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 |
|---|---|---|---|---|---|
``xyz`` is now a 3-column float array and can now probably be treated as a data frame.Here we use a ``pandas`` method to build the dataframe. | df = pandas.DataFrame(xyz) | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson08/OriginalPowerpoint/ENGR-1330-Lesson8-Dev.ipynb | dustykat/engr-1330-psuedo-course |
Get some info, yep three columns (ordered triples to be precise!) | df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 774 entries, 0 to 773
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 774 non-null float64
1 1 774 non-null float64
2 2 774 non-null float64
dtypes: float64(3)
memory usage... | CC0-1.0 | 1-Lessons/Lesson08/OriginalPowerpoint/ENGR-1330-Lesson8-Dev.ipynb | dustykat/engr-1330-psuedo-course |
And some summary statistics (meaningless for these data), but now have taken data from the internet and prepared it for analysis. | df.describe() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson08/OriginalPowerpoint/ENGR-1330-Lesson8-Dev.ipynb | dustykat/engr-1330-psuedo-course |
And lets look at the first few rows | df.head() | _____no_output_____ | CC0-1.0 | 1-Lessons/Lesson08/OriginalPowerpoint/ENGR-1330-Lesson8-Dev.ipynb | dustykat/engr-1330-psuedo-course |
--- ReferencesOverland, B. (2018). Python Without Fear. Addison-Wesley ISBN 978-0-13-468747-6. Grus, Joel (2015). Data Science from Scratch: First Principles with Python O’ReillyMedia. Kindle Edition.Precord, C. (2010) wxPython 2.8 Application Development Cookbook Packt Publishing Ltd. Birmingham , B27 6PA, UK ISBN 978... | # Preamble script block to identify host, user, and kernel
import sys
! hostname
! whoami
print(sys.executable)
print(sys.version)
print(sys.version_info) | atomickitty.aws
engr1330content
/opt/conda/envs/python/bin/python
3.8.3 (default, Jul 2 2020, 16:21:59)
[GCC 7.3.0]
sys.version_info(major=3, minor=8, micro=3, releaselevel='final', serial=0)
| CC0-1.0 | 1-Lessons/Lesson08/OriginalPowerpoint/ENGR-1330-Lesson8-Dev.ipynb | dustykat/engr-1330-psuedo-course |
Create a histogram to visualize the most common salary ranges for employees. | #display salaries table
salaries = pd.read_sql('select * from salaries', connection)
salaries.head()
#finding the maximum value of salary
max_salary = salaries["salary"].max()
max_salary
#finding the minimum value of salary
min_salary = salaries["salary"].min()
min_salary
#calculate gap for 6 bins
bin_value = (max_sala... | _____no_output_____ | ADSL | bonus.ipynb | SandraTeh/sql-challenge |
Create a bar chart of average salary by title. | #display salaries table
salaries = pd.read_sql('select * from salaries', connection)
salaries.head()
#display titles table
titles = pd.read_sql('select * from titles', connection)
titles.head()
#display employees table
employees= pd.read_sql('select * from employees', connection)
employees.head()
#Merge salaries table ... | _____no_output_____ | ADSL | bonus.ipynb | SandraTeh/sql-challenge |
Measles Incidence in Altair This is an example of reproducing the Wall Street Journal's famous [Measles Incidence Plot](http://graphics.wsj.com/infectious-diseases-and-vaccines/b02g20t20w15) in Python using [Altair](http://github.com/ellisonbg/altair/). The DataWe'll start by downloading the data. Fortunately, others... | import pandas as pd
url = 'https://raw.githubusercontent.com/blmoore/blogR/master/data/measles_incidence.csv'
data = pd.read_csv(url, skiprows=2, na_values='-')
data.head() | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
Data Munging with Pandas This data needs to be cleaned-up a bit; we can do this with the Pandas library.We first need to aggregate the incidence data by year: | annual = data.drop('WEEK', axis=1).groupby('YEAR').sum()
annual.head() | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
Next, because Altair is built to handle data where each row corresponds to a single sample, we will stack the data, re-labeling the columns for clarity: | measles = annual.reset_index()
measles = measles.melt('YEAR', var_name='state', value_name='incidence')
measles.head() | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
Initial Visualization Now we can use Altair's syntax for generating a heat map: | import altair as alt
alt.Chart(measles).mark_rect().encode(
x='YEAR:O',
y='state:N',
color='incidence'
).properties(
width=600,
height=400
) | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
Adjusting Aesthetics All operative components of the visualization appear above, we now just have to adjust the aesthetic features to reproduce the original plot.Altair allows a wide range of flexibility for such adjustments, including size and color of markings, axis labels and titles, and more.Here is the data visua... | # Define a custom colormape using Hex codes & HTML color names
colormap = alt.Scale(domain=[0, 100, 200, 300, 1000, 3000],
range=['#F0F8FF', 'cornflowerblue', 'mediumseagreen', '#FFEE00', 'darkorange', 'firebrick'],
type='sqrt')
alt.Chart(measles).mark_rect().encode(
alt.X... | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
The result clearly shows the impact of the the measles vaccine introduced in the mid-1960s. Layering & SelectionsHere is another view of the data, using layering and selections to allow zooming-in | hover = alt.selection_single(on='mouseover', nearest=True, fields=['state'], empty='none')
line = alt.Chart().mark_line().encode(
alt.X('YEAR:Q',
scale=alt.Scale(zero=False),
axis=alt.Axis(format='f', title='year')
),
alt.Y('incidence:Q', axis=alt.Axis(title='measles incidence')),
detai... | _____no_output_____ | BSD-3-Clause | notebooks/09-Measles.ipynb | pydatawrangler/altair_notebooks |
Sparkify Project WorkspaceThis workspace contains a tiny subset (128MB) of the full dataset available (12GB). Feel free to use this workspace to build your project, or to explore a smaller subset with Spark before deploying your cluster on the cloud. Instructions for setting up your Spark cluster is included in the la... | # import libraries
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pyspark.sql import SparkSession
from pyspark.ml.feature import RegexTokenizer, VectorAssembler, Normalizer, StandardScaler
from pyspark.sql.functions import isnan, when, count, col
from pyspark.sql.funct... | _____no_output_____ | MIT | .ipynb_checkpoints/Sparkify-checkpoint.ipynb | rabadzhiyski/projectSpark |
Load and Clean DatasetIn this workspace, the mini-dataset file is `mini_sparkify_event_data.json`. Load and clean the dataset, checking for invalid or missing data - for example, records without userids or sessionids. | pwd
path = '/home/freemo/Projects/largeData/mini_sparkify_event_data.json'
df = spark.read.json(file)
df.take(1)
print((df.count(), len(df.columns)))
mising_values.show()
df.show(5)
spark.conf.set('spark.sql.repl.eagerEval.enabled', True)
df
df.show(1, vertical=True)
df.printSchema()
df.select([count(when(col(c).isNull... | +---------------+----------+---------+------+-------------+--------+---------+-----+--------------------+------+-----------+-------------+---------+--------------------+------+-------------+--------------------+------+
| artist| auth|firstName|gender|itemInSession|lastName| length|level| locat... | MIT | .ipynb_checkpoints/Sparkify-checkpoint.ipynb | rabadzhiyski/projectSpark |
USER INPUT | tickers = get_ticker_list()
asset_list = [ticker['Ticker'] for ticker in tickers]
#asset_list.remove("GAIL")
#asset_list = ['ICICIBANK', 'HDFC', 'HDFCBANK', 'INFY', 'RELIANCE', 'ASIANPAINT', 'TCS', 'MARUTI', 'TATAMOTORS', 'CIPLA']
asset_list = ["ABBOTINDIA", "ACC", "ADANIENT", "ADANIGREEN", "ADANITRANS", "ALKEM", "AMBU... | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Portfolio Strategy Tools | strat = PortfolioStrategy(asset_list, period = 1000, cob = cob) | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Correlation Matrix | fig = strat.plot_correlation_matrix()
fig.show() | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Correlated Stocks | corr_pair = strat.get_correlation_pair()
print("Highly Correlated:")
display_html(corr_pair[corr_pair['Correlation'] > .95])
print("\nInversely Correlated:")
display_html(corr_pair[corr_pair['Correlation'] < -.90]) | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Efficient Frontier | market_portfolio = strat.get_efficient_market_portfolio(plot = True, num_ports = 500, show_frontier =True) #plot =True to plot Frontier | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Market Portfolio | market_portfolio = market_portfolio.T
market_portfolio = market_portfolio[market_portfolio['MP'] != 0.00]
plt.pie(market_portfolio.iloc[:-3,0], labels=market_portfolio.index.tolist()[:-3])
market_portfolio | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
**Ticker Performance** | strat.calcStat(format_result = True) | _____no_output_____ | MIT | jupyter notebooks/Analysis/Stocks/.ipynb_checkpoints/Portfolio Analysis-checkpoint 5.ipynb | rahulspsec/JupyterNB_PortfolioTools |
Bayes's Theorem Think Bayes, Second EditionCopyright 2020 Allen B. DowneyLicense: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) In the previous chapter, we derived Bayes's Theorem:$$P(A|B) = \frac{P(A) P(B|A)}{P(B)}$$As an example, we use... | import pandas as pd
table = pd.DataFrame(index=['Bowl 1', 'Bowl 2']) | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Now I'll add a column to represent the priors: | table['prior'] = 1/2, 1/2
table | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
And a column for the likelihoods: | table['likelihood'] = 3/4, 1/2
table | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Here we see a difference from the previous method: we compute likelihoods for both hypotheses, not just Bowl 1:* The chance of getting a vanilla cookie from Bowl 1 is 3/4.* The chance of getting a vanilla cookie from Bowl 2 is 1/2.You might notice that the likelihoods don't add up to 1. That's OK; each of them is a pr... | table['unnorm'] = table['prior'] * table['likelihood']
table | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
I call the result `unnorm` because these values are the **"unnormalized posteriors"**. Each of them is the product of a prior and a likelihood:$$P(B_i)~P(D|B_i)$$which is the numerator of Bayes's Theorem. If we add them up, we have$$P(B_1)~P(D|B_1) + P(B_2)~P(D|B_2)$$which is the denominator of Bayes's Theorem, $P(D)$... | prob_data = table['unnorm'].sum()
prob_data | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Notice that we get 5/8, which is what we got by computing $P(D)$ directly.And we can compute the posterior probabilities like this: | table['posterior'] = table['unnorm'] / prob_data
table | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
The posterior probability for Bowl 1 is 0.6, which is what we got using Bayes's Theorem explicitly.As a bonus, we also get the posterior probability of Bowl 2, which is 0.4.When we add up the unnormalized posteriors and divide through, we force the posteriors to add up to 1. **This process is called "normalization"**,... | table2 = pd.DataFrame(index=[6, 8, 12]) | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
I'll use fractions to represent the prior probabilities and the likelihoods. That way they don't get rounded off to floating-point numbers. | from fractions import Fraction
table2['prior'] = Fraction(1, 3)
table2['likelihood'] = Fraction(1, 6), Fraction(1, 8), Fraction(1, 12)
table2 | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Once you have priors and likelhoods, the remaining steps are always the same, so I'll put them in a function: | def update(table):
"""Compute the posterior probabilities."""
table['unnorm'] = table['prior'] * table['likelihood']
prob_data = table['unnorm'].sum()
table['posterior'] = table['unnorm'] / prob_data
return prob_data | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
And call it like this. | prob_data = update(table2) | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Here is the final Bayes table: | table2 | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
The posterior probability of the 6-sided die is 4/9, which is a little more than the probabilities for the other dice, 3/9 and 2/9.Intuitively, the 6-sided die is the most likely because it had the highest likelihood of producing the outcome we saw. The Monty Hall ProblemNext we'll use a Bayes table to solve one of th... | table3 = pd.DataFrame(index=['Door 1', 'Door 2', 'Door 3'])
table3['prior'] = Fraction(1, 3)
table3 | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
The data is that Monty opened Door 3 and revealed a goat. So let'sconsider the probability of the data under each hypothesis:* If the car is behind Door 1, Monty chooses Door 2 or 3 at random, so the probability he opens Door 3 is $1/2$.* If the car is behind Door 2, Monty has to open Door 3, so the probability of the ... | table3['likelihood'] = Fraction(1, 2), 1, 0
table3 | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Now that we have priors and likelihoods, we can use `update` to compute the posterior probabilities. | update(table3)
table3 | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
After Monty opens Door 3, the posterior probability of Door 1 is $1/3$;the posterior probability of Door 2 is $2/3$.So you are better off switching from Door 1 to Door 2. As this example shows, our intuition for probability is not alwaysreliable. Bayes's Theorem can help by providing a divide-and-conquer strategy:1. F... | # Solution goes here
coins=pd.DataFrame(index=['Tricky','Normal'])
coins['prior']=Fraction(1,2)
coins['likelihood']=1,Fraction(1,2)
update(coins)
coins.loc['Tricky','posterior'] | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
**My Answer was right!** ✅ **Exercise:** Suppose you meet someone and learn that they have two children.You ask if either child is a girl and they say yes.What is the probability that both children are girls?Hint: Start with four equally likely hypotheses. | # Solution goes here | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
**My Answer was wrong!** ❎ :I don't understand problem correctly. **Exercise:** There are many variations of the [Monty Hall problem](https://en.wikipedia.org/wiki/Monty_Hall_problem). For example, suppose Monty always chooses Door 2 if he can, andonly chooses Door 3 if he has to (because the car is behind Door 2).If ... | # Solution goes here
monty_1=pd.DataFrame(index=['Door 1','Door 2','Door 3'])
monty_1['prior']=Fraction(1,3)
monty_1['likelihood']=1,0,1
update(monty_1)
monty_1.loc['Door 3','posterior'] | _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
**My Answer was right!** ✅ Solution goes heremonty_2=pd.DataFrame(index=['Door 1','Door 2','Door 3'])monty_2['prior']=Fraction(1,3)monty_2['likelihood']=0,1,0update(monty_2)monty_2.loc['Door 2','posterior']monty_2 **My Answer was right!** ✅ **Exercise:** M&M's are small candy-coated chocolates that come in a variety o... | # Solution goes here
MandM=pd.DataFrame(index=['From 1994','From 1996'])
MandM['prior']=Fraction(1,2)
MandM['likelihood']=Fraction(2,10),Fraction(13,100)
update(MandM)
MandM.loc['From 1994','posterior']
MandM
| _____no_output_____ | MIT | notebooks/chap02.ipynb | anvaari/ThinkBayes2 |
Using the Prediction Model Environment | import getpass
import json
import os
import sys
import time
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from seffnet.constants import (
DEFAULT_EMBEDDINGS_PATH, DEFAULT_GRAPH_PATH,
DEFAULT_MAPPING_PATH, DEFAULT_PREDICTIVE_MODEL_PATH,
RESOURCES
)
from seffnet.literature import query_europe_p... | aldis
| MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
Loading the Data | from seffnet.default_predictor import predictor
print(f"""Loaded default predictor using paths:
embeddings: {DEFAULT_EMBEDDINGS_PATH}
graph: {DEFAULT_GRAPH_PATH}
model: {DEFAULT_PREDICTIVE_MODEL_PATH}
mapping: {DEFAULT_MAPPING_PATH}
""") | Loaded default predictor using paths:
embeddings: c:\users\aldis\documents\github\seffnet\resources\embeddings\0812_weighted_node2vec_emb.embeddings
graph: c:\users\aldis\documents\github\seffnet\resources\basic_graphs\fullgraph_with_chemsim.edgelist
model: c:\users\aldis\documents\github\seffnet\resources\p... | MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
Examples of different kinds of predictions with literature evidence side effect - target association | r = predictor.find_new_relation(
source_name='EGFR_HUMAN',
target_name='Papulopustular rash',
)
print(json.dumps(r, indent=2))
#PMID: 18165622
r = predictor.find_new_relation(
source_id='9451', # Histamine receptor H1
target_id='331', # Drowsiness
)
print(json.dumps(r, indent=2))
#PMID: 26626077
r = pre... | {
"source": {
"node_id": "8670",
"namespace": "uniprot",
"identifier": "P22303",
"name": "ACES_HUMAN",
"entity_type": "target"
},
"target": {
"node_id": "309",
"namespace": "umls",
"identifier": "C0428977",
"name": "Bradycardia",
"entity_type": "phenotype"
},
"lor": 0.8... | MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
drug- side effect association | r = predictor.find_new_relation(
source_id='3534', # diazepam
target_id='670', # Libido decreased
)
print(json.dumps(r, indent=2))
#PMID: 29888057
r = predictor.find_new_relation(
source_id='1148', # Cytarabine
target_id='1149', # Anaemia megaloblastic
)
print(json.dumps(r, indent=2))
# PMID: 23157... | {
"source": {
"node_id": "1148",
"namespace": "pubchem.compound",
"identifier": "6175",
"name": "Cytidine",
"entity_type": "experimental drug"
},
"target": {
"node_id": "1149",
"namespace": "umls",
"identifier": "C0002888",
"name": "Anaemia megaloblastic",
"entity_type": "p... | MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
drug-target association | r = predictor.find_new_relation(
source_id='14672', # Sertindole
target_id='9350', # CHRM1 receptor
)
print(json.dumps(r, indent=2))
# PMID: 29942259 | {
"source": {
"node_id": "14672",
"namespace": "pubchem.compound",
"identifier": "60149",
"name": "Sertindole",
"entity_type": "approved drug"
},
"target": {
"node_id": "9350",
"namespace": "uniprot",
"identifier": "P11229",
"name": "ACM1_HUMAN",
"entity_type": "target"
}... | MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
Example of predicting relations using node2vec model and embeddings | def get_predictions_df(curie, results_type=None):
results = predictor.find_new_relations(
node_curie=curie,
results_type=results_type,
k=50,
)
results_df = pd.DataFrame(results['predictions'])
results_df = results_df[['node_id', 'namespace', 'identifier', 'name', 'lor', 'novel']]... | {
"source": {
"node_id": "5265",
"namespace": "pubchem.compound",
"identifier": "37542",
"name": "Ribavirin",
"entity_type": "approved drug"
},
"target": {
"node_id": "947",
"namespace": "umls",
"identifier": "C0006840",
"name": "Candida infection",
"entity_type": "phenotyp... | MIT | notebooks/Using the Predictive Model.ipynb | AldisiRana/SE_KGE |
from google.colab import drive
drive.mount('/content/drive')
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
cardata_df = pd.read_csv('/content/drive/MyDr... | Dataset after preprocessing
Price Age KM ... Automatic CC Doors
0 0.763763 -1.822802e+00 -0.583476 ... 0.0 2.314976 3.0
1 0.832715 -1.822802e+00 0.115551 ... 0.0 2.314976 3.0
2 0.887877 -1.767012e+00 -0.725566 ... 0.0 2.314976 3.0
3 ... | MIT | lab2/Lab_02_Data_Preprocessing.ipynb | dnhirapara/049_DarshikHirapara | |
Submission Instructions | # Now click the 'Submit Assignment' button above. | _____no_output_____ | MIT | ConvolutionalNeuralNetworks/Week4/Exercise_4_Multi_class_classifier_Question-FINAL.ipynb | wilsonjr/Tensorflow-Developer |
When you're done or would like to take a break, please run the two cells below to save your work and close the Notebook. This will free up resources for your fellow learners. | %%javascript
<!-- Save the notebook -->
IPython.notebook.save_checkpoint();
%%javascript
IPython.notebook.session.delete();
window.onbeforeunload = null
setTimeout(function() { window.close(); }, 1000); | _____no_output_____ | MIT | ConvolutionalNeuralNetworks/Week4/Exercise_4_Multi_class_classifier_Question-FINAL.ipynb | wilsonjr/Tensorflow-Developer |
octavemagic: Octave inside IPython Installation The `octavemagic` extension provides the ability to interact with Octave. It is provided by the `oct2py` package,which may be installed using `pip` or `easy_install`.To enable the extension, load it as follows: | %load_ext oct2py.ipython | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
Overview Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.Here you see an example of how to execute a single line of Octave, and how ... | x = %octave [1 2; 3 4];
x
a = [1, 2, 3]
%octave_push a
%octave a = a * 2;
%octave_pull a
a | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together. Unlikewith the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables. Also note the use of the semicolon to suppress the Octave output. | %%octave -i x -o U,S,V
[U, S, V] = svd(x);
print(U, S, V) | [[-0.40455358 -0.9145143 ]
[-0.9145143 0.40455358]] [[ 5.4649857 0. ]
[ 0. 0.36596619]] [[-0.57604844 0.81741556]
[-0.81741556 -0.57604844]]
| MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
Plotting Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported). | %%octave -f svg
p = [12 -2.5 -8 -0.1 8];
x = 0:0.01:1;
polyout(p, 'x')
plot(x, polyval(p, x)); | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
The width or the height can be specified to constrain the image while maintaining the original aspect ratio. | %%octave -f png -w 600
% butterworth filter, order 2, cutoff pi/2 radians
b = [0.292893218813452 0.585786437626905 0.292893218813452];
a = [1 0 0.171572875253810];
freqz(b, a, 32);
%%octave -s 600,200 -f png
% Note: On Windows, this will not show the plots unless Ghostscript is installed.
subplot(121);
[x, y] = ... | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
Multiple figures can be drawn. Note that when using imshow the image will be created as a PNG with the rawimage dimensions. | %%octave -f svg -h 300
sombrero
figure
imshow(rand(200,200)) | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
Plots can be drawn inline (default) or bring up the Octave plotting GUI by using the -g (or --gui) flag: | %%octave -g
plot([1,2,3])
# brings up an Octave plotting GUI | _____no_output_____ | MIT | example/octavemagic_extension.ipynb | ankostis/oct2py |
Copyright (c) Microsoft Corporation. All rights reserved.Licensed under the MIT License.  Automated Machine Lea... | import json
import logging
import warnings
import numpy as np
import pandas as pd
import azureml.core
from azureml.core.workspace import Workspace
from azureml.core.experiment import Experiment
from azureml.train.automl import AutoMLConfig | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
Accessing the Azure ML workspace requires authentication with Azure.The default authentication is interactive authentication using the default tenant. Executing the ws = Workspace.from_config() line in the cell below will prompt for authentication the first time that it is run.If you have multiple Azure tenants, you ca... | ws = Workspace.from_config()
ds = ws.get_default_datastore()
# choose a name for the run history container in the workspace
experiment_name = 'automl-grouping-oj'
# project folder
project_folder = './sample_projects/{}'.format(experiment_name)
experiment = Experiment(ws, experiment_name)
output = {}
output['SDK vers... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
DataUpload data to your default datastore and then load it as a `TabularDataset` | from azureml.core.dataset import Dataset
# upload training and test data to your default datastore
ds = ws.get_default_datastore()
ds.upload(src_dir='./data', target_path='groupdata', overwrite=True, show_progress=True)
# load data from your datastore
data = Dataset.Tabular.from_delimited_files(path=ds.path('groupdata/... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
Compute Create or Attach existing AmlComputeYou will need to create a compute target for your automated ML run. In this tutorial, you create AmlCompute as your training compute resource. Creation of AmlCompute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace this code will ... | from azureml.core.compute import AmlCompute
from azureml.core.compute import ComputeTarget
# Choose a name for your cluster.
amlcompute_cluster_name = "cpu-cluster-11"
found = False
# Check if this compute target already exists in the workspace.
cts = ws.compute_targets
if amlcompute_cluster_name in cts and cts[amlco... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
AutoMLConfig Create a base AutoMLConfigThis configuration will be used for all the groups in the pipeline. | target_column = 'Quantity'
time_column_name = 'WeekStarting'
grain_column_names = ['Brand']
group_column_names = ['Store']
max_horizon = 20
automl_settings = {
"iteration_timeout_minutes" : 5,
"experiment_timeout_minutes" : 15,
"primary_metric" : 'normalized_mean_absolute_error',
"time_column_name": tim... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
PipelineWe've written a script to generate the individual pipeline steps used to create each automl step. Calling this script will return a list of PipelineSteps that will train multiple groups concurrently and then deploy these models.This step requires an Enterprise workspace to gain access to this feature. To learn... | from azureml.core.webservice import Webservice
from azureml.exceptions import WebserviceException
service_name = 'grouped-model'
try:
# if you want to get existing service below is the command
# since aci name needs to be unique in subscription deleting existing aci if any
# we use aci_service_name to crea... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
TrainUse the list of steps generated from above to build the pipeline and submit it to your compute for remote training. | from azureml.pipeline.core import Pipeline
pipeline = Pipeline(
description="A pipeline with one model per data group using Automated ML.",
workspace=ws,
steps=steps)
pipeline_run = experiment.submit(pipeline)
from azureml.widgets import RunDetails
RunDetails(pipeline_run).show()
pipeline_run.wait_for_... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
TestNow we can use the holdout set to test our models and ensure our web-service is running as expected. | from azureml.core.webservice import AciWebservice
service = AciWebservice(ws, service_name)
X_test = data_test.to_pandas_dataframe()
# Drop the column we are trying to predict (target column)
x_pred = X_test.drop(target_column, inplace=False, axis=1)
x_pred.head()
# Get Predictions
test_sample = X_test.drop(target_colu... | _____no_output_____ | MIT | how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb | MustAl-Du/MachineLearningNotebooks |
ex05-Filtering a Query with WHERESometimes, you’ll want to only check the rows returned by a query, where one or more columns meet certain criteria. This can be done with a WHERE statement. The WHERE clause is an optional clause of the SELECT statement. It appears after the FROM clause as the following statement:>SELE... | %load_ext sql | _____no_output_____ | MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
1. Connet to the given database of demo.db3 | %sql sqlite:///data/demo.db3 | _____no_output_____ | MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
If you do not remember the tables in the demo data, you can always use the follow command to query. Here we select the table of watershed_yearly as an example. | %sql SELECT name FROM sqlite_master WHERE type='table' | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2. Retrieving data with WHERETake the table of ***rch*** as an example. 2.1 Check the table colums firstly. | %sql SELECT * From rch LIMIT 5 | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2.2 Check the number of rowsThere should be 8280 rows. This can be done with the SQLite ***COUNT*** function. We will touch other SQLite function over the next few notebooks. | %sql SELECT COUNT(*) as nrow From rch | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2.3 Use WHERE to retrieve dataLet’s say we are interested in records for only the year 1981. Using a WHERE is pretty straightforward for a simple criterion like this. | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR=1981 | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2.4 use *AND* to further filter dataThere are 23 RCHs. We are only intersted in the 10th RCH. We can add another filter condition with an ***AND*** statement. | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR=1981 AND RCH=10 | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2.5 More combinations of filters We also can further filter data with the operators of ***!=*** or ****** to get data except 1981. | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR<>1981 and RCH=10 and MO=6 | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
We can further filter the data to spefic months using ***OR*** statement. For example, we'd like check the data in the months of 3, 6 and 9. However, we have to use ***()*** to make them as one condition.:) It is a trick. You can try! | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR>2009 and RCH=10 and (MO=3 or MO=6 or MO=9 or MO=12) | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
Or we can simplify the above filter using the ***IN*** statement. | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR>2009 and RCH=10 and MO in (3, 6, 9, 12) | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
Or the months are ***NOT*** in 3, 6, 9, 12 | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR>2009 and RCH=10 and MO NOT IN (3,6,9,12) | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
2.6 Filter with math operatorsFor example, we could use the modulus operator (%) to filter the MOs. | %sql SELECT RCH, YR, MO, FLOW_INcms, FLOW_OUTcms From rch WHERE YR>2009 and RCH=10 and MO % 3 = 0 | * sqlite:///data/demo.db3
Done.
| MIT | ex05-Filtering a Query with WHERE.ipynb | hoganbyun/Practice-SQL-with-SQLite-and-Jupyter-Notebook |
Scheme 2Non-transfer of xMore secureRequires one-time additional sharing of a dictionaryFrom the two dictionaries is inferred the total number of output states(in the cell below, 2 + 2 = 4) | constant_index_dictionary = {}
constant_index_dictionary['0000'] = [0, 2]
constant_index_dictionary['0001'] = [2, 3]
constant_index_dictionary['0010'] = [0, 1]
constant_index_dictionary['0011'] = [1, 3]
constant_index_dictionary['0100'] = [2, 3]
constant_index_dictionary['0101'] = [1, 2]
constant_index_dictionary['0110... | Message: 0101
1110 10
0100 00
1110 11
0100 00
0010 00
1110 10
0100 00
0010 00
1100 00
REJECT
2.47572660446167
| MIT | Quantum Cryptography/Quantum_inspired_digital_signatures.ipynb | NimishMishra/research |
k-Path dependent scheme | constant_index_dictionary = {}
constant_index_dictionary['0000'] = [0, 2]
constant_index_dictionary['0001'] = [2, 3]
constant_index_dictionary['0010'] = [0, 1]
constant_index_dictionary['0011'] = [1, 3]
constant_index_dictionary['0100'] = [2, 3]
constant_index_dictionary['0101'] = [1, 2]
constant_index_dictionary['0110... | _____no_output_____ | MIT | Quantum Cryptography/Quantum_inspired_digital_signatures.ipynb | NimishMishra/research |
Creating a long message100 *bits* | def create_random_message(NUMBER_OF_BITS):
message = ""
c = qiskit.ClassicalRegister(1)
q = qiskit.QuantumRegister(1)
s = qiskit.Aer.get_backend('qasm_simulator')
for i in range(NUMBER_OF_BITS):
print(i)
random_circuit = qiskit.QuantumCircuit(q, c)
random_circuit.h(q[0])
random_circuit.measur... | 0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
101100000101101011001111101101... | MIT | Quantum Cryptography/Quantum_inspired_digital_signatures.ipynb | NimishMishra/research |
Signing a long message | def sign_message(message):
signature = ""
ITER = int(len(message)/4)
start_time = time.time()
STEPS = 5 # security parameter: length of the walk
iter = 0
while True:
if(iter == ITER):
break
state = message[0:4]
initialise_starting_state(int(state[0]), int(s... | _____no_output_____ | MIT | Quantum Cryptography/Quantum_inspired_digital_signatures.ipynb | NimishMishra/research |
Table of Contents1 Goal2 Var3 Init4 Merging4.1 SV artifact4.2 rep-seqs4.3 Taxonomy5 Alignment5.1 Creating alignment5.2 Masking alignment6 Phylogeny6.1 Unrooted tree6.2 Rooted tr... | work_dir = '/ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged/'
run_dir = '/ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/'
miseq_runs = c('Run0116', 'Run0122', 'Run0126', 'Run0189', 'Run0190')
# params
conda_env = 'qiime2-2019.10'
... | _____no_output_____ | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Init | library(dplyr)
library(tidyr)
library(ggplot2)
library(LeyLabRMisc)
df.dims()
make_dir(work_dir) | Directory already exists: /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged/
| MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Merging SV artifact | # artifacts for individual runs
P = file.path(run_dir, '{run}', 'table_merged_filt.qza')
runs = miseq_runs %>% as.list %>%
lapply(function(x) glue::glue(P, run=x))
runs
# function to merge tables
merge_tables = function(in_tbls, out_tbl, conda_env){
cmd = 'qiime feature-table merge --i-tables {in_tbls}... | CMD: qiime feature-table merge --i-tables /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0116/table_merged_filt.qza /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0122/table_merged_filt.qza /ebio/abt3_projects/Georg_animal_feces/d... | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
rep-seqs | # artifacts for individual runs
P = file.path(run_dir, '{run}', 'rep-seqs_merged_filt.qza')
runs = miseq_runs %>% as.list %>%
lapply(function(x) glue::glue(P, run=x))
runs
# function to merge seqs
merge_seqs = function(in_seqs, out_seq, conda_env){
cmd = 'qiime feature-table merge-seqs --i-data {in_se... | CMD: qiime feature-table merge-seqs --i-data /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0116/rep-seqs_merged_filt.qza /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0122/rep-seqs_merged_filt.qza /ebio/abt3_projects/Georg_anima... | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Taxonomy | # artifacts for individual runs
P = file.path(run_dir, '{run}', 'taxonomy.qza')
runs = miseq_runs %>% as.list %>%
lapply(function(x) glue::glue(P, run=x))
runs
# function to merge tax
merge_tax = function(in_taxs, out_tax, conda_env){
cmd = 'qiime feature-table merge-taxa --i-data {in_seqs} --o-merged... | CMD: qiime feature-table merge-taxa --i-data /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0116/taxonomy.qza /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA//Run0122/taxonomy.qza /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/Mi... | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Alignment Creating alignment | aln_file = file.path(work_dir, 'aligned-rep-seqs_filt.qza')
cmd = 'qiime alignment mafft --p-n-threads {threads} --i-sequences {in_seq} --o-alignment {out_aln}'
cmd = glue::glue(cmd, threads=threads, in_seq=seqs_merged_file, out_aln=aln_file)
bash_job(cmd, conda_env=conda_env, stderr=TRUE)
| Saved FeatureData[AlignedSequence] to: /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged//aligned-rep-seqs_filt.qza | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Masking alignment | aln_mask_file = file.path(work_dir, 'aligned-rep-seqs_filt_masked.qza')
cmd = 'qiime alignment mask --i-alignment {in_aln} --o-masked-alignment {out_aln}'
cmd = glue::glue(cmd, in_aln=aln_file, out_aln=aln_mask_file)
bash_job(cmd, conda_env=conda_env, stderr=TRUE)
| Saved FeatureData[AlignedSequence] to: /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged//aligned-rep-seqs_filt_masked.qza | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Phylogeny Unrooted tree | phy_unroot_file = file.path(work_dir, 'aligned-rep-seqs_filt_masked_unroot-tree.qza')
cmd = 'qiime phylogeny fasttree --p-n-threads {threads} --i-alignment {in_aln} --o-tree {out_phy}'
cmd = glue::glue(cmd, threads=threads, in_aln=aln_mask_file, out_phy=phy_unroot_file)
bash_job(cmd, conda_env=conda_env, stderr=TRUE)
| Saved Phylogeny[Unrooted] to: /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged//aligned-rep-seqs_filt_masked_unroot-tree.qza | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Rooted tree | phy_root_file = file.path(work_dir, 'aligned-rep-seqs_filt_masked_midroot-tree.qza')
cmd = 'qiime phylogeny midpoint-root --i-tree {in_phy} --o-rooted-tree {out_phy}'
cmd = glue::glue(cmd, in_phy=phy_unroot_file, out_phy=phy_root_file)
bash_job(cmd, conda_env=conda_env, stderr=TRUE)
| Saved Phylogeny[Rooted] to: /ebio/abt3_projects/Georg_animal_feces/data/16S_arch/MiSeq-Runs-116-122-126-189-190/LLA/merged//aligned-rep-seqs_filt_masked_midroot-tree.qza | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
sessionInfo | sessionInfo() | _____no_output_____ | MIT | 01_LLA/02_LLA_run_merge.ipynb | leylabmpi/16S-arc_vertebrate_paper |
Test web application locally This notebook pulls some images and tests them against the local web app running inside the Docker container we made previously. | import matplotlib.pyplot as plt
import numpy as np
from testing_utilities import *
import requests
%matplotlib inline
%load_ext autoreload
%autoreload 2
docker_login = 'fboylu'
image_name = docker_login + '/kerastf-gpu' | _____no_output_____ | MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Run the Docker conatainer in the background and open port 80. Notice we are using nvidia-docker and not docker command. | %%bash --bg -s "$image_name"
nvidia-docker run -p 80:80 $1 | _____no_output_____ | MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Wait a few seconds for the application to spin up and then check that everything works. | !curl 'http://0.0.0.0:80/'
!curl 'http://0.0.0.0:80/version' | 1.4.1 | MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Pull an image of a Lynx to test our local web app with. | IMAGEURL = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Lynx_lynx_poing.jpg/220px-Lynx_lynx_poing.jpg"
plt.imshow(to_img(IMAGEURL))
jsonimg = img_url_to_json(IMAGEURL)
jsonimg[:100]
headers = {'content-type': 'application/json'}
%time r = requests.post('http://0.0.0.0:80/score', data=jsonimg, headers=hea... | CPU times: user 4.86 ms, sys: 0 ns, total: 4.86 ms
Wall time: 2.4 s
<Response [200]>
| MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Let's try a few more images. | images = ('https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Lynx_lynx_poing.jpg/220px-Lynx_lynx_poing.jpg',
'https://upload.wikimedia.org/wikipedia/commons/3/3a/Roadster_2.5_windmills_trimmed.jpg',
'http://www.worldshipsociety.org/wp-content/themes/construct/lib/scripts/timthumb/thumb.php?s... | /anaconda/envs/AKSDeploymentKeras/lib/python3.5/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.
warnings.warn(message, mplDeprecation, stacklevel=1)
| MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Next let's quickly check what the request response performance is for the locally running Docker container. | image_data = list(map(img_url_to_json, images)) # Retrieve the images and data
timer_results = list()
for img in image_data:
res=%timeit -r 1 -o -q requests.post(url, data=img, headers=headers)
timer_results.append(res.best)
timer_results
print('Average time taken: {0:4.2f} ms'.format(10**3 * np.mean(timer_resu... | edb629d68eca
| MIT | Keras_Tensorflow/03_TestLocally.ipynb | marabout2015/AKSDeploymentTutorial |
Homework part I: Prohibited Comment Classification (3 points)__In this notebook__ you will build an algorithm that classifies social media comments into normal or toxic.Like in many real-world cases, you only have a small (10^3) d... | import pandas as pd
data = pd.read_csv("comments.tsv", sep='\t')
texts = data['comment_text'].values
target = data['should_ban'].values
data[50::200]
from sklearn.model_selection import train_test_split
texts_train, texts_test, y_train, y_test = train_test_split(texts, target, test_size=0.5, random_state=42) | _____no_output_____ | MIT | week02_classification/homework_part1.ipynb | andnlv/natural-language-processing |
__Note:__ it is generally a good idea to split data into train/test before anything is done to them.It guards you against possible data leakage in the preprocessing stage. For example, should you decide to select words present in obscene tweets as features, you should only count those words over the training set. Other... | from nltk.tokenize import TweetTokenizer
tokenizer = TweetTokenizer()
preprocess = lambda text: ' '.join(tokenizer.tokenize(text.lower()))
text = 'How to be a grown-up at work: replace "fuck you" with "Ok, great!".'
print("before:", text,)
print("after:", preprocess(text),)
# task: preprocess each comment in train and... | _____no_output_____ | MIT | week02_classification/homework_part1.ipynb | andnlv/natural-language-processing |
Solving it: bag of wordsOne traditional approach to such problem is to use bag of words features:1. build a vocabulary of frequent words (use train data only)2. for each training sample, count the number of times a word occurs in it (for each word in vocabulary... | # task: find up to k most frequent tokens in texts_train,
# sort them by number of occurences (highest first)
k = 10000
from collections import Counter
bow_vocabulary = list(zip(*Counter((' '.join(texts_train)).split()).most_common(k)))[0]
print('example features:', sorted(bow_vocabulary)[::100])
def text_to_bow(tex... | _____no_output_____ | MIT | week02_classification/homework_part1.ipynb | andnlv/natural-language-processing |
Machine learning stuff: fit, predict, evaluate. You know the drill. | from sklearn.linear_model import LogisticRegression
bow_model = LogisticRegression().fit(X_train_bow, y_train)
from sklearn.metrics import roc_auc_score, roc_curve
for name, X, y, model in [
('train', X_train_bow, y_train, bow_model),
('test ', X_test_bow, y_test, bow_model)
]:
proba = model.predict_proba(... | _____no_output_____ | MIT | week02_classification/homework_part1.ipynb | andnlv/natural-language-processing |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.