tfrecords'
Export format 'fiftyone.types.dataset_types... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Let's verify that the conversion happened as expected: | ls -lah /tmp/fiftyone/cifar10-tfrecords | total 29696
drwxr-xr-x 3 voxel51 wheel 96B Jul 14 11:24 .
drwxr-xr-x 4 voxel51 wheel 128B Jul 14 11:24 ..
-rw-r--r-- 1 voxel51 wheel 14M Jul 14 11:24 tf.records
| Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Convert KITTI dataset When you downloaded the validation split of the KITTI dataset above, it was written to disk as a dataset in [fiftyone.types.FiftyOneImageDetectionDataset](https://voxel51.com/docs/fiftyone/user_guide/dataset_creation/datasets.htmlfiftyoneimagedetectiondataset) format.You can verify this by printi... | fiftyone zoo datasets info kitti | ***** Dataset description *****
KITTI contains a suite of vision tasks built using an autonomous
driving platform.
The full benchmark contains many tasks such as stereo, optical flow, visual
odometry, etc. This dataset contains the object detection dataset,
including the monocular images and bounding b... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
The snippet below uses `fiftyone convert` to convert the test split of the CIFAR-10 dataset to [fiftyone.types.COCODetectionDataset](https://voxel51.com/docs/fiftyone/user_guide/export_datasets.htmlcocodetectiondataset) format, which writes the dataset to disk with annotations in [COCO format](https://cocodataset.org/f... | INPUT_DIR=$(fiftyone zoo datasets find kitti --split validation)
OUTPUT_DIR=/tmp/fiftyone/kitti-coco
fiftyone convert \
--input-dir ${INPUT_DIR} --input-type fiftyone.types.FiftyOneImageDetectionDataset \
--output-dir ${OUTPUT_DIR} --output-type fiftyone.types.COCODetectionDataset | Loading dataset from '~/fiftyone/kitti/validation'
Input format 'fiftyone.types.dataset_types.FiftyOneImageDetectionDataset'
100% |███████| 423/423 [1.2s elapsed, 0s remaining, 351.0 samples/s]
Import complete
Exporting dataset to '/tmp/fiftyone/kitti-coco'
Export format 'fiftyone.types.dataset_types.COCODete... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Let's verify that the conversion happened as expected: | ls -lah /tmp/fiftyone/kitti-coco/
ls -lah /tmp/fiftyone/kitti-coco/data | head
cat /tmp/fiftyone/kitti-coco/labels.json | python -m json.tool 2> /dev/null | head -20
echo "..."
cat /tmp/fiftyone/kitti-coco/labels.json | python -m json.tool 2> /dev/null | tail -20 | {
"info": {
"year": "",
"version": "",
"description": "Exported from FiftyOne",
"contributor": "",
"url": "https://voxel51.com/fiftyone",
"date_created": "2020-07-14T11:24:40"
},
"licenses": [],
"categories": [
{
"id": 0,
"n... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Now let's convert from COCO format to [CVAT Image format](https://voxel51.com/docs/fiftyone/user_guide/export_datasets.htmlcvatimageformat) format! | INPUT_DIR=/tmp/fiftyone/kitti-coco
OUTPUT_DIR=/tmp/fiftyone/kitti-cvat
fiftyone convert \
--input-dir ${INPUT_DIR} --input-type fiftyone.types.COCODetectionDataset \
--output-dir ${OUTPUT_DIR} --output-type fiftyone.types.CVATImageDataset | Loading dataset from '/tmp/fiftyone/kitti-coco'
Input format 'fiftyone.types.dataset_types.COCODetectionDataset'
100% |███████| 423/423 [2.0s elapsed, 0s remaining, 206.4 samples/s]
Import complete
Exporting dataset to '/tmp/fiftyone/kitti-cvat'
Export format 'fiftyone.types.dataset_types.CVATImageDataset'
100%... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Let's verify that the conversion happened as expected: | ls -lah /tmp/fiftyone/kitti-cvat
cat /tmp/fiftyone/kitti-cvat/labels.xml | head -20
echo "..."
cat /tmp/fiftyone/kitti-cvat/labels.xml | tail -20 | <?xml version="1.0" encoding="utf-8"?>
<annotations>
<version>1.1</version>
<meta>
<task>
<size>423</size>
<mode>annotation</mode>
<labels>
<label>
<name>Car</name>
<attributes>
</attributes>
... | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
CleanupYou can cleanup the files generated by this recipe by running the command below: | rm -rf /tmp/fiftyone | _____no_output_____ | Apache-2.0 | docs/source/recipes/convert_datasets.ipynb | pixta-dev/fiftyone |
Session 1 Homework Solution=========================This is the homework for the first session of the MolSSI Python Scripting Level 2 Workshop.This homework is intended to give you practice with the material covered in the first session. Goals: - Utilize pandas to read in and work with the data in a csv file. - Utilize... | # Import necessary packages for the homework:
import os
import pandas as pd
import matplotlib.pyplot as plt
# %matplotlib notebook
# Create a filepath to the periodic table csv file.
file_path = os.path.join("data", "PubChemElements_all.csv")
# Use pandas to read the csv file into a table.
df = pd.read_csv(file_path)... | _____no_output_____ | BSD-3-Clause | book/homework_1_solutions.ipynb | janash/python-analysis |
Exercise 2 Solution | # Create a set of subplots of the two trends: Ionization Energy and Electronegativity.
comparison_fig, comparison_ax = plt.subplots(1, 2)
# Add the first subplot.
comparison_ax[0].scatter('AtomicNumber', 'IonizationEnergy', data=df)
comparison_ax[0].set_xlabel('Atomic Number')
comparison_ax[0].set_ylabel('Ionization')... | _____no_output_____ | BSD-3-Clause | book/homework_1_solutions.ipynb | janash/python-analysis |
Exercise 3 Solution | # Determine possible states stored in the Dataframe
states = pd.unique(df['StandardState'])
states
# Create a function that returns a different color for each type of standard state.
def assign_state_color(standard_state):
state_markers = {'Gas': 'r',
'Solid': 'b',
'Liquid'... | _____no_output_____ | BSD-3-Clause | book/homework_1_solutions.ipynb | janash/python-analysis |
Generator function to armstrong numbers | def genFunc():
start = 1
end = 1000
for i in range(start, end + 1):
if i >= 10:
order = len(str(i))
sum = 0
temp = i
while temp > 0:
dig = temp % 10
sum += dig ** order
temp //= 10
if i == sum:
yield i
for x in genFunc():
print(x) | 153
370
371
407
| Apache-2.0 | Assignment Day 9 q2.ipynb | gopi2650/letsupgrade-python |
define categorical / numeric columns | d.hist(figsize=[20, 20], sharey=False, bins=50)
d.hist(figsize=[20, 20], sharey=True)
print()
col_identity = {'ignore': ['accident_id','provider_and_id','provider_code'],
'numeric' : ['license_acquiring_date', 'accident_year','accident_month'],
'category' : ['age_group', 'sex', 'vehicle_ty... | _____no_output_____ | MIT | datascience/2018_10_27_anyway_data_trial_1.ipynb | neuhofmo/anyway_projects |
balance the data and train forest of decision trees | from sklearn.model_selection import train_test_split
from imblearn.ensemble import BalancedRandomForestClassifier # !!!!!! balanced!
from sklearn.metrics import f1_score, precision_score, recall_score, balanced_accuracy_score
del df
X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=42, test_size=0.... | _____no_output_____ | MIT | datascience/2018_10_27_anyway_data_trial_1.ipynb | neuhofmo/anyway_projects |
Data Support | fan_smoothing_window = 60 # time width of smoothing wndow
def load_df(df_name):
df = pd.read_csv(
df_name,
usecols=[0, 1, 2, 3, 4, 5, 6]
)
df.rename(index=str, columns={ # remove units for easier indexing
'Time (s)': 'Time',
'Temperature': 'Thermistor',
'Error (deg... | _____no_output_____ | BSD-3-Clause | results/Temperature Control Plots.ipynb | ethanjli/punchcard-microfluidics |
Plotting Support | figure_width = 17.5
figure_temps_height = 4
figure_complete_height = 7.5
figure_complete_height_ratio = (3, 2)
box_width_shrink_factor = 0.875 # to fit the figure legend on the right
ylabel_position = -0.08
min_temp = 20
max_temp = 100
legend_location = 'center right'
reached_color = 'gainsboro' # light gray
setpo... | _____no_output_____ | BSD-3-Clause | results/Temperature Control Plots.ipynb | ethanjli/punchcard-microfluidics |
Stepwise Sequence | df_stepwise = load_df('20190117 Thermal Subsystem Testing Data - Fifth Test.csv')
smooth_fan(df_stepwise)
df_stepwise
fig_plot_temps(df_stepwise, 'Stepwise Adjustment Control Sequence')
plt.savefig('stepwise_control.pdf', format='pdf')
plt.savefig('stepwise_control.png', format='png')
fig_plot_complete(df_stepwise, 'St... | _____no_output_____ | BSD-3-Clause | results/Temperature Control Plots.ipynb | ethanjli/punchcard-microfluidics |
Lysis Sequence | df_lysis = load_df('20190117 Thermal Subsystem Testing Data - Fourth Test.csv')
smooth_fan(df_lysis)
df_lysis
fig_plot_temps(df_lysis, 'Thermal Lysis Control Sequence')
fig_plot_complete(df_lysis, 'Thermal Lysis Control Sequence')
plt.savefig('thermal_lysis.pdf', format='pdf')
plt.savefig('thermal_lysis.png', format='p... | 'HelveticaNeueLTStd_Md.otf' can not be subsetted into a Type 3 font. The entire font will be embedded in the output.
'HelveticaNeueLTStd_Roman.otf' can not be subsetted into a Type 3 font. The entire font will be embedded in the output.
'HelveticaNeueLTStd_Md.otf' can not be subsetted into a Type 3 font. The entire fon... | BSD-3-Clause | results/Temperature Control Plots.ipynb | ethanjli/punchcard-microfluidics |
**Table of Contents:*** Introduction* The RMS Titanic* Import Libraries* Getting the Data* Data Exploration/Analysis* Data Preprocessing - Missing Data - Converting Features - Creating Categories - Creating new Features* Building Machine Learning Models - Training 8 different models - Which is the be... | # linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
import seaborn as sns
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import style
# Algorithms
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.en... | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
**Getting the Data** | test_df = pd.read_csv("../input/test.csv")
train_df = pd.read_csv("../input/train.csv") | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
**Data Exploration/Analysis** | train_df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp ... | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
**The training-set has 891 examples and 11 features + the target variable (survived)**. 2 of the features are floats, 5 are integers and 5 are objects. Below I have listed the features with a short description: survival: Survival PassengerId: Unique Id of a passenger. pclass: Ticket class sex: Sex Age:... | train_df.describe() | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
Above we can see that **38% out of the training-set survived the Titanic**. We can also see that the passenger ages range from 0.4 to 80. On top of that we can already detect some features, that contain missing values, like the 'Age' feature. | train_df.head(15) | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
From the table above, we can note a few things. First of all, that we **need to convert a lot of features into numeric** ones later on, so that the machine learning algorithms can process them. Furthermore, we can see that the **features have widely different ranges**, that we will need to convert into roughly the same... | total = train_df.isnull().sum().sort_values(ascending=False)
percent_1 = train_df.isnull().sum()/train_df.isnull().count()*100
percent_2 = (round(percent_1, 1)).sort_values(ascending=False)
missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])
missing_data.head(5) | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
The Embarked feature has only 2 missing values, which can easily be filled. It will be much more tricky, to deal with the 'Age' feature, which has 177 missing values. The 'Cabin' feature needs further investigation, but it looks like that we might want to drop it from the dataset, since 77 % of it are missing. | train_df.columns.values | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
Above you can see the 11 features + the target variable (survived). **What features could contribute to a high survival rate ?** To me it would make sense if everything except 'PassengerId', 'Ticket' and 'Name' would be correlated with a high survival rate. **1. Age and Sex:** | survived = 'survived'
not_survived = 'not survived'
fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))
women = train_df[train_df['Sex']=='female']
men = train_df[train_df['Sex']=='male']
ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)
ax = sns.dis... | _____no_output_____ | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
You can see that men have a high probability of survival when they are between 18 and 30 years old, which is also a little bit true for women but not fully. For women the survival chances are higher between 14 and 40.For men the probability of survival is very low between the age of 5 and 18, but that isn't true for wo... | FacetGrid = sns.FacetGrid(train_df, row='Embarked', size=4.5, aspect=1.6)
FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None, order=None, hue_order=None )
FacetGrid.add_legend() | /opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional index... | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
Embarked seems to be correlated with survival, depending on the gender. Women on port Q and on port S have a higher chance of survival. The inverse is true, if they are at port C. Men have a high survival probability if they are on port C, but a low probability if they are on port Q or S. Pclass also seems to be correl... | sns.barplot(x='Pclass', y='Survived', data=train_df) | /opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a ... | MIT | titanic/end-to-end-project-with-python.ipynb | MLVPRASAD/KaggleProjects |
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