Spent some time feeding the graveyard ducks with Emily in the morning. The ducks and geese were generally well behaved. As we left I noticed that someone had broken the face off a small Buddha statue that is near the back “entrance” to the cemetery.
Errands and cleaning followed by a visit from Laura, Cedar and Sam.
Spent some time in the evening recovering lost notes for Memento. The virtual machine is gone but I set up a new wiki for project notes and identified a couple books to read.
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 continuesCreation of test setsStratified samplingsklearn’s StratifiedShuffleSplitVisualizing data with matplotlibCoorelation 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% of the data set to[…]
HAL8999 – 3/100 Chapter 2 of Hands on MLCost functionsvirtualenv setupcode 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 a linear regression[…]
Achievement: HAL 8999 – 2/100 Completed chapter 1 of Hands On ML and worked through the exercisesModified 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 an[…]
Achievement: HAL 8999 – 1/100 Set up virtualenv for HAL8999Installed sklearn, pandas, numpy, matplotlibUnable 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 LearningAuthor is a little fast and loose with the example[…]
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[…]
Just out of curiosity I started looking into how to get jupyter to display text and math formatted as markdown and latex in output cells. This got into my head when I was looking at symbolic integration and differentiation of functions and how to format the output in a more civilized manner. I don’t have[…]
