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 |
|---|---|---|---|---|---|
--- Question 2 Write the training loop* Create the loss function. This should be a loss function suitable for multi-class classification.* Create the metric accumulator. This should the compute and store the accuracy of the model during training* Create the trainer with the `adam` optimizer and learning rate of `0.002`... | def train(network, training_dataloader, batch_size, epochs):
"""
Should take an initialized network and train that network using data from the data loader.
:param network: initialized gluon network to be trained
:type network: gluon.Block
:param training_dataloader: the training DataLoader... | _____no_output_____ | MIT | Module_5_LeNet_on_MNIST (1).ipynb | vigneshb-it19/AWS-Computer-Vision-GluonCV |
Let's define and initialize a network to test the train function. | net = gluon.nn.Sequential()
net.add(gluon.nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
gluon.nn.MaxPool2D(pool_size=2, strides=2),
gluon.nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
gluon.nn.MaxPool2D(pool_size=2, strides=2),
gluon.nn.Flatten(),
gluon.nn.... | 0 0.93415
1 0.9572583333333333
2 0.9668111111111111
3 0.972375
4 0.97606
| MIT | Module_5_LeNet_on_MNIST (1).ipynb | vigneshb-it19/AWS-Computer-Vision-GluonCV |
--- Question 3 Write the validation loop* Create the metric accumulator. This should the compute and store the accuracy of the model on the validation set* Write the validation loop | def validate(network, validation_dataloader):
"""
Should compute the accuracy of the network on the validation set.
:param network: initialized gluon network to be trained
:type network: gluon.Block
:param validation_dataloader: the training DataLoader provides batches for data for every i... | _____no_output_____ | MIT | Module_5_LeNet_on_MNIST (1).ipynb | vigneshb-it19/AWS-Computer-Vision-GluonCV |
Callbacks and Multiple inputs | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.preprocessing import scale
from keras.optimizers import SGD
from keras.layers import Dense, Input, concatenate, BatchNormalization
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
from keras.m... | _____no_output_____ | MIT | solutions_do_not_open/Lab_05_DL Callbacks and Multiple Inputs_solution.ipynb | Dataweekends/global_AI_conference_Jan_2018 |
HypothesisI believe that random forest will have a better score since the data frame has a lot of catergorical data and a lot of columns in general. | # Train the Logistic Regression model on the unscaled data and print the model score
classifier = LogisticRegression()
classifier.fit(X_dummies_train, y_label_1)
print(f"Training Data Score: {classifier.score(X_dummies_train, y_label_1)}")
print(f"Testing Data Score: {classifier.score(X_dummies_test, y_label_2)}")
# Tr... | Training Score: 1.0
Testing Score: 0.646958740961293
| MIT | Credit Risk Evaluator.ipynb | J-Schea29/Supervised-Machine-Learning-Challenge |
Hypothesis 2I think that by scaling my scores are going to get better and that the testing and training will be less spread out. | # Scale the data
scaler = StandardScaler().fit(X_dummies_train)
X_train_scaled = scaler.transform(X_dummies_train)
X_test_scaled = scaler.transform(X_dummies_test)
X_test_scaled
# Train the Logistic Regression model on the scaled data and print the model score
classifier = LogisticRegression()
classifier.fit(X_train_sc... | Training Score: 1.0
Testing Score: 0.6480221182475542
| MIT | Credit Risk Evaluator.ipynb | J-Schea29/Supervised-Machine-Learning-Challenge |
IntroHere's a simple example where we produce a set of plots, called a tear sheet, for a single stock. Imports and Settings | # silence warnings
import warnings
warnings.filterwarnings('ignore')
import yfinance as yf
import pyfolio as pf
%matplotlib inline | _____no_output_____ | Apache-2.0 | pyfolio/examples/single_stock_example.ipynb | MBounouar/pyfolio-reloaded |
Download daily stock prices using yfinance Pyfolio expects tz-aware input set to UTC timezone. You may have to import `yfinance` first by running:```bashpip install yfinance``` | fb = yf.Ticker('FB')
history = fb.history('max')
history.index = history.index.tz_localize('utc')
history.info()
returns = history.Close.pct_change() | _____no_output_____ | Apache-2.0 | pyfolio/examples/single_stock_example.ipynb | MBounouar/pyfolio-reloaded |
Create returns tear sheetThis will show charts and analysis about returns of the single stock. | pf.create_returns_tear_sheet(returns, live_start_date='2020-1-1') | _____no_output_____ | Apache-2.0 | pyfolio/examples/single_stock_example.ipynb | MBounouar/pyfolio-reloaded |
Import Data | import numpy as np
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
# load data
import os
from google.colab import drive
drive.mount('/content/drive')
filedir = './drive/My Drive/Final/CNN_data'
with open(filedir + '/' + 'feature_extracted', 'rb') as f:
X = np.load(f)
with open(filedir... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
CLF1 Ridge Classifier | '''
from sklearn.linear_model import RidgeClassifier
parameters = {'alpha':[1]}
rc = RidgeClassifier(alpha = 1)
clf = GridSearchCV(rc, parameters, cv=3)
clf.fit(X[:30], Y[:30])
clf.best_estimator_.fit(X[:30], Y[:30]).score(X, Y)
clf.best_index_
'''
from sklearn.linear_model import RidgeClassifier
def clf_RidgeClassifi... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
CLF2 SVM | from sklearn.svm import SVC
def clf_SVM(X_train, Y_train, X_test, Y_test):
parameters = {'C':[10, 1, 1e-1, 1e-2, 1e-3]}
svc = SVC(kernel='linear')
clf = GridSearchCV(svc, parameters, cv=3, return_train_score=True, iid=False)
clf.fit(X_train, Y_train)
results = clf.cv_results_
opt_index = clf.best_index_
t... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
CLF3 LDA | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
def clf_lda(Xtrain, Ytrain, Xtest, Ytest):
"""
Input: training data, labels, testing data, labels
Output: training set mean prediciton accuracy, validation accuracy = None, testing set mean prediction accuracy
Note: LDA has no hyperparamete... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
CLF4 KNN | from sklearn.neighbors import KNeighborsClassifier
def clf_KNN(X_train, Y_train, X_test, Y_test):
parameters = {'n_neighbors':[1,5,20]}
knn = KNeighborsClassifier(algorithm='auto', weights='uniform')
clf = GridSearchCV(knn, parameters, cv=3, return_train_score=True, iid=False)
clf.fit(X_train, Y_train)
result... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
CLF5 Decision Tree | from sklearn.tree import DecisionTreeClassifier
def clf_DecisionTree(X_train, Y_train, X_test, Y_test):
parameters = {'max_depth':[5,10,15,20,25], 'criterion':['entropy', 'gini']}
dtc = DecisionTreeClassifier()
clf = GridSearchCV(dtc, parameters, cv=3, return_train_score=True, iid=False)
clf.fit(X_train, Y_trai... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
Testing On Data | clf_list = [clf_RidgeClassifier, clf_SVM, clf_lda, clf_KNN, clf_DecisionTree]
def test_trial(X_shuffled, Y_shuffled):
global clf_list
error = np.zeros((3,5,3)) # partition(3) * clf(5) * error(3)
# (8/2,5/5,2/8) * (clf_list) * (trn,val,tst)
opt_param = np.empty((3,5), dtype=dict) # part... | _____no_output_____ | MIT | cnn_classifier.ipynb | Poxls88/triggerword |
View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine API and geemapInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The **geemap** Python package is built upon the [ipy... | # Installs geemap package
import subprocess
try:
import geemap
except ImportError:
print('geemap package not installed. Installing ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])
# Checks whether this notebook is running on Google Colab
try:
import google.colab
import gee... | _____no_output_____ | MIT | Image/extract_value_to_points.ipynb | YuePanEdward/earthengine-py-notebooks |
Create an interactive map The default basemap is `Google Satellite`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/geemap.pyL13) can be added using the `Map.add_basemap()` function. | Map = emap.Map(center=[40,-100], zoom=4)
Map.add_basemap('ROADMAP') # Add Google Map
Map | _____no_output_____ | MIT | Image/extract_value_to_points.ipynb | YuePanEdward/earthengine-py-notebooks |
Add Earth Engine Python script | # Add Earth Engine dataset
# Input imagery is a cloud-free Landsat 8 composite.
l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1')
image = ee.Algorithms.Landsat.simpleComposite(**{
'collection': l8.filterDate('2018-01-01', '2018-12-31'),
'asFloat': True
})
# Use these bands for prediction.
bands = ['B2', 'B3', 'B... | _____no_output_____ | MIT | Image/extract_value_to_points.ipynb | YuePanEdward/earthengine-py-notebooks |
Display Earth Engine data layers | Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.
Map | _____no_output_____ | MIT | Image/extract_value_to_points.ipynb | YuePanEdward/earthengine-py-notebooks |
FmriprepToday, many excellent general-purpose, open-source neuroimaging software packages exist: [SPM](https://www.fil.ion.ucl.ac.uk/spm/) (Matlab-based), [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki), [AFNI](https://afni.nimh.nih.gov/), and [Freesurfer](https://surfer.nmr.mgh.harvard.edu/) (with a shell interface). W... | import os
print(os.listdir('bids/derivatives/fmriprep')) | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
As said, Fmriprep outputs a directory with results (`sub-03`) and an associated HTML-file with a summary of the (intermediate and final) results. Let's check the directory with results first: | from pprint import pprint # pprint stands for "pretty print",
sub_path = os.path.join('bids/derivatives/fmriprep', 'sub-03')
pprint(sorted(os.listdir(sub_path))) | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
The `figures` directory contains several figures with the result of different preprocessing stages (like functional → high-res anatomical registration), but these figures are also included in the HTML-file, so we'll leave that for now. The other two directories, `anat` and `func`, contain the preprocessed anatomic... | anat_path = os.path.join(sub_path, 'anat')
pprint(os.listdir(anat_path)) | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
Here, we see a couple of different files. There are both (preprocessed) nifti images (`*.nii.gz`) and associated meta-data (plain-text files in JSON format: `*.json`).Importantly, the nifti outputs are in two different spaces: one set of files are in the original "T1 space", so without any resampling to another space (... | func_path = os.path.join(sub_path, 'func')
pprint(os.listdir(func_path)) | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
Again, like the files in the `anat` folder, the functional outputs are available in two spaces: `T1w` and `MNI152NLin2009cAsym`. In terms of actual images, there are preprocessed BOLD files (ending in `preproc_bold.nii.gz`), the functional volume used for "functional → anatomical" registration (ending in `boldref.... | import pandas as pd
conf_path = os.path.join(func_path, 'sub-03_task-flocBLOCKED_desc-confounds_regressors.tsv')
conf = pd.read_csv(conf_path, sep='\t')
conf.head() | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
Confound files from Fmriprep contain a large set of confounds, ranging from motion parameters (`rot_x`, `rot_y`, `rot_z`, `trans_x`, `trans_y`, and `trans_z`) and their derivatives (`*derivative1`) and squares (`*_power2`) to the average signal from the brain's white matter and cerebrospinal fluid (CSF), which should c... | from IPython.display import IFrame
IFrame(src='./bids/derivatives/fmriprep/sub-03.html', width=700, height=600) | _____no_output_____ | MIT | NI-edu/fMRI-introduction/week_4/fmriprep.ipynb | lukassnoek/NI-edu |
Desafio 1 do [Paulo Silveira](https://twitter.com/paulo_caelum) Encontrar quantos filmes não possuem avaliações e quais são esses filmes | count_rating_by_movieId = movies_rating.pivot_table(index=['movieId'], aggfunc='size').rename('votes')
count_rating_by_movieId
movies_with_votes = movies.join(count_rating_by_movieId, on="movieId")
movies_with_votes[movies_with_votes['votes'].isnull()] | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 2 do [Guilherme Silveira](https://twitter.com/guilhermecaelum) Alterar o nome da coluna nota do dataframe filmes_com_media para nota_média após o join. | rating = movies_rating.groupby("movieId")['rating'].mean()
rating
filmes_com_media = movies.join(rating, on="movieId").rename(columns={'rating': 'nota_média'})
filmes_com_media | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 3 do [Guilherme Silveira](https://twitter.com/guilhermecaelum) Adicionar ao filmes_com_media o total de votos de cada filme | movies_with_rating_and_votes = filmes_com_media.join(count_rating_by_movieId, on="movieId")
movies_with_rating_and_votes | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 4 do [Thiago Gonçalves](https://twitter.com/tgcsantos) Arredondar as médias (coluna de nota média) para duas casas decimais. | movies_with_rating_and_votes = movies_with_rating_and_votes.round({'nota_média':2})
movies_with_rating_and_votes | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 5 do [Allan Spadini](https://twitter.com/allanspadini) Descobrir os generos dos filmes (quais são eles, únicos). (esse aqui o bicho pega) | genres_split = movies.genres.str.split("|")
genres_split
genres = pd.DataFrame({'genre':np.concatenate(genres_split.values)})
list_genres = genres.groupby('genre').size().reset_index(name='count')
list_genres['genre'] | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 6 da [Thais André](https://twitter.com/thais_tandre) Contar o número de aparições de cada genero. | list_genres | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Desafio 7 do [Guilherme Silveira](https://twitter.com/guilhermecaelum) Plotar o gráfico de aparições de cada genero. Pode ser um gráfico de tipo igual a barra. | list_genres[['genre', 'count']].sort_values(by=['genre'], ascending=True).plot(x='genre', kind='barh', title="Generos") | _____no_output_____ | MIT | desafios_aula01.ipynb | justapixel/QuarentenaDados |
Write a program to remove characters from a string starting from zero up to n and return a new string.__Example:__remove_char("Untitled", 4) so output must be tled. Here we need to remove first four characters from a string | def remove_char(a, b):
# Write your code here
print("started")
a="Untitled"
b=4
remove_char(a,b) | started
| MIT | test.ipynb | sharaththota/Test |
Write a program to find how many times substring appears in the given string.__Example:__"You can use Markdown to format documentation you add to Markdown cells" sub_string: MarkdownIn the above the substring Markdown is appeared two times.So the count is two | def sub_string(m_string,s_string):
# Write your code here
print("started")
m_string="You can use Markdown to format documentation you add to Markdown cells"
s_string="Markdown"
sub_string(m_string,s_string)
| started
| MIT | test.ipynb | sharaththota/Test |
Write a program to check if the given number is a palindrome number.__Exapmle:__A palindrome number is a number that is same after reverse. For example 242, is the palindrome number | def palindrom_check(a):
# Write your code here
print("started")
palindrom_check(242) | started
| MIT | test.ipynb | sharaththota/Test |
Write a program to Extract Unique values from dictionary values__Example:__test= {"gfg': [5, 6, 7, 8], 'is': [10, 11, 7, 5], 'best' : [6, 12, 10, 8], 'for': [1, 2, 5]}out_put: [1, 2, 5, 6, 7, 8, 10, 11, 12] | def extract_unique(a):
# Write your code here
print("started")
test= {'gfg': [5, 6, 7, 8], 'is': [10, 11, 7, 5], 'best' : [6, 12, 10, 8], 'for': [1, 2, 5]}
extract_unique(test) | started
| MIT | test.ipynb | sharaththota/Test |
Write a program to find the dictionary with maximum count of pairs__Example:__Input: test_list = [{"gfg": 2, "best":4}, {"gfg": 2, "is" : 3, "best": 4, "CS":9}, {"gfg":2}] Output: 4 | def max_count(a):
# Write your code here
print("started")
test_list = [{"gfg": 2, "best":4}, {"gfg": 2, "is" : 3, "best": 4, "CS":9}, {"gfg":2}]
max_count(test_list)
| started
| MIT | test.ipynb | sharaththota/Test |
Access the value of key 'history' from the below dict | def key_access(a):
# Write your code here
print("started")
sampleDict = {
"class":{
"student":{
"name": "Mike",
"marks" : {
"physics":70,
"history":80
}
}
}
}
key_access(sampleDict) | started
| MIT | test.ipynb | sharaththota/Test |
Print the value of key hair Print the third element of the key interested in | def third_ele(a):
# Write your code here
print("started")
info={
"personal data":{
"name":"Lauren",
"age":20,
"major":"Information Science",
"physical_features":{
"color":{
"eye":"blue",
"hair":"brown"
},
"hei... | _____no_output_____ | MIT | test.ipynb | sharaththota/Test |
Print the Unique values from attempts column | def un_values(df):
# Write your code here
print("started")
un_values(df) | started
| MIT | test.ipynb | sharaththota/Test |
Print the top five rows from the data frame | def top_five(df):
# Write your code here
print("started")
top_five(df) | started
| MIT | test.ipynb | sharaththota/Test |
Print the max and min values of the coulmn attempts | def min_max(df):
# Write your code here
print("started")
min_max(df) | started
| MIT | test.ipynb | sharaththota/Test |
Import data | df = pd.read_hdf('data/car.h5')
df.shape
df.columns | _____no_output_____ | MIT | matrix_two/day3.ipynb | kmwolowiec/data_workshop |
Dummy Model | df.select_dtypes(np.number).columns
X = df['car_id']
y = df['price_value']
model = DummyRegressor()
model.fit(X, y)
y_pred = model.predict(X)
mae(y, y_pred)
[x for x in df.columns if 'price' in x]
df['price_currency'].value_counts()
df = df[ df.price_currency == 'PLN']
df.shape | _____no_output_____ | MIT | matrix_two/day3.ipynb | kmwolowiec/data_workshop |
Features | df.sample(5)
suffix_cat = '__cat'
for feat in df.columns:
if isinstance(df[feat][0], list):continue
factorized_values = df[feat].factorize()[0]
if suffix_cat in feat:
df[feat] = factorized_values
else:
df[feat+suffix_cat] = factorized_values
cat_feats = [x for x in df.columns i... | Counting objects: 1
Counting objects: 4, done.
Delta compression using up to 2 threads.
Compressing objects: 25% (1/4)
Compressing objects: 50% (2/4)
Compressing objects: 75% (3/4)
Compressing objects: 100% (4/4)
Compressing objects: 100% (4/4), done.
Writing objects: 25% (1/4)
Writing objects: 5... | MIT | matrix_two/day3.ipynb | kmwolowiec/data_workshop |
Pattern Mining Library | source("https://raw.githubusercontent.com/eogasawara/mylibrary/master/myPreprocessing.R")
loadlibrary("arules")
loadlibrary("arulesViz")
loadlibrary("arulesSequences")
data(AdultUCI)
dim(AdultUCI)
head(AdultUCI) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Removing attributes | AdultUCI$fnlwgt <- NULL
AdultUCI$"education-num" <- NULL
| _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Conceptual Hierarchy and Binning | AdultUCI$age <- ordered(cut(AdultUCI$age, c(15,25,45,65,100)),
labels = c("Young", "Middle-aged", "Senior", "Old"))
AdultUCI$"hours-per-week" <- ordered(cut(AdultUCI$"hours-per-week",
c(0,25,40,60,168)),
... | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Convert to transactions | AdultTrans <- as(AdultUCI, "transactions")
| _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
A Priori | rules <- apriori(AdultTrans, parameter=list(supp = 0.5, conf = 0.9, minlen=2, maxlen= 10, target = "rules"),
appearance=list(rhs = c("capital-gain=None"), default="lhs"), control=NULL)
inspect(rules)
rules_a <- as(rules, "data.frame")
head(rules_a) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Analysis of Rules | imrules <- interestMeasure(rules, transactions = AdultTrans)
head(imrules) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Removing redundant rules | nrules <- rules[!is.redundant(rules)]
arules::inspect(nrules) | lhs rhs support confidence
[1] {hours-per-week=Full-time} => {capital-gain=None} 0.5435895 0.9290688
[2] {sex=Male} => {capital-gain=None} 0.6050735 0.9051455
[3] {workclass=Private} => {capital-gain=None} 0.6413742 0.9239073
[4] ... | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Showing the transactions that support the rulesIn this example, we can see the transactions (trans) that support rules 1. | st <- supportingTransactions(nrules[1], AdultTrans)
trans <- unique(st@data@i)
length(trans)
print(c(length(trans)/length(AdultTrans), nrules[1]@quality$support)) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Now we can see the transactions (trans) that support rules 1 and 2. As can be observed, the support for both rules is not the sum of the support of each rule. | st <- supportingTransactions(nrules[1:2], AdultTrans)
trans <- unique(st@data@i)
length(trans)
print(c(length(trans)/length(AdultTrans), nrules[1:2]@quality$support)) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Rules visualization | options(repr.plot.width=7, repr.plot.height=4)
plot(rules)
options(repr.plot.width=7, repr.plot.height=4)
plot(rules, method="paracoord", control=list(reorder=TRUE)) | _____no_output_____ | MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Sequence Mining | x <- read_baskets(con = system.file("misc", "zaki.txt", package = "arulesSequences"), info = c("sequenceID","eventID","SIZE"))
as(x, "data.frame")
s1 <- cspade(x, parameter = list(support = 0.4), control = list(verbose = TRUE))
as(s1, "data.frame") |
parameter specification:
support : 0.4
maxsize : 10
maxlen : 10
algorithmic control:
bfstype : FALSE
verbose : TRUE
summary : FALSE
tidLists : FALSE
preprocessing ... 1 partition(s), 0 MB [0.046s]
mining transactions ... 0 MB [0.032s]
reading sequences ... [0.027s]
total elapsed time: 0.105s
| MIT | todo/Pattern.ipynb | lucasgiutavares/mylibrary |
Load data | df = pd.DataFrame({
'x': [4.5, 4.9, 5.0, 4.8, 5.8, 5.6, 5.7, 5.8],
'y': [35, 38, 45, 49, 59, 65, 73, 82],
'z': [0, 0, 0, 0, 1, 1, 1, 1]
})
df
plt.scatter(df['x'], df['y'], c=df['z']) | _____no_output_____ | MIT | docs/!ml/notebooks/Perceptron.ipynb | a-mt/dev-roadmap |
Train model | def fit(X, y, max_epochs=500):
"""
X : numpy 2D array. Each row corresponds to one training example.
y : numpy 1D array. Label (0 or 1) of each example.
"""
n = X.shape[1]
# Initialize weights
weights = np.zeros((n, ))
bias = 0.0
for _ in range(max_epochs):
errors = 0
... | _____no_output_____ | MIT | docs/!ml/notebooks/Perceptron.ipynb | a-mt/dev-roadmap |
Plot predictions | def plot_decision_boundary():
# Draw points
plt.scatter(X[:,0], X[:,1], c=y)
a = -weights[0]/weights[1]
b = -bias/weights[1]
# Draw hyperplane with margin
_X = np.arange(X[:,0].min(), X[:,0].max()+1, .1)
_Y = _X * a + b
plt.plot(_X, _Y)
plot_decision_boundary()
def plot_contour():
... | _____no_output_____ | MIT | docs/!ml/notebooks/Perceptron.ipynb | a-mt/dev-roadmap |
Compare with logistic regression | from sklearn.linear_model import LogisticRegression
model = LogisticRegression(C=1e20, solver='liblinear', random_state=0)
model.fit(X, y)
weights = model.coef_[0]
bias = model.intercept_[0]
plot_decision_boundary() | _____no_output_____ | MIT | docs/!ml/notebooks/Perceptron.ipynb | a-mt/dev-roadmap |
Compare with SVM | from sklearn import svm
model = svm.SVC(kernel='linear', C=1.0)
model.fit(X, y)
weights = model.coef_[0]
bias = model.intercept_[0]
plot_decision_boundary() | _____no_output_____ | MIT | docs/!ml/notebooks/Perceptron.ipynb | a-mt/dev-roadmap |
Gradient Boosting | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
cancer.data, cancer.target, random_state=0)
gbrt = GradientBoostingClassif... | _____no_output_____ | MIT | notebooks/extra - Gradient Boosting.ipynb | lampsonnguyen/ml-training-advance |
import numpy as np
import matplotlib.pyplot as plt | _____no_output_____ | MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics | |
Introduction to vectors Plot vector that has notation (2,4,4). Another vector has notation (1,2,3). Find the direction cosines of each vector, the angles of each vector to the three axes, and the angle between the two vectors! | from mpl_toolkits.mplot3d import axes3d
X = np.array((0, 0))
Y= np.array((0, 0))
Z = np.array((0, 0))
U = np.array((2, 1))
V = np.array((4, 2))
W = np.array((4, 3))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.quiver(X, Y, Z, U, V, W)
ax.set_xlim([-4, 4])
ax.set_ylim([-4, 4])
ax.set_zlim([-4, 4])
... | Angle between vector A and B: 11.490459903731518 degrees
| MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
Exercise 10-3. Effective, Normal, and Shear Stress on a Plane Consider a plane that makes an angle 60 degrees with $\sigma_1$ and 60 degrees with $\sigma_3$. The principal stresses are: -600, -400, -200 MPa. Calculate:* Total effective stress* Normal stress* Shear stress | # principle stresses
sigma_1 = -600; sigma_2 = -400; sigma_3 = -200
# calculate the angle of plane to second principal stress sigma 2
# using pythagorean
alpha = 60; gamma = 60
l = np.cos(np.deg2rad(alpha))
n = np.cos(np.deg2rad(gamma))
m = np.sqrt(1 - l**2 - n**2)
beta = np.rad2deg(np.arccos(m))
print("The second pr... | The second principal stress sigma 2 makes angle: 45.000000000000014 degrees to the plane
The effective stress is: -424.26406871192853 MPa (minus because it's compressive)
The normal stress is: -400.0 MPa
The shear stress is: 141.4213562373095 MPa
| MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
Stress Tensor Components | stress_tensor = [[sigma_xx, sigma_xy, sigma_xz],
[sigma_yx, sigma_yy, sigma_yz],
[sigma_zx, sigma_zy, sigma_zz]]
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
# point of cube
points = np.array([[-5, -5, -5],
[5, -5, -5... | _____no_output_____ | MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
Exercise 10-7 Total Stress, Deviatoric Stress, Effective Stress, Cauchy Summation$$\sigma_{ij}=\tau_{ij}+P_{ij}$$$$P_{ij}=P \cdot \delta_{ij}$$Pressure is: $P=|\sigma_{mean}|=|\frac{\sigma_{xx}+\sigma_{yy}+\sigma_{zz}}{3}|$Knorecker Delta is: $\delta_{ij}=\begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatri... | # known
l, m, n = 0.7, 0.5, 0.5 # direction cosines
alpha, beta, gamma = 45, 60, 60 # angles
stress_ij = np.array([[-40, -40, -35],
[-40, 45, -50],
[-35, -50, -20]]) # total stress tensor
# calculate pressure
P = np.abs(np.mean(np.array([(stress_ij[0][0]), (stress_ij[1][1]),... | The total effective stress is: -93.59887819840577 MPa
X component of principal stress: -93.57142857142858 MPa
Y component of principal stress: -61.0 MPa
Z component of principal stress: -119.0 MPa
The normal stress is: -90.85 MPa
Because normal stress -90.85 MPa nearly equals to sigma 1 -93.57142857142858 MPa, the plan... | MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
Exercise 10-8 Transforming Stress Tensor (Containing all the 9 tensors of shear and normal) to Principal Stress Tensor using Cubic Equation | sigma_ij = np.array([[0, 0, 100],
[0, 0, 0],
[-100, 0, 0]]) # stress tensor
# cubic equation
coeff3 = 1
coeff2 = -((sigma_ij[0][0] + sigma_ij[1][1] + sigma_ij[2][2]))
coeff1 = (sigma_ij[0][0] * sigma_ij[1][1]) + (sigma_ij[1][1] * sigma_ij[2][2]) + (sigma_ij[2][2] * sigma_ij[0][... | _____no_output_____ | MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
*** | from mpl_toolkits.mplot3d import axes3d
X = np.array((0))
Y= np.array((0))
U = np.array((0))
V = np.array((4))
fig, ax = plt.subplots()
q = ax.quiver(X, Y, U, V,units='xy' ,scale=1)
plt.grid()
ax.set_aspect('equal')
plt.xlim(-5,5)
plt.ylim(-5,5)
from mpl_toolkits.mplot3d import axes3d
X = np.array((0))
Y= np.arra... | _____no_output_____ | MIT | delft course dr weijermars/stress_tensor.ipynb | rksin8/reservoir-geomechanics |
AutoGluon Tabular with SageMaker[AutoGluon](https://github.com/awslabs/autogluon) automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text da... | # Make sure docker compose is set up properly for local mode
!./setup.sh
# Imports
import os
import boto3
import sagemaker
from time import sleep
from collections import Counter
import numpy as np
import pandas as pd
from sagemaker import get_execution_role, local, Model, utils, fw_utils, s3
from sagemaker.estimator im... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Build docker images First, build autogluon package to copy into docker image. | if not os.path.exists('package'):
!pip install PrettyTable -t package
!pip install --upgrade boto3 -t package
!pip install bokeh -t package
!pip install --upgrade matplotlib -t package
!pip install autogluon -t package | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Now build the training/inference image and push to ECR | training_algorithm_name = 'autogluon-sagemaker-training'
inference_algorithm_name = 'autogluon-sagemaker-inference'
!./container-training/build_push_training.sh {account} {region} {training_algorithm_name} {ecr_uri_prefix} {registry_id} {registry_uri}
!./container-inference/build_push_inference.sh {account} {region} {i... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Get the data In this example we'll use the direct-marketing dataset to build a binary classification model that predicts whether customers will accept or decline a marketing offer. First we'll download the data and split it into train and test sets. AutoGluon does not require a separate validation set (it uses bagged... | # Download and unzip the data
!aws s3 cp --region {region} s3://sagemaker-sample-data-{region}/autopilot/direct_marketing/bank-additional.zip .
!unzip -qq -o bank-additional.zip
!rm bank-additional.zip
local_data_path = './bank-additional/bank-additional-full.csv'
data = pd.read_csv(local_data_path)
# Split train/tes... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Check the data | train.head(3)
train.shape
test.head(3)
test.shape
X_test.head(3)
X_test.shape | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Upload the data to s3 | train_file = 'train.csv'
train.to_csv(train_file,index=False)
train_s3_path = session.upload_data(train_file, key_prefix='{}/data'.format(prefix))
test_file = 'test.csv'
test.to_csv(test_file,index=False)
test_s3_path = session.upload_data(test_file, key_prefix='{}/data'.format(prefix))
X_test_file = 'X_test.csv'
X_t... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Hyperparameter SelectionThe minimum required settings for training is just a target label, `fit_args['label']`.Additional optional hyperparameters can be passed to the `autogluon.task.TabularPrediction.fit` function via `fit_args`.Below shows a more in depth example of AutoGluon-Tabular hyperparameters from the exampl... | # Define required label and optional additional parameters
fit_args = {
'label': 'y',
# Adding 'best_quality' to presets list will result in better performance (but longer runtime)
'presets': ['optimize_for_deployment'],
}
# Pass fit_args to SageMaker estimator hyperparameters
hyperparameters = {
'fit_args': f... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
TrainFor local training set `train_instance_type` to `local` . For non-local training the recommended instance type is `ml.m5.2xlarge`. **Note:** Depending on how many underlying models are trained, `train_volume_size` may need to be increased so that they all fit on disk. | %%time
instance_type = 'ml.m5.2xlarge'
#instance_type = 'local'
ecr_image = f'{ecr_uri_prefix}/{training_algorithm_name}:latest'
estimator = Estimator(image_name=ecr_image,
role=role,
train_instance_count=1,
train_instance_type=instance_type,
... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Create Model | # Create predictor object
class AutoGluonTabularPredictor(RealTimePredictor):
def __init__(self, *args, **kwargs):
super().__init__(*args, content_type='text/csv',
serializer=csv_serializer,
deserializer=StringDeserializer(), **kwargs)
ecr_image = f'{ecr_u... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Batch Transform For local mode, either `s3:////output/` or `file:///` can be used as outputs.By including the label column in the test data, you can also evaluate prediction performance (In this case, passing `test_s3_path` instead of `X_test_s3_path`). | output_path = f's3://{bucket}/{prefix}/output/'
# output_path = f'file://{os.getcwd()}'
transformer = model.transformer(instance_count=1,
instance_type=instance_type,
strategy='MultiRecord',
max_payload=6,
... | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Endpoint Deploy remote or local endpoint | instance_type = 'ml.m5.2xlarge'
#instance_type = 'local'
predictor = model.deploy(initial_instance_count=1,
instance_type=instance_type) | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Attach to endpoint (or reattach if kernel was restarted) | # Select standard or local session based on instance_type
if instance_type == 'local':
sess = local_session
else:
sess = session
# Attach to endpoint
predictor = AutoGluonTabularPredictor(predictor.endpoint, sagemaker_session=sess) | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Predict on unlabeled test data | results = predictor.predict(X_test.to_csv(index=False)).splitlines()
# Check output
print(Counter(results)) | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Predict on data that includes label column Prediction performance metrics will be printed to endpoint logs. | results = predictor.predict(test.to_csv(index=False)).splitlines()
# Check output
print(Counter(results)) | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Check that classification performance metrics match evaluation printed to endpoint logs as expected | y_results = np.array(results)
print("accuracy: {}".format(accuracy_score(y_true=y_test, y_pred=y_results)))
print(classification_report(y_true=y_test, y_pred=y_results, digits=6)) | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Clean up endpoint | predictor.delete_endpoint() | _____no_output_____ | Apache-2.0 | advanced_functionality/autogluon-tabular/AutoGluon_Tabular_SageMaker.ipynb | phiamazon/amazon-sagemaker-examples |
Berdasarkan isu [73](https://github.com/taruma/hidrokit/issues/73): **request: mengolah berkas dari data bmkg**Deskripsi:- mengolah berkas excel yang diperoleh dari data online bmkg untuk siap dipakai- memeriksa kondisi dataFungsi yang diharapkan:__Umum / General__- Memeriksa apakah data lengkap atau tidak? Jika tidak... | # AKSES GOOGLE DRIVE
from google.colab import drive
drive.mount('/content/gdrive')
# DRIVE PATH
DRIVE_DROP_PATH = '/content/gdrive/My Drive/Colab Notebooks/_dropbox'
DRIVE_DATASET_PATH = '/content/gdrive/My Drive/Colab Notebooks/_dataset/uma_pamarayan'
DATASET_PATH = DRIVE_DATASET_PATH + '/klimatologi_geofisika_tanger... | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
FUNGSI | import pandas as pd
import numpy as np
from operator import itemgetter
from itertools import groupby
def _read_bmkg(io):
return pd.read_excel(
io, skiprows=8, skipfooter=16, header=0, index_col=0, parse_dates=True,
date_parser=lambda x: pd.to_datetime(x, format='%d-%m-%Y')
)
def _have_nan(data... | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
PENGGUNAAN Fungsi `_read_bmkg`Tujuan: Impor berkas excel bmkg ke dataframe | dataset = _read_bmkg(DATASET_PATH)
dataset.head()
dataset.tail() | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_have_nan()`Tujuan: Memeriksa apakah di dalam tabel memiliki nilai yang hilang (np.nan) | _have_nan(dataset) | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_get_index1D()`Tujuan: Memperoleh index data yang hilang untuk setiap array | _get_index1D(dataset['RH_avg'].isna().values) | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_get_nan()`Tujuan: Memperoleh index data yang hilang untuk setiap kolom dalam bentuk `dictionary` | _get_nan(dataset).keys()
print(_get_nan(dataset)['RH_avg']) | [852, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1220, 1221, 1222, 1223, 1224, 1628, 1629, 1697, 2657]
| MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_get_nan_columns()`Tujuan: Memperoleh nama kolom yang memiliki nilai yang hilang `NaN`. | _get_nan_columns(dataset) | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_check_nan()`Tujuan: Gabungan dari `_have_nan()` dan `_get_nan()`. Memeriksa apakah dataset memiliki `NaN`, jika iya, memberikan nilai hasil `_get_nan()`, jika tidak memberikan nilai `None`. | _check_nan(dataset).items()
# Jika tidak memiliki nilai nan
print(_check_nan(dataset.drop(_get_nan_columns(dataset), axis=1))) | None
| MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_group_as_list()`Tujuan: Mengelompokkan kelompok array yang bersifat kontinu (nilainya berurutan) dalam masing-masing list.Referensi: https://stackoverflow.com/a/15276206 (dimodifikasi untuk Python 3.x dan kemudahan membaca) | missing_dict = _get_nan(dataset)
missing_RH_avg = missing_dict['RH_avg']
print(missing_RH_avg)
print(_group_as_list(missing_RH_avg)) | [[852], [1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067], [1220, 1221, 1222, 1223, 1224], [1628, 1629], [1697], [2657]]
| MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_group_as_index()`Tujuan: Mengubah hasil pengelompokkan menjadi jenis index dataset (dalam kasus ini dalam bentuk tanggal dibandingkan dalam bentuk angka-index dataset). | _group_as_index(_group_as_list(missing_RH_avg), index=dataset.index, date_format='%d %b %Y') | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Fungsi `_get_missing()`Tujuan: Memperoleh index yang memiliki nilai tidak terukur (bernilai `8888` atau `9999`) untuk setiap kolomnya | _get_missing(dataset) | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Penerapan Menampilkan index yang bermasalahTujuan: Setelah memperoleh index dari hasil `_get_missing()` atau `_get_nan()`, bisa menampilkan potongan index tersebut dalam dataframe. | dataset.iloc[_get_missing(dataset)['RR']]
_group_as_list(_get_missing(dataset)['RR'])
_group_as_index(_, index=dataset.index, date_format='%d %b %Y', format_date='{} sampai {}') | _____no_output_____ | MIT | hidrokit/contrib_taruma/ipynb/taruma_hk73_bmkg.ipynb | hidrokit/manual |
Homework - Random Walks (18 pts) Continuous random walk in three dimensionsWrite a program simulating a three-dimensional random walk in a continuous space. Let 1000 independent particles all start at random positions within a cube with corners at (0,0,0) and (1,1,1). At each time step each particle will move in a ra... | import numpy as np
numTimeSteps = 2000
numParticles = 1000
positions = np.zeros( (numParticles, 3, numTimeSteps) )
# initialize starting positions on first time step
positions[:,:,0] = np.random.random( (numParticles, 3) ) | _____no_output_____ | Unlicense | homework/key-random_walks.ipynb | nishadalal120/NEU-365P-385L-Spring-2021 |
2. (3 pts) Write code to run your simulation for 2000 time steps. | for t in range(numTimeSteps-1):
# 2 * [0 to 1] - 1 --> [-1 to 1]
jumpsForAllParticles = 2 * np.random.random((numParticles, 3)) - 1
positions[:,:,t+1] = positions[:,:,t] + jumpsForAllParticles
# just for fun, here's another way to run the simulation above without a loop
jumpsForAllParticlesAndAllTimeSteps =... | _____no_output_____ | Unlicense | homework/key-random_walks.ipynb | nishadalal120/NEU-365P-385L-Spring-2021 |
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