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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Download File | # def tt(j):
# type_ = test['type'][j]
# return models[type_].predict(np.expand_dims(X[j], axis=0))
# def get_dataframe():
# with Pool(8) as p:
# a = p.map(tt,[i for i in range(sub.shape[0])])
# return pd.DataFrame(np.array(a))
# %%time
# sub['scalar_coupling_constant'] = get_dataframe()
# s... | _____no_output_____ | MIT | Gradient Boosting for molecular properties/pmp-lightgbm-inference.ipynb | dhananjayraut/ML_projects |
02 | Decision Tree. A Supervised Classification Model - Subscribe to my [Blog ↗](https://blog.pythonassembly.com/)- Let's keep in touch on [LinkedIn ↗](www.linkedin.com/in/jsulopz) 😄 Discipline to Search Solutions in Google > Apply the following steps when **looking for solutions in Google**:>> 1. **Necesity**: How t... | import seaborn as sns
sns.load_dataset(name='titanic').iloc[:, :4] | _____no_output_____ | MIT | II Machine Learning & Deep Learning/02_Decision Tree. A Supervised Classification Model/02session_decision-tree.ipynb | Blanes3/resolving-python-data-science |
`DecisionTreeClassifier()` Model in Python Build the Model > 1. **Necesity**: Build Model> 2. **Google**: How do you search for the solution?> 3. **Solution**: Find the `function()` that makes it happen Code Thinking> Which function computes the Model?> - `fit()`>> How could can you **import the function in Python**... | explanatory = ?
target = ? | _____no_output_____ | MIT | II Machine Learning & Deep Learning/02_Decision Tree. A Supervised Classification Model/02session_decision-tree.ipynb | Blanes3/resolving-python-data-science |
Finally `fit()` the Model Calculate a Prediction with the Model > - `model.predict_proba()` Model Visualization > - `tree.plot_tree()` Model Interpretation > Why `sex` is the most important column? What has to do with **EDA** (Exploratory Data Analysis)? | %%HTML
<iframe width="560" height="315" src="https://www.youtube.com/embed/7VeUPuFGJHk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | _____no_output_____ | MIT | II Machine Learning & Deep Learning/02_Decision Tree. A Supervised Classification Model/02session_decision-tree.ipynb | Blanes3/resolving-python-data-science |
Copy paddleocr.py to cp_pdocr.py | !python cp_pdocr.py --image_dir doc/imgs_en/can-ho.jpg --lang=en | Namespace(cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/root/.paddleocr/cls', cls_thresh=0.9, det=True, det_algorithm='DB', det_db_box_thresh=0.5, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.6, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_lim... | Apache-2.0 | use_PaddleOCR_colab.ipynb | hieu28022000/PaddleOCR |
CRRT Mortality Prediction Model Construction Christopher V. Cosgriff, David Sasson, Colby Wilkinson, Kanhua YinThe purpose of this notebook is to build a deep learning model that predicts ICU mortality in the CRRT population. The data is extracted in the `extract_cohort_and_features` notebook and stored in the `data` ... | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import SVG
import os
from keras.optimizers import Adam, SGD, rmsprop
from keras.models import Sequential,Model
from keras.layers import Dense, Activation, Dropout, Input, Dropout, concatenate
from keras.layers.recurrent im... | /home/cosgriffc/env/ML/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Usin... | MIT | mortality_model.ipynb | cosgriffc/bst261-crrt |
Step 1: Load and Prepare DataHere will we load in the data, and create train, validation, and testing splits. | # set tensors to float 32 as this is what GPUs expect
features_sequence = np.load('./features_sequence.npy').astype(np.float32)
features_static = np.load('./features_static.npy').astype(np.float32)
labels = np.load('./labels.npy').astype(np.float32)
x_seq_full_train, x_seq_test, x_static_full_train, x_static_test, y_f... | _____no_output_____ | MIT | mortality_model.ipynb | cosgriffc/bst261-crrt |
Next we need to remove NANs from the data; we'll impute the trianing population mean, the simplest method suggested by David Sontag. | def impute_mean(source_data, input_data):
'''
Takes the source data, and uses it to determine means for all
features; it then applies them to the input data.
inputs:
source_data: a tensor to provide means
input_data: the data to fill in NA for
output:
output_data: data with nan... | _____no_output_____ | MIT | mortality_model.ipynb | cosgriffc/bst261-crrt |
Step 2: Build Model Model 1Base model, no regularization. | # Define inputs
sequence_input = Input(shape = (x_seq_train.shape[1], x_seq_train.shape[2], ), dtype = 'float32', name = 'sequence_input')
static_input = Input(shape = (x_static_train.shape[1], ), name = 'static_input')
# Network architecture
seq_x = GRU(units = 128)(sequence_input)
# Seperate output for the GRU late... | _____no_output_____ | MIT | mortality_model.ipynb | cosgriffc/bst261-crrt |
10% Dropout | # Define inputs
sequence_input = Input(shape = (x_seq_train.shape[1], x_seq_train.shape[2], ), dtype = 'float32', name = 'sequence_input')
static_input = Input(shape = (x_static_train.shape[1], ), name = 'static_input')
# Network architecture
seq_x = GRU(units = 128)(sequence_input)
# Seperate output for the GRU late... | _____no_output_____ | MIT | mortality_model.ipynb | cosgriffc/bst261-crrt |
First Deep Learning Model | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_circles
X, y = make_circles(n_samples=1000,
noise=0.1,
factor=0.2,
random_state=0)
X
X.shape
plt.figure(figsize=(5, 5))
plt.plot(X[y==0, 0... | _____no_output_____ | MIT | course/1 First Deep Learning Model.ipynb | Zhenxingzhang/zero_to_deep_learning_video |
models.unet Type an introduction of the package here. | from fastai.gen_doc.nbdoc import *
from fastai.models.unet import * | _____no_output_____ | Apache-2.0 | docs_src/models.unet.ipynb | Gokkulnath/fastai_v1 |
Global Variable Definitions: | show_doc(DynamicUnet) | _____no_output_____ | Apache-2.0 | docs_src/models.unet.ipynb | Gokkulnath/fastai_v1 |
[`DynamicUnet`](/models.unet.htmlDynamicUnet) | show_doc(UnetBlock) | _____no_output_____ | Apache-2.0 | docs_src/models.unet.ipynb | Gokkulnath/fastai_v1 |
[`UnetBlock`](/models.unet.htmlUnetBlock) | show_doc(UnetBlock.forward) | _____no_output_____ | Apache-2.0 | docs_src/models.unet.ipynb | Gokkulnath/fastai_v1 |
$\color{black}{}$ 1. Fitting data--- Input data | import pandas as pd
import seaborn as sns
data = pd.read_csv('https://milliams.com/courses/applied_data_analysis/linear.csv')
data.head() | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
Let's check how many rows we have | data.count() | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
We have 50 rows here. In the input data, each row is often called a sample (though sometimes also called an instance, example or observation). For example, it could be the information about a single person from a census or the measurements at a particular time from a weather station. Let's have a look at what the data ... | sns.scatterplot(data=data, x="x", y="y") | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
We can clearly visually see here that there is a linear relationship between the x and y values but we need to be able to extract the exact parameters programmatically. Setting up our model We import the model and create an instance of it. By default the LinearRegression model will fit the y-intercept, but since we do... | from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=True) | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
Fitting the data Once we have created our model, we can fit it to the data by calling the `fit()` method on it. This takes two arguments:1. The input data as a two-dimensional structure of the size ($N_{samples}$,$N_{features}$)2. The labels or targets of the data as a one-dimensional data structure of size ($N_{sampl... | X = data[['x']]
y = data['y']
model.fit(X, y) | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
Making predictions using the model We can use this to plot the fit over the original data to compare the result. By getting the predicted *__y__* values for the minimum and maximum x values, we can plot a straight line between them to visualise the model. The `predict()` function takes an array of the same shape as th... | x_fit = pd.DataFrame({'x': [0, 10]})
y_pred = model.predict(x_fit)
import matplotlib.pyplot as plt
plt.figure(figsize=(20,20))
fig, ax = plt.subplots()
sns.scatterplot(data=data, x='x', y='y', ax=ax)
ax.plot(x_fit['x'], y_pred, linestyle=':', color='red')
plt.show() | _____no_output_____ | MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
As well as plotting the line in a graph, we can also extract the calculated values of the gradient and y-intercept. The gradient is available as a list of values, `model.coef_`, one for each dimension or feature. The intercept is available as `model.intercept_`. | print(f'Model gradient: {model.coef_[0]}')
print(f'Model intercept: {model.intercept_}') | Model gradient: 1.9776566003853107
Model intercept: -4.903310725531115
| MIT | fitting_data.ipynb | bibliotekue/python-applied-data-analysis |
Implement Canny edge detection | # Try Canny using "wide" and "tight" thresholds
wide = cv2.Canny(gray, 30, 100)
tight = cv2.Canny(gray, 200, 240)
# Display the images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.set_title('wide')
ax1.imshow(wide, cmap='gray')
ax2.set_title('tight')
ax2.imshow(tight, cmap='gray') | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/5. Canny Edge Detection.ipynb | Echo9k/CVND_Exercises |
TODO: Try to find the edges of this flowerSet a small enough threshold to isolate the boundary of the flower. | # Read in the image
image = cv2.imread('images/sunflower.jpg')
# Change color to RGB (from BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
## TODO: Define lower and upper thresholds for hysteresis
# right now the th... | _____no_output_____ | MIT | 1_2_Convolutional_Filters_Edge_Detection/5. Canny Edge Detection.ipynb | Echo9k/CVND_Exercises |
Loading Raw Data | import tensorflow as tf
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train_flatten = x_train.reshape(x_train.shape[0], x_train.shape[1]*x_train.shape[2])/255.0
x_test_flatten = x_test.reshape(x_test.shape[0], x_test.shape[1]*x_test.shape[2])/255.0
print(x_train_flatten.shape, y_train.sh... | (980, 784)
(1135, 784)
(1032, 784)
(1010, 784)
(982, 784)
(892, 784)
(958, 784)
(1028, 784)
(974, 784)
(1009, 784)
| MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
Selecting the datasetOutput: X_train, Y_train, X_test, Y_test | num_sample = 300
n_class = 4
mult_test = 0.25
X_train = x_train_list[0][:num_sample, :]
X_test = x_test_list[0][:int(mult_test*num_sample), :]
Y_train = np.zeros((n_class*X_train.shape[0],), dtype=int)
Y_test = np.zeros((n_class*X_test.shape[0],), dtype=int)
for i in range(n_class-1):
X_train = np.concatenate((X_t... | (1200, 784) (1200,)
(300, 784) (300,)
| MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
Dataset Preprocessing (Standardization + PCA) Standardization | def normalize(X, use_params=False, params=None):
"""Normalize the given dataset X
Args:
X: ndarray, dataset
Returns:
(Xbar, mean, std): tuple of ndarray, Xbar is the normalized dataset
with mean 0 and standard deviation 1; mean and std are the
mean and standard deviatio... | _____no_output_____ | MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
PCA | from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
num_component = 9
pca = PCA(n_components=num_component, svd_solver='full')
pca.fit(X_train)
np.cumsum(pca.explained_variance_ratio_)
X_train = pca.transform(X_train)
X_test = pca.transform(X_test)
print(X_train.shape, Y_train.shape)
print(X_test... | (1200, 9) (1200,)
(300, 9) (300,)
| MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
Norm | X_train = (X_train.T / np.sqrt(np.sum(X_train ** 2, -1))).T
X_test = (X_test.T / np.sqrt(np.sum(X_test ** 2, -1))).T
plt.scatter(X_train[:100, 0], X_train[:100, 1])
plt.scatter(X_train[100:200, 0], X_train[100:200, 1])
plt.scatter(X_train[200:300, 0], X_train[200:300, 1]) | _____no_output_____ | MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
Quantum | import pennylane as qml
from pennylane import numpy as np
from pennylane.optimize import AdamOptimizer, GradientDescentOptimizer
qml.enable_tape()
# Set a random seed
np.random.seed(42)
def plot_data(x, y, fig=None, ax=None):
"""
Plot data with red/blue values for a binary classification.
Args:
... | _____no_output_____ | MIT | PennyLane/Data Reuploading Classifier/.ipynb_checkpoints/DRC MNIST MultiClass PCA-Copy1-checkpoint.ipynb | Graciaira/quantum_image_classifier |
Constant definition | np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed_all(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
lr = 2e-3
seq_len = 12
beta1 = 0.5
content_dim = 128
pose_dim = 10
channels = 3
normalize = False
sd_nf = 100
image_width = 64
batch_size = 100
log_dir = './logs/0522... | _____no_output_____ | MIT | train.ipynb | TMYuan/video-interpolation |
Data Loader | train_data = MovingMNIST(True, '../data_uni/', seq_len=seq_len)
test_data = MovingMNIST(False, '../data_uni/', seq_len=seq_len)
train_loader = DataLoader(
train_data,
batch_size=batch_size,
num_workers=16,
shuffle=True,
drop_last=True,
pin_memory=True
)
test_loader = DataLoader(
test_data,
... | _____no_output_____ | MIT | train.ipynb | TMYuan/video-interpolation |
Model definition | # # netEC = dcgan_unet_models.content_encoder(content_dim, channels).to(device)
# netEC = dcgan_models.content_encoder(content_dim, channels).to(device)
# netEP = dcgan_models.pose_encoder(pose_dim, channels).to(device)
# # netD = dcgan_unet_models.decoder(content_dim, pose_dim, channels).to(device)
# netD = dcgan_mode... | _____no_output_____ | MIT | train.ipynb | TMYuan/video-interpolation |
Plot function | # --------- plotting funtions ------------------------------------
def plot_rec(x, epoch, dtype):
x_c = x[0]
x_p = x[np.random.randint(1, len(x))]
h_c = netEC(x_c)
h_p = netEP(x_p, h_c)
rec = netD([h_c, h_p])
x_c, x_p, rec = x_c.data, x_p.data, rec.data
fname = '{}-{}.png'.format(dtype, ep... | _____no_output_____ | MIT | train.ipynb | TMYuan/video-interpolation |
Training function | def train(x):
optimizerEC.zero_grad()
optimizerEP.zero_grad()
optimizerD.zero_grad()
# x_c1 = x[0]
# x_c2 = x[1]
# x_p1 = x[2]
# x_p2 = x[3]
x_c1 = x[np.random.randint(len(x))]
x_c2 = x[np.random.randint(len(x))]
x_p1 = x[np.random.randint(len(x))]
x_p2 = x[np.random.randint... | _____no_output_____ | MIT | train.ipynb | TMYuan/video-interpolation |
$H(curl, \Omega)$ Elliptic Problems $\newcommand{\dd}{\,{\rm d}}$$\newcommand{\uu}{\mathbf{u}}$$\newcommand{\vv}{\mathbf{v}}$$\newcommand{\nn}{\mathbf{n}}$$\newcommand{\ff}{\mathbf{f}}$$\newcommand{\Hcurlzero}{\mathbf{H}_0(\mbox{curl}, \Omega)}$$\newcommand{\Curl}{\nabla \times}$Let $\Omega \subset \mathbb{R}^d$ be an... | import numpy as np
from sympy import pi, cos, sin, sqrt, Matrix, Tuple, lambdify
from scipy.sparse.linalg import spsolve
from scipy.sparse.linalg import gmres as sp_gmres
from scipy.sparse.linalg import minres as sp_minres
from scipy.sparse.linalg import cg as sp_cg
from scipy.sparse.linalg import bicg as sp_bicg
from ... | 0.0029394893438220407
| MIT | lessons/Chapter5/03_Hcurl_elliptic_problem.ipynb | pyccel/IGA-Python |
Emotion recognition using Emo-DB dataset and scikit-learn Database: Emo-DB database (free) 7 emotionsThe data can be downloaded from http://emodb.bilderbar.info/index-1024.htmlCode of emotionsW->Anger->WutL->Boredom->LangeweileE->Disgust->EkelA->Anxiety/Fear->AngstF->Happiness->FreudeT->Sadness->TrauerN->Neutral![imag... | import requests
import zipfile
import os
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as st
import itertools
import sys
sys.path.append("../")
from plots_examples import plot_confusion_matrix, plot_ROC, plot_histogram
# disvoice imports
from phonation.phonation import Phonation
from articulat... | Using TensorFlow backend.
/home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.i... | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
Download and unzip data | def download_url(url, save_path, chunk_size=128):
r = requests.get(url, stream=True)
with open(save_path, 'wb') as fd:
for chunk in r.iter_content(chunk_size=chunk_size):
fd.write(chunk)
PATH_data="http://emodb.bilderbar.info/download/download.zip"
download_url(PATH_data, "./dow... | _____no_output_____ | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
prepare labels from the datasetwe will get labels for two classification problems: 1. high vs. low arousal emotions2. positive vs. negative emotions | PATH_AUDIO=os.path.abspath("./emo-db/wav")+"/"
labelsd='WLEAFTN'
labelshl= [0, 1, 0, 0, 0, 1, 1] # 0 high arousal emotion, 1 low arousal emotions
labelspn= [0, 0, 0, 0, 1, 0, 1] # 0 negative valence emotion, 1 positive valence emotion
hf=os.listdir(PATH_AUDIO)
hf.sort()
yArousal=np.zeros(len(hf))
yValence=np.zeros(... | _____no_output_____ | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
compute features using disvoice: phonation, articulation, prosody, phonological | phonationf=Phonation()
articulationf=Articulation()
prosodyf=Prosody()
phonologicalf=Phonological()
replearningf=RepLearning('CAE')
| WARNING:tensorflow:From /home/camilo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
| MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
phonation features | Xphonation=phonationf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt="npy")
print(Xphonation.shape) | Processing 14a07Aa.wav: 69%|██████▉ | 368/535 [00:10<00:04, 40.87it/s]../phonation/phonation.py:161: WavFileWarning: Chunk (non-data) not understood, skipping it.
fs, data_audio=read(audio)
Processing 16b10Wb.wav: 100%|██████████| 535/535 [00:14<00:00, 36.70it/s] | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
articulation features | Xarticulation=articulationf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt="npy")
print(Xarticulation.shape) | Processing 03a01Fa.wav: 0%| | 0/535 [00:00<?, ?it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecat... | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
prosody features | Xprosody=prosodyf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt="npy")
print(Xprosody.shape) | Processing 14a07Aa.wav: 69%|██████▉ | 370/535 [01:12<00:33, 4.96it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-data) not understood, skipping it.
fs, data_audio=read(audio)
Processing 14a07Fd.wav: 70%|██████▉ | 372/535 [01:12<00:29, 5.57it/s]../prosody/prosody.py:300: WavFileWarning: Chunk (non-da... | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
phonological features | Xphonological=phonologicalf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt="npy")
print(Xphonological.shape) | Processing 14a07Aa.wav: 69%|██████▉ | 370/535 [02:24<00:57, 2.89it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/phonet/phonet.py:235: WavFileWarning: Chunk (non-data) not understood, skipping it.
fs, signal=read(audio_file)
Processing 16b10Wb.wav: 100%|██████████| 535/535 [03:28<00:00, 2.57it/s] | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
representation learning features | Xrep=replearningf.extract_features_path(PATH_AUDIO, static=True, plots=False, fmt="npy")
print(Xrep.shape) | Processing 03a01Fa.wav: 0%| | 0/535 [00:00<?, ?it/s]/home/camilo/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1639: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.
warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.")
Processing 08b01W... | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
Emotion classification using an SVM classifier | def classify(X, y):
# train test split
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.30, random_state=42)
# z-score standarization
scaler = preprocessing.StandardScaler().fit(Xtrain)
Xtrain=scaler.transform(Xtrain)
Xtest=scaler.transform(Xtest)
Results=[]
... | _____no_output_____ | MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
classify high vs. low arousal with the different feature sets | classify(Xphonation, yArousal)
classify(Xarticulation, yArousal)
classify(Xprosody, yArousal)
classify(Xphonological, yArousal)
classify(Xrep, yArousal) | Fitting 10 folds for each of 500 candidates, totalling 5000 fits
| MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
classify positive vs. negative valence with the different feature sets | classify(Xphonation, yValence)
classify(Xarticulation, yValence)
classify(Xprosody, yValence)
classify(Xphonological, yValence)
classify(Xrep, yValence) | Fitting 10 folds for each of 500 candidates, totalling 5000 fits
| MIT | notebooks_examples/emotion classification using SVMs in scikit-learn.ipynb | virtualcharacters/DisVoice |
!pip install keras-tuner
import tensorflow as tf
from tensorflow import keras
import numpy as np
from matplotlib import pyplot
mnist=keras.datasets.mnist
(train_x, train_y), (test_x, test_y) = mnist.load_data()
for i in range(9):
# define subplot
pyplot.subplot(330 + 1 + i)
# plot raw pixel data
pyplot.imsho... | _____no_output_____ | Apache-2.0 | Keras_mnist_digit_dataset.ipynb | Nikhitha-S-Pavan/Deep-learning-examples-using-keras | |
07 - Ensemble Methodsby [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)version 1.3, June 2018 Part of the class [Applied Deep Learning](https://github.com/albahnsen/AppliedDeepLearningClass)This notebook is licensed under a [Creative Commons Attribution-ShareAlik... | import numpy as np
# set a seed for reproducibility
np.random.seed(1234)
# generate 1000 random numbers (between 0 and 1) for each model, representing 1000 observations
mod1 = np.random.rand(1000)
mod2 = np.random.rand(1000)
mod3 = np.random.rand(1000)
mod4 = np.random.rand(1000)
mod5 = np.random.rand(1000)
# each m... | 0.841
| MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
**Note:** As you add more models to the voting process, the probability of error decreases, which is known as [Condorcet's Jury Theorem](http://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem). What is ensembling?**Ensemble learning (or "ensembling")** is the process of combining several predictive models in order to... | # read in and prepare the vehicle training data
import zipfile
import pandas as pd
with zipfile.ZipFile('../datasets/vehicles_train.csv.zip', 'r') as z:
f = z.open('vehicles_train.csv')
train = pd.io.parsers.read_table(f, index_col=False, sep=',')
with zipfile.ZipFile('../datasets/vehicles_test.csv.zip', 'r') a... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Train different models | from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsRegressor
models = {'lr': LinearRegression(),
'dt': DecisionTreeRegressor(),
'nb': GaussianNB(),
'nn': KNeigh... | lr 2138.3579028745116
dt 1414.213562373095
nb 5477.2255750516615
nn 1671.3268182295567
| MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Evaluate the error of the mean of the predictions | np.sqrt(mean_squared_error(y_pred.mean(axis=1), y_test)) | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Comparing manual ensembling with a single model approach**Advantages of manual ensembling:**- Increases predictive accuracy- Easy to get started**Disadvantages of manual ensembling:**- Decreases interpretability- Takes longer to train- Takes longer to predict- More complex to automate and maintain- Small gains in accu... | # set a seed for reproducibility
np.random.seed(1)
# create an array of 1 through 20
nums = np.arange(1, 21)
print(nums)
# sample that array 20 times with replacement
print(np.random.choice(a=nums, size=20, replace=True)) | [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
[ 6 12 13 9 10 12 6 16 1 17 2 13 8 14 7 19 6 19 12 11]
| MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
**How does bagging work (for decision trees)?**1. Grow B trees using B bootstrap samples from the training data.2. Train each tree on its bootstrap sample and make predictions.3. Combine the predictions: - Average the predictions for **regression trees** - Take a vote for **classification trees**Notes:- **Each bo... | # set a seed for reproducibility
np.random.seed(123)
n_samples = train.shape[0]
n_B = 10
# create ten bootstrap samples (will be used to select rows from the DataFrame)
samples = [np.random.choice(a=n_samples, size=n_samples, replace=True) for _ in range(1, n_B +1 )]
samples
# show the rows for the first decision tre... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Build one tree for each sample | from sklearn.tree import DecisionTreeRegressor
# grow each tree deep
treereg = DecisionTreeRegressor(max_depth=None, random_state=123)
# DataFrame for storing predicted price from each tree
y_pred = pd.DataFrame(index=test.index, columns=[list(range(n_B))])
# grow one tree for each bootstrap sample and make predicti... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Results of each tree | for i in range(n_B):
print(i, np.sqrt(mean_squared_error(y_pred[i], y_test))) | 0 1621.7274740226856
1 2942.7877939124323
2 1825.7418583505537
3 1000.0
4 1276.7145334803704
5 1414.213562373095
6 1414.213562373095
7 1000.0
8 1554.5631755148024
9 1914.854215512676
| MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Results of the ensemble | y_pred.mean(axis=1)
np.sqrt(mean_squared_error(y_test, y_pred.mean(axis=1))) | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Bagged decision trees in scikit-learn (with B=500) | # define the training and testing sets
X_train = train.iloc[:, 1:]
y_train = train.iloc[:, 0]
X_test = test.iloc[:, 1:]
y_test = test.iloc[:, 0]
# instruct BaggingRegressor to use DecisionTreeRegressor as the "base estimator"
from sklearn.ensemble import BaggingRegressor
bagreg = BaggingRegressor(DecisionTreeRegressor(... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Estimating out-of-sample errorFor bagged models, out-of-sample error can be estimated without using **train/test split** or **cross-validation**!On average, each bagged tree uses about **two-thirds** of the observations. For each tree, the **remaining observations** are called "out-of-bag" observations. | # show the first bootstrap sample
samples[0]
# show the "in-bag" observations for each sample
for sample in samples:
print(set(sample))
# show the "out-of-bag" observations for each sample
for sample in samples:
print(sorted(set(range(n_samples)) - set(sample))) | [4, 5, 7, 8]
[5, 6, 8, 10, 11, 12]
[1, 5, 10, 11]
[4, 7, 9, 13]
[0, 1, 5, 7, 8, 9]
[2, 3, 8, 12, 13]
[6, 7, 10, 11, 13]
[0, 4, 8, 10, 12, 13]
[0, 2, 4, 5, 10, 13]
[2, 7, 9, 12]
| MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
How to calculate **"out-of-bag error":**1. For every observation in the training data, predict its response value using **only** the trees in which that observation was out-of-bag. Average those predictions (for regression) or take a vote (for classification).2. Compare all predictions to the actual response values in ... | # compute the out-of-bag R-squared score (not MSE, unfortunately!) for B=500
bagreg.oob_score_ | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Estimating feature importanceBagging increases **predictive accuracy**, but decreases **model interpretability** because it's no longer possible to visualize the tree to understand the importance of each feature.However, we can still obtain an overall summary of **feature importance** from bagged models:- **Bagged reg... | # read in the data
with zipfile.ZipFile('../datasets/hitters.csv.zip', 'r') as z:
f = z.open('hitters.csv')
hitters = pd.read_csv(f, sep=',', index_col=False)
# remove rows with missing values
hitters.dropna(inplace=True)
hitters.head()
# encode categorical variables as integers
hitters['League'] = pd.factoriz... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Predicting salary with a decision treeFind the best **max_depth** for a decision tree using cross-validation: | # list of values to try for max_depth
max_depth_range = range(1, 21)
# list to store the average RMSE for each value of max_depth
RMSE_scores = []
# use 10-fold cross-validation with each value of max_depth
from sklearn.model_selection import cross_val_score
for depth in max_depth_range:
treereg = DecisionTreeReg... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Predicting salary with a Random Forest | from sklearn.ensemble import RandomForestRegressor
rfreg = RandomForestRegressor()
rfreg | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Tuning n_estimatorsOne important tuning parameter is **n_estimators**, which is the number of trees that should be grown. It should be a large enough value that the error seems to have "stabilized". | # list of values to try for n_estimators
estimator_range = range(10, 310, 10)
# list to store the average RMSE for each value of n_estimators
RMSE_scores = []
# use 5-fold cross-validation with each value of n_estimators (WARNING: SLOW!)
for estimator in estimator_range:
rfreg = RandomForestRegressor(n_estimators... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Tuning max_featuresThe other important tuning parameter is **max_features**, which is the number of features that should be considered at each split. | # list of values to try for max_features
feature_range = range(1, len(feature_cols)+1)
# list to store the average RMSE for each value of max_features
RMSE_scores = []
# use 10-fold cross-validation with each value of max_features (WARNING: SLOW!)
for feature in feature_range:
rfreg = RandomForestRegressor(n_esti... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Fitting a Random Forest with the best parameters | # max_features=8 is best and n_estimators=150 is sufficiently large
rfreg = RandomForestRegressor(n_estimators=150, max_features=8, max_depth=3, oob_score=True, random_state=1)
rfreg.fit(X, y)
# compute feature importances
pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values('impo... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Reducing X to its most important features | # check the shape of X
X.shape
rfreg
# set a threshold for which features to include
from sklearn.feature_selection import SelectFromModel
print(SelectFromModel(rfreg, threshold=0.1, prefit=True).transform(X).shape)
print(SelectFromModel(rfreg, threshold='mean', prefit=True).transform(X).shape)
print(SelectFromModel(rf... | _____no_output_____ | MIT | notebooks/07_EnsembleMethods.ipynb | Tionick/AppliedDeepLearningClass |
Examples with the SolarAbund Class (v1.1) | # import
from linetools.abund import solar as labsol | _____no_output_____ | BSD-3-Clause | docs/examples/SolarAbund.ipynb | jchowk/linetools |
Init | sol = labsol.SolarAbund()
sol | _____no_output_____ | BSD-3-Clause | docs/examples/SolarAbund.ipynb | jchowk/linetools |
Usage | # Simple calls
print(sol['C'])
print(sol[6])
# Ratio
print(sol.get_ratio('C/Fe'))
# Multiple calls
print(sol[6,12,'S']) | [ 8.43 7.53 7.15]
| BSD-3-Clause | docs/examples/SolarAbund.ipynb | jchowk/linetools |
Bits and pieces | from linetools.abund import ions as laions
# Ion name
laions.ion_name((6,2))
# Name to ion
laions.name_ion('CII')
from linetools.abund.elements import ELEMENTS
ele = ELEMENTS['C']
ele.eleconfig_dict | _____no_output_____ | BSD-3-Clause | docs/examples/SolarAbund.ipynb | jchowk/linetools |
Plotting Various Scan Reports **Covid Patient** | plt.imshow(cv2.imread("../input/covid19-image-dataset/Covid19-dataset/train/Covid/022.jpeg")) | _____no_output_____ | CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
**Pneumomnia Patient** | plt.imshow(cv2.imread("../input/covid19-image-dataset/Covid19-dataset/train/Viral Pneumonia/020.jpeg")) | _____no_output_____ | CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
**Normal Patient** | plt.imshow(cv2.imread("../input/covid19-image-dataset/Covid19-dataset/train/Normal/018.jpeg")) | _____no_output_____ | CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
Preprocessing the images | train_datagen=ImageDataGenerator(rescale=1/255,
shear_range=0.2,
zoom_range=2,
horizontal_flip=True)
training_set=train_datagen.flow_from_directory('../input/covid19-image-dataset/Covid19-dataset/train',
... | _____no_output_____ | CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
Building VGG-16 architecture & Neural Network | model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32,(3,3),strides=(1, 1),activation='relu',padding='same', input_shape=(224, 224, 3)),
tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
tf.keras.layers.Conv2D(64,(3,3),strides=(1, 1) ,padding='same',activation='relu'),
tf.keras.layers.MaxPoo... | Epoch 1/71
8/8 [==============================] - 16s 2s/step - loss: 1.1425 - accuracy: 0.3302 - val_loss: 1.0993 - val_accuracy: 0.4091
Epoch 2/71
8/8 [==============================] - 14s 2s/step - loss: 1.0259 - accuracy: 0.5257 - val_loss: 1.0013 - val_accuracy: 0.4394
Epoch 3/71
8/8 [============================... | CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
Prediction | import numpy as np
from keras.preprocessing import image
test_image = image.load_img('../input/covid19-image-dataset/Covid19-dataset/test/Viral Pneumonia/0112.jpeg', target_size = (224, 224))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = model.predict(test_image)... | [[0. 0.00101711 0.99898285]]
| CC0-1.0 | covid-19-prediction-using-cnn.ipynb | AdityaIyer2609/Covid-19-Prediction-Using-CNN |
Collaborative filtering on Google Analytics dataThis notebook demonstrates how to implement a WALS matrix refactorization approach to do collaborative filtering. | import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"]... | 1.13.1
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Create raw datasetFor collaborative filtering, we don't need to know anything about either the users or the content. Essentially, all we need to know is userId, itemId, and rating that the particular user gave the particular item.In this case, we are working with newspaper articles. The company doesn't ask their users... | from google.cloud import bigquery
bq = bigquery.Client(project = PROJECT)
sql = """
WITH CTE_visitor_page_content AS (
SELECT
# Schema: https://support.google.com/analytics/answer/3437719?hl=en
# For a completely unique visit-session ID, we combine combination of fullVisitorId and visitNumber:
... | 7337153711992174438,100074831,0.2321051400452234
5190801220865459604,100170790,1.0
2293633612703952721,100510126,0.2481776360816793
5874973374932455844,100510126,0.16690549004998828
1173698801255170595,100676857,0.05464232805149575
883397426232997550,10083328,0.9487035095774818
1808867070685560283,100906145,1.0
7615995... | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Create dataset for WALSThe raw dataset (above) won't work for WALS: The userId and itemId have to be 0,1,2 ... so we need to create a mapping from visitorId (in the raw data) to userId and contentId (in the raw data) to itemId. We will need to save the above mapping to a file because at prediction time, we'll need to ... | import pandas as pd
import numpy as np
def create_mapping(values, filename):
with open(filename, 'w') as ofp:
value_to_id = {value:idx for idx, value in enumerate(values.unique())}
for value, idx in value_to_id.items():
ofp.write("{},{}\n".format(value, idx))
return value_to_id
df =... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Creating rows and columns datasets | import pandas as pd
import numpy as np
mapped_df = pd.read_csv(filepath_or_buffer = "data/collab_mapped.csv", header = None, names = ["userId", "itemId", "rating"])
mapped_df.head()
NITEMS = np.max(mapped_df["itemId"]) + 1
NUSERS = np.max(mapped_df["userId"]) + 1
mapped_df["rating"] = np.round(mapped_df["rating"].value... | total 31908
-rw-r--r-- 1 jupyter jupyter 13152765 Jul 31 20:41 collab_raw.csv
-rw-r--r-- 1 jupyter jupyter 2134511 Jul 31 20:41 users.csv
-rw-r--r-- 1 jupyter jupyter 82947 Jul 31 20:41 items.csv
-rw-r--r-- 1 jupyter jupyter 7812739 Jul 31 20:41 collab_mapped.csv
-rw-r--r-- 1 jupyter jupyter 2252828 Jul 31 20:41 ... | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
To summarize, we created the following data files from collab_raw.csv: ```collab_mapped.csv``` is essentially the same data as in ```collab_raw.csv``` except that ```visitorId``` and ```contentId``` which are business-specific have been mapped to ```userId``` and ```itemId``` which are enumerated in 0,1,2,.... The map... | import os
import tensorflow as tf
from tensorflow.python.lib.io import file_io
from tensorflow.contrib.factorization import WALSMatrixFactorization
def read_dataset(mode, args):
def decode_example(protos, vocab_size):
# TODO
return
def remap_keys(sparse_tensor):
# Current indice... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
This code is helpful in developing the input function. You don't need it in production. | def try_out():
with tf.Session() as sess:
fn = read_dataset(
mode = tf.estimator.ModeKeys.EVAL,
args = {"input_path": "data", "batch_size": 4, "nitems": NITEMS, "nusers": NUSERS})
feats, _ = fn()
print(feats["input_rows"].eval())
print(feats["input_r... | 284,5609,36
284,2754,42
284,3168,534
2621,5528,2694
4409,5295,343
5161,3267,3369
5479,1335,55
5479,1335,55
4414,284,5572
284,241,2359
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Run as a Python moduleLet's run it as Python module for just a few steps. | os.environ["NITEMS"] = str(NITEMS)
os.environ["NUSERS"] = str(NUSERS)
%%bash
rm -rf wals.tar.gz wals_trained
gcloud ai-platform local train \
--module-name=walsmodel.task \
--package-path=${PWD}/walsmodel \
-- \
--output_dir=${PWD}/wals_trained \
--input_path=${PWD}/data \
--num_epochs=0.01 --ni... | Will train for 2 steps, evaluating once every 162 steps
| Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Run on Cloud | %%bash
gsutil -m cp data/* gs://${BUCKET}/wals/data
%%bash
OUTDIR=gs://${BUCKET}/wals/model_trained
JOBNAME=wals_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
--region=$REGION \
--module-name=walsmodel.task \
--package-pat... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
This took 10 minutes for me. Get row and column factorsOnce you have a trained WALS model, you can get row and column factors (user and item embeddings) from the checkpoint file. We'll look at how to use these in the section on building a recommendation system using deep neural networks. | def get_factors(args):
with tf.Session() as sess:
estimator = tf.contrib.factorization.WALSMatrixFactorization(
num_rows = args["nusers"],
num_cols = args["nitems"],
embedding_dimension = args["n_embeds"],
model_dir = args["output_dir"])
row_... | INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_environment': 'local', '_is_chief': True, '_keep_checkpoint_every_n_hours': 10000, '_num_worker_replicas': 0, '_session_config': None, '_task_type': None, '_eval_distribute': None, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}... | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
You can visualize the embedding vectors using dimensional reduction techniques such as PCA. | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
pca = PCA(n_components = 3)
pca.fit(user_embeddings)
user_embeddings_pca = pca.transform(user_embeddings)
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(111, projection = "3d")
xs, ys, zs = user_embed... | _____no_output_____ | Apache-2.0 | courses/machine_learning/deepdive/10_recommend/labs/wals.ipynb | KayvanShah1/training-data-analyst |
Welcome back to Data Science using Pythonဒီအပါတ်မှာ Python Programming Language ကို အသုံးပြုရပြီး Program တွေ ရေးသားရမှာ ဖြစ်ပါတယ်။ | # ဒီ cell ကို run လိုက်ပါ။
!git clone https://github.com/neolaw84/data_science_using_python_labs.git
%cd week02 | _____no_output_____ | Apache-2.0 | week02/01_hello_world.ipynb | neolaw84/data_science_using_python_labs |
Hello World"Hello World" လို့ print ထုတ်တဲ့ statement တကြောင်းထဲပါတဲ့ Python program ကို ရေးကြည့်ရအောင်။```python beginning of programprint("Hello World") end of program```အပေါ်က program ကို အောက်က cell မှာ ကူးရေးပါ။ | # copy the program here and run
# you will see the output underneath
%%writefile lab_hello_world.py
# copy the program here to make the program file called "lab_hello_world.py"
# program မှန် မမှန်ကို ဒီ cell ကို run ပြီး စမ်းသပ်ပါ။
!pytest test_hello_world.py
# မင်္ဂလာပါ ကမ္ဘာကြီး လို့ output ထုတ်ပေးတဲ့ program ကို ... | _____no_output_____ | Apache-2.0 | week02/01_hello_world.ipynb | neolaw84/data_science_using_python_labs |
Most Frequent ElementWrite a function that finds the most frequent element in an array.`most_frequent([1, 2, 2, 3]) 2` | numbers = [1, 2, 2, 3] | _____no_output_____ | ADSL | 04-Career-Modules/Interview_Questions/09-Most_Frequent/Unsolved/Most_Frequent.ipynb | anirudhmungre/sneaky-lessons |
Building a Stack in Python Before we start let us reiterate they key components of a stack. A stack is a data structure that consists of two main operations: push and pop. A push is when you add an element to the **top of the stack** and a pop is when you remove an element from **the top of the stack**. Python 3.x con... | class Stack:
def __init__(self):
# TODO: Initialize the Stack
def size(self):
# TODO: Check the size of the Stack
def push(self, item):
# TODO: Push item onto Stack
def pop(self):
# TODO: Pop item off of the Stack | _____no_output_____ | MIT | data structure/stack and queues/stack/python_stack_practice.ipynb | salviosage/Data-Structures-Algorithms-Nanodegree-playground-o |
Test the Stack | MyStack = Stack()
MyStack.push("Web Page 1")
MyStack.push("Web Page 2")
MyStack.push("Web Page 3")
print (MyStack.items)
MyStack.pop()
MyStack.pop()
print ("Pass" if (MyStack.items[0] == 'Web Page 1') else "Fail")
MyStack.pop()
print ("Pass" if (MyStack.pop() == None) else "Fail") | _____no_output_____ | MIT | data structure/stack and queues/stack/python_stack_practice.ipynb | salviosage/Data-Structures-Algorithms-Nanodegree-playground-o |
Hide Solution | # Solution
class Stack:
def __init__(self):
self.items = []
def size(self):
return len(self.items)
def push(self, item):
self.items.append(item)
def pop(self):
if self.size()==0:
return None
else:
return self.items.pop()
... | _____no_output_____ | MIT | data structure/stack and queues/stack/python_stack_practice.ipynb | salviosage/Data-Structures-Algorithms-Nanodegree-playground-o |
Custom URL prefix with Seldon and AmbassadorThis notebook shows how you can deploy Seldon Deployments with custom Ambassador configuration. Setup Seldon CoreUset the setup notebook to [Setup Cluster](../../../notebooks/seldon_core_setup.ipynbSetup-Cluster) with [Ambassador Ingress](../../../notebooks/seldon_core_setu... | !pygmentize model_custom_ambassador.json
!kubectl create -f model_custom_ambassador.json
!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=example-custom -o jsonpath='{.items[0].metadata.name}') | _____no_output_____ | Apache-2.0 | examples/ambassador/custom/ambassador_custom.ipynb | pachyderm/seldon-core |
Get predictions | from seldon_core.seldon_client import SeldonClient
sc = SeldonClient(deployment_name="example-custom",namespace="seldon") | _____no_output_____ | Apache-2.0 | examples/ambassador/custom/ambassador_custom.ipynb | pachyderm/seldon-core |
REST Request | r = sc.predict(gateway="ambassador",transport="rest",gateway_prefix="/mycompany/ml")
assert(r.success==True)
print(r)
!kubectl delete -f model_custom_ambassador.json | _____no_output_____ | Apache-2.0 | examples/ambassador/custom/ambassador_custom.ipynb | pachyderm/seldon-core |
Install the packages | !pip install -Uqq datasets pythainlp==2.2.4 transformers==4.4.0 tensorflow==2.4.0 tensorflow_text emoji seqeval sentencepiece fuzzywuzzy
!npx degit --force https://github.com/vistec-AI/thai2transformers#dev
%load_ext autoreload
%autoreload 2
import pythainlp, transformers
pythainlp.__version__, transformers.__version_... | _____no_output_____ | MIT | fine_tuning_roBERTsaikwan.ipynb | Sav-eng/roBERTsaikwan-model |
Prepare function for calculate metrics | !pip install rouge
from rouge import Rouge
rouge = Rouge()
def cal_rouge_score(hyps, refs, get_average_f1=True):
'''
argument: cands, refs [list of string], get_average_f1=True
return dict of r1, r2, rl score
if get_average_f1 == True return mean of rouge-1, rouge-2, rouge-L
'''
r1 = dict(); r1['precision'... | _____no_output_____ | MIT | fine_tuning_roBERTsaikwan.ipynb | Sav-eng/roBERTsaikwan-model |
Utility functions for calculate label in our use. | def tokenize_with_space(texts, tokenizer):
output = []
encoded_texts = tokenizer(texts, max_length=416, truncation=True)
for text in encoded_texts['input_ids']:
tokenized_text = " ".join(tokenizer.convert_ids_to_tokens(text, skip_special_tokens=True))
if(len(tokenized_text)==0):
output.append("")
... | _____no_output_____ | MIT | fine_tuning_roBERTsaikwan.ipynb | Sav-eng/roBERTsaikwan-model |
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