markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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Training the model | max_epochs = 100
vec_size = 50
alpha = 0.025
# Distributed memory model
model = Doc2Vec(vector_size=vec_size,
alpha=alpha,
min_alpha=0.00025,
min_count=1,
dm=1,
workers=8)
# Initialize the model
model.build_vocab(documents)
# Train the mod... | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
Generate the document dictionary that can be used to access a document by tag | # Generate the document dictionary that can be used to access a document by tag
document_dic = {}
for doc,tag in documents:
document_dic[tag[0]] = doc | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
Test the model for Random Data from ACLED after Jan-1-2019 | article = "On July 15, a long protest march by farmers, from Mandsaur in Madhya Pradesh to New Delhi, demanding loan waiver and fair price for their produce, reached Jaipur."
article = ' '.join(generate_document_vocabulary(article)) | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
Find the closest Document | # Reference : https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-lee.ipynb
# find the vector
vec = model.infer_vector(sabri)
#find 10 closest documents
sims = model.docvecs.most_similar([vec])
# Print the document
for doc_tag,score in sims:
print("Document has score : "score, "\nConte... | _____no_output_____ | MIT | TOI/Doc2Vec.ipynb | aashish-jain/Social-unrest-prediction |
https://github.com/pysal/mgwr/pull/56 | import sys
sys.path.append("C:/Users/msachde1/Downloads/Research/Development/mgwr")
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
from mgwr.gwr import GWR
from spglm.family import Gaussian, Binomial, Poisson
from mgwr.gwr import MGWR
from mgwr.sel_bw import Sel_BW
import mult... | _____no_output_____ | MIT-0 | Notebooks/Binomial_MGWR_approaches_tried.ipynb | mehak-sachdeva/MGWR_book |
Fundamental equation By simple algebraic manipulation, the probability that Y=1 is: \begin{align}p = 1 / (1 + exp (-{\beta} & _k x _{k,i}) ) \\\end{align} Approaches tried:1. Changing XB to : `1 / (1 + np.exp (-1*np.sum(np.multiply(X,params),axis=1)))` - these are the predicted probabilities ~(0,1)2. Chan... | data_p = pd.read_csv("C:/Users/msachde1/Downloads/logistic_mgwr_data/landslides.csv")
data_p.head() | _____no_output_____ | MIT-0 | Notebooks/Binomial_MGWR_approaches_tried.ipynb | mehak-sachdeva/MGWR_book |
Helper functions - hardcoded here for simplicity in the notebook workflowPlease note: A separate bw_func_b will not be required when changes will be made in the repository | kernel='bisquare'
fixed=False
spherical=False
search_method='golden_section'
criterion='AICc'
interval=None
tol=1e-06
max_iter=500
X_glob=[]
def gwr_func(y, X, bw,family=Gaussian(),offset=None):
return GWR(coords, y, X, bw, family,offset,kernel=kernel,
fixed=fixed, constant=False,
sphe... | _____no_output_____ | MIT-0 | Notebooks/Binomial_MGWR_approaches_tried.ipynb | mehak-sachdeva/MGWR_book |
GWR Binomial model with independent variable, x = slope | coords = list(zip(data_p['X'],data_p['Y']))
y = np.array(data_p['Landslid']).reshape((-1,1))
elev = np.array(data_p['Elev']).reshape((-1,1))
slope = np.array(data_p['Slope']).reshape((-1,1))
SinAspct = np.array(data_p['SinAspct']).reshape(-1,1)
CosAspct = np.array(data_p['CosAspct']).reshape(-1,1)
X = np.hstack([elev,... | _____no_output_____ | MIT-0 | Notebooks/Binomial_MGWR_approaches_tried.ipynb | mehak-sachdeva/MGWR_book |
Multi_bw changes | def multi_bw(init,coords,y, X, n, k, family=Gaussian(),offset=None, tol=1e-06, max_iter=20, multi_bw_min=[None], multi_bw_max=[None],rss_score=True,bws_same_times=3,
verbose=True):
if multi_bw_min==[None]:
multi_bw_min = multi_bw_min*X.shape[1]
if multi_bw_max==[None]:
... | _____no_output_____ | MIT-0 | Notebooks/Binomial_MGWR_approaches_tried.ipynb | mehak-sachdeva/MGWR_book |
Transfer LearningIn this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html). ImageNet is a massiv... | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
Most of the pretrained models require the input to be 224x224 images. Also, we'll need to match the normalization used when the models were trained. Each color channel was normalized separately, the means are `[0.485, 0.456, 0.406]` and the standard deviations are `[0.229, 0.224, 0.225]`. | data_dir = 'Cat_Dog_data'
# TODO: Define transforms for the training data and testing data
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
... | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
We can load in a model such as [DenseNet](http://pytorch.org/docs/0.3.0/torchvision/models.htmlid5). Let's print out the model architecture so we can see what's going on. | model = models.densenet121(pretrained=True)
model | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
This model is built out of two main parts, the features and the classifier. The features part is a stack of convolutional layers and overall works as a feature detector that can be fed into a classifier. The classifier part is a single fully-connected layer `(classifier): Linear(in_features=1024, out_features=1000)`. T... | # Freeze parameters so we don't backprop through them
for param in model.parameters():
param.requires_grad = False
from collections import OrderedDict
classifier = nn.Sequential(OrderedDict([
('fc1', nn.Linear(1024, 500)),
('relu', nn.ReLU()),
... | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
With our model built, we need to train the classifier. However, now we're using a **really deep** neural network. If you try to train this on a CPU like normal, it will take a long, long time. Instead, we're going to use the GPU to do the calculations. The linear algebra computations are done in parallel on the GPU lea... | import time
for device in ['cpu', 'cuda']:
criterion = nn.NLLLoss()
# Only train the classifier parameters, feature parameters are frozen
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)
model.to(device)
for ii, (inputs, labels) in enumerate(trainloader):
# Move input and ... | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
You can write device agnostic code which will automatically use CUDA if it's enabled like so:```python at beginning of the scriptdevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")... then whenever you get a new Tensor or Module this won't copy if they are already on the desired deviceinput = data.t... | # Use GPU if it's available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.densenet121(pretrained=True)
# Freeze parameters so we don't backprop through them
for param in model.parameters():
param.requires_grad = False
model.classifier = nn.Sequential(nn.Linear(1024, 256... | _____no_output_____ | MIT | deep_learning/new-intro-to-pytorch/Part 8 - Transfer Learning (Solution).ipynb | willcanniford/python-learning |
PNW Focal Species Sanity CheckWe'll use GBIF to select a species with lots of occurrences in the PNW and assess the impact of MHWs on that species. | plankton = pd.read_csv("../data/Phytoplankton_temperature_growth_rate_dataset_2016_01_29/traits_derived_2016_01_29.csv", engine='python')
plankton = plankton[(plankton.minqual == "good") &
(plankton.maxqual == "good") &
(plankton.curvequal == "good")]
plankton = plankton[plankton... | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
GBIF Occurrences*For locating PNW species...* | genera = plankton.genus.dropna().unique()
genera
genera = [pygbif.species.name_lookup(q=g, rank='genus') for g in genera]
genera = [g for g in genera if len(g['results']) > 0]
genera = [max(g['results'], key=lambda x: x['numDescendants']) for g in genera]
[g['key'] for g in genera]
genera_counts = [pygbif.occurrences.c... | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
TPC From Genus | def tpc(T, a, b, z, w):
'''
https://science.sciencemag.org/content/sci/suppl/2012/10/25/science.1224836.DC1/Thomas.SM.pdf
'''
return a * np.exp(b*T) * (1 - ((T - z)/(w / 2))**2)
def plot_tpc(sample, ax=None):
T = np.arange(sample['mu.c.opt.list'] - (sample['mu.wlist'] / 2), sample['mu.c.opt.list'] +... | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
**NOTE** Overrode above analysis and chose a single species | sample_species = this_genus.sample(1).iloc[0]
plot_tpc(sample_species) | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
Enter MHW and SST | mhw_time = slice('2014-01-01', '2015-05-31')
mhws = xr.open_dataset("../mhw_pipeline/pnw_mhw_intensity.nc").rename({
'__xarray_dataarray_variable__': 'mhw_intensity'
})
mhws
mhw_recent = mhws.sel(time=mhw_time)
mhw_count = mhw_recent.median(dim='time').mhw_intensity
plt.figure(figsize=(10,10))
ax = plt.axes(proj... | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
SST | fs = gcsfs.GCSFileSystem(project=GCP_PROJECT_ID, token="/home/jovyan/gc-pangeo.json")
oisst = xr.open_zarr(fs.get_mapper(OISST_GCP))
oisst = oisst.assign_coords(lon=(((oisst.lon + 180) % 360) - 180)).sortby('lon')
PNW_LAT = slice(mhw_count.lat.min(), mhw_count.lat.max())
PNW_LON = slice(mhw_count.lon.min(), mhw_count.l... | MovieWriter ffmpeg unavailable; trying to use <class 'matplotlib.animation.PillowWriter'> instead.
| MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
Compute Performance Detriment | def perf_det(T, T_opt, tpc, axis=1):
return tpc(T_opt) - tpc(T)
def tsm(T, T_opt, axis):
return T - T_opt
def plot_det(s, ax):
this_tpc = partial(tpc, a=s['mu.alist'], b=s['mu.blist'], z=s['mu.c.opt.list'], w=s['mu.wlist'])
T = np.arange(s['mu.g.opt.list'] - (s['mu.wlist'] / 2), s['mu.c.opt.list'... | _____no_output_____ | MIT | analysis/distribution-analysis.ipynb | HuckleyLab/phyto-mhw |
Data Analysis Research Question: Examine the top 3 countries who have competed at the olympics, and examine their preformance over the past 120 years. For Each top 3 country, these aspects should be examined in order to answer the question:- Which years did this country win the most medals? - Most gold medals?- Wh... | import pandas as pd
import numpy as np
from matplotlib import pyplot
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('../data/raw/olympic_dataset.csv', low_memory=False, encoding = 'utf-8')
top3Countries=(data.dropna(subset=['Medal'])).value_counts(subset=['NOC'])
top3Countries.head(3) | _____no_output_____ | MIT | analysis/.ipynb_checkpoints/Task_5-checkpoint_LOCAL_1236.ipynb | data301-2020-winter2/course-project-group_1001 |
Based on this, we can see that the United States leads the dataset with the most amount of medals, with the Soviet Union coming in second, and Germany in third. Therefore, our data analysis will focus on these three countries.Although, with the Soviet Union and Germany, there have been many historical changes to those ... | us =(data["NOC"] == "USA")
athletes=data[(us)]
print("Number of events particpated in by American athletes: "+str(athletes.shape[0]))
athletesUnique=data[us].drop_duplicates(subset=['Name'])
print("Number of American athletes who have competed at the olympics: "+str(athletesUnique.shape[0]))
maleAthletesUnique=data[us&... | _____no_output_____ | MIT | analysis/.ipynb_checkpoints/Task_5-checkpoint_LOCAL_1236.ipynb | data301-2020-winter2/course-project-group_1001 |
Other Graphs to Note | sns.countplot(data=medals, x="Medal", hue="Sex", palette='pastel')
plt.title("Types of medals won by male and female American athletes")
sns.countplot(data=medals, x="Year", hue="Sex", palette='pastel')
plt.title("Medals won by American olympic athletes per year compared to sex") | _____no_output_____ | MIT | analysis/.ipynb_checkpoints/Task_5-checkpoint_LOCAL_1236.ipynb | data301-2020-winter2/course-project-group_1001 |
K26 - Heated Wall
Interface at 90°.
Variable fluid densities => but very small variations
Also no Heat capacity => infinitely fast heat conduction
Height of the domain is reduced
Implicit RK timestepping, to allow for an adaption of the maximum step size
Also only the "start" of the process is simulated, i.... | #r "..\..\..\src\L4-application\BoSSSpad\bin\Release\net5.0\BoSSSpad.dll"
using System;
using System.Collections.Generic;
using System.Linq;
using ilPSP;
using ilPSP.Utils;
using BoSSS.Platform;
using BoSSS.Foundation;
using BoSSS.Foundation.XDG;
using BoSSS.Foundation.Grid;
using BoSSS.Foundation.Grid.Classi... | _____no_output_____ | Apache-2.0 | examples/XNSFE_Solver/HeatedWall_VariableDensity/HeatedWall90DegVariableDensity.ipynb | FDYdarmstadt/BoSSS |
Setup Workflowmanagement, Batchprocessor and Database | ExecutionQueues
static var myBatch = BoSSSshell.GetDefaultQueue();
static var myDb = myBatch.CreateOrOpenCompatibleDatabase("XNSFE_HeatedWall");
myDb.Path
BoSSSshell.WorkflowMgm.Init($"HeatedWall_VariableDensity"); | Project name is set to 'HeatedWall_VariableDensity'.
| Apache-2.0 | examples/XNSFE_Solver/HeatedWall_VariableDensity/HeatedWall90DegVariableDensity.ipynb | FDYdarmstadt/BoSSS |
Setup Simulationcontrols | using BoSSS.Application.XNSFE_Solver;
int[] hRes = {16, 24, 32, 48};
int[] pDeg = {2};
double[] Q = {0.2};
double[] dRho_B = {0.0, -1e-3, -5e-3, -1e-2, -5e-2, -1e-1};
List<XNSFE_Control> Controls = new List<XNSFE_Control>();
foreach(int h in hRes){
foreach(int p in pDeg){
foreach(double q in Q){
... | _____no_output_____ | Apache-2.0 | examples/XNSFE_Solver/HeatedWall_VariableDensity/HeatedWall90DegVariableDensity.ipynb | FDYdarmstadt/BoSSS |
Start simulations on Batch processor | foreach(var C in Controls) {
Type solver = typeof(BoSSS.Application.XNSFE_Solver.XNSFE<XNSFE_Control>);
string jobName = C.SessionName;
var oneJob = new Job(jobName, solver);
oneJob.NumberOfMPIProcs = 1;
oneJob.SetControlObject(C);
oneJob.Activate(myBatch, true);
} | _____no_output_____ | Apache-2.0 | examples/XNSFE_Solver/HeatedWall_VariableDensity/HeatedWall90DegVariableDensity.ipynb | FDYdarmstadt/BoSSS |
Hyperexponential CaseThroughout this document, the following packages are required: | import numpy as np
import scipy
import math
from scipy.stats import binom, erlang, poisson
from scipy.optimize import minimize
from functools import lru_cache | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
Plot Phase-Type Fit | from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
def SCV_to_params(SCV):
# weighted Erlang case
if SCV <= 1:
K = math.floor(1/SCV)
p = ((K + 1) * SCV - math.sqrt((K + 1) * (1 - K * SCV))) / (SCV + 1)
mu... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
The recursion of the dynamic program is given as follows. For $i=1,\dots,n-1$, $k=1,\dots,i$, and $m\in\mathbb{N}_0$,\begin{align*}\xi_i(k,m) &= \inf_{t\in \mathbb{N}_0}\Big(\omega \bar{f}^{\circ}_{k,m\Delta}(t\Delta) + (1-\omega)\bar{h}^{\circ}_{k,m\Delta} +\sum_{\ell=2}^{k}\sum_{j=0}^{t}\bar{q}_{k\ell,mj}(t)\xi_{i+1}... | @lru_cache(maxsize=128)
def B_sf(t):
"""The survival function P(B > t)."""
return p * np.exp(-mu1 * t) + (1 - p) * np.exp(-mu2 * t)
@lru_cache(maxsize=128)
def gamma(z, t):
"""Computes P(Z_t = z | B > t)."""
gamma_circ = B_sf(t)
if z == 1:
return p * np.exp(-mu1 * t) / gamma_circ
... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
Next, we implement $\bar{f}^{\circ}_{k,u}(t)$, which depends on $\bar{f}_{k,z}(t)$: | @lru_cache(maxsize=128)
def f_bar(k,z,t):
if z == 1:
return sum([binom.pmf(m, k-1, p) * sigma(t, m+1, k-1-m) for m in range(k)])
elif z == 2:
return sum([binom.pmf(m, k-1, p) * sigma(t, m, k-m) for m in range(k)])
@lru_cache(maxsize=128)
def f_circ(k, u, t):
return gamma(1, u) * f_bar(... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
In here, we need to evaluate the object $\sigma_{t}[m,k]$, which depends on $\rho_{t}[m,k]$: | @lru_cache(maxsize=512)
def sigma(t,m,k):
return (t - k / mu2) * erlang.cdf(t, m, scale=1/mu1) - (m / mu1) * erlang.cdf(t, m+1, mu1) + \
(mu1 / mu2) * sum([(k-i) * rho_t(t, m-1, i) for i in range(k)])
@lru_cache(maxsize=512)
def rho_t(t,m,k):
if not k:
return np.exp(-mu2 * t) * (m... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
We do the same for $\bar{h}^{\circ}_{k,u}(t)$, which only depends on $\bar{h}_{k,z}$: | @lru_cache(maxsize=128)
def h_bar(k, z):
if k == 1:
return 0
elif z <= K:
return ((k - 1) * (K + 1 - p) + 1 - z) / mu
elif z == K + 1:
return ((k - 2) * (K + 1 - p) + 1) / mu
@lru_cache(maxsize=128)
def h_circ(k, u):
return gamma(1, u) * h_bar() sum([gamma(z, u) * h_bar(k, z) f... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
The next objective is to implement $\bar{q}_{k\ell,mj}(t)$. This function depends on $q_{k\ell,z,v}(t)$, which depends on $\psi_{vt}[k,\ell]$: TODO | # TODO
poisson.pmf(3,0) | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
Finally, we implement the remaining transition probabilities $P^{\uparrow}_{k,u}(t)$ and $P^{\downarrow}_{k,u}(t)$: | # @lru_cache(maxsize=128)
def P_up(k, u, t):
"""Computes P(N_t- = k | N_0 = k, B_0 = u)."""
return B_sf(u + t) / B_sf(u)
@lru_cache(maxsize=128)
def P_down(k, u, t):
"""Computes P(N_t- = 0 | N_0 = k, B_0 = u)."""
return sum([binom.pmf(m, k, p) * Psi(t, m, k-m) for m in range(k+1)])
@lru_cache(maxsize=... | _____no_output_____ | MIT | .ipynb_checkpoints/Hyperexponential Case-checkpoint.ipynb | Roshanmahes/Appointment-Scheduling |
Reading and Writing filesSo far, we have typed all of our "data" into the code of our software (e.g. the names of the students, and their ages.Most of the time, this kind of data is stored in files. We need to read (and write) files so that we can create and use permanent copies of these data (and exchange these data... | studentfile = open("students.csv", "r")
print(studentfile) | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Does the output of that print statement surprise you? What it tells you is that 'studentfile' is a Python "object" (again, we will discuss objects in more detail later, but you will start to see how they work now...)studentfile is an object of type "TextIOWrapper" (from the "io" Python library, which is automatically ... | print(studentfile.read()) | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Now we need to talk about a feature of file input/output, called a "pointer". The pointer is the position where the code "is" in the file - is it at the beginning? is it at the end"? Is it at line 5?Where is the pointer now? Let's try the same command again! |
print(studentfile.read()) | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Nada de nada! That's because the pointer is at the end of the file - when we say file.read it tries to read starting from the end of the file...and of course, there is nothing there. To reset back to the beginning, we will use the "seek" function, and set it to position '0': | studentfile.seek(0)
print(studentfile.read())
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
More refined file access - line-by-lineMost of the time, you do not want to read the enire file into memory (tell me why this can be very very bad!.... please)MOST of the time, a file will have one "record" per line. e.g. our CSV file has the "name,age" for one student per line. We want to read those lines one-at-a-... | studentfile.seek(0) # set it back to the beginning again for this lesson...
print(studentfile.readlines()) | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
You will see that this returns a list, which means we can use it in a FOR loop... | studentfile.seek(0) # set it back to the beginning again for this lesson...
for line in studentfile.readlines():
print("the current record is", line)
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
We're getting closer to what we want! We have each record as a string in the format "Mark,50". What we want is to separate the "Mark" and the "50" so that we could put them into separate variables (e.g. *name* and *age*)There is a ***correct*** way to this, but you already know one way to solve this problem! In the ... | # put your amazing solution here!
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
OK, so now you have solved the problem using regular expressions, however... the solution isn't very "abstract". In another case, you might have a more complex record: Mark,50,190cm,95kg,163483,113mmhg,29mg/mlYour regular expression would start to get ugly! What is the one thing that is constant in this CSV file? ... | studentfile.seek(0) # set it back to the beginning again for this lesson...
for line in studentfile.readlines():
print("the current record is", line)
name, age = line.split(',')
print("Name:", name, " Age:", age)
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Much better! But... *Still not quite right!!* What are all of those blank lines? We didn't ask for blank lines...Remember just a few minutes ago we looked at the output from readlines(): studentfile.seek(0) set it back to the beginning again for this lesson... print(studentfile.readlines()) ==> ['Ma... | studentfile.seek(0) # set it back to the beginning again for this lesson...
for line in studentfile.readlines():
line = line.rstrip()
print("the current record is", line)
name, age = line.split(',')
print("Name:", name, " Age:", age)
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
When you have finished with an open file, it is a very good idea to close it! studentfile.close() it's a good idea to close a file once you are finished with it! We are... | studentfile.close() # it's a good idea to close a file once you are finished with it! We are... | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Writing to a fileWriting to a file is very straightforward. Use the same "open" command that you have already learned, but using the "w" flag ("open for **w**rite), then write information to that open file using the *write()* method.Python will help you by creating the file if it doesn't exist. For example, the box ... | olderstudents = open("OLDERstudents.csv", "w")
olderstudents.write("hello, I am writing stuff to a file!\nThis is very cool!") # the write function, using \n (newline)
olderstudents.close()
checkcontent = open("OLDERstudents.csv", "r")
print(checkcontent.read()) # print the content of the file
checkcontent.close() | _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
Now you!* create the file OLDERstudents.csv* using the data from the original students.csv, make everyone 5 years older* write the new older student data to the OLDERstudents.csv file, in an identical format (Mark,55....)* do this again, but this time, create a "header line" (Student Name, Student Age) Student ... | # put your amazing code here!
| _____no_output_____ | CC-BY-4.0 | Lesson 6 - File access in Python3.ipynb | mjmtnez/Accelerated_Intro_to_CompBio_Part_2 |
SymPy Symbolic ComputationFree, Open Source, Python- solve equations - simplify expressions- compute derivatives, integrals, limits- work with matrices, - plotting & printing- code gen - physics - statitics - combinatorics- number theory - geometry - logic---- Modules[SymPy Core](http://docs.sympy.org/latest/modules/c... | # declare variable first
x, y = symbols('x y')
# Declare expression
expr = x + 3*y
# Print expressions
print("expr =", expr)
print("expr + 1 =", expr + 1)
print("expr - x =", expr - x) # auto-simplify
print("x * expr =", x * expr) | expr = x + 3*y
expr + 1 = x + 3*y + 1
expr - x = 3*y
x * expr = x*(x + 3*y)
| MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
---- Substitution | x = symbols('x')
expr = x + 1
print(expr)
display(expr)
# Evaluate expression at a point
print("expr(2)=", expr.subs(x, 2))
# Replace sub expression with another sub expression
# 1. For expressions with symmetry
x, y = symbols('x y')
expr2 = x ** y
expr2 = expr2.subs(y, x**y)
expr2 = expr2.subs(y, x**x)
display(expr2)... | _____no_output_____ | MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
---- Equality & Equivalence | # do not use == between symbols and variables, will return false
x = symbols('x')
x+1==4
# Create a symbolic equality expression
expr2 = Eq(x+1, 4)
print(expr2)
display(expr2)
print("if x=3, then", expr2.subs(x,3))
# two equivalent formulas
expr3 = (x + 1)**2 # we use pythons ** exponentiation (instead of ^)
expr4 = ... | expr3
| MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
---- SymPy Types & Casting | print( "1 =", type(1) )
print( "1.0 =", type(1.0) )
print( "Integer(1) =", type(Integer(1)) )
print( "Integer(1)/Integer(3) =", type(Integer(1)/Integer(3)) )
print( "Rational(0.5) =", type(Rational(0.5)) )
print( "Rational(1/3) =", type(Rational(1,3)) )
#... | _____no_output_____ | MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
---- Evaluating Expressions | # evaluate as float using .evalf(), and N
display( sqrt(8) )
display( sqrt(8).evalf() )
display( sympy.N(sqrt(8)) )
# evaluate as float to nearest n decimals
display(sympy.pi)
display(sympy.pi.evalf(100)) | _____no_output_____ | MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
---- SymPy Types Number Class[Number](http://docs.sympy.org/latest/modules/core.htmlnumber) - [Float](http://docs.sympy.org/latest/modules/core.htmlfloat) - [Rational](http://docs.sympy.org/latest/modules/core.htmlrational) - [Integer](http://docs.sympy.org/latest/modules/core.htmlinteger) - [RealNumber](http://docs.sy... | # Rational Numbers
expr_rational = Rational(1)/3
print("expr_rational")
display( type(expr_rational) )
display( expr_rational )
eval_rational = expr_rational.evalf()
print("eval_rational")
display( type(eval_rational) )
display( eval_rational )
neval_rational = N(expr_rational)
print("neval_rational")
display( type(n... | expr_rational
| MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
Complex Numbers | # Complex Numbers supported.
expr_cplx = 2.0 + 2*sympy.I
print("expr_cplx")
display( type(expr_cplx) )
display( expr_cplx )
print("expr_cplx.evalf()")
display( type(expr_cplx.evalf()) )
display( expr_cplx.evalf() )
print("float() - errors")
print(" ")
# this errors complex cannot be converted to float
#display( floa... | _____no_output_____ | MIT | notebooks/python-data-science/sympy/sympy.ipynb | sparkboom/my_jupyter_notes |
This is statregistration.py but then for multiple .nlp's. Edited from Tobias' script: Calculates shifts from CPcorrected and create a shift corrected stack. The calculations implements the drift correction algorithm as described in section 4 of the paper. https://doi.org/10.1016/j.ultramic.2019.112913 https... | import numpy as np
import matplotlib.pyplot as plt
import dask.array as da
from dask.distributed import Client, LocalCluster
import scipy.ndimage as ndi
import os
import time
import napari
%gui qt
from pyL5.lib.analysis.container import Container
from pyL5.analysis.CorrectChannelPlate.CorrectChannelPlate import Cor... | _____no_output_____ | MIT | StatRegistration_multiNLP.ipynb | thagusta/LEEM-analysis-1 |
Step 0: choose areaChoose the (rectangular) area on which to perform drift correction. | center = [dim//2 for dim in original.shape[1:]]
extent = (center[0]-fftsize, center[0]+fftsize, center[1]-fftsize, center[1]+fftsize)
extent
viewer = napari.view_image(np.swapaxes(original, -1, -2), name='original')
# create the square in napari
center = np.array(original.shape[1:]) // 2
square = np.array([[center[1]+... | The extent in x,y is: (214, 726, 646, 1158) pixels, which makes the largest side/2 256 pixels.
| MIT | StatRegistration_multiNLP.ipynb | thagusta/LEEM-analysis-1 |
Now starting the steps of the algorithm | def crop_and_filter_extent(images, extent, sigma=11, mode='nearest'):
"""Crop images to extent chosen and apply the filters. Cropping is initially with a margin of sigma,
to prevent edge effects of the filters. extent = minx,maxx,miny,maxy of ROI"""
result = images[:, extent[0]-sigma:extent[1]+sigma,
... | Saving stack as .png
Now saving movie stack.mp4
| MIT | StatRegistration_multiNLP.ipynb | thagusta/LEEM-analysis-1 |
Create a Class To create a class, use the keyword class: | class car:
x = 5
print(car) | <class '__main__.car'>
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Object Creation | class car:
x = 5
company="Maruti"
p1 = car()
print(p1.x)
print(p1.company) | 5
Maruti
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Object Creation & The self Parameter | class Jeep:
# A simple class
# attribute
company = "Maruti"
location = "India"
# A sample method
def test(self):
print("I'm from", self.company)
print("I'm made in ", self.location)
# Object instantiation
jimny = Jeep()
# Accessing class attributes
... | Maruti
I'm from Maruti
I'm made in India
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
The self Parameter It does not have to be named *self* , you can call it whatever you like, but it has to be the first parameter of any function in the class: | class Car:
def __init__(sampleobject, name, price):
sampleobject.name = name
sampleobject.price = price
def myfunc(abc):
print("Hello , my car is " + abc.name)
p1 = Car("BMW", 1)
p1.myfunc()
| Hello , my car is BMW
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
The __init__() Function Note: The __init__() function is called automatically every time the class is being used to create a new object. | class Car:
def __init__(self, company):
self.company = company
p1 = Car("Maruti" )
print(p1.company) | Maruti
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Modify Object Properties | class Car:
def __init__(self, model, rate):
self.model = model
self.rate = rate
def myfunc(self):
print("Hello my name is " + self.model)
p1 = Car("Swift", 500000)
p1.myfunc()
print(p1.model)
print("Previous rate of my model was " , p1.rate)
p1.rate ... | Hello my name is Swift
Swift
Previous rate of my model was 500000
New rate is 400000
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Delete Object Properties You can delete properties on objects by using the del keyword: | class Car:
def __init__(self, model, rate):
self.model = model
self.rate = rate
def myfunc(self):
print("Hello my name is " + self.model)
p1 = Car("Swift",500000 )
del p1.rate
print(p1.rate) | _____no_output_____ | MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Delete Object You can delete objects by using the del keyword: | class Car:
def __init__(self, model, rate):
self.model = model
self.rate = rate
def myfunc(self):
print("Hello my name is " + self.model)
p1 = Car("Swift",500000 )
del p1
print(p1.rate) | _____no_output_____ | MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
The pass Statement | class Person:
pass
class Car:
# Class Variable
fav_car = 'Verna'
# The init method or constructor
def __init__(self, model, color):
# Instance Variable
self.model = model
self.color = color
# Objects of Dog class ... | Moderna details:
Moderna is a Verna
Model: swift
Color: brown
Buziga details:
Buziga is a Verna
Model: wagonR
Color: black
Accessing class variable using class name
Verna
| MIT | Ananthakrishnan_Python_Class&Objects.ipynb | VarunBhaaskar/Open-contributions |
Lambda School Data Science - Survival Analysishttps://xkcd.com/881/The aim of survival analysis is to analyze the effect of different risk factors and use them to predict the duration of time between one event ("birth") and anoth... | !pip install lifelines
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lifelines
# lifelines comes with some datasets to get you started playing around with it.
# Most of the datasets are cleaned-up versions of real datasets. Here we will
# use their Leukemia dataset comparing 2 different ... | _____no_output_____ | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
You can you any Pandas DataFrame with lifelines. The only requirement is that the DataFrame includes features that describe:* a duration of time for the observation* a binary column regarding censorship (`1` if the death event was observed, `0` if the death event was not observed)Sometimes, you will have to engineer ... | leukemia.info()
leukemia.describe()
time = leukemia.t.values
event = leukemia.status.values
ax = lifelines.plotting.plot_lifetimes(time, event_observed=event)
ax.set_xlim(0, 40)
ax.grid(axis='x')
ax.set_xlabel("Time in Months")
ax.set_title("Lifelines for Survival of Leukemia Patients");
plt.plot(); | _____no_output_____ | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Kaplan-Meier survival estimate The Kaplan-Meier method estimates survival probability from observed survival times. It results in a step function that changes value only at the time of each event, and confidence intervals can be computer for the survival probabilities. The KM survival curve,a plot of KM survival proba... | kmf = lifelines.KaplanMeierFitter()
kmf.fit(time, event_observed=event)
!pip install -U matplotlib # Colab has matplotlib 2.2.3, we need >3
kmf.survival_function_.plot()
plt.title('Survival Function Leukemia Patients');
print(f'Median Survival: {kmf.median_} months after treatment')
kmf.survival_function_.plot.line()... | Median survival time with Treatment 1: 8.0 months
Median survival time with Treatment 0: 23.0 months
| MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Cox Proportional Hazards Model -- Survival RegressionIt assumes the ratio of death event risks (hazard) of two groups remains about the same over time.This ratio is called the hazards ratio or the relative risk.All Cox regression requires is an assumption that ratio of hazards is constant over time across groups.*The ... | # Using Cox Proportional Hazards model
cph = lifelines.CoxPHFitter()
cph.fit(leukemia, 't', event_col='status')
cph.print_summary() | <lifelines.CoxPHFitter: fitted with 42 observations, 12 censored>
duration col = 't'
event col = 'status'
number of subjects = 42
number of events = 30
log-likelihood = -69.59
time fit was run = 2019-01-20 19:09:48 UTC
---
coef exp(coef) se(coef) z p log(p) lower 0.95 upper ... | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Interpreting the Results`coef`: usually denoted with $b$, the coefficient`exp(coef)`: $e^{b}$, equals the estimate of the hazard ratio. Here, we can say that participants who received treatment 1 had ~4.5 times the hazard risk (risk of death) compared to those who received treatment 2. And for every unit the `logWBC` ... | cph.plot_covariate_groups(covariate='logWBC', groups=np.arange(1.5,5,.5))
cph.plot_covariate_groups(covariate='sex', groups=[0,1]) | _____no_output_____ | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Remember how the Cox model assumes the ratio of death events between groups remains constant over time?Well we can check for that. | cph.check_assumptions(leukemia)
# We can see that the sex variable is not very useful by plotting the coefficients
cph.plot()
# Let's do what the check_assumptions function suggested
cph = lifelines.CoxPHFitter()
cph.fit(leukemia, 't', event_col='status', strata=['sex'])
cph.print_summary()
cph.baseline_cumulative_haza... | <lifelines.CoxPHFitter: fitted with 42 observations, 12 censored>
duration col = 't'
event col = 'status'
strata = ['sex']
number of subjects = 42
number of events = 30
log-likelihood = -55.73
time fit was run = 2019-01-20 19:09:51 UTC
---
coef exp(coef) se(coef) z ... | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Notice that this regression has `Likelihood ratio test = 74.90 on 2 df, log(p)=-37.45`, while the one that included `sex` had `Likelihood ratio test = 47.19 on 3 df, log(p)=-21.87`. The LRT is higher and log(p) is lower, meaning this is likely a better fitting model. | cph.plot()
cph.compute_residuals(leukemia, kind='score')
cph.predict_cumulative_hazard(leukemia[:5])
surv_func = cph.predict_survival_function(leukemia[:5])
exp_lifetime = cph.predict_expectation(leukemia[:5])
plt.plot(surv_func)
exp_lifetime
# lifelines comes with some datasets to get you started playing around wit... | _____no_output_____ | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
These are the "lifelines" of the study participants as they attempt to avoid recidivism | recidivism_sample = recidivism.sample(n=25)
duration = recidivism_sample.week.values
arrested = recidivism_sample.arrest.values
ax = lifelines.plotting.plot_lifetimes(duration, event_observed=arrested)
ax.set_xlim(0, 78)
ax.grid(axis='x')
ax.vlines(52, 0, 25, lw=2, linestyles='--')
ax.set_xlabel("Time in Weeks")
ax.s... |
<lifelines.StatisticalResult>
test_name = proportional_hazard_test
null_distribution = chi squared
degrees_of_freedom = 1
---
test_statistic p log(p)
age identity 12.06 <0.005 -7.57 **
km 11.03 <0.005 -7.02 **
log 13.07 <0.00... | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
The Intuition - Hazard and Survival Functions Hazard Function - the dangerous bathtubThe hazard function represents the *instantaneous* likelihood of failure. It can be treated as a PDF (probability density function), and with real-world data comes in three typical shapes. | _____no_output_____ | MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
Assignment - Customer ChurnTreselle Systems, a data consulting service, [analyzed customer churn data using logistic regression](http://www.treselle.com/blog/customer-churn-logistic-regression-with-r/). For simply modeling whether or not a customer left this can work, but if we want to model the actual tenure of a cus... | import pandas as pd
# Loading the data to get you started
df = pd.read_csv(
'https://raw.githubusercontent.com/treselle-systems/'
'customer_churn_analysis/master/WA_Fn-UseC_-Telco-Customer-Churn.csv')
df.head()
df.info() # A lot of these are "object" - some may need to be fixed...
pd.set_option('display.max_ro... | /usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
FutureWarning)
| MIT | module2-survival-analysis/LS_DS_232_Survival_Analysis.ipynb | macscheffer/DS-Unit-2-Sprint-3-Advanced-Regression |
$$p(word|C_k)$$ | def nk(word,given_tag,data):
if word not in voc1:
return 0
else:
word_in_sen=[(word in dc['s{}'.format(i)]) and (tag[i]==given_tag) for i in range(len(data))]
return sum(word_in_sen)
nk('find',4,twitter)
def n(given_tag):
if given_tag==0:
return len(text_0)
elif given_tag... | _____no_output_____ | MIT | Twitter Sentiment Analysis/Untitled2.ipynb | mmaleki/Deep-Learning-with-Tensorfow |
MAT281 Aplicaciones de la Matemática en la Ingeniería Módulo 04 Laboratorio Clase 06: Proyectos de Machine Learning Instrucciones* Completa tus datos personales (nombre y rol USM) en siguiente celda.* La escala es de 0 a 4 considerando solo valores enteros.* Debes _pushear_ tus cambios a tu repositorio personal del ... | import pandas as pd
import altair as alt
import numpy as np
from vega_datasets import data
alt.themes.enable('opaque')
%matplotlib inline
gapminder = data.gapminder_health_income()
gapminder.head() | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
1. Análisis exploratorio (1 pto)Como mínimo, realizar un `describe` del dataframe y una visualización adecuada, una _scatter matrix_ con los valores numéricos. | desc=gapminder.describe()
print(desc)
pd.plotting.scatter_matrix(gapminder, alpha=0.3, figsize=(9,9), diagonal='kde') | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
2. Preprocesamiento (1 pto) Aplicar un escalamiento a los datos antes de aplicar nuestro algoritmo de clustering. Para ello, definir la variable `X_raw` que corresponde a un `numpy.array` con los valores del dataframe `gapminder` en las columnas _income_, _health_ y _population_. Luego, definir la variable `X` que de... | from sklearn.preprocessing import StandardScaler
X_raw = gapminder.drop('country', axis=1).values
X = StandardScaler().fit_transform(X_raw) | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
3. Clustering (1 pto) | from sklearn.cluster import KMeans | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
Definir un _estimator_ `KMeans` con `k=3` y `random_state=42`, luego ajustar con `X` y finalmente, agregar los _labels_ obtenidos a una nueva columna del dataframe `gapminder` llamada `cluster`. Finalmente, realizar el mismo gráfico del principio pero coloreado por los clusters obtenidos. | k = 3
kmeans = KMeans(k, random_state=42)
kmeans.fit(X)
clusters = kmeans.labels_
gapminder['cluster']=clusters
##gapminder.head()
colors_palette = {0: "orange", 1: "green", 2: "violet"}
colors = [colors_palette[c] for c in list(gapminder['cluster'])]
pd.plotting.scatter_matrix(gapminder, alpha=0.3, figsize=(9,9), c=co... | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
4. Regla del codo (1 pto) __¿Cómo escoger la mejor cantidad de _clusters_?__En este ejercicio hemos utilizado que el número de clusters es igual a 3. El ajuste del modelo siempre será mejor al aumentar el número de clusters, pero ello no significa que el número de clusters sea el apropiado. De hecho, si tenemos que aj... | elbow = pd.Series(name="inertia").rename_axis(index="k")
for k in range(1, 10):
kmeans = KMeans(n_clusters=k, random_state=42).fit(X)
elbow.loc[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center
elbow = elbow.reset_index()
alt.Chart(elbow).mark_line(point=True).encode(
... | _____no_output_____ | MIT | m04_machine_learning/m04_c06_ml_workflow/m04_c06_lab.ipynb | Crhlb/mat281_repositorio |
03 ImageClustering using PCA, Kmeans, DBSCAN Learning Objectives* Explore and interpret the image dataset* Apply Intel® Extension for Scikit-learn* patches to Principal Components Analysis (PCA), Kmeans,and DBSCAN algorithms and target GPU Library Dependencies: - **p... | from __future__ import print_function
data_path = ['data']
# Notebook time start
from datetime import datetime
start_time = datetime.now()
current_time = start_time.strftime("%H:%M:%S")
print("Current Time =", current_time) | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Define image manipulation and Reading functions | from lab.Read_Transform_Images import *
| _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Actually read the images- [Back to Sections](Back_to_Sections) | resultsDict = {}
#resultsDict = Read_Transform_Images(resultsDict,imagesFilenameList = imagesFilenameList)
resultsDict = Read_Transform_Images(resultsDict)
#resultsDict.keys() | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Display ImageGrid Random SamplingThis should give an idea of how closely or differently the various images appear. Notice that some of the collard lizard images have much differnet white balance and this will affect the clustering. For this dataset the images are clustered based on the similarity in RGB colorspace onl... | img_arr = []
ncols = 8
imageGrid=(ncols,3)
for pil in random.sample(resultsDict['list_PIL_Images'], imageGrid[0]*imageGrid[1]) :
img_arr.append(np.array(pil))
#displayImageGrid(img_arr, imageGrid=imageGrid)
displayImageGrid2(img_arr, ncols=ncols) | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Review Python file prior to submission Set SYCL Device Context- [Back to Sections](Back-to-Sections)Paste this code in cell below and run it (twice) once to load, once to execute the cell:%load batch_clustering_Streamlined.py | %load batch_clustering_Streamlined.py | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Submit batch_clustering_Streamlined.py- [Back to Sections](Back_to_Sections)batch_clustering_Streamlined.py executed with a Python* command inside a shell script - run_clustering_streamlined.sh.run_clustering_streamlined.sh is submitted as a batch job to a node which has GPUs | #!python batch_kmeans.py # works on Windows
! chmod 755 q; chmod 755 run_clustering_streamlined.sh; if [ -x "$(command -v qsub)" ]; then ./q run_clustering_streamlined.sh; else ./run_clustering_streamlined.sh; fi | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Read Results of the Dictionary After GPU Computation- [Back to Sections](Back_to_Sections) | # read results from json file in results folder
resultsDict = read_results_json()
# get list_PIL_Images from Read_Tansform_Images
resultsDict = Read_Transform_Images(resultsDict) | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Plot Kmeans Clusters Plot a histogram of the using GPU results- [Back to Sections](Back_to_Sections) | resultsDict.keys()
#resultsDict = Compute_kmeans_db_histogram_labels(resultsDict, knee = 6, gpu_available = gpu_available) #knee = 5
counts = np.asarray(resultsDict['counts'])
bins = np.asarray(resultsDict['bins'])
plt.xlabel("Weight")
plt.ylabel("Probability")
plt.title("Histogram with Probability Plot}")
slice = min(... | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Print Kmeans Related Data as a Sanity Check | print(resultsDict['bins'])
print(resultsDict['counts'])
resultsDict.keys() | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Display Similar ImagesVisually compare image which have been clustered by the allgorithm | clusterRank = 2
d = {i:cts for i, cts in enumerate(resultsDict['counts'])}
sorted_d = sorted(d.items(), key=operator.itemgetter(1), reverse=True)
id = sorted_d[clusterRank][0]
indexCluster = np.where(np.asarray(resultsDict['km_labels']) == id )[0].tolist()
img_arr = []
for idx in indexCluster:
img_arr.append(np.a... | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
PlotPlot Seaborn Kmeans ClustersIndicates numbers of images that are close in color space | %matplotlib inline
n_components = 4
columns = ['PC{:0d}'.format(c) for c in range(n_components)]
data = pd.DataFrame(np.asarray(resultsDict['PCA_fit_transform'])[:,:n_components], columns = columns)
#k_means = resultsDict['model']
data['cluster'] = resultsDict['km_labels']
data.head()
# similarlyNamedImages = [9,6,6,... | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Find DBSCAN EPS parameterDensity-Based Spatial Clustering of Applications with Noise (DBSCAN) finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.EPS: "epsilon" value in sklearn is the maximum distance between two samples for one to be considered ... | from sklearn.neighbors import NearestNeighbors
PCA_images = resultsDict['PCA_fit_transform']
neighbors = NearestNeighbors(n_neighbors=2)
#X = StandardScaler().fit_transform(PCA_images)
neighbors_fit = neighbors.fit(PCA_images)
distances, indices = neighbors_fit.kneighbors(PCA_images)
distances = np.sort(distances, a... | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Use DBSCAN to find clusterswe will use initial estiamtes from KNN above (find elbow) to given initial trial for DBSCAN EPS values In the plot above, there is a plateau in the y values somewherre near 350 indicating that a cluster distance (aka EPS) might work well somewhere near this value. We used this value in the b... | #%%write_and_run lab/compute_DBSCANClusterRank.py
def compute_DBSCANClusterRank(n, EPS):
d = {index-1:int(cnt) for index, cnt in enumerate(counts )}
sorted_d = sorted(d.items(), key=operator.itemgetter(1), reverse=True)
for i in range(0, len(d)):
idx = sorted_d[i][0]
print('cluster = ', ... | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
DBSCAN Cluster PlotPlot a histogram of the using GPU results- [Back to Sections](Back_to_Sections) To indicate numbers of images in each cluster. color each point by its membership in a cluster | %matplotlib inline
sns.set_context('notebook');
g = sns.pairplot(data[columns], hue="cluster", palette="Paired", diag_kws=dict(hue=None));
g.fig.suptitle("DBSCAN pairplot"); | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
Print Filenames of Outliers | print('Outlier/problematic images are: \n',
[resultsDict['imagesFilenameList'][f] for f in list(data[data['cluster'] == -1].index)]
) | _____no_output_____ | MIT | 03_scikit-learn-intelex_Image_Cluster/03_ImageClustering.ipynb | IntelSoftware/scikit-learn_essentials |
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