So the gym fell through this afternoon so I spent some time going through pad drills one through five on the beat up old wavemaster in my garage. Not as rough as the typical class at the dojang but I at least got my heart rate up, worked up a sweat and wore the skin off one of my knuckles.
I asked the girls for a set of boxers hand wraps for Fathers Day.
Coming away from the first class on the Bodhisattva Precepts at Buddha Eye Temple I have the following to roll around in my head for a while. The Bodhisattva Precepts used in the Soto Zen tradition encompass and expand on the ethics / sila encompassed in the Noble Eightfold Path. The sixteen Bodhisattva Precepts include[…]
I don’t know why I continue to use Windows. Every single god damned thing feels like pushing a giant rock up a hill. I’ll admit that the fact that I’ve been doing most of my work on Linux for the past twenty years inclines me to use the linux idiom for getting things done and[…]
HAL8999 7/100 I was sick yesterday but did spend some time looking over some “cheat sheets” that people had put together for various machine learning topics. Some were good, some were just stupid (I’m looking at you Machine Learning in Emoji). Also went through a very simply classifier based on the iris data set.
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from sklearn import neighbors, datasets, preprocessing from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # load the data into training and test sets iris = datasets.load_iris() X, y = iris.data[:, :2], iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13) # scale the data scaler = preprocessing.StandardScaler() scaler.fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) # train a k-nearest neighbors model knn = neighbors.KNeighborsClassifier(n_neighbors = 5) knn.fit(X_train, y_train) # Test the model y_pred = knn.predict(X_test) print(accuracy_score(y_test, y_pred)) |
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HAL8999 6/100 Watched the “Learn how to Learn” Google talk on youtube Updated the jupyter notebooks for handson-ml from github and read through the Ch2 notebook to address the CategoricalEncoder issue from yesterday Looked at a basic transformer Part of building a data pipeline is likely to include the creation of custom transformer classes to[…]
HAL8999 – 5/100 Today while going back through the Hands On Machine Learning book Ch2 I learned that the CategoricalEncoder referenced in the section on handling categorical attributes still isn’t in scikit-learn. I checked the reqirements.txt which shows scikit-learn=0.19.1. Checking my virtualenv, I should be good.
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$ grep scikit-learn handson-ml/requirements.txt scikit-learn==0.19.1 $ pip freeze |grep scikit-learn scikit-learn==0.19.2 |
Turns out that the CategoricalEncoder isn’t going to[…]
HAL8999 – [3,4]/100 Chapter 2 of Hands on ML continues Creation of test sets Stratified sampling sklearn’s StratifiedShuffleSplit Visualizing data with matplotlib Coorelation coefficients Getting a good train-test split Since you can’t train a model and just expect it to work well right out of the box it’s standard practice to split off about 20%[…]
HAL8999 – 3/100 Chapter 2 of Hands on ML Cost functions virtualenv setup code to get the dataset The chapter follows a rudimentary machine learning project from business case to final product. California census data is analyzed to build a model which will predict media housing price in a district based on other factors using[…]
Achievement: HAL 8999 – 2/100 Completed chapter 1 of Hands On ML and worked through the exercises Modified yesterdays example to also do both k-nearest-neighbors with both three and four neighbors. Four neighbors was further from the linear regression than three demonstrating that more is not always better. Short list but Ch1 is something of[…]
Achievement: HAL 8999 – 1/100 Set up virtualenv for HAL8999 Installed sklearn, pandas, numpy, matplotlib Unable to install tensorflow since I’m on python 3.7 and the pip installs only work for 3.6 and earlier. I can sort that out later. Read up through Example 1-1 in Hands-On Machine Learning Author is a little fast and[…]
Some druids apparently wandered by and cast Wall of Thorns in my yard so I spent a good sized chunk of the morning clearing blackberry brambles. While my hands were busy being impaled by the thorns which unerringly find the weak points in my gloves I got to thinking about some of the things on[…]