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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5.3) Predicting Comments for Specific Factor | comments_ = []
for i in df['views']:
comments_.append(int(i * .01388)) | _____no_output_____ | Apache-2.0 | Week -12/TED Talk - Correlation - Comments/Correlation - Comments.ipynb | AshishJangra27/Data-Science-Specialization |
6. Combining Factor + Error + Ratios | comments = np.array(df['comments'])
error = []
for i in tqdm(range(st,end + 1 , 1)): # Creating Start and Ending Reage for Factors
factor = i/100000
comments_ = []
for i in df['views']: # Predicting Likes for Specific Factor
comments_... | 100%|██████████████████████████████████████| 5416/5416 [00:07<00:00, 770.16it/s]
| Apache-2.0 | Week -12/TED Talk - Correlation - Comments/Correlation - Comments.ipynb | AshishJangra27/Data-Science-Specialization |
Finding Best Factor that Fits the Likes and Views | final_factor = error.sort_values(by = 'Error').head(10)['Factor'].mean()
final_factor
comments_ = []
for i in df['views']:
comments_.append(int(i * final_factor))
df['pred_comments'] = comments_
df.head() | _____no_output_____ | Apache-2.0 | Week -12/TED Talk - Correlation - Comments/Correlation - Comments.ipynb | AshishJangra27/Data-Science-Specialization |
Actual to Predicted Likes with best Fit Factor | data = []
for i in df.values:
data.append([i[2],i[4],i[10]])
df_ = pd.DataFrame(data, columns = ['views','comments','pred_comments'])
views = list(df_.sort_values(by = 'views')['views'])
likes = list(df_.sort_values(by = 'views')['comments'])
likes_ = list(df_.sort_values(by = 'views')['pred_comments... | _____no_output_____ | Apache-2.0 | Week -12/TED Talk - Correlation - Comments/Correlation - Comments.ipynb | AshishJangra27/Data-Science-Specialization |
Multicollinearity and Regression AnalysisIn this tutorial, we will be using a spatial dataset of county-level election and demographic statistics for the United States. This time, we'll explore different methods to diagnose and account for multicollinearity in our data. Specifically, we'll calculate variance inflation... | import numpy as np
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFo... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
First, we're going to load the 'Elections' dataset from the libpysal library, which is a very easy to use API that accesses the Geodata Center at the University of Chicago.* More on spatial data science resources from UC: https://spatial.uchicago.edu/* A list of datasets available through lipysal: https://geodacenter.g... | from libpysal.examples import load_example
elections = load_example('Elections')
#note the folder where your data now lives:
#First, let's see what files are available in the 'Elections' data example
elections.get_file_list() | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
When you are out in the world doing research, you often will not find a ready-made function to download your data. That's okay! You know how to get this dataset without using pysal! Do a quick internal review of online data formats and automatic data downloads. TASK 1: Use urllib functions to download this file directl... | # Task 1 code here:
#import required function:
import urllib.request
#define online filepath (aka url):
url = "https://geodacenter.github.io/data-and-lab//data/election.zip"
#define local filepath:
local = '../../elections.zip'
#download elections data:
urllib.request.urlretrieve(url, local)
#unzip file: see if goo... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame "votes" | # TASK 2: Use geopandas to read in this shapefile. Call your geopandas.DataFrame "votes"
votes = gpd.read_file("H:\EnvDataSci\election/election.shp") | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
EXTRA CREDIT TASK (+2pts): use os to delete the elections data downloaded by pysal in your C: drive that you are no longer using. | # Extra credit task:
#Let's view the shapefile to get a general idea of the geometry we're looking at:
%matplotlib inline
votes.plot()
#View the first few line]s of the dataset
votes.head()
#Since there are too many columns for us to view on a signle page using "head", we can just print out the column names so we have... | STATEFP
COUNTYFP
GEOID
ALAND
AWATER
area_name
state_abbr
PST045214
PST040210
PST120214
POP010210
AGE135214
AGE295214
AGE775214
SEX255214
RHI125214
RHI225214
RHI325214
RHI425214
RHI525214
RHI625214
RHI725214
RHI825214
POP715213
POP645213
POP815213
EDU635213
EDU685213
VET605213
LFE305213
HSG010214
HSG445213
HSG096213
HSG... | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
You can use pandas summary statistics to get an idea of how county-level data varies across the United States. TASK 3: For example, how did the county mean percent Democratic vote change between 2012 (pct_dem_12) and 2016 (pct_dem_16)?Look here for more info on pandas summary statistics:https://www.earthdatascience.o... | #Task 3
demchange = votes["pct_dem_16"].mean() - votes["pct_dem_12"].mean()
print("The mean percent Democrative vote changed by ", demchange, "between 2012 and 2016.")
| The mean percent Democrative vote changed by -0.06783446699806961 between 2012 and 2016.
| MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
We can also plot histograms of the data. Below, smoothed histograms from the seaborn package (imported as sns) let us get an idea of the distribution of percent democratic votes in 2012 (left) and 2016 (right). | # Plot histograms:
f,ax = plt.subplots(1,2, figsize=(2*3*1.6, 2))
for i,col in enumerate(['pct_dem_12','pct_dem_16']):
sns.kdeplot(votes[col].values, shade=True, color='slategrey', ax=ax[i])
ax[i].set_title(col.split('_')[1])
# Plot spatial distribution of # dem vote in 2012 and 2016 with histogram.
f,ax = plt.... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 4: Make a new column on your geopandas dataframe called "pct_dem_change" and plot it using the syntax above. Explain the plot. | # Task 4: add new column pct_dem_change to votes:
votes["pct_dem_change"] = votes.pct_dem_16 - votes.pct_dem_12
f, ax = plt
plt.show(votes.pct_dem_change)
#Task 4: plot your pct_dem_change variable on a map:
| _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Click on this url to learn more about the variables in this dataset: https://geodacenter.github.io/data-and-lab//county_election_2012_2016-variables/As you can see, there are a lot of data values available in this dataset. Let's say we want to learn more about what county-level factors influence percent change in democ... | # Task 4: create a subset of votes called "my list" with all your subset variables.
#my_list = ["pct_pt_16", <list your variables here>]
#check to make sure all your columns are there:
votes[my_list].head() | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Scatterplot matrixWe call the process of getting to know your data (ranges and distributions of the data, as well as any relationships between variables) "exploratory data analysis". Pairwise plots of your variables, called scatterplots, can provide a lot of insight into the type of relationships you have between vari... | #Use seaborn.pairplot to plot a scatterplot matrix of you 10 variable subset:
sns.pairplot(votes[my_list]) | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 7: Do you observe any collinearity in this dataset? How would you describe the relationship between your two "incidentally collinear" variables that you selected based on looking at variable descriptions? *Type answer here* TASK 8: What is plotted on the diagonal panels of the scatterplot matrix?*Type answer here... | #VIF = 1/(1-R2) of a pairwise OLS regression between two predictor variables
#We can use a built-in function "variance_inflation_factor" from statsmodel.api to calculate VIF
#Learn more about the function
?variance_inflation_factor
#Calculate VIFs on our dataset
vif = pd.DataFrame()
vif["VIF Factor"] = [variance_inflat... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Collinearity is always present in observational data. When is it a problem?Generally speaking, VIF > 10 are considered "too much" collinearity. But this value is somewhat arbitrary: the extent to which variance inflation will impact your analysis is highly context dependent. There are two primary contexts where varian... | #first, forumalate the model. See weather_trend.py in "Git_101" for a refresher on how.
#extract variable that you want to use to "predict"
X = np.array(votes[my_list[1:10]].values)
#standardize data to assist in interpretation of coefficients
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
#extract variable that we... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 9: Answer: which coefficients indicate a statisticall significant relationship between parameter and pct_dem_change? What is your most important predictor variable? How can you tell?*Type answer here* TASK10: Are any of these parameters subject to variance inflation? How can you tell?*Type answer here* Now, let... | #Add model residuals to our "votes" geopandas dataframe:
votes['lm_resid']=OLS(Y,X).fit().resid
sns.kdeplot(votes['lm_resid'].values, shade=True, color='slategrey')
| _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 11: Are our residuals normally distributed with a mean of zero? What does that mean?*Type answer here* Penalized regression: ridge penaltyIn penalized regression, we intentionally bias the parameter estimates to stabilize them given collinearity in the dataset.From https://www.analyticsvidhya.com/blog/2016/01/ri... | # when L2=0, Ridge equals OLS
model = Ridge(alpha=1)
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
#force scores to be positive
scores = absolute(scores)
print('Mea... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Penalized regression: lasso penaltyFrom https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/"LASSO stands for Least Absolute Shrinkage and Selection Operator. I know it doesn’t give much of an idea but there are 2 key words here – ‘absolute‘ and ‘selection‘.Lets consider the fo... | # when L1=0, Lasso equals OLS
model = Lasso(alpha=0)
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model, X, Y, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1)
#force scores to be positive
scores = absolute(scores)
print('Mea... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Penalized regression: elastic net penaltyIn other words, the lasso penalty shrinks unimportant coefficients down towards zero, automatically "selecting" important predictor variables. The ridge penalty shrinks coefficients of collinear predictor variables nearer to each other, effectively partitioning the magnitude of... | # when L1 approaches infinity, certain coefficients will become exactly zero, and MAE equals the variance of our response variable:
model = ElasticNet(alpha=1, l1_ratio=0.2)
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model, X, ... | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
TASK 11: Match these elastic net coefficients up with your original data. Do you see a logical grouping(s) between variables that have non-zero coefficients?Explain why or why not.*Type answer here* | # Task 11 scratch cell: | _____no_output_____ | MIT | CodeSprints/multicollinearity_methods.ipynb | jjessamy/EnvDatSci2021 |
Testinnsening av person skattemelding med næringspesifikasjon Denne demoen er ment for å vise hvordan flyten for et sluttbrukersystem kan hente et utkast, gjøre endringer, validere/kontrollere det mot Skatteetatens apier, for å sende det inn via Altinn3 | try:
from altinn3 import *
from skatteetaten_api import main_relay, base64_decode_response, decode_dokument
import requests
import base64
import xmltodict
import xml.dom.minidom
from pathlib import Path
except ImportError as e:
print("Mangler en eller avhengighet, installer dem via pip,... | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Generer ID-porten tokenTokenet er gyldig i 300 sekunder, rekjørt denne biten om du ikke har kommet frem til Altinn3 biten før 300 sekunder | idporten_header = main_relay() | https://oidc-ver2.difi.no/idporten-oidc-provider/authorize?scope=skatteetaten%3Aformueinntekt%2Fskattemelding%20openid&acr_values=Level3&client_id=8d7adad7-b497-40d0-8897-9a9d86c95306&redirect_uri=http%3A%2F%2Flocalhost%3A12345%2Ftoken&response_type=code&state=5lCEToPZskoHXWGs-ghf4g&nonce=1638258045740949&resource=http... | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Hent utkast og gjeldendeHer legger vi inn fødselsnummeret vi logget oss inn med, Dersom du velger et annet fødselsnummer så må den du logget på med ha tilgang til skattemeldingen du ønsker å hente Parten nedenfor er brukt for internt test, pass på bruk deres egne testparter når dere tester01014700230 har fått en myndi... | s = requests.Session()
s.headers = dict(idporten_header)
fnr="29114501318" #oppdater med test fødselsnummerene du har fått tildelt | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Utkast | url_utkast = f'https://mp-test.sits.no/api/skattemelding/v2/utkast/2021/{fnr}'
r = s.get(url_utkast)
r
print(r.text) | <skattemeldingOgNaeringsspesifikasjonforespoerselResponse xmlns="no:skatteetaten:fastsetting:formueinntekt:skattemeldingognaeringsspesifikasjon:forespoersel:response:v2"><dokumenter><skattemeldingdokument><id>SKI:138:41694</id><encoding>utf-8</encoding><content>PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz48c2thdH... | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Gjeldende | url_gjeldende = f'https://mp-test.sits.no/api/skattemelding/v2/2021/{fnr}'
r_gjeldende = s.get(url_gjeldende)
r_gjeldende | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
FastsattHer får en _http 404_ om vedkommende ikke har noen fastsetting, rekjørt denne etter du har sendt inn og fått tilbakemdling i Altinn at den har blitt behandlet, du skal nå ha en fastsatt skattemelding om den har blitt sent inn som Komplett | url_fastsatt = f'https://mp-test.sits.no/api/skattemelding/v2/fastsatt/2021/{fnr}'
r_fastsatt = s.get(url_fastsatt)
r_fastsatt | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Svar fra hent gjeldende Gjeldende dokument referanse: I responsen på alle api kallene, være seg utkast/fastsatt eller gjeldene, så følger det med en dokumentreferanse. For å kalle valider tjenesten, er en avhengig av å bruke korrekt referanse til gjeldende skattemelding. Cellen nedenfor henter ut gjeldende dokumentre... | sjekk_svar = r_gjeldende
sme_og_naering_respons = xmltodict.parse(sjekk_svar.text)
skattemelding_base64 = sme_og_naering_respons["skattemeldingOgNaeringsspesifikasjonforespoerselResponse"]["dokumenter"]["skattemeldingdokument"]
sme_base64 = skattemelding_base64["content"]
dokref = sme_og_naering_respons["skattemelding... | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Valider utkast sme med næringsopplysninger | def valider_sme(payload):
url_valider = f'https://mp-test.sits.no/api/skattemelding/v2/valider/2021/{fnr}'
header = dict(idporten_header)
header["Content-Type"] = "application/xml"
return s.post(url_valider, headers=header, data=payload)
valider_respons = valider_sme(naering_enk)
resultatAvValidering ... | validertMedFeil
<?xml version="1.0" ?>
<skattemeldingOgNaeringsspesifikasjonResponse xmlns="no:skatteetaten:fastsetting:formueinntekt:skattemeldingognaeringsspesifikasjon:response:v2">
<avvikVedValidering>
<avvik>
<avvikstype>xmlValideringsfeilPaaNaeringsopplysningene</avvikstype>
</avvik>
</avvikVedValiderin... | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Altinn 3 1. Hent Altinn Token2. Oppretter en ny instans av skjemaet3. Last opp vedlegg til skattemeldingen4. Oppdater skattemelding xml med referanse til vedlegg_id fra altinn3.5. Laster opp skattemeldingen og næringsopplysninger som et vedlegg | #1
altinn3_applikasjon = "skd/formueinntekt-skattemelding-v2"
altinn_header = hent_altinn_token(idporten_header)
#2
instans_data = opprett_ny_instans_med_inntektsaar(altinn_header, fnr, "2021", appnavn=altinn3_applikasjon) | {'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsImtpZCI6IjI3RTAyRTk4M0FCMUEwQzZEQzFBRjAyN0YyMUZFMUVFNENEQjRGRjEiLCJ4NXQiOiJKLUF1bURxeG9NYmNHdkFuOGhfaDdremJUX0UiLCJ0eXAiOiJKV1QifQ.eyJuYW1laWQiOiI4NTMzNyIsInVybjphbHRpbm46dXNlcmlkIjoiODUzMzciLCJ1cm46YWx0aW5uOnVzZXJuYW1lIjoibXVuaGplbSIsInVybjphbHRpbm46cGFydHlpZCI6NTAxMTA0OTU... | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Last opp skattemelding Last først opp vedlegg som hører til skattemeldingenEksemplet nedenfor gjelder kun generelle vedlegg for skattemeldingen, ```xml En unik id levert av altinn når du laster opp vedleggsfilen vedlegg_eksempel_sirius_stjerne.jpg jpg dokumentertMarkedsverdi ```men samme p... | vedleggfil = "vedlegg_eksempel_sirius_stjerne.jpg"
opplasting_respons = last_opp_vedlegg(instans_data,
altinn_header,
vedleggfil,
content_type="image/jpeg",
data_ty... | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Sett statusen klar til henting av skatteetaten. | req_bekreftelse = endre_prosess_status(instans_data, altinn_header, "next", appnavn=altinn3_applikasjon)
req_bekreftelse = endre_prosess_status(instans_data, altinn_header, "next", appnavn=altinn3_applikasjon)
req_bekreftelse | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Sjekk status på altinn3 instansen om skatteetaten har hentet instansen.Denne statusen vil til å begynne med ha verdien "none". Oppdatering skjer når skatteetaten har behandlet innsendingen.- Ved **komplett**-innsending vil status oppdateres til Godkjent/Avvist når innsendingen er behandlet.- Ved **ikkeKomplett**-innse... | instans_etter_bekreftelse = hent_instans(instans_data, altinn_header, appnavn=altinn3_applikasjon)
response_data = instans_etter_bekreftelse.json()
print(f"Instans-status: {response_data['status']['substatus']}") | Instans-status: None
| Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Se innsending i AltinnTa en slurk av kaffen og klapp deg selv på ryggen, du har nå sendt inn, la byråkratiet gjøre sin ting... og det tar litt tid. Pt så sjekker skatteeaten hos Altinn3 hvert 30 sek om det har kommet noen nye innsendinger. Skulle det gå mer enn et par minutter så har det mest sansynlig feilet. Før der... | print("Resultat av hent fastsatt før fastsetting")
print(r_fastsatt.text)
print("Resultat av hent fastsatt etter fastsetting")
r_fastsatt2 = s.get(url_fastsatt)
r_fastsatt2.text
#r_fastsatt.elapsed.total_seconds() | _____no_output_____ | Apache-2.0 | docs/test/testinnsending/.ipynb_checkpoints/person-enk-med-vedlegg-2021-checkpoint.ipynb | Skatteetaten/skattemelding |
Full Run In order to run the scripts, we need to be in the base directory. This will move us out of the notebooks directory and into the base directory | import os
os.chdir('..') | _____no_output_____ | MIT | notebooks/Full Run.ipynb | joelmpiper/ga_project |
Define where each of the datasets are stored | Xtrain_dir = 'solar/data/kaggle_solar/train/'
Xtest_dir = 'solar/data/kaggle_solar/test'
ytrain_file = 'solar/data/kaggle_solar/train.csv'
station_file = 'solar/data/kaggle_solar/station_info.csv'
import numpy as np | _____no_output_____ | MIT | notebooks/Full Run.ipynb | joelmpiper/ga_project |
Define the parameters needed to run the analysis script. | # Choose up to 98 stations; not specifying a station means to use all that fall within the given lats and longs. If the
# parameter 'all' is given, then it will use all stations no matter the provided lats and longs
station = ['all']
# Determine which dates will be used to train the model. No specified date means use ... | _____no_output_____ | MIT | notebooks/Full Run.ipynb | joelmpiper/ga_project |
Define the directories that contain the code needed to run the analysis | import solar.report.submission
import solar.wrangle.wrangle
import solar.wrangle.subset
import solar.wrangle.engineer
import solar.analyze.model | _____no_output_____ | MIT | notebooks/Full Run.ipynb | joelmpiper/ga_project |
Reload the modules to load in any code changes since the last run. Load in all of the data needed for the run and store in a pickle file. The 'external' flag determines whether to look to save the pickle file in a connected hard drive or to store locally. The information in pink shows what has been written to the log ... | # test combination of station names and grid
reload(solar.wrangle.wrangle)
reload(solar.wrangle.subset)
reload(solar.wrangle.engineer)
from solar.wrangle.wrangle import SolarData
#external = True
input_data = SolarData.load(Xtrain_dir, ytrain_file, Xtest_dir, station_file, \
train_da... | _____no_output_____ | MIT | notebooks/Full Run.ipynb | joelmpiper/ga_project |
"Jupyter notebook"> "Setup and snippets for a smooth jupyter notebook experience"- toc: False- branch: master- categories: [code snippets, jupyter, python] Start jupyter notebook on boot Edit the crontab for your user. | crontab -e | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
Add the following line. | @reboot source ~/.venv/venv/bin/activate; ~/.venv/venv/bin/jupyter-notebook | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
--- Magic Commands Autoreload imports when file changes were made. | %load_ext autoreload
%autoreload 2 | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
Show matplotlib plots inside the notebook. | import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
Measure excecution time of a cell. | %%time | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
`pip install` from jupyter notebook. | import sys
!{sys.executable} -m pip install numpy | _____no_output_____ | Apache-2.0 | _notebooks/2020-03-02-jupyter-notebook.ipynb | fabge/snippets |
Data Science Academy - Python Fundamentos - Capítulo 10 Download: http://github.com/dsacademybr | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version()) | Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.7.6
| MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Lab 4 - Construindo um Modelo de Regressão Linear com TensorFlow Use como referência o Deep Learning Book: http://www.deeplearningbook.com.br/ Obs: Embora a versão 2.x do TensorFlow já esteja disponível, este Jupyter Notebook usar a versão 1.15, que também é mantida pela equipe do Google.Caso queira aprender TensorFlo... | # Versão do TensorFlow a ser usada
!pip install -q tensorflow==1.15.2
# Imports
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline | _____no_output_____ | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Definindo os hyperparâmetros do modelo | # Hyperparâmetros do modelo
learning_rate = 0.01
training_epochs = 2000
display_step = 200 | _____no_output_____ | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Definindo os datasets de treino e de teste Considere X como o tamanho de uma casa e y o preço de uma casa | # Dataset de treino
train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]
# Dataset de teste
test_X = np.asarray(... | _____no_output_____ | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Placeholders e variáveis | # Placeholders para as variáveis preditoras (x) e para variável target (y)
X = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
# Pesos e bias do modelo
W = tf.Variable(np.random.randn(), name="weight")
b = tf.Variable(np.random.randn(), name="bias") | _____no_output_____ | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Construindo o modelo | # Construindo o modelo linear
# Fórmula do modelo linear: y = W*X + b
linear_model = W*X + b
# Mean squared error (erro quadrado médio)
cost = tf.reduce_sum(tf.square(linear_model - y)) / (2*n_samples)
# Otimização com Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) | _____no_output_____ | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Executando o grafo computacional, treinando e testando o modelo | # Definindo a inicialização das variáveis
init = tf.global_variables_initializer()
# Iniciando a sessão
with tf.Session() as sess:
# Iniciando as variáveis
sess.run(init)
# Treinamento do modelo
for epoch in range(training_epochs):
# Otimização com Gradient Descent
sess.run(optimiz... | Epoch: 200 Custo (Erro): 0.2628 W:0.4961 b:-0.934
Epoch: 400 Custo (Erro): 0.1913 W:0.4433 b:-0.5603
Epoch: 600 Custo (Erro): 0.1473 W: 0.402 b:-0.2672
Epoch: 800 Custo (Erro): 0.1202 W:0.3696 b:-0.03732
Epoch: 1000 Custo (Erro): 0.1036 W:0.3441 b: 0.143
Epoch: 120... | MIT | pyfund/Cap10/Notebooks/DSA-Python-Cap10-Lab04.ipynb | guimaraesalves/Data-Science-Academy |
Bay Area Bike Share Analysis Introduction> **Tip**: Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook.[Bay Area Bike Share](http://www.bayareabikeshare.com/) is a company that provides on-demand bike rentals for customers in San Francisco, Redwood City, Palo Alt... | # import all necessary packages and functions.
import csv
from datetime import datetime
import numpy as np
import pandas as pd
from babs_datacheck import question_3
from babs_visualizations import usage_stats, usage_plot
from IPython.display import display
%matplotlib inline
# file locations
file_in = '201402_trip_dat... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Condensing the Trip DataThe first step is to look at the structure of the dataset to see if there's any data wrangling we should perform. The below cell will read in the sampled data file that you created in the previous cell, and print out the first few rows of the table. | sample_data = pd.read_csv('201309_trip_data.csv')
display(sample_data.head()) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
In this exploration, we're going to concentrate on factors in the trip data that affect the number of trips that are taken. Let's focus down on a few selected columns: the trip duration, start time, start terminal, end terminal, and subscription type. Start time will be divided into year, month, and hour components. We... | # Display the first few rows of the station data file.
station_info = pd.read_csv('201402_station_data.csv')
display(station_info.head())
# This function will be called by another function later on to create the mapping.
def create_station_mapping(station_data):
"""
Create a mapping from station IDs to cities,... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
You can now use the mapping to condense the trip data to the selected columns noted above. This will be performed in the `summarise_data()` function below. As part of this function, the `datetime` module is used to **p**arse the timestamp strings from the original data file as datetime objects (`strptime`), which can t... | def summarise_data(trip_in, station_data, trip_out):
"""
This function takes trip and station information and outputs a new
data file with a condensed summary of major trip information. The
trip_in and station_data arguments will be lists of data files for
the trip and station information, respectiv... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
**Question 3**: Run the below code block to call the `summarise_data()` function you finished in the above cell. It will take the data contained in the files listed in the `trip_in` and `station_data` variables, and write a new file at the location specified in the `trip_out` variable. If you've performed the data wran... | # Process the data by running the function we wrote above.
station_data = ['201402_station_data.csv']
trip_in = ['201309_trip_data.csv']
trip_out = '201309_trip_summary.csv'
summarise_data(trip_in, station_data, trip_out)
# Load in the data file and print out the first few rows
sample_data = pd.read_csv(trip_out)
disp... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
> **Tip**: If you save a jupyter Notebook, the output from running code blocks will also be saved. However, the state of your workspace will be reset once a new session is started. Make sure that you run all of the necessary code blocks from your previous session to reestablish variables and functions before picking up... | trip_data = pd.read_csv('201309_trip_summary.csv')
usage_stats(trip_data) | There are 27345 data points in the dataset.
The average duration of trips is 27.60 minutes.
The median trip duration is 10.72 minutes.
25% of trips are shorter than 6.82 minutes.
25% of trips are longer than 17.28 minutes.
| MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
You should see that there are over 27,000 trips in the first month, and that the average trip duration is larger than the median trip duration (the point where 50% of trips are shorter, and 50% are longer). In fact, the mean is larger than the 75% shortest durations. This will be interesting to look at later on.Let's s... | usage_plot(trip_data, 'subscription_type') | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Seems like there's about 50% more trips made by subscribers in the first month than customers. Let's try a different variable now. What does the distribution of trip durations look like? | usage_plot(trip_data, 'duration') | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Looks pretty strange, doesn't it? Take a look at the duration values on the x-axis. Most rides are expected to be 30 minutes or less, since there are overage charges for taking extra time in a single trip. The first bar spans durations up to about 1000 minutes, or over 16 hours. Based on the statistics we got out of `u... | usage_plot(trip_data, 'duration', ['duration < 60']) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
This is looking better! You can see that most trips are indeed less than 30 minutes in length, but there's more that you can do to improve the presentation. Since the minimum duration is not 0, the left hand bar is slighly above 0. We want to be able to tell where there is a clear boundary at 30 minutes, so it will loo... | usage_plot(trip_data, 'duration', ['duration < 60'], boundary = 0, bin_width = 5) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
**Question 4**: Which five-minute trip duration shows the most number of trips? Approximately how many trips were made in this range?**Answer**: 5 to 10 minutes trip, with approximately 9.000 trips. Visual adjustments like this might be small, but they can go a long way in helping you understand the data and convey you... | station_data = ['201402_station_data.csv',
'201408_station_data.csv',
'201508_station_data.csv' ]
trip_in = ['201402_trip_data.csv',
'201408_trip_data.csv',
'201508_trip_data.csv' ]
trip_out = 'babs_y1_y2_summary.csv'
# This function will take in the station data a... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Since the `summarise_data()` function has created a standalone file, the above cell will not need to be run a second time, even if you close the notebook and start a new session. You can just load in the dataset and then explore things from there. | trip_data = pd.read_csv('babs_y1_y2_summary.csv')
display(trip_data.head()) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Now it's your turn to explore the new dataset with `usage_stats()` and `usage_plot()` and report your findings! Here's a refresher on how to use the `usage_plot()` function:- first argument (required): loaded dataframe from which data will be analyzed.- second argument (required): variable on which trip counts will be... | usage_stats(trip_data)
usage_plot(trip_data,'start_hour',['subscription_type == Subscriber']) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Explore some different variables using the functions above and take note of some trends you find. Feel free to create additional cells if you want to explore the dataset in other ways or multiple ways.> **Tip**: In order to add additional cells to a notebook, you can use the "Insert Cell Above" and "Insert Cell Below" ... | # Final Plot 1
usage_plot(trip_data,'start_hour',['subscription_type == Subscriber'],bin_width=1)
usage_plot(trip_data,'weekday',['subscription_type == Subscriber']) | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
**Question 5a**: What is interesting about the above visualization? Why did you select it?**Answer**: Both graphs show that most Subscribers use the service to go to work, since vast majority happened on weekdays, and between 7-9 AM and 4-5 PM. | # Final Plot 2
usage_plot(trip_data,'start_month',['subscription_type == Customer'], boundary = 1)
usage_plot(trip_data,'start_month',['subscription_type == Customer'], boundary = 1, n_bins=12)
usage_plot(trip_data,'weekday',['subscription_type == Customer','start_month > 6'],bin_width=30)
usage_plot(trip_data,'start_c... | _____no_output_____ | MIT | Project Bike Sharing/Bay_Area_Bike_Share_Analysis.ipynb | afshimono/data_analyst_nanodegree |
Apsidal Motion Age for HD 144548Here, I am attempting to derive an age for the triple eclipsing hierarchical triple HD 144548 (Upper Scoripus member) based on the observed orbital precession (apsidal motion) of the inner binary system's orbit about the tertiary companion (star A). A value for the orbital precession is... | def c2(masses, radii, e, a, rotation=None):
f = (1.0 - e**2)**-2
g = (8.0 + 12.0*e**2 + e**4)*f**(5.0/2.0) / 8.0
if rotation == None:
omega_ratio_sq = 0.0
elif rotation == 'synchronized':
omega_ratio_sq = (1.0 + e)/(1.0 - e)**3
else:
omega_ratio_sq = 0.0
c2_0 = (ome... | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
What complicates the issue is that the interior structure constants for the B components also vary as a function of age, so we need to compute a mean mass track using the $c_2$ coefficients and the individual $k_2$ values. | import numpy as np
trk_Ba = np.genfromtxt('/Users/grefe950/evolve/dmestar/trk/gs98/p000/a0/amlt1884/m0980_GS98_p000_p0_y28_mlt1.884.trk')
trk_Bb = np.genfromtxt('/Users/grefe950/evolve/dmestar/trk/gs98/p000/a0/amlt1884/m0940_GS98_p000_p0_y28_mlt1.884.trk') | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
Create tracks with equally spaced time steps. | from scipy.interpolate import interp1d
log10_age = np.arange(6.0, 8.0, 0.01) # log10(age/yr)
ages = 10**log10_age
icurve = interp1d(trk_Ba[:,0], trk_Ba, kind='linear', axis=0)
new_trk_Ba = icurve(ages)
icurve = interp1d(trk_Bb[:,0], trk_Bb, kind='linear', axis=0)
new_trk_Bb = icurve(ages) | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
Now, compute the $c_2$ coefficients for each age. | mean_trk_B = np.empty((len(ages), 3))
for i, age in enumerate(ages):
c2s = c2(masses, [10**new_trk_Ba[i, 4], 10**new_trk_Bb[i, 4]], e, a,
rotation='synchronized')
avg_k2 = (c2s[0]*new_trk_Ba[i, 10] + c2s[1]*new_trk_Bb[i, 10])/(sum(c2s))
mean_trk_B[i] = np.array([age, 10**new_trk_Ba[i, 4] ... | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
With that, we have an estimate for the mean B component properties as a function of age. One complicating factor is the "radius" of the average B component. If we are modeling the potential created by the Ba/Bb components as that of a single star, we need to assume that the A component never enters into any region of t... | e2 = 0.2652
a2 = 66.2319
masses_2 = [1.44, 1.928]
trk_A = np.genfromtxt('/Users/grefe950/evolve/dmestar/trk/gs98/p000/a0/amlt1884/m1450_GS98_p000_p0_y28_mlt1.884.trk',
usecols=(0,1,2,3,4,5,6,7,8,9,10))
icurve = interp1d(trk_A[:,0], trk_A, kind='linear', axis=0)
new_trk_A = icurve(ages) | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
We are now in a position to compute the classical apsidal motion rate from the combined A/B tracks. | cl_apsidal_motion_rate = np.empty((len(ages), 2))
for i, age in enumerate(ages):
c2_AB = c2(masses_2, [10**new_trk_A[i, 4], a + 0.5*mean_trk_B[i, 1]], e2, a2)
cl_apsidal_motion_rate[i] = np.array([age, 360.0*(c2_AB[0]*new_trk_A[i, 10] + c2_AB[1]*mean_trk_B[i, 2])])
GR_apsidal_motion_rate = 5.45e-4*(sum(masses)/... | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
One can see from this that the general relativistic component is a very small contribution to the total apsidal motion of the system. Let's look at the evolution of the apsidal motion for the A/B binary system. | %matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1, figsize=(8., 8.), sharex=True)
ax.grid(True)
ax.tick_params(which='major', axis='both', length=15., labelsize=18.)
ax.set_xlabel('Age (yr)', fontsize=20., family='serif')
ax.set_ylabel('Apsidal Motion Rate (deg / cycle)', fontsize=20., fam... | _____no_output_____ | MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
How sensitive is this to the properties of the A component, which are fairly uncertain? | icurve = interp1d(cl_apsidal_motion_rate[:,1], cl_apsidal_motion_rate[:,0], kind='linear')
print icurve(0.0235)/1.0e6, icurve(0.0255)/1.0e6, icurve(0.0215)/1.0e6 | 11.2030404132 9.66153795127 12.8365039818
| MIT | Projects/upper_sco_age/apsidal_motion_age.ipynb | gfeiden/Notebook |
CORD19 Analysis | %matplotlib inline
# import nltk
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('averaged_perceptron_tagger')
import json
import yaml
import os
import nltk
import matplotlib.pyplot as plt
import re
import pandas as pd
from nltk.corpus import stopwords
#import plotly.graph_objects as go
import ne... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Configurations | # import configurations
with open('config.yaml','r') as ymlfile:
cfg = yaml.load(ymlfile) | C:\Users\david\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
This is separate from the ipykernel package so we can avoid doing imports until
| MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
General Functions | def get_papers(path):
# get list of papers .json from path
papers = []
for file_name in os.listdir(path):
papers.append(file_name)
return papers
def extract_authors(authors_list):
'''
Function to extract authors metadata from list of authors
'''
authors = []
for curr_au... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Objects | class Author:
def __init__(self, firstname, middlename, lastname):
self.firstName = firstname
self.middleName = middlename
self.lastName = lastname
def __str__(self):
return '{} {} {}'.format(self.firstName, self.middleName, self.lastName)
class Paper:
def __init__(self,... | C:\Users\david\OneDrive\Bureau\CORD-19-research-challenge\\2020-03-13\biorxiv_medrxiv\biorxiv_medrxiv
| MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Metadata | meta = '2020-03-13/all_sources_metadata_2020-03-13.csv'
df_meta = pd.read_csv(cfg['data-path'] + meta)
df_meta.head()
df_meta[df_meta['has_full_text']==True]
df_meta.info()
df_meta['source_x'].unique()
paper_ids = set(df_meta.iloc[:,0])
paper_ids.pop()
paper_ids
df_meta[df_meta['source_x']=='biorxiv'][['sha','doi']] | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
biorxiv_medrxiv | biorxiv = '\\2020-03-13\\biorxiv_medrxiv\\biorxiv_medrxiv'
path = cfg['data-path'] + biorxiv
papers = get_papers(path)
cnt = 0
# check if paper are in metadata dataframe
for paper in papers:
if paper[:-5] not in paper_ids:
print(paper)
else:
cnt += 1
print('There are {}/{} papers present in the ... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
pmc_custom_license | pmc = '2020-03-13\pmc_custom_license\pmc_custom_license'
path = cfg['data-path'] + pmc
pmc_papers = get_papers(path)
pmc_papers[:5]
cnt = 0
# check if paper are in metadata dataframe
for paper in pmc_papers:
if paper[:-5] not in paper_ids:
print(paper)
else:
cnt += 1
print('There are {}/{} paper... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
comm_use_subset noncomm_use_subset | # extract data from all papers
all_papers_data = []
for paper_name in papers:
file_path = os.path.join(path,paper_name)
with open(file_path, 'r') as f:
paper_info = extract_paper_metadata(json.load(f))
all_papers_data.append(paper_info)
for i in range(10):
print('- {}'.format(all_papers_data[i][... | 0015023cc06b5362d332b3baf348d11567ca2fbb
| MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Authors | def are_equal(author1, author2):
if (author1['first'][0] == author2['first'][0]) and (author1['mid'] == author2['mid']) and (author1['last'] == author2['last']):
return True
class Author:
def __init__(self, firstname, middlename, lastname):
self.firstName = firstname
self.middleName = mi... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Co-Authors | from itertools import combinations
co_authors_net = nx.Graph()
# for each paper
for i in range(len(all_papers_data)):
# get list of authors
co_authors = []
for author in all_papers_data[i]['authors']:
author_full_name = ''
# only keep authors with first and last names
if aut... | C:\Users\david\Anaconda3\lib\site-packages\networkx\drawing\nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)
if cb.is_numlike(alpha):
| MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Reference Authors | for i in range(3):
for author in all_papers_data[i]['authors']:
print(author)
# referenced authors
for ref in all_papers_data[i]['refs']:
for author in ref['authors']:
print(author)
print('-'*60) | {'first': 'Joseph', 'middle': ['C'], 'last': 'Ward'}
{'first': 'Lidia', 'middle': [], 'last': 'Lasecka-Dykes'}
{'first': 'Chris', 'middle': [], 'last': 'Neil'}
{'first': 'Oluwapelumi', 'middle': [], 'last': 'Adeyemi'}
{'first': 'Sarah', 'middle': [], 'last': ''}
{'first': '', 'middle': [], 'last': 'Gold'}
{'first': 'Ni... | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Extracting Key Words | paper_json['body_text']
stop_sentences = ['All rights reserved.','No reuse allowed without permission.','Abstract','author/funder','The copyright holder for this preprint (which was not peer-reviewed) is the']
abstract_text = extract_abstract(paper_json['abstract'])
body_text = ''
for t in paper_json['body_text']:
... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
TEST | paper_info = get_paper_data(cfg['data-path'] + biorxiv, papers[10])
paper_info
def get_sections_from_body(body):
sections = {}
for section in body:
if section['section'].isupper():
if section['section'] not in sections:
sections[section['section']] = ''
else:
... | _____no_output_____ | MIT | CORD19_Analysis.ipynb | davidcdupuis/CORD-19 |
Implementing the Gradient Descent AlgorithmIn this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data. | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#Some helper functions for plotting and drawing lines
def plot_points(X, y):
admitted = X[np.argwhere(y==1)]
rejected = X[np.argwhere(y==0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', ... | _____no_output_____ | MIT | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI |
Reading and plotting the data | data = pd.read_csv('data.csv', header=None)
X = np.array(data[[0,1]])
y = np.array(data[2])
plot_points(X,y)
plt.show() | _____no_output_____ | MIT | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI |
TODO: Implementing the basic functionsHere is your turn to shine. Implement the following formulas, as explained in the text.- Sigmoid activation function$$\sigma(x) = \frac{1}{1+e^{-x}}$$- Output (prediction) formula$$\hat{y} = \sigma(w_1 x_1 + w_2 x_2 + b)$$- Error function$$Error(y, \hat{y}) = - y \log(\hat{y}) - (... | # Implement the following functions
# Activation (sigmoid) function
def sigmoid(x):
sigmoid_result = 1/(1 + np.exp(-x))
return sigmoid_result
# Output (prediction) formula
def output_formula(features, weights, bias):
x = features.dot(weights) + bias
print(x)
output= sigmoid(x)
return output
# ... | _____no_output_____ | MIT | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI |
Training functionThis function will help us iterate the gradient descent algorithm through all the data, for a number of epochs. It will also plot the data, and some of the boundary lines obtained as we run the algorithm. | np.random.seed(44)
epochs = 100
learnrate = 0.01
def train(features, targets, epochs, learnrate, graph_lines=False):
errors = []
n_records, n_features = features.shape
last_loss = None
weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
bias = 0
for e in range(epochs):
... | _____no_output_____ | MIT | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI |
Time to train the algorithm!When we run the function, we'll obtain the following:- 10 updates with the current training loss and accuracy- A plot of the data and some of the boundary lines obtained. The final one is in black. Notice how the lines get closer and closer to the best fit, as we go through more epochs.- A ... | train(X, y, epochs, learnrate, True) | -0.473530635888
0.125936869166
-0.0361573775337
0.256515978501
0.0843418004565
-0.0179345755974
0.198924764205
0.10068384058
0.270812660579
0.158779604536
0.172757003252
0.306288885566
0.328800256305
0.209874987271
0.267438947578
0.047722264903
0.0292851983816
0.156739365126
0.309723821939
0.299274606528
0.276005815744... | MIT | Lesson 3: Introduction to Neural Networks/4 GradientDescent.ipynb | makeithappenlois/Udacity-AI |
Monitoring & Reporting What `pipeline.py` is doing:- Load: - Monitoring & Reporting Data- Link MPRNs to GPRNs CaveatThe M&R data is publicly available, however, the user still needs to [create their own s3 credentials](https://aws.amazon.com/s3/) to fully reproduce the pipeline this pipeline (*i.e. they need an AWS... | conda env create --file environment.yml
conda activate hdd | _____no_output_____ | MIT | combine-monitoring-and-reporting-mprns-and-gprns/README.ipynb | Rebeccacachia/projects |
Run | python pipeline.py | _____no_output_____ | MIT | combine-monitoring-and-reporting-mprns-and-gprns/README.ipynb | Rebeccacachia/projects |
Time and Dates The `astropy.time` package provides functionality for manipulating times and dates. Specific emphasis is placed on supporting time scales (e.g. UTC, TAI, UT1, TDB) and time representations (e.g. JD, MJD, ISO 8601) that are used in astronomy and required to calculate, e.g., sidereal times and barycentric... | import numpy as np
from astropy.time import Time
times = ['1999-01-01T00:00:00.123456789', '2010-01-01T00:00:00']
t = Time(times, format='isot', scale='utc')
t
t[1] | _____no_output_____ | Unlicense | day4/06. Astropy - Time.ipynb | ubutnux/bosscha-python-workshop-2022 |
The `format` argument specifies how to interpret the input values, e.g. ISO or JD or Unix time. The `scale` argument specifies the time scale for the values, e.g. UTC or TT or UT1. The `scale` argument is optional and defaults to UTC except for Time from epoch formats. We could have written the above as: | t = Time(times, format='isot') | _____no_output_____ | Unlicense | day4/06. Astropy - Time.ipynb | ubutnux/bosscha-python-workshop-2022 |
When the format of the input can be unambiguously determined then the format argument is not required, so we can simplify even further: | t = Time(times)
t | _____no_output_____ | Unlicense | day4/06. Astropy - Time.ipynb | ubutnux/bosscha-python-workshop-2022 |
Now let’s get the representation of these times in the JD and MJD formats by requesting the corresponding Time attributes: | t.jd
t.mjd | _____no_output_____ | Unlicense | day4/06. Astropy - Time.ipynb | ubutnux/bosscha-python-workshop-2022 |
The default representation can be changed by setting the `format` attribute: | t.format = 'fits'
t
t.format = 'isot'
t | _____no_output_____ | Unlicense | day4/06. Astropy - Time.ipynb | ubutnux/bosscha-python-workshop-2022 |
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