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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Note that it is possible to nest multiple timeouts. | with ExpectTimeout(5):
with ExpectTimeout(3):
long_running_test()
long_running_test() | Start
0 seconds have passed
1 seconds have passed
| MIT | docs/notebooks/ExpectError.ipynb | bjrnmath/debuggingbook |
That's it, folks – enjoy! SynopsisThe `ExpectError` class allows you to catch and report exceptions, yet resume execution. This is useful in notebooks, as they would normally interrupt execution as soon as an exception is raised. Its typical usage is in conjunction with a `with` clause: | with ExpectError():
x = 1 / 0 | Traceback (most recent call last):
File "<ipython-input-1-264328004f25>", line 2, in <module>
x = 1 / 0
ZeroDivisionError: division by zero (expected)
| MIT | docs/notebooks/ExpectError.ipynb | bjrnmath/debuggingbook |
The `ExpectTimeout` class allows you to interrupt execution after the specified time. This is useful for interrupting code that might otherwise run forever. | with ExpectTimeout(5):
long_running_test() | Start
0 seconds have passed
1 seconds have passed
2 seconds have passed
3 seconds have passed
| MIT | docs/notebooks/ExpectError.ipynb | bjrnmath/debuggingbook |
Export JSON to TXT + CSVThe WE1S workflows use JSON format internally for manipulating data. However, you may wish to export JSON data from a project to plain text files with a CSV metadata file for use with other external tools.This notebook uses JSON project data to export a collection of plain txt files — one... | # Python imports
from pathlib import Path
from IPython.display import display, HTML
# Get path to project_dir
current_dir = %pwd
project_dir = str(Path(current_dir).parent.parent)
json_dir = project_dir + '/project_data/json'
config_path = project_dir + '/config/config.py... | _____no_output_____ | MIT | src/templates/v0.1.9/modules/export/json_to_txt_csv.ipynb | whatevery1says/we1s-templates |
ConfigurationThe default configuration assumes:1. There are JSON files in `project_data/json`.2. Each JSON has the required fields `pub_date`, `title`, `author`.3. Each JSON file has either: - a `content` field, or - a `bag_of_words` field created using the `import` module tokenizer (see the "Export Features Table... | limit = 10 # limit files exported -- 0 = unlimited.
txt_dir = project_dir + '/project_data/txt'
metafile = project_dir + '/project_data/txt/metadata.csv'
zipfile = project_dir + '/project_data/txt/txt.zip'
# The listed fields will be checked in order.
# The first one encountered will be the export content.
# Docum... | _____no_output_____ | MIT | src/templates/v0.1.9/modules/export/json_to_txt_csv.ipynb | whatevery1says/we1s-templates |
ExportStart the export. | # Optionally, clear the cache
if clear_cache:
clear_txt(txt_dir, metafile=metafile, zipfile=zipfile)
# Perform the export
json_to_txt_csv(json_dir=json_dir,
txt_dir=txt_dir,
txt_content_fields=txt_content_fields,
csv_export_fields=csv_export_fields,
... | _____no_output_____ | MIT | src/templates/v0.1.9/modules/export/json_to_txt_csv.ipynb | whatevery1says/we1s-templates |
Export Features TablesIf your data contains features tables (lists of lists containing linguistic features), use the cell below to export features tables as CSV files for each document in your JSON folder. Set the `save_path` to a directory where you wish to save the CSV files. If you are using WE1S public data, this ... | # Configuration
save_path = ''
# Run the export
export_features_tables(save_path, json_dir) | _____no_output_____ | MIT | src/templates/v0.1.9/modules/export/json_to_txt_csv.ipynb | whatevery1says/we1s-templates |
import ipynb into other ipynb> 路過的神器，import ipynb form other ipynb[Source](https://stackoverflow.com/questions/20186344/importing-an-ipynb-file-from-another-ipynb-file) | # import ipynb.fs.full.try1 as try1
# try1.good() | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
本 ipynb 的目標> 做 feature extracion 的 function Referance1. 品妤學姊碩論2. 清彥學長碩論3. 杰勳學長碩論4. This paper (science report, 2019)```A Machine Learning Approach forthe Identification of a Biomarker ofHuman Pain using fNIRS > Raul Fernandez Rojas1,9, Xu Huang1 & Keng-Liang Ou2,3,4,5,6,7,8```5. bbox --> annotation的bbox可以不用指定位置 | import os
import glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# 老樣子，導到適合的資料夾
print(os.getcwd())
# path = 'C:\\Users\\BOIL_PO\\Desktop\\VFT(2)\\VFT'
# os.chdir(path)
all_csv = glob.glob('Filtered//*.csv')
all_csv[:5] | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
Time_Host 設成 index的原因:1. 可用loc切，即用index_name，可以準確地切30秒，不然用iloc還要算筆數舉例:`iloc` 取30秒，必須算 30秒有多少筆 `.iloc[:筆]``loc` 取30秒，打`[:30]`他會自己取 < 30的 index | check_df = pd.read_csv(all_csv[5], index_col= 'Unnamed: 0').drop(columns= ['Time_Arduino', 'easingdata'])
# print(check_df.dtypes)
check_df = check_df.set_index('Time_Host')
check_df.head()
# 讀了誰
cols = check_df.columns
print(check_df.columns)
# 畫圖確認
stage1 = 30
stage2 = 90
stage3 = 160
text_size = 25
plt.figure(figs... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
濾波請用for> 一定要用for，不然是文組>> 用 for i in range(len(AA)) 還好，但若是後面沒用到`**位置**`資訊，都是 AA[i]，那不是文組，但你寫的是C>> Python 的 for 是神>> for str 可以出字母，for list 可以出元素，for model 可以出layer，還有好用的list comprehension `[x**3 for i in range(10) if x%2 == 0]` Feature Extraction (From Lowpass filter) 清彥1. 階段起始斜率 (8s) $\checkmark$ * Task * Reco... | # 就重寫，沒意義
exam_df = pd.read_csv(all_csv[0], index_col= 'Unnamed: 0').drop(columns= ['Time_Arduino', 'easingdata'])
# print(exam_df.dtypes)
exam_df = exam_df.set_index('Time_Host')
exam_df.head() | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段起始斜率 2*6= 120. 定義 階段開始前"八秒"，單位 `?/S`1. return list2. 30~38 -> Task 3. 90~98 -> Recovery---- | def stage_begin_slope(dataframe, plot= False, figsize= (10, 6), use_col= 0):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of slope
... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# stage_begin_slope(exam_df, plot= True, use_col= i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段平均 3*6 = 181. 0~30 -> Rest2. 30~90 -> Task3. 90~ 160 -> Recovery | def stage_mean(dataframe, plot= False, figsize= (10, 6), use_col= 0):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of mean
# ... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# stage_mean(exam_df, plot= True, use_col=i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段平均的差 -> 2*6 = 12 * Task mean – Rest mean * Task mean – Recovery mean 活化值 -> 1*6 * Recovery mean – Rest mean | def stage_mean_diff(dataframe, plot= False, figsize= (10, 6), use_col= 0):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of mean dif... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫畫看 channel | stage_mean_diff(exam_df, plot= True, use_col= 4) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段峰值 1*6 = 6 * Task | def stage_acivation(dataframe, plot= False, figsize= (10, 6), use_col= 0):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of 峰值
#... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# stage_acivation(exam_df, plot= True, use_col= i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段標準差 * 三個 標準差不能歸一化 | def stage_std(dataframe, plot= False, figsize= (10, 6), use_col= 0):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of std
# ... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# stage_std(exam_df, plot= True, use_col= i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
階段起始斜率 的差 * Task - Recovery | def stage_begin_slope_diff(dataframe):
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of slope diff
# Tuple[1] : List of ... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
Stage skewness -> use scipy * 三個階段> 資料分布靠左"正">> 資料分布靠右"負" [好用圖中圖](https://www.itread01.com/p/518289.html) | def stage_skew(dataframe, plot= False, figsize= (10, 6), use_col= 0):
from scipy.stats import skew
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# ... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# a = stage_skew(exam_df, plot= True, use_col= i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
Stage kurtosis 峰度(尖度) * 三個 | def stage_kurtosis(dataframe):
from scipy.stats import kurtosis
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# Tuple[0] : List of kurtosis
#... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
AUC -> use sklearn * 三個1. 看了很多，好比說scipy.integrate, numpy.trap2. 還是 sklearn的好用，(這邊其他的也可以試試不強制) | def stage_auc(dataframe, plot= False, figsize= (10, 6), use_col= 0):
from sklearn.metrics import auc
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Return:
# Tuple:
# ... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
畫全部 channel | # for i in range(6):
# stage_auc(exam_df, plot=True, use_col= i) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
FFT1. 取樣頻率要是頻率的兩倍.shape[-1]
# 從 0 ~ 12Hz 取 N/個
fft_window = np.fft.fft(y)
freq = np.fft.fftfreq(N, d=1/24)
# 為啥要平方??
fft_ps = np.abs(fft_window)**2
fft_window.shape, freq.shape, freq.max(), freq.min()
plt.plot(freq) | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
0.12Hz (cut down frequency)之後decay很快，看起來合理 | import matplotlib.pyplot as plt
fig = plt.figure(figsize=(14, 7))
plt.plot(freq, 2.0/N *np.abs(fft_window), label= 'FFT')
# plt.plot(freq, np.log10(fft_ps))
plt.ylim(0, 0.08)
plt.xlim(0.005, 0.4)
plt.vlines(0.12, 0, 100, colors= 'r', linestyles= '--', label= 'Cut down Freq (low pass)', )
plt.xlabel("Frequency")
plt.y... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
ML Fundamentals -> scipy | from scipy.fft import fft
def get_fft_values(y_values, T, N, f_s):
f_values = np.linspace(0.0, 1.0/(2.0*T), N//2)
fft_values_ = fft(y_values)
# 歸一化嗎??
fft_values = 2.0/N * np.abs(fft_values_[0:N//2])
return f_values, fft_values
f_s = 24
T = 1/f_s
N = np.array(y).shape[-1]
f_values, fft_values =... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
找峰值 -> scipy > 下面網站有很多方法[1. 好用網站](https://www.delftstack.com/zh-tw/howto/python/find-peaks-in-python/) | import numpy as np
from scipy.signal import argrelextrema
peaks = argrelextrema(fft_values, np.greater)
print(peaks)
f_values[5], fft_values[5]
for ind in peaks[0]:
print(f_values[ind], fft_values[ind])
peaks[0]
plt.figure(figsize= (14, 7))
plt.plot(f_values, fft_values, linestyle='-', color='blue')
plt.xlabel('... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
FFT * 一張圖 3 個峰值，共有六種血氧(3channel * 含氧/缺氧) * 一個峰值會有兩數值，一個是 amp，一個是 峰值的頻率 | def FFT(dataframe, f_s = 24, plot= False):
from scipy.fft import fft
import numpy as np
from scipy.signal import argrelextrema
#============================
# Parameter:
# dataframe: input dataframe
# plot : whether to plot
# figsize: plt.figure(figsize= figsize)
# Ret... | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
顛倒ndarray -> 帥`fft_values[real_ind][::-1]` Numpy 神技 | test= np.arange(1, 10)
test
test[::-1]
test[::-2]
test[::-3]
test[::1]
test[::2]
test[::3] | _____no_output_____ | MIT | fNIRS signal analysis/Randonstate/Extraction.ipynb | JulianLee310514065/Complete-Project |
Danger zone. This notebook is just here to be convenient for development | %matplotlib ipympl
import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np
%load_ext autoreload
%autoreload 2
from mpl_interactions import *
plt.close()
fig, ax = plt.subplots()
zoom_factory(ax)
ph = panhandler(fig)
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
a... | _____no_output_____ | BSD-3-Clause | docs/examples/devlop/devlop-scatter.ipynb | samwelborn/mpl-interactions |
EDA: Named Entity RecognitionNamed entity recognition is the process of identifing particular elements from text, such as names, places, quantities, percentages, times/dates, etc. Identifying and quantifying what the general content types an article contains seems like a good predictor of what type of article it is. W... | import articledata
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import operator
data = pd.read_pickle('/Users/teresaborcuch/capstone_project/notebooks/pickled_data.pkl') | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
Counting Named EntitiesHere is my count_entities function. The idea is to count the total mentions of a person or a place in an article's body or title and save them as columns in my existing data structure. | def count_entities(data = None, title = True):
# set up tagger
os.environ['CLASSPATH'] = "/Users/teresaborcuch/stanford-ner-2013-11-12/stanford-ner.jar"
os.environ['STANFORD_MODELS'] = '/Users/teresaborcuch/stanford-ner-2013-11-12/classifiers'
st = StanfordNERTagger('english.all.3class.distsim.crf.ser.g... | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
These graphs indicate that person and place counts from article are both strongly right skewed. It might be more interesting to compare mean person and place counts among different sections. | data.pivot_table(
index = ['condensed_section'],
values = ['total_persons_title', 'total_places_title']).sort_values('total_persons_title', ascending = False) | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
From this pivot table, it seems there are a few distinctions to be made between different sections. Entertainment and sports contain more person mentions on average than any other sections, and world news contains more places in the title than other sections. Finding Common Named EntitiesNow, I'll try to see which peo... | def evaluate_entities(data = None, section = None, source = None):
section_mask = (data['condensed_section'] == section)
source_mask = (data['source'] == source)
if section and source:
masked_data = data[section_mask & source_mask]
elif section:
masked_data = data[section_mask]
el... | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
Commonly Mentioned People in World News and Entertainment | world_persons, world_places = articledata.evaluate_entities(data = data, section = 'world', source = None)
# get top 20 people from world news article bodies
sorted_wp = sorted(world_persons.items(), key=operator.itemgetter(1))
sorted_wp.reverse()
sorted_wp[:20] | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
Perhaps as expected, Trump is the most commonly mentioned person in world news, with 1,237 mentions in 467 articles, with Obama and Putin coming in second and third. It's interesting to note that most of these names are political figures, but since the tagger only receives unigrams, partial names and first names are me... | entertainment_persons, entertainment_places = articledata.evaluate_entities(data = data, section = 'entertainment', source = None)
sorted_ep = sorted(entertainment_persons.items(), key=operator.itemgetter(1))
sorted_ep.reverse()
sorted_ep[:20] | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
Now, I'll compare the top 20 people mentioned in entertainment articles. Trump still takes the number one spot, but interestingly, he's followed by a string of first names. NLTK provides a corpus of male and female-tagged first names, so counting the number of informal mentions or even the ratio of men to women might b... | # get top 20 places from world news article bodies
sorted_wp = sorted(world_places.items(), key=operator.itemgetter(1))
sorted_wp.reverse()
sorted_wp[:20]
# get top 20 places from entertainment article bodies
sorted_ep = sorted(entertainment_places.items(), key=operator.itemgetter(1))
sorted_ep.reverse()
sorted_ep[:20] | _____no_output_____ | MIT | notebooks/ner_blog_post.ipynb | teresaborcuch/teresaborcuch.github.io |
Analysis of Red Wine Quality Index 1. Reading the data and importing the libraries 2. EDA 3. Correlation Matrix 4. Modeling - Linear Model - Weighted KNN - Random Forest - Conditional Inference Random Forest - Decision Tree Model 5. Modeling Results Table 6. Conclusion 1. Reading the data and i... | library(tidyverse)
library(grid)
library(gridExtra)
library(e1071)
library(caret)
df1 <- read.csv("C:/Users/kausha2/Documents/Data Analytics/DataSets/winequality/winequality/winequality-red.csv", sep = ";")
head(df1)
summary(df1$quality) | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
Creating a new variable --> WineAttribute : Good (1) or bad (0) for binary classification | df1$wine_attribute <- ifelse(df1$quality > 5, 1, 0 )
head(df1) | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
2. EDA How is the wine quality distributed? | qplot(df1$quality, geom="histogram", binwidth = 1) | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- The dataset is dominated by values 5 and 6. There are less wines with a quality of 4 and 7 whereas there are hardly any wines that have values less 3 and 8- there are two options : either split the quality variable into 3 parts by quantiles : top 20, middle 60 and bottom 20 or split based on the mean i.e. Good wines ... | p1 <- qplot(df1$pH, geom="histogram", binwidth = 0.05)
p2 <- qplot(df1$alcohol, geom="histogram",binwidth = 0.099)
p3 <- qplot(df1$volatile.acidity, geom="histogram",binwidth = 0.05)
p4 <- qplot(df1$citric.acid, geom="histogram",binwidth = 0.05)
grid.arrange(p1,p2,p3,p4, ncol=2, nrow=2) | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- We see that pH looks normally distributed - Volatile acidity, Alcohol and citric acid have a positive skew shape but dont seem to follow a particular distribution | p1 <- qplot(df1$residual.sugar, geom="histogram", binwidth = 0.1)
p2 <- qplot(df1$chlorides, geom="histogram",binwidth = 0.01)
p3 <- qplot(df1$density, geom="histogram",binwidth = 0.001)
p4 <- qplot(df1$free.sulfur.dioxide, geom="histogram",binwidth = 1)
grid.arrange(p1,p2,p3,p4, ncol=2, nrow=2) | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- Density seems to follow a normal distribution. - Residual sugar and chlorides seem to follow a normal distribution initially but flatten out later- Free sulfur dioxide content seems to have a positive skew shaped distribution | p1 <- qplot(df1$pH, geom="density")
p2 <- qplot(df1$alcohol, geom="density")
p3 <- qplot(df1$volatile.acidity, geom="density")
p4 <- qplot(df1$citric.acid, geom="density")
grid.arrange(p1,p2,p3,p4, ncol=2, nrow=2)
p1 <- qplot(df1$residual.sugar, geom="density")
p2 <- qplot(df1$chlorides, geom="density")
p3 <- qplo... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- The kernel density plots seem to agree with the histograms and our conclusions | p1 <- ggplot(df1, aes(x="pH", y=pH)) + stat_boxplot(geom ='errorbar') + geom_boxplot()
p2 <- ggplot(df1, aes(x="alcohol", y=alcohol)) + stat_boxplot(geom ='errorbar') + geom_boxplot()
p3 <- ggplot(df1, aes(x="volatile.acidity", y=volatile.acidity)) + stat_boxplot(geom ='errorbar') + geom_boxplot()
p4 <- ggplot(df1, aes... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- pH and acidity seem to have a lot of outliers.- The pH of an acidic substance is usally below 5 but for wines it seems to concentrate in the area between 2.7 and 4.0.- The alcohol content is between 8.4 to 15 but there seem to be many outliers. The Age of the wine also affects its alcohol content. This could explain ... | #data(attitude)
df2 <- df1
df2$wine_attribute <- NULL
library(ggplot2)
library(reshape2)
#(cor(df1) ) # correlation matrix
cormat <- cor(df2)
melted_cormat <- melt(cor(df2))
#ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
# geom_tile()
# Get lower triangle of the correlation matrix
get_lower_t... | Warning message:
"package 'reshape2' was built under R version 3.3.2"
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
| Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
- The values in Red are positively correlated while those in Blue are negatively correlated. The density of the color determines the strength of correlation.- Quality has a negative correlation with volatile acidity, and total sulfur dioxide content. While it has a positive correlation with alcohol content and citric a... | p1 <- ggplot(df1, aes(x= volatile.acidity, y= quality)) + geom_point() + geom_smooth(method=lm)
p2 <- ggplot(df1, aes(x= total.sulfur.dioxide, y= quality)) + geom_point() + geom_smooth(method=lm)
p3 <- ggplot(df1, aes(x= alcohol, y= quality)) + geom_point() + geom_smooth(method=lm)
p4 <- ggplot(df1, aes(x= citric.acid,... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
This confirms our analysis from the correlation matrix 4. Modeling We'll be using a 10-fold cross validationWe perform the 10 fold CV on the learning dataset and try to predict the valid dataset | # Train Test Split
m <- dim(df1)[1] # Select the rows of iris
val <- sample(1:m, size = round(m/3), replace = FALSE, prob = rep(1/m, m))
df1.learn <- df1[-val,] # train
df1.valid <- df1[val,] # test
# 10 Fold CV
library(caret)
# define training control
train_control <- trainControl(method="cv", number=10)
#trC... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
The linear model : trying to predict wine quality from the variables | head(df1,1)
model1 <- lm(as.numeric(quality)~ 0 + volatile.acidity + chlorides
+ log(free.sulfur.dioxide) + log(total.sulfur.dioxide) + density + pH + sulphates + alcohol, data = df1)
summary(model1)
df1.valid$prediction <- predict(model1,df1.valid)
df1.valid$prediction_lm <- round(df1.valid$prediction)
x... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
From the above graph we see that this is not what we intended. Therefore we move on to classification models Weighted KNNUsing multiple K, distance metrics and kernels | require(kknn)
model2 <- kknn( factor(wine_attribute) ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar
+ chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH
+ sulphates + alcohol, df1.learn, df1.valid)
x <- confusionMatrix(df1.valid$wine_attribute, m... |
Call:
train.kknn(formula = factor(wine_attribute) ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar + chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH + sulphates + alcohol, data = df1.learn, kmax = 15, distance = 5, kernel = c("triangular", "epanechnikov", "biweigh... | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
Weighted KKNN gave us decent results. Lets see if we can improve on it. Tree Models Random Forest | library(randomForest)
model5 <- randomForest(as.factor(wine_attribute) ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar
+ chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH
+ sulphates + alcohol, df1.learn,trControl = train_control, importance=TRUE,... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
Ensembling Random Forest with the Conditional Inference Tree | library(party)
model5x <- cforest(as.factor(wine_attribute) ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar
+ chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH
+ sulphates + alcohol, df1.learn, controls=cforest_unbiased(ntree=2000, mtry=3))
df1.v... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
Decision Trees using Rpart | library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(rpart)
rpart.grid <- expand.grid(.cp=0.2)
model6 <- train(as.factor(wine_attribute) ~ fixed.acidity + volatile.acidity + citric.acid + residual.sugar
+ chlorides + free.sulfur.dioxide + total.sulfur.dioxide + density + pH
... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
5. Modeling Results Table | Model_Name <- c("Linear Model", "Simple_KKNN","KKNN_dist1","KKNN_dist2", "RandomForest", "Conditional Forest", "Decision Tree")
Overall_Accuracy <- c(acc_lm*100, acc_kknn1*100, acc_kknn2*100, acc_kknn3*100, acc_rf*100, acc_cf*100, acc_dt*100)
final <- data.frame(Model_Name,Overall_Accuracy)
final$Overall_Accuracy <- ... | _____no_output_____ | Apache-2.0 | Red Wine Quality Analysis.ipynb | RagsX137/Red-Wine-Quality-Analysis |
Optimal treatment for varied lengths of time horizons | cs = c(3.6,3.2)
options(repr.plot.width=cs[1],repr.plot.height=cs[2])
cln_nms = c("T","time","sigma","resistance","size")
read.table(file="../figures/draft/Fig7X-trjs_optimal.csv",header=FALSE,sep=",",col.names=cln_nms) %>%
as.data.frame -> df_optimal
print("Optimal")
df_optimal %>% tail(1) %>% .$size
sz = 1.5;... | _____no_output_____ | MIT | scripts/.ipynb_checkpoints/E. Figures 6 and 7 [R]-checkpoint.ipynb | aakhmetz/AkhmKim2019Scripts |
Another figure for solution of the optimal control problem | cs = c(4.2,2.75)
options(repr.plot.width=cs[1],repr.plot.height=cs[2])
cln_nms = c("trajectory","time","sigma","resistance","size")
read.table(file="../figures/draft/Fig6-trjs_optimal-final.csv",header=TRUE,sep=",",col.names=cln_nms) %>%
as.data.frame %>% mutate(time=time/30,resistance=100*resistance) -> df
# cl... | _____no_output_____ | MIT | scripts/.ipynb_checkpoints/E. Figures 6 and 7 [R]-checkpoint.ipynb | aakhmetz/AkhmKim2019Scripts |
Accessing Items 1. Write a program to retrieve the keys/values of dictionary **Use the dictionary**dictionary2 = {0:3, 'x':5, 1:2} | dictionary2 = {0:3, 'x':5, 1:2}
dictionary2 | _____no_output_____ | MIT | Arvind/08 - Accessing Items.ipynb | Arvind-collab/Data-Science |
2. Write a program to get the value for 'Age' from the dictionary **Use the dictionary**dictionary3 = {'Weight': 67, 'BMI': 25, 'Age': 27, 'Profession': 'CA'} | dictionary3 = {'Weight': 67, 'BMI': 25, 'Age': 27, 'Profession': 'CA'}
dictionary3['Age'] | _____no_output_____ | MIT | Arvind/08 - Accessing Items.ipynb | Arvind-collab/Data-Science |
 Kindle eBook Recommendation System: Data PreparationAuthors: Daniel Burdeno --- Contents- Overview- Business Understanding - Data Understanding - Data Preparation - Imports and Functions - Meta Data - Review Data - CSV Files Overview > This project aims ... | import pandas as pd
import numpy as np
import matplotlib as plt
import warnings
# Show plots in notebook
%matplotlib inline
warnings.filterwarnings('ignore')
# Set matplotlib style to match other notebook graphics
plt.style.use('fast')
# Function that takes a list and returns the third value, used in a .apply below to... | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
Load Data > As stated in the Data Understanding section, we have two seperate JSON files to load in. One containing individual user reviews and the other containing book meta data. The meta data will need to be heavily cleaned to extract relevant information for this project. These large initial data files were loaded... | # Meta data load-in, file stored as compressed JSON, each line is a JSON entry hence the lines=True arguement
path = 'C:\\Users\\danie\\Documents\\Flatiron\\Projects\\Capstone\\Rawdata\\meta_Kindle_Store.gz'
df_meta = pd.read_json(path, compression='gzip', lines=True)
# Review data load-in, file stored as compressed JS... | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
Meta Data | df_meta.info()
df_meta.head() | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
> Taking a look at the .info() and .head() of the meta data shows a plethora of cleaning that needs to occur. There are a multitude of unusable columns with blank information including tech1, tech2, fit, description, rank, main_cat, price, and image columns. Within the category column (a list) and the details column (a... | # Taking a look at what is within the category columns, it contains lists
df_meta['category'].value_counts()
# Using a dual nested lambda function and .apply I can subset the dataframe to only contain categories that denote eBooks
df_meta = df_meta.loc[
lambda df: df.category.apply(
lambda l: 'Kindle eBooks... | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
> After running the clean function on my meta data I noticed that there was still blank entries for things like title and I should check for null values. However a lot of these were just blank entries not actually denoated as NaN so I had to parse through the dataframe and replace any blank entry with NaN in order to a... | # Converting blank entries to NaN using regex expression and looking at nulls
df_meta = df_meta.replace(r'^\s*$', np.nan, regex=True)
df_meta.isna().sum()
# Dropping nulls and using groupby to sort by genre so I can fill in any print_length nulls based on mean genre value
df_meta['print_length'] = df_meta.groupby(['gen... | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
Review Data | df_rev.info()
df_rev.head()
# I thought style would contain genre but it did not, entries will be subsetted using the meta data, so ignore column
df_rev['style'].value_counts() | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
> Taking a look at the review data shows some cleaning that needs to occur as well, including dropping unneeded columns and exploring several others. The overall column denotes the rating that a user gave to the item (critical information). Verified is either True or False, and looking it up it designates if a reviewer... | # Matching reviewed products with products with meta data
df_rev = df_rev[df_rev['asin'].isin(asin_list)]
df_rev.verified.value_counts()
# Dropping any rows that were not verified
indexNames = df_rev[df_rev['verified'] == False].index
df_rev.drop(indexNames , inplace=True)
# Dropping unused columns
df_rev_use = df_rev.... | <class 'pandas.core.frame.DataFrame'>
Int64Index: 1398682 entries, 2754 to 2222982
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 overall 1398682 non-null int64
1 reviewerID 1398682 non-null object
2 asin 1398682 non-null ob... | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
CSV Files > For ease of use in further notebooks the cleaned and compiled dataframes were saved to individual csv files within the data folder. These files can be saved locally and were not pushed to github due to size constraints. The meta data files were saved and pushed to github inorder for heroku/streamlit to hav... | # Save cleaned review dataframe to csv for use in other notebook, this set includes reviewers with less than 5 reviews
df_rev_use.to_csv('Data/df_rev_all.csv')
# Subsetting review data to only include reviewers with 5 or more reviews
df_rev5 = df_rev_use[df_rev_use['reviewerID'].map(df_rev_use['reviewerID'].value_count... | _____no_output_____ | MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
> In order to conduct natural language processing and produce content based on review text I needed to aggregate review text based on individual books. I used the unique product number, 'asin', to groupby and then join review text for each book into a new data dataframe below. This dataframe will be used to produce a d... | # Groupby using 'asin' and custom aggregate to join all review text
df_books_rev = df_rev_use.groupby(['asin'], as_index = False).agg({'reviewText': ' '.join})
df_books_rev.to_csv('Data/df_books_rev.csv')
df_books_rev.head()
df_books_rev.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 94211 entries, 0 to 94210
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 asin 94211 non-null object
1 reviewText 94211 non-null object
dtypes: object(2)
memory usage: 1.4+ MB
| MIT | DataPrepFinal.ipynb | danielburdeno/Kindle-eBook-Recommendations |
URL: http://bokeh.pydata.org/en/latest/docs/gallery/unemployment.htmlMost examples work across multiple plotting backends, this example is also available for:* [Matplotlib - US unemployment example](../matplotlib/us_unemployment.ipynb) | import pandas as pd
import holoviews as hv
from holoviews import opts
hv.extension('bokeh') | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/us_unemployment.ipynb | ppwadhwa/holoviews |
Defining data | from bokeh.sampledata.unemployment1948 import data
data = pd.melt(data.drop('Annual', 1), id_vars='Year', var_name='Month', value_name='Unemployment')
heatmap = hv.HeatMap(data, label="US Unemployment (1948 - 2013)") | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/us_unemployment.ipynb | ppwadhwa/holoviews |
Plot | colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
heatmap.opts(
opts.HeatMap(width=900, height=400, xrotation=45, xaxis='top', labelled=[],
tools=['hover'], cmap=colors)) | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/us_unemployment.ipynb | ppwadhwa/holoviews |
TensorFlow 2 Complete Project Workflow in Amazon SageMaker Data Preprocessing -> Training -> Automatic Model Tuning -> Deployment 1. [Introduction](Introduction)2. [SageMaker Processing for dataset transformation](SageMakerProcessing)5. [SageMaker hosted training](SageMakerHostedTraining)6. [Automatic Model Tuning]... | import boto3
import os
import sagemaker
import tensorflow as tf
sess = sagemaker.session.Session()
bucket = sess.default_bucket()
region = boto3.Session().region_name
data_dir = os.path.join(os.getcwd(), 'data')
os.makedirs(data_dir, exist_ok=True)
train_dir = os.path.join(os.getcwd(), 'data/train')
os.makedirs(tra... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
SageMaker Processing for dataset transformation Next, we'll import the dataset and transform it with SageMaker Processing, which can be used to process terabytes of data in a SageMaker-managed cluster separate from the instance running your notebook server. In a typical SageMaker workflow, notebooks are only used for ... | import numpy as np
from tensorflow.python.keras.datasets import boston_housing
from sklearn.preprocessing import StandardScaler
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
np.save(os.path.join(raw_dir, 'x_train.npy'), x_train)
np.save(os.path.join(raw_dir, 'x_test.npy'), x_test)
np.save(os.path.... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Next, simply supply an ordinary Python data preprocessing script as shown below. For this example, we're using a SageMaker prebuilt Scikit-learn framework container, which includes many common functions for processing data. There are few limitations on what kinds of code and operations you can run, and only a minimal... | %%writefile preprocessing.py
import glob
import numpy as np
import os
from sklearn.preprocessing import StandardScaler
if __name__=='__main__':
input_files = glob.glob('{}/*.npy'.format('/opt/ml/processing/input'))
print('\nINPUT FILE LIST: \n{}\n'.format(input_files))
scaler = StandardScaler()
f... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Before starting the SageMaker Processing job, we instantiate a `SKLearnProcessor` object. This object allows you to specify the instance type to use in the job, as well as how many instances. Although the Boston Housing dataset is quite small, we'll use two instances to showcase how easy it is to spin up a cluster fo... | from sagemaker import get_execution_role
from sagemaker.sklearn.processing import SKLearnProcessor
sklearn_processor1 = SKLearnProcessor(framework_version='0.23-1',
role=get_execution_role(),
instance_type='ml.m5.xlarge',
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
We're now ready to run the Processing job. To enable distributing the data files equally among the instances, we specify the `ShardedByS3Key` distribution type in the `ProcessingInput` object. This ensures that if we have `n` instances, each instance will receive `1/n` files from the specified S3 bucket. It may take... | from sagemaker.processing import ProcessingInput, ProcessingOutput
from time import gmtime, strftime
processing_job_name = "tf-2-workflow-{}".format(strftime("%d-%H-%M-%S", gmtime()))
output_destination = 's3://{}/{}/data'.format(bucket, s3_prefix)
sklearn_processor1.run(code='preprocessing.py',
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
In the log output of the SageMaker Processing job above, you should be able to see logs in two different colors for the two different instances, and that each instance received different files. Without the `ShardedByS3Key` distribution type, each instance would have received a copy of **all** files. By spreading the ... | x_train_in_s3 = '{}/train/x_train.npy'.format(output_destination)
y_train_in_s3 = '{}/train/y_train.npy'.format(output_destination)
x_test_in_s3 = '{}/test/x_test.npy'.format(output_destination)
y_test_in_s3 = '{}/test/y_test.npy'.format(output_destination)
!aws s3 cp {x_train_in_s3} ./data/train/x_train.npy
!aws s3 c... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
SageMaker hosted training Now that we've prepared a dataset, we can move on to SageMaker's model training functionality. With SageMaker hosted training the actual training itself occurs not on the notebook instance, but on a separate cluster of machines managed by SageMaker. Before starting hosted training, the data ... | s3_prefix = 'tf-2-workflow'
traindata_s3_prefix = '{}/data/train'.format(s3_prefix)
testdata_s3_prefix = '{}/data/test'.format(s3_prefix)
train_s3 = sess.upload_data(path='./data/train/', key_prefix=traindata_s3_prefix)
test_s3 = sess.upload_data(path='./data/test/', key_prefix=testdata_s3_prefix)
inputs = {'train':t... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
We're now ready to set up an Estimator object for hosted training. We simply call `fit` to start the actual hosted training. | from sagemaker.tensorflow import TensorFlow
train_instance_type = 'ml.c5.xlarge'
hyperparameters = {'epochs': 30, 'batch_size': 128, 'learning_rate': 0.01}
hosted_estimator = TensorFlow(
source_dir='tf-2-workflow-smpipelines',
entry_point='train.py',
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
After starting the hosted training job with the `fit` method call below, you should observe the valication loss converge with each epoch. Can we do better? We'll look into a way to do so in the **Automatic Model Tuning** section below. In the meantime, the hosted training job should take about 3 minutes to complete. | hosted_estimator.fit(inputs) | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
The training job produces a model saved in S3 that we can retrieve. This is an example of the modularity of SageMaker: having trained the model in SageMaker, you can now take the model out of SageMaker and run it anywhere else. Alternatively, you can deploy the model into a production-ready environment using SageMake... | !aws s3 cp {hosted_estimator.model_data} ./model/model.tar.gz | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
The unzipped archive should include the assets required by TensorFlow Serving to load the model and serve it, including a .pb file: | !tar -xvzf ./model/model.tar.gz -C ./model | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Automatic Model Tuning So far we have simply run one Hosted Training job without any real attempt to tune hyperparameters to produce a better model. Selecting the right hyperparameter values to train your model can be difficult, and typically is very time consuming if done manually. The right combination of hyperpara... | from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner
hyperparameter_ranges = {
'learning_rate': ContinuousParameter(0.001, 0.2, scaling_type="Logarithmic"),
'epochs': IntegerParameter(10, 50),
'batch_size': IntegerParameter(64, 256),
}
metric_definitions =... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Next we specify a HyperparameterTuner object that takes the above definitions as parameters. Each tuning job must be given a budget: a maximum number of training jobs. A tuning job will complete after that many training jobs have been executed. We also can specify how much parallelism to employ, in this case five j... | tuner = HyperparameterTuner(hosted_estimator,
objective_metric_name,
hyperparameter_ranges,
metric_definitions,
max_jobs=15,
max_parallel_jobs=5,
object... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
After the tuning job is finished, we can use the `HyperparameterTuningJobAnalytics` object from the SageMaker Python SDK to list the top 5 tuning jobs with the best performance. Although the results vary from tuning job to tuning job, the best validation loss from the tuning job (under the FinalObjectiveValue column) l... | tuner_metrics = sagemaker.HyperparameterTuningJobAnalytics(tuning_job_name)
tuner_metrics.dataframe().sort_values(['FinalObjectiveValue'], ascending=True).head(5) | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
The total training time and training jobs status can be checked with the following lines of code. Because automatic early stopping is by default off, all the training jobs should be completed normally. For an example of a more in-depth analysis of a tuning job, see the SageMaker official sample [HPO_Analyze_TuningJob_... | total_time = tuner_metrics.dataframe()['TrainingElapsedTimeSeconds'].sum() / 3600
print("The total training time is {:.2f} hours".format(total_time))
tuner_metrics.dataframe()['TrainingJobStatus'].value_counts() | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
SageMaker hosted endpoint Assuming the best model from the tuning job is better than the model produced by the individual hosted training job above, we could now easily deploy that model to production. A convenient option is to use a SageMaker hosted endpoint, which serves real time predictions from the trained mode... | tuning_predictor = tuner.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge') | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
We can compare the predictions generated by this endpoint with the actual target values: | results = tuning_predictor.predict(x_test[:10])['predictions']
flat_list = [float('%.1f'%(item)) for sublist in results for item in sublist]
print('predictions: \t{}'.format(np.array(flat_list)))
print('target values: \t{}'.format(y_test[:10].round(decimals=1))) | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
To avoid billing charges from stray resources, you can delete the prediction endpoint to release its associated instance(s). | sess.delete_endpoint(tuning_predictor.endpoint_name) | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Workflow Automation with SageMaker Pipelines In the previous parts of this notebook, we prototyped various steps of a TensorFlow project within the notebook itself, with some steps being run on external SageMaker resources (hosted training, model tuning, hosted endpoints). Notebooks are great for prototyping, but gen... | from sagemaker.workflow.parameters import (
ParameterInteger,
ParameterString,
)
# raw input data
input_data = ParameterString(name="InputData", default_value=raw_s3)
# processing step parameters
processing_instance_type = ParameterString(name="ProcessingInstanceType", default_value="ml.m5.xlarge")
processing... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Processing Step The first step in the pipeline will preprocess the data to prepare it for training. We create a `SKLearnProcessor` object similar to the one above, but now parameterized so we can separately track and change the job configuration as needed, for example to increase the instance type size and count to ac... | from sagemaker.sklearn.processing import SKLearnProcessor
role = sagemaker.get_execution_role()
framework_version = "0.23-1"
sklearn_processor = SKLearnProcessor(
framework_version=framework_version,
instance_type=processing_instance_type,
instance_count=processing_instance_count,
base_job_name="tf-2-... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Training and Model Creation Steps The following code sets up a pipeline step for a training job. We start by specifying which SageMaker prebuilt TensorFlow 2 training container to use for the job. | from sagemaker.tensorflow import TensorFlow
from sagemaker.inputs import TrainingInput
from sagemaker.workflow.steps import TrainingStep
from sagemaker.workflow.step_collections import RegisterModel
tensorflow_version = '2.3.1'
python_version = 'py37'
image_uri_train = sagemaker.image_uris.retrieve(
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Next, we specify an `Estimator` object, and define a `TrainingStep` to insert the training job in the pipeline with inputs from the previous SageMaker Processing step. | import time
model_path = f"s3://{bucket}/TF2WorkflowTrain"
training_parameters = {'epochs': 44, 'batch_size': 128, 'learning_rate': 0.0125, 'for_pipeline': 'true'}
estimator = TensorFlow(
image_uri=image_uri_train,
source_dir='tf-2-workflow-smpipelines',
entry_point='train.py',
instance_type=training_... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
As another step, we create a SageMaker `Model` object to wrap the model artifact, and associate it with a separate SageMaker prebuilt TensorFlow Serving inference container to potentially use later. | from sagemaker.model import Model
from sagemaker.inputs import CreateModelInput
from sagemaker.workflow.steps import CreateModelStep
image_uri_inference = sagemaker.image_uris.retrieve(
framework="tensorflow",
region=region,
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Batch Scoring Step The final step in this pipeline is offline, batch scoring (inference/prediction). The inputs to this step will be the model we trained earlier, and the test data. A simple, ordinary Python script is all we need to do the actual batch inference. | %%writefile batch-score.py
import os
import subprocess
import sys
import numpy as np
import pathlib
import tarfile
def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])
if __name__ == "__main__":
install('tensorflow==2.3.1')
model_path = f"/opt/ml/processing/... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
In regard to the SageMaker features we could use to perform batch scoring, we have several choices, including SageMaker Processing and SageMaker Batch Transform. We'll use SageMaker Processing here. | batch_scorer = SKLearnProcessor(
framework_version=framework_version,
instance_type=batch_instance_type,
instance_count=batch_instance_count,
base_job_name="tf-2-workflow-batch",
sagemaker_session=sess,
... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
Creating and executing the pipeline With all of the pipeline steps now defined, we can define the pipeline itself as a `Pipeline` object comprising a series of those steps. Parallel and conditional steps also are possible. | from sagemaker.workflow.pipeline import Pipeline
pipeline_name = f"TF2Workflow"
pipeline = Pipeline(
name=pipeline_name,
parameters=[input_data,
processing_instance_type,
processing_instance_count,
training_instance_type,
training_instance_cou... | _____no_output_____ | Apache-2.0 | notebooks/tf-2-workflow-smpipelines.ipynb | yegortokmakov/amazon-sagemaker-workshop |
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