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# Count the number of covid meetups for R-Ladies and WiMLDS

library(meetupr)
library(tidyverse)

# Look up all R-Ladies & WiMLDS groups by "topic id" & count the events.  
# You can find topic ids for associated tags by querying 
# [here](https://secure.meetup.com/meetup_api/console/?path=/find/topics).  
# The `topic_id` for topic, "R-Ladies", is 1513883.
# The `topic_id` for topic, "WiMLDS", is 1517030.

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# Meetups since quarantine started (feel free to adjust the date to your local lockdown date)

library(meetupr)
library(tidyverse)

meetup_urlname <- "Bay-Area-Women-in-Machine-Learning-and-Data-Science"  #insert your meetup urlnamne here
events <- get_events(urlname = meetup_urlname, 
                     event_status = "past")

events %>%

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install.packages("h2o", repos="http://h2o-release.s3.amazonaws.com/h2o/latest_stable_R", method="curl")

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library(R.utils)
# Note: For saving H2O AutoML objects, if path is NULL (default), 
# then save in pwd with project_name as folder name
# This function (or something similar to it) will be part of H2O soon...
# Written by: https://github.com/tomasfryda

.dump_aml_frames <- function(aml, path) {
  frames <- c(attr(aml@leaderboard, "id"), attr(aml@event_log, "id"))
  frames <- c(frames,
              unlist(sapply(aml@leaderboard$model_id, function(model_id)

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# Example of monotonic constraints in H2O AutoML (using h2o v3.30.0.1)
# monotone constraints: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/monotone_constraints.html
# H2O AutoML: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

library(h2o)
h2o.init()

# Import the prostate dataset
file <- "http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip"
prostate <- h2o.importFile(file)

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# How to get k-fold metrics for all the H2O AutoML models in R
# Adapted from: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

library(h2o)

h2o.init()

# Import a sample binary outcome train/test set into H2O
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")

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# Example of how to do Stacking using clustered (aka. "pooled repeated measures") data:
# Since stacking uses cross-validation, we must ensure that the observations from 
# the same clusters are all in the same fold.  We borrow the SuperLearner::CVFolds()
# function and use H2O Stacked Ensembles and AutoML to train stacked ensembles.

library(SuperLearner)
library(h2o)
h2o.init()

# Import a sample binary outcome train/test set into H2O

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# My version of the code at this blog post:
# https://towardsdatascience.com/automl-a-tool-to-improve-your-workflow-1a132248371f

import h2o
from h2o.automl import H2OAutoML

h2o.init()

train = h2o.import_file("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data")
y = "C1" #e = edible, p = poisonous

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### Kaggle Days SF: Hackathon submission (8th place) 

# I used the latest version of H2O (3.24.0.1)
# Latest stable always here: http://h2o-release.s3.amazonaws.com/h2o/latest_stable.html
# H2O 3.24.0.1: http://h2o-release.s3.amazonaws.com/h2o/rel-yates/1/index.html
# If you are a Python user, you can use the demo Python code available on the H2O AutoML User Guide 
# instead: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

# Unfortunately it was a private competition, so the data is not publicly available! 

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library("ggplot2")
library("scales")
library("devtools")
install_github("rladies/meetupr", ref = "topic_id")
library("meetupr")  #Requires topic_id branch...
api_key = "API_KEY"  #Use your own meetup.com API key...

meetups <- find_groups(topic_id = 1513883, api_key = api_key)  #all groups tagged with "R-Ladies" topic id
meetups <- meetups[-nrow(meetups), ]  #remove Joburg-R-Users-Group
tc <- find_groups(text = "r ladies twin cities", api_key = api_key) #add Twin Cities