Archive for the ‘Programming’ Category
TM Philosophy via gutenbergr
The gutenbergr
package is an excellent API wrapper for Project Gutenberg, which provides unlimited free access to public domain books and materials.
Inspired by an analytical philosopher working in DS, I decided to spin up a Shiny app to allow myself and the public to text-mine 1 or more works at a time.
I started a new blog for the project at:
The app itself can be found at https://hume.xyz, however the free ShinyApps.io is a bit slow, so it won’t load quickly until the project gets moved to AWS, Azure, a NameCheap VM or some other affordable production environment.
Please see the initial, and currently only, blog article at hume.xyz/blog for GitHub links to the full source code – and feel free to contribute!
LightGBM Grid Search Example in R
library(data.table) library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.traindtrain <- lgb.Dataset(train$data, label = train$label, free_raw_data = FALSE) data(agaricus.test, package = "lightgbm") test <- agaricus.testdtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) valids <- list(test = dtest) grid_search <- expand.grid(Depth = 8, L1 = 0:5, L2 = 0:5) model <- list() perf <- numeric(nrow(grid_search)) for (i in 1:nrow(grid_search)) { model[[i]] <- lgb.train(list(objective = "regression", metric = "l2", lambda_l1 = grid_search[i, "L1"], lambda_l2 = grid_search[i, "L2"], max_depth = grid_search[i, "Depth"]), dtrain, 2, valids, min_data = 1, learning_rate = 1, early_stopping_rounds = 5) perf[i] <- min(rbindlist(model[[i]]$record_evals$test$l2)) } Result: > cat("Model ", which.min(perf), " is lowest loss: ", min(perf), sep = "") Model 1 is lowest loss: 1.972152e-31> print(grid_search[which.min(perf), ]) Depth L1 L21 8 0 0
Example XGboost Grid Search in Python
import sys import math import numpy as np from sklearn.grid_search import GridSearchCV sys.path.append('xgboost/wrapper/')import xgboost as xgb class XGBoostClassifier(): def __init__(self, num_boost_round=10, **params): self.clf = None self.num_boost_round = num_boost_round self.params = params self.params.update({'objective': 'multi:softprob'}) def fit(self, X, y, num_boost_round=None): num_boost_round = num_boost_round or self.num_boost_round self.label2num = dict((label, i) for i, label in enumerate(sorted(set(y)))) dtrain = xgb.DMatrix(X, label=[self.label2num[label] for label in y]) self.clf = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=num_boost_round) def predict(self, X): num2label = dict((i, label)for label, i in self.label2num.items()) Y = self.predict_proba(X) y = np.argmax(Y, axis=1) return np.array([num2label[i] for i in y]) def predict_proba(self, X): dtest = xgb.DMatrix(X) return self.clf.predict(dtest) def score(self, X, y): Y = self.predict_proba(X) return 1 / logloss(y, Y) def get_params(self, deep=True): return self.params def set_params(self, **params): if 'num_boost_round' in params: self.num_boost_round = params.pop('num_boost_round') if 'objective' in params: del params['objective'] self.params.update(params) return self def logloss(y_true, Y_pred): label2num = dict((name, i) for i, name in enumerate(sorted(set(y_true)))) return -1 * sum(math.log(y[label2num[label]]) if y[label2num[label]] > 0 else -np.inf for y, label in zip(Y_pred, y_true)) / len(Y_pred) def main(): clf = XGBoostClassifier( eval_metric = 'auc', num_class = 2, nthread = 4, eta = 0.1, num_boost_round = 80, max_depth = 12, subsample = 0.5, colsample_bytree = 1.0, silent = 1, ) parameters = { 'num_boost_round': [100, 250, 500], 'eta': [0.05, 0.1, 0.3], 'max_depth': [6, 9, 12], 'subsample': [0.9, 1.0], 'colsample_bytree': [0.9, 1.0], } clf = GridSearchCV(clf, parameters, n_jobs=1, cv=2) clf.fit([[1,2], [3,4], [2,1], [4,3], [1,0], [4,5]], ['a', 'b', 'a', 'b', 'a', 'b']) best_parameters, score, _ = max(clf.grid_scores_, key=lambda x: x[1]) print(score) for param_name in sorted(best_parameters.keys()): print("%s: %r" % (param_name, best_parameters[param_name])) print(clf.predict([[1,2]])) if __name__ == '__main__': main()
Raspberry Pi #antisec LED Alert Script
Just a little Python script I wrote to make an LED blink on a Raspberry Pi and to print a message to the screen when there’s a #antisec tweet:
# Jason D. Miller # github.com/hack-r from twython import TwythonStreamer import RPi.GPIO as GPIO import time C_KEY = "" C_SECRET = "" A_TOKEN = "-" A_SECRET = "" GPIO.setmode(GPIO.BCM) GPIO.setwarnings(False) GPIO.setup(18,GPIO.OUT) def blink(): GPIO.output(18, GPIO.HIGH) time.sleep(1) GPIO.output(18, GPIO.LOW) class MyStreamer(TwythonStreamer): def on_success(self, data): if 'text' in data: blink() print("Antisec Tweet detected. Call the FBI.") stream = MyStreamer(C_KEY, C_SECRET, A_TOKEN, A_SECRET) stream.statuses.filter(track="#antisec")
Happy Pi Day 2016!
Has it really been a whole year?
On Pi Day 2015 hack-r.com posted a tribute to Pi (π) Day, published on GitHub, wherein we created fractals in R based on π, scraped and displayed information on Pi and other fun stuff.
This year, find out how Fibonacci numbers, which are sequences of integers, have a freaky relationship with π! View the entire script on GitHub.
# Pi Fibonacci Sequence --------------------------------------------------- cat("This year, we'll look at the relationship between Pi and Fibonacci sequences. \n") cat("Until very recently there were just two methods used to compute pi (π), one invented by the Greek mathematician Archimedes, and the other by the Scottish mathematician James Gregory. \n") cat("If we use Sir Gregory's arc tangent method, you'll notice a pattern...") pi/4 atan(1) pi/4 == atan(1) atan(1/3) atan(1/5) + atan(1/8) atan(1/8) atan(1/13) + atan(1/21) cat("We can combine what we saw above") pi/4 atan(1/2) + atan(1/3) atan(1/2) + atan(1/5) + atan(1/8) atan(1/21) atan(1/34) + atan(1/55) cat("You'll notice that the pattern is a Fibonacci sequence! \n") cat(" We have just seen that there are infinitely many formulae for π using the Fibonacci numbers!")
R: beginning and end of month
date.end.month <- seq(as.Date("2012-02-01"),length=4,by="months")-1
R: Better detach package function
<div>detach_package <- function(pkg, character.only = FALSE)</div>
<div>{</div>
<div> if(!character.only)</div>
<div> {</div>
<div> pkg <- deparse(substitute(pkg))</div>
<div> }</div>
<div> search_item <- paste("package", pkg, sep = ":")</div>
<div> while(search_item %in% search())</div>
<div> {</div>
<div> detach(search_item, unload = TRUE, character.only = TRUE)</div>
<div> }</div>
<div>}</div>
<div></div>
<div>
R: Remove constant and identical features programmatically
<div>##### Removing constant features</div>
<div>cat("\n## Removing the constants features.\n")</div>
<div>for (f in names(train)) {</div>
<div> if (length(unique(train[[f]])) == 1) {</div>
<div> cat(f, "is constant in train. We delete it.\n")</div>
<div> train[[f]] <- NULL</div>
<div> test[[f]] <- NULL</div>
<div> }</div>
<div>}</div>
<div></div>
<div>##### Removing identical features</div>
<div>features_pair <- combn(names(train), 2, simplify = F)</div>
<div>toRemove <- c()</div>
<div>for(pair in features_pair) {</div>
<div> f1 <- pair[1]</div>
<div> f2 <- pair[2]</div>
<div></div>
<div> if (!(f1 %in% toRemove) & !(f2 %in% toRemove)) {</div>
<div> if (all(train[[f1]] == train[[f2]])) {</div>
<div> cat(f1, "and", f2, "are equals.\n")</div>
<div> toRemove <- c(toRemove, f2)</div>
<div> }</div>
<div> }</div>
<div>}</div>
<div></div>
<div>