markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
We fit the O-C points measured above using MCMC by calling the run_mcmc() function We plot both the fit, as well as the triangle plot showing the two- (and one-)dimensional posterior distributions (these can be suppressed by setting the optional parameters "plot_oc" and "plot_triangle" to False) | sampler, fit_mcmc, oc_sigmas, param_means, param_sigmas, fit_at_points, K =\
octs.run_mcmc(oc_jd, oc_oc, oc_sd,
prior_ranges, pos,
nsteps = 31000, discard = 1000,
thin = 300, processes=1) | 100%|ββββββββββ| 31000/31000 [03:08<00:00, 164.32it/s]
100%|βββββββββββββββββββββββββββββββββββ| 20000/20000 [00:02<00:00, 8267.13it/s]
| MIT | 06498_oc.ipynb | gerhajdu/rrl_binaries_1 |
The estimated LTTE parameters are: | print("Orbital period: {:d} +- {:d} [d]".format(int(param_means[0]),
int(param_sigmas[0])))
print("Projected semi-major axis: {:.3f} +- {:.3f} [AU]".format(param_means[2]*173.144633,
... | Orbital period: 2803 +- 3 [d]
Projected semi-major axis: 2.492 +- 0.010 [AU]
Eccentricity: 0.136 +- 0.008
Argumen of periastron: -76 +- 3 [deg]
Periastron passage time: 6538 +- 24 [HJD-2450000]
Period-change rate: -0.002 +- 0.005 [d/Myr]
RV semi-amplitude: 9.76 +- 0... | MIT | 06498_oc.ipynb | gerhajdu/rrl_binaries_1 |
Consensus Optimization This notebook contains the code for the toy experiment in the paper [The Numerics of GANs](https://arxiv.org/abs/1705.10461). | %load_ext autoreload
%autoreload 2
import tensorflow as tf
from tensorflow.contrib import slim
import numpy as np
import scipy as sp
from scipy import stats
from matplotlib import pyplot as plt
import sys, os
from tqdm import tqdm_notebook
tf.reset_default_graph()
def kde(mu, tau, bbox=[-5, 5, -5, 5], save_file="", xl... | MIT | notebooks/mog-eigval-dist.ipynb | LMescheder/TheNumericsOfGANs | |
Getting Started with Tensorflow | import tensorflow as tf
# Create TensorFlow object called tensor
hello_constant = tf.constant('Hello World!')
with tf.Session() as sess:
# Run the tf.constant operation in the session
output = sess.run(hello_constant)
print(output);
A = tf.constant(1234)
B = tf.constant([123, 456, 789])
C = tf.constant(... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Build a Neural Network with Tensorflow | # Coding example for building a neural network with tensorflow
# Quiz Solution
import tensorflow as tf
output = None
hidden_layer_weights = [
[0.1, 0.2, 0.4],
[0.4, 0.6, 0.6],
[0.5, 0.9, 0.1],
[0.8, 0.2, 0.8]]
out_weights = [
[0.1, 0.6],
[0.2, 0.1],
[0.7, 0.9]]
# Weights and biases
weights... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Deep Neural Networks in Tensorflow | # For stacking muliple layers --> Deep NN
# Store layers weight & bias
weights = {
'hidden_layer': tf.Variable(tf.random_normal([n_input, n_hidden_layer])),
'out': tf.Variable(tf.random_normal([n_hidden_layer, n_classes]))
}
biases = {
'hidden_layer': tf.Variable(tf.random_normal([n_hidden_layer])),
'o... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Saving Variables and trained Models and load them backYou save the particular **session** in a file | import tensorflow as tf
# The file path to save the data
save_file = './model.ckpt'
# Two Tensor Variables: weights and bias
weights = tf.Variable(tf.truncated_normal([2, 3]))
bias = tf.Variable(tf.truncated_normal([3]))
# Class used to save and/or restore Tensor Variables
saver = tf.train.Saver()
with tf.Session()... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
... same works for models. Just train a NN like shown above and save the session afterwards Dropout for regularization in Tensorflow | # In tensorflow, dropout is just another "layer" in the model
#During training, a good starting value for keep_prob is 0.5.
#During testing, use a keep_prob value of 1.0 to keep all units and maximize the power of the model.
keep_prob = tf.placeholder(tf.float32) # probability to keep units
hidden_layer = tf.add(tf.m... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Convolutinal Neural Network (CNN) | # Note the output shape of conv will be [1, 16, 16, 20].
# It's 4D to account for batch size, but more importantly, it's not [1, 14, 14, 20].
# This is because the padding algorithm TensorFlow uses is not exactly the same as the one above.
# An alternative algorithm is to switch padding from 'SAME' to 'VALID'
input ... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Example code for constructing a CNN | # Load data set
# Batch, scale and one-hot-encode it
# Set Parameters
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(".", one_hot=True, reshape=False)
import tensorflow as tf
# Parameters
learning_rate = 0.00001
epochs = 10
batch_size = 128
# Number of samples to calcu... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
LeNet Architecture Load DataLoad the MNIST data, which comes pre-loaded with TensorFlow.You do not need to modify this section. | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mn... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Split up data into training, validation and test set | import numpy as np
# Pad images with 0s
X_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
print("Updated Image Shape: {}".format(X_train[0].shape)) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Visualize DataView a sample from the dataset.You do not need to modify this section. | import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
index = random.randint(0, len(X_train))
image = X_train[index].squeeze()
plt.figure(figsize=(1,1))
plt.imshow(image, cmap="gray")
print(y_train[index]) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Preprocess DataShuffle the training data.You do not need to modify this section. | from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Setup TensorFlowThe `EPOCH` and `BATCH_SIZE` values affect the training speed and model accuracy. | import tensorflow as tf
EPOCHS = 10
BATCH_SIZE = 128 | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
InputThe LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case. Architecture**Layer 1: Convolutional.** The output shape should be 28x28x6.**Activation.** Your choice of activation function.**Pooling.** The output shape shoul... | from tensorflow.contrib.layers import flatten
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.1
weights = {
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 6])),
'wc2': tf.Variable(tf.ra... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Features and LabelsTrain LeNet to classify [MNIST](http://yann.lecun.com/exdb/mnist/) data.`x` is a placeholder for a batch of input images.`y` is a placeholder for a batch of output labels. | x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 10) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Training PipelineCreate a training pipeline that uses the model to classify MNIST data. | rate = 0.001
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Model EvaluationEvaluate how well the loss and accuracy of the model for a given dataset. | correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in ra... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Train the ModelRun the training data through the training pipeline to train the model.Before each epoch, shuffle the training set.After each epoch, measure the loss and accuracy of the validation set.Save the model after training. | with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH... | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
Evaluate the Model (on the test set)Once you are completely satisfied with your model, evaluate the performance of the model on the test set.Be sure to only do this once!If you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model... | with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy)) | _____no_output_____ | MIT | TensorFlowIntro/.ipynb_checkpoints/TensorFlowIntroduction-checkpoint.ipynb | dschmoeller/03TrafficSignClassifierCNN |
pip install pydicom
# Import tensorflow
import logging
import tensorflow as tf
import keras.backend as K
# Helper libraries
import math
import numpy as np
import pandas as pd
import pydicom
import os
import sys
import time
# Imports for dataset manipulation
from sklearn.model_selection import train_test_split
from k... | _____no_output_____ | Apache-2.0 | ipynbs/reshape_demo.ipynb | zbytes/fsqs-tips-tricks-notes | |
20 Sept 2019 RULESDate: Level 2 heading Example Heading: Level 3 heading Method Heading: Level 4 heading References 1. [Forester_W._Isen;_J._Moura]_DSP_for_MATLAB_and_La Volume II(z-lib.org)2. H. K. Dass, Advanced Engineering Mathematics3. [Forester_W._Isen;_J._Moura]_DSP_for_MATLAB_and_La Volume I(z-lib.org)4. [Jo... | import numpy as np
from sympy import oo
import math
import sympy as sp
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
from IPython.display import display
from IPython.display import display_latex
from sympy import latex
import math
from scipy import signal
from datetime... | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Setup | sp.init_printing(use_latex = True)
z, f, i = sp.symbols('z f i')
x, k = sp.symbols('x k') | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Methods | # Usage: display_equation('u_x', x)
def display_equation(idx, symObj):
if(isinstance(idx, str)):
eqn = '\\[' + idx + ' = ' + latex(symObj) + '\\]'
display_latex(eqn, raw=True)
else:
eqn = '\\[' + latex(idx) + ' = ' + latex(symObj) + '\\]'
display_latex(eqn, raw=True)
return
#... | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Z Transform | display_full_latex('X(z) = \sum_{-\infty}^{\infty} x[n]z^{-n}') | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Tests Convert Symbolic to Numeric | f = x**2
f = sp.lambdify(x, f, 'numpy')
f(2)
display_equation('f(x)', sp.summation(3**k, ( k, -oo, oo )))
display_equation('F(z)', sp.summation(3**k/z**k, ( k, -oo, oo ))) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Partial Fractions | f = 1/(x**2 + x - 6)
display_equation('f(x)', f)
f = sp.apart(f)
display_equation('f(x)_{canonical}', f) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Piecewise | f1 = 5**k
f2 = 3**k
f = sp.Piecewise((f1, k < 0), (f2, k >= 0))
display_equation('f(k)', f) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
21 Sept 2019 Positive Time / Causal | f1 = k **2
f2 = 3**k
f = f1 * sp.Heaviside(k)
# or
#f = sp.Piecewise((0, k < 0), (f1, k >= 0))
display_equation('f(k)', f)
sp.plot(f, (k, -10, 10)) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Stem Plot | x = np.linspace(0.1, 2 * np.pi, 41)
y = np.sin(x)
plt.stem(x, y)
plt.show() | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
zplane Plot | b = np.array([1, 1, 0, 0])
a = np.array([1, 1, 1])
zplane(b,a) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Filter | g = (1 + z**-2)/(1-1.2*z**-1+0.81*z**-2)
display_equation('F(z)', g)
b = np.array([1,1])
a = np.array([1,-1.2,0.81])
x = np.ones((1, 8))
# Response
y = signal.lfilter(b, a, x)
# Reverse
signal.lfilter(a, b, y) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[1] Example 2.2 | radFreq = np.arange(0, 2*np.pi, 2*np.pi/499)
g = np.exp(1j*radFreq)
Zxform= 1/(1-0.7*g**(-1))
plt.plot(radFreq/np.pi,abs(Zxform))
plt.title('Graph')
plt.xlabel('Frequency, Units of Ο')
plt.ylabel('H(x)')
plt.grid(True)
plt.show() | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[2] Chapter 19, Example 5 | f = 3**(-k)
display_ztrans(f, k, (-4, 3)) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[2] Example 9 | f1 = 5**k
f2 = 3**k
f = sp.Piecewise((f1, k < 0), (f2, k >= 0))
display_ztrans(f, k, (-3, 3))
p = sum_of_GP(z/5, z/5)
q = sum_of_GP(1, 3/z)
display_equation('F(z)', sp.ratsimp(q + p)) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
28 Sept, 2019 [3] Folding formula fperceived = [ f - fsampling * NINT( f / fsampling ) ] 9 Oct, 2019 [3] Section 4.3 Equations | display_full_latex('F \\rightarrow analog')
display_full_latex('f \\rightarrow discrete')
display_full_latex('Nyquist frequency = F_s')
display_full_latex('Folding frequency = \\frac{F_s}{2}')
display_full_latex('F_{max} = \\frac{F_s}{2}')
display_full_latex('T = \\frac{1}{F_s}')
display_full_latex('f = \\frac{F}{F_s}'... | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
14 Oct, 2019 | n = sp.symbols('n')
x = np.arange(0, 10, 1)
y = x * np.heaviside(x, 1)
f = sp.Piecewise((0, n < 0), (n, n >= 0))
display_equation('u_r(n)', f)
plt.stem(x, y)
plt.plot(x, y, 'g-')
plt.xticks(np.arange(0, 10, 1))
plt.yticks(np.arange(0, 10, 1))
plt.xlabel('n')
plt.ylabel('x(n)')
plt.grid(True)
plt.show()
display_full_... | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
16 Oct, 2019 General form of the input-output relationships | display_full_latex('y(n) = -\\sum^N _{k = 1}a_k y(n-k) + \\sum^M _{k = 0}b_k x(n-k)') | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[4] Example 3.2 | h = np.array([1, 2, 1, -1])
x = np.array([1, 2, 3, 1])
y = np.convolve(h, x, mode='full')
#y = signal.convolve(h, x, mode='full', method='auto')
print(y)
fig, (ax_orig, ax_h, ax_x) = plt.subplots(3, 1, sharex=True)
ax_orig.plot(h)
ax_orig.set_title('Impulse Response')
ax_orig.margins(0, 0.1)
ax_h.plot(x)
ax_h.set_titl... | [ 1 4 8 8 3 -2 -1]
| MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
17 Oct, 2019 Sum of an AP with common ratio r and first term a, starting from the zeroth term | a, r = sp.symbols('a r')
s = sp.summation(a*r**k, ( k, 0, n ))
display_equation('S_n', s) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Sum of positive powers of a | a = sp.symbols('a')
s = sp.summation(a**k, ( k, 0, n ))
display_equation('S_n', s) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[3] 4.12.3 Single Pole IIR | SR = 24
b = 1
p = 0.8
y = np.zeros((1, SR)).ravel()
x = np.zeros((1, SR + 1)).ravel()
x[0] = 1
y[0] = b * x[0]
for n in range(1, SR):
y[n] = b * x[n] + p * y[n - 1]
plt.stem(y) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Copying the method above for [4] 4.1 Averaging | x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y[0] = b * x[0]
for n in range(1, len(x)):
y[n] = (n/(n + 1)) * y[n - 1] + (1/(n + 1)) * x[n]
print(y[n], '\n') | 5.5
| MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
My Recursive Averaging Implementation | def avg(x, n):
if (n < 0):
return 0
else:
return (n/(n + 1)) * avg(x, n - 1) + (1/(n + 1)) * x[n]
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
average = avg(x, len(x) - 1)
print(average) | 5.5
| MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
Performance Comparism | from timeit import timeit
code_rec = '''
import numpy as np
def avg(x, n):
if (n < 0):
return 0
else:
return (n/(n + 1)) * avg(x, n - 1) + (1/(n + 1)) * x[n]
x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
average = avg(x, len(x) - 1)
'''
code_py = '''
import numpy as np
x = np.array(... | Running time using my recursive average function:
9.264100000000001e-05
Running time using python sum function:
4.1410000000000005e-05
Running time using loop python function:
7.479999999999987e-06
| MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
[4] Example 4.1 | def rec_sqrt(x, n):
if (n == -1):
return 1
else:
return (1/2) * (rec_sqrt(x, n - 1) + (x[n]/rec_sqrt(x, n - 1)))
A = 2
x = np.ones((1, 5)).ravel() * A
print(rec_sqrt(x, len(x) - 1))
b = np.array([1, 1, 1, 1, 1])
a = np.array([1, 0, 0])
zplane(b,a) | _____no_output_____ | MIT | .ipynb_checkpoints/DSP-checkpoint.ipynb | Valentine-Efagene/Jupyter-Notebooks |
langages de script β Python Modules et packages M1 IngΓ©nierie Multilingue β INaLCOclement.plancq@ens.fr Les modules et les packages permettent d'ajouter des fonctionnalitΓ©s Γ PythonUn module est un fichier (```.py```) qui contient des fonctions et/ou des classes. Et de la documentation bien sΓ»rUn package est un rΓ©pe... | %%file operations.py
# -*- coding: utf-8 -*-
"""
Module pour le cours sur les modules
OpΓ©rations arithmΓ©tiques
"""
def addition(a, b):
""" Ben une addition quoi : a + b """
return a + b
def soustraction(a, b):
""" Une soustraction :Β a - b """
return a - b | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
Pour l'utiliser on peut :* l'importer par son nom | import operations
operations.addition(2, 4) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
* l'importer et modifier son nom | import operations as op
op.addition(2, 4) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
* importer une partie du module | from operations import addition
addition(2, 4) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
* importer l'intΓ©gralitΓ© du module | from operations import *
addition(2, 4)
soustraction(4, 2) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
En réalité seules les fonctions et/ou les classes ne commençant pas par '_' sont importées. L'utilisation de `import *` n'est pas recommandée. Parce que, comme vous le savez « *explicit is better than implicit* ». Et en ajoutant les fonctions dans l'espace de nommage du script vous pouvez écraser des fonctions existant... | import operations
type(operations) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
``import`` ajoute des attributs au module | import operations
print(f"name : {operations.__name__}")
print(f"file : {operations.__file__}")
print(f"doc :Β {operations.__doc__}") | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
Un package | ! tree operations_pack | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
Un package python peut contenir des modules, des rΓ©pertoires et sous-rΓ©pertoires, et bien souvent du non-python :Β de la doc html, des donnΓ©es pour les tests, etcβ¦ Le rΓ©pertoire principal et les rΓ©pertoires contenant des modules python doivent contenir un fichier `__init__.py` `__init__.py` peut Γͺtre vide, contenir du c... | import operations_pack.simple
operations_pack.simple.addition(2, 4)
from operations_pack import simple
simple.soustraction(4, 2) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
``__all__`` dans ``__init__.py`` dΓ©finit quels seront les modules qui seront importΓ©s avec ``import *`` | from operations_pack.avance import *
multi.multiplication(2,4) | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
OΓΉ sont les modules et les packages ? Pour que ``import`` fonctionne il faut que les modules soient dans le PATH. | import sys
sys.path | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
``sys.path`` est une liste, vous pouvez la modifier | sys.path.append("[...]") # le chemin vers le dossier operations_pack
sys.path | _____no_output_____ | MIT | modules.ipynb | LoicGrobol/python-im |
DSPT6 - Adding Data Science to a Web ApplicationThe purpose of this notebook is to demonstrate:- Simple online analysis of data from a user of the Twitoff app or an API- Train a more complicated offline model, and serialize the results for online use | import sqlite3
import pickle
import pandas as pd
# Connect to sqlite database
conn = sqlite3.connect('../twitoff/twitoff.sqlite')
def get_data(query, conn):
'''Function to get data from SQLite DB'''
cursor = conn.cursor()
result = cursor.execute(query).fetchall()
# Get columns from cursor object
... | _____no_output_____ | MIT | notebooks/LS333_DSPT6_Model_Demo.ipynb | DrewRust/DSPT6-Twitoff |
Working with Watson Machine Learning This notebook should be run in a Watson Studio project, using **Default Python 3.7.x** runtime environment. **If you are viewing this in Watson Studio and do not see Python 3.7.x in the upper right corner of your screen, please update the runtime now.** It requires service credent... | import warnings
warnings.filterwarnings('ignore')
!rm -rf /home/spark/shared/user-libs/python3.7*
!pip install --upgrade pandas==1.2.3 --no-cache | tail -n 1
!pip install --upgrade requests==2.23 --no-cache | tail -n 1
!pip install --upgrade numpy==1.20.3 --user --no-cache | tail -n 1
!pip install SciPy --no-cache | t... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Provision services and configure credentials If you have not already, provision an instance of IBM Watson OpenScale using the [OpenScale link in the Cloud catalog](https://cloud.ibm.com/catalog/services/watson-openscale). Your Cloud API key can be generated by going to the [**Users** section of the Cloud console](http... | CLOUD_API_KEY = "***"
IAM_URL="https://iam.ng.bluemix.net/oidc/token" | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
If you have not already, provision an instance of IBM Watson OpenScale using the [OpenScale link in the Cloud catalog](https://cloud.ibm.com/catalog/services/watson-openscale).Your Cloud API key can be generated by going to the [**Users** section of the Cloud console](https://cloud.ibm.com/iam/users). From that page, c... | WML_CREDENTIALS = {
"url": "https://us-south.ml.cloud.ibm.com",
"apikey": CLOUD_API_KEY
} | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
WML credentials example using IAM_token **NOTE**: If IAM_TOKEN is used for authentication and you receive unauthorized/expired token error at any steps, please create a new token and reinitiate clients authentication. | # #uncomment this cell if want to use IAM_TOKEN
# import requests
# def generate_access_token():
# headers={}
# headers["Content-Type"] = "application/x-www-form-urlencoded"
# headers["Accept"] = "application/json"
# auth = HTTPBasicAuth("bx", "bx")
# data = {
# "grant_type": "urn:ibm:params... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Cloud object storage detailsIn next cells, you will need to paste some credentials to Cloud Object Storage. If you haven't worked with COS yet please visit [getting started with COS tutorial](https://cloud.ibm.com/docs/cloud-object-storage?topic=cloud-object-storage-getting-started). You can find `COS_API_KEY_ID` and ... | COS_API_KEY_ID = "***"
COS_RESOURCE_CRN = "***" # eg "crn:v1:bluemix:public:cloud-object-storage:global:a/3bf0d9003abfb5d29761c3e97696b71c:d6f04d83-6c4f-4a62-a165-696756d63903::"
COS_ENDPOINT = "***" # Current list avaiable at https://control.cloud-object-storage.cloud.ibm.com/v2/endpoints
BUCKET_NAME = "***"
training... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
This tutorial can use Databases for PostgreSQL, Db2 Warehouse, or a free internal verison of PostgreSQL to create a datamart for OpenScale.If you have previously configured OpenScale, it will use your existing datamart, and not interfere with any models you are currently monitoring. Do not update the cell below.If you ... | DB_CREDENTIALS = None
#DB_CREDENTIALS= {"hostname":"","username":"","password":"","database":"","port":"","ssl":True,"sslmode":"","certificate_base64":""}
KEEP_MY_INTERNAL_POSTGRES = True | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Run the notebookAt this point, the notebook is ready to run. You can either run the cells one at a time, or click the **Kernel** option above and select **Restart and Run All** to run all the cells. Model building and deployment In this section you will learn how to train Spark MLLib model and next deploy it as web-... | !rm house_price_regression.csv
!wget https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/house_price/house_price_regression.csv
import pandas as pd
import numpy as np
pd_data = pd.read_csv("house_price_regression.csv")
pd_data.head() | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Explore data Save training data to Cloud Object Storage | import ibm_boto3
from ibm_botocore.client import Config, ClientError
cos_client = ibm_boto3.resource("s3",
ibm_api_key_id=COS_API_KEY_ID,
ibm_service_instance_id=COS_RESOURCE_CRN,
ibm_auth_endpoint="https://iam.bluemix.net/oidc/token",
config=Config(signature_version="oauth"),
endpoint_url=COS_ENDP... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Create a model | from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
pd_data.dropna(axis=0, subset=['SalePrice'], inplace=True)
label = pd_data.SalePrice
feature_data = pd_data.drop(['SalePrice'], axis=1).select_dtypes(exclude=['object'])
train_X, test_X, train_y, test_y = train_test_split(fea... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
wrap xgboost with scikit pipeline | from sklearn.pipeline import Pipeline
xgb_model_imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
pipeline = Pipeline(steps=[('Imputer', xgb_model_imputer), ('xgb', model)])
model_xgb=pipeline.fit(train_X, train_y)
# make predictions
predictions = model_xgb.predict(test_X)
from sklearn.metrics import mean... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Publish the model | import json
from ibm_watson_machine_learning import APIClient
wml_client = APIClient(WML_CREDENTIALS)
wml_client.version | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Listing all the available spaces | wml_client.spaces.list(limit=10)
WML_SPACE_ID='***' # use space id here
wml_client.set.default_space(WML_SPACE_ID) | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Remove existing model and deployment | MODEL_NAME="house_price_xgbregression"
DEPLOYMENT_NAME="house_price_xgbregression_deployment"
deployments_list = wml_client.deployments.get_details()
for deployment in deployments_list["resources"]:
model_id = deployment["entity"]["asset"]["id"]
deployment_id = deployment["metadata"]["id"]
if deployment["me... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Deploy the model The next section of the notebook deploys the model as a RESTful web service in Watson Machine Learning. The deployed model will have a scoring URL you can use to send data to the model for predictions. | deployment_details = wml_client.deployments.create(
model_uid,
meta_props={
wml_client.deployments.ConfigurationMetaNames.NAME: "{}".format(DEPLOYMENT_NAME),
wml_client.deployments.ConfigurationMetaNames.ONLINE: {}
}
)
scoring_url = wml_client.deployments.get_scoring_href(deployment_details... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Sample scoring | fields = feature_data.columns.tolist()
values = [
test_X[0].tolist()
]
scoring_payload = {"input_data": [{"fields": fields, "values": values}]}
scoring_payload
scoring_response = wml_client.deployments.score(deployment_uid, scoring_payload)
scoring_response | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Configure OpenScale The notebook will now import the necessary libraries and set up a Python OpenScale client. | from ibm_cloud_sdk_core.authenticators import IAMAuthenticator,BearerTokenAuthenticator
from ibm_watson_openscale import *
from ibm_watson_openscale.supporting_classes.enums import *
from ibm_watson_openscale.supporting_classes import *
authenticator = IAMAuthenticator(apikey=CLOUD_API_KEY)
wos_client = APIClient(au... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Create schema and datamart Set up datamart Watson OpenScale uses a database to store payload logs and calculated metrics. If database credentials were **not** supplied above, the notebook will use the free, internal lite database. If database credentials were supplied, the datamart will be created there **unless** th... | wos_client.data_marts.show()
data_marts = wos_client.data_marts.list().result.data_marts
if len(data_marts) == 0:
if DB_CREDENTIALS is not None:
if SCHEMA_NAME is None:
print("Please specify the SCHEMA_NAME and rerun the cell")
print('Setting up external datamart')
added_data_m... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Remove existing service provider connected with used WML instance. Multiple service providers for the same engine instance are avaiable in Watson OpenScale. To avoid multiple service providers of used WML instance in the tutorial notebook the following code deletes existing service provder(s) and then adds new one. | SERVICE_PROVIDER_NAME = "xgboost_WML V2"
SERVICE_PROVIDER_DESCRIPTION = "Added by tutorial WOS notebook."
service_providers = wos_client.service_providers.list().result.service_providers
for service_provider in service_providers:
service_instance_name = service_provider.entity.name
if service_instance_name == S... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Add service provider Watson OpenScale needs to be bound to the Watson Machine Learning instance to capture payload data into and out of the model. **Note:** You can bind more than one engine instance if needed by calling `wos_client.service_providers.add` method. Next, you can refer to particular service provider usin... | added_service_provider_result = wos_client.service_providers.add(
name=SERVICE_PROVIDER_NAME,
description=SERVICE_PROVIDER_DESCRIPTION,
service_type=ServiceTypes.WATSON_MACHINE_LEARNING,
deployment_space_id = WML_SPACE_ID,
operational_space_id = "production",
credentials=... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Subscriptions Remove existing House price model subscriptions This code removes previous subscriptions to the House price model to refresh the monitors with the new model and new data. | wos_client.subscriptions.show() | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
This code removes previous subscriptions to the House price model to refresh the monitors with the new model and new data. | subscriptions = wos_client.subscriptions.list().result.subscriptions
for subscription in subscriptions:
sub_model_id = subscription.entity.asset.asset_id
if sub_model_id == model_uid:
wos_client.subscriptions.delete(subscription.metadata.id)
print('Deleted existing subscription for model', sub_m... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
This code creates the model subscription in OpenScale using the Python client API. Note that we need to provide the model unique identifier, and some information about the model itself. This code creates the model subscription in OpenScale using the Python client API. Note that we need to provide the model unique iden... | feature_cols=feature_data.columns.tolist()
#categorical_cols=X.select_dtypes(include=['object']).columns
from ibm_watson_openscale.base_classes.watson_open_scale_v2 import ScoringEndpointRequest
subscription_details = wos_client.subscriptions.add(
data_mart_id=data_mart_id,
service_provider_id=service_p... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Get subscription list | wos_client.subscriptions.show() | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Score the model so we can configure monitors | import random
fields = feature_data.columns.tolist()
values = random.sample(test_X.tolist(), 2)
scoring_payload = {"input_data": [{"fields": fields, "values": values}]}
predictions = wml_client.deployments.score(deployment_uid, scoring_payload)
print("Single record scoring result:", "\n fields:", prediction... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Check if WML payload logging worked else manually store payload records | import uuid
from ibm_watson_openscale.supporting_classes.payload_record import PayloadRecord
time.sleep(5)
pl_records_count = wos_client.data_sets.get_records_count(payload_data_set_id)
print("Number of records in the payload logging table: {}".format(pl_records_count))
if pl_records_count == 0:
print("Payload logg... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Quality monitoring and feedback logging Enable quality monitoring | import time
time.sleep(10)
target = Target(
target_type=TargetTypes.SUBSCRIPTION,
target_id=subscription_id
)
parameters = {
"min_feedback_data_size": 50
}
quality_monitor_details = wos_client.monitor_instances.create(
data_mart_id=data_mart_id,
background_mode=False,
monitor_definition... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Feedback logging The code below downloads and stores enough feedback data to meet the minimum threshold so that OpenScale can calculate a new accuracy measurement. It then kicks off the accuracy monitor. The monitors run hourly, or can be initiated via the Python API, the REST API, or the graphical user interface. Ge... | feedback_dataset_id = None
feedback_dataset = wos_client.data_sets.list(type=DataSetTypes.FEEDBACK,
target_target_id=subscription_id,
target_target_type=TargetTypes.SUBSCRIPTION).result
print(feedback_dataset)
feedback_dat... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Fairness, drift monitoring and explanations Fairness configurationThe code below configures fairness monitoring for our model. It turns on monitoring for one features, MSSubClass. In each case, we must specify: * Which model feature to monitor * One or more **majority** groups, which are values of that feature tha... | wos_client.monitor_instances.show()
#wos_client.monitor_instances.delete(drift_monitor_instance_id,background_mode=False)
target = Target(
target_type=TargetTypes.SUBSCRIPTION,
target_id=subscription_id
)
parameters = {
"features": [
{
"feature": "MSSubClass",
"majori... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Drift configuration Note: you can choose to enable/disable (True or False) model or data drift within config | monitor_instances = wos_client.monitor_instances.list().result.monitor_instances
for monitor_instance in monitor_instances:
monitor_def_id=monitor_instance.entity.monitor_definition_id
if monitor_def_id == "drift" and monitor_instance.entity.target.target_id == subscription_id:
wos_client.monitor_instan... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Score the model again now that monitoring is configured This next section randomly selects 200 records from the data feed and sends those records to the model for predictions. This is enough to exceed the minimum threshold for records set in the previous section, which allows OpenScale to begin calculating fairness. | !wget https://raw.githubusercontent.com/IBM/watson-openscale-samples/main/IBM%20Cloud/WML/assets/data/house_price/custom_scoring_payloads_50_regression.json
with open('custom_scoring_payloads_50_regression.json', 'r') as scoring_file:
scoring_data = json.load(scoring_file)
import random
with open('custom_scoring_p... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Run fairness monitor Kick off a fairness monitor run on current data. The monitor runs hourly, but can be manually initiated using the Python client, the REST API, or the graphical user interface. | run_details = wos_client.monitor_instances.run(monitor_instance_id=fairness_monitor_instance_id, background_mode=False)
time.sleep(10)
wos_client.monitor_instances.show_metrics(monitor_instance_id=fairness_monitor_instance_id) | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Run drift monitorKick off a drift monitor run on current data. The monitor runs every hour, but can be manually initiated using the Python client, the REST API. | drift_run_details = wos_client.monitor_instances.run(monitor_instance_id=drift_monitor_instance_id, background_mode=False)
time.sleep(5)
wos_client.monitor_instances.show_metrics(monitor_instance_id=drift_monitor_instance_id) | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Configure Explainability Finally, we provide OpenScale with the training data to enable and configure the explainability features. | target = Target(
target_type=TargetTypes.SUBSCRIPTION,
target_id=subscription_id
)
parameters = {
"enabled": True
}
explainability_details = wos_client.monitor_instances.create(
data_mart_id=data_mart_id,
background_mode=False,
monitor_definition_id=wos_client.monitor_definitions.MONITORS.EXPLAI... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Run explanation for sample record | pl_records_resp = wos_client.data_sets.get_list_of_records(data_set_id=payload_data_set_id, limit=1, offset=0).result
scoring_ids = [pl_records_resp["records"][0]["entity"]["values"]["scoring_id"]]
print("Running explanations on scoring IDs: {}".format(scoring_ids))
explanation_types = ["lime", "contrastive"]
result = ... | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Additional data to help debugging | print('Datamart:', data_mart_id)
print('Model:', model_uid)
print('Deployment:', deployment_uid) | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
Identify transactions for Explainability Transaction IDs identified by the cells below can be copied and pasted into the Explainability tab of the OpenScale dashboard. | wos_client.data_sets.show_records(payload_data_set_id, limit=5) | _____no_output_____ | Apache-2.0 | IBM Cloud/WML/notebooks/regression/xgboost_scikit_wrapper/Watson OpenScale and Watson ML Engine Regression.ipynb | arsuryan/watson-openscale-samples |
This Notebook uses a Session Event Dataset from E-Commerce Website (https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store and https://rees46.com/) to build an Outlier Detection based on an Autoencoder. | import mlflow
import numpy as np
import os
import shutil
import pandas as pd
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow_hub as hub
from itertools import product
# enable gpu growth if gpu is available
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
for device in gpu_de... | INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK
Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: GeForce RTX 2070 SUPER, compute capability 7.5
numpy 1.19.4
mlflow 1.14.1
tensorflow 2.4.0
tensorflo... | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Setting Registry and Tracking URI for MLflow | # Use this registry uri when mlflow is created by docker container with a mysql db backend
#registry_uri = os.path.expandvars('mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@localhost:3306/${MYSQL_DATABASE}')
# Use this registry uri when mlflow is running locally by the command:
# "mlflow server --backend-store-uri s... | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
The Data is taken from https://www.kaggle.com/mkechinov/ecommerce-behavior-data-from-multi-category-store and https://rees46.com/ Each record/line in the file has the following fields:1. event_time: When did the event happened (UTC)2. event_type: Event type: one of [view, cart, remove_from_cart, purchase] 3. product_i... | # Read first 500.000 Rows
for chunk in pd.read_table("2019-Dec.csv",
sep=",", header=0,
infer_datetime_format=True, low_memory=False, chunksize=500000):
# Filter out other event types than 'view'
chunk = chunk[chunk['event_type'] == 'view']
# Filter out ... | Mean: 284.7710546866538
Std: 349.46740231584886
Sessions: (61296,)
Unique Products: (38515,)
Unique category_code: (134,)
| MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
Delete Rows with equal or less than 6 Product Occurrences | count_product_id_mapped = df.groupby('product_id').count()
products_to_delete = count_product_id_mapped.loc[count_product_id_mapped['embedding_0'] <= 6].index
products_to_delete | _____no_output_____ | MIT | outlier_detection/training_outlier_detection.ipynb | felix-exel/kfserving-advanced |
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