ryanbbrown

hammerspoon-charmstone

A Charmstone-style app launcher built on Hammerspoon. Hold a modifier, tap one or two arrow keys, release to launch. Eight directions total (4 cardinals + 4 diagonals).

Install

1. Install Hammerspoon: brew install --cask hammerspoon
2. Copy hammerspoon-charmstone.lua to ~/.hammerspoon/init.lua
3. Launch Hammerspoon and grant accessibility permissions
4. Reload config (cmd+alt+r, or from the Hammerspoon menu)

ryanbbrown

classifier = KNeighborsClassifier()

pipe = Pipeline([('cleaner', clean_transformer()),
                 ('vectorizer', tfidf_vector),
                 ('classifier', classifier)])

params = {'classifier__n_neighbors':[1,3,5,7,9,11],
         'classifier__weights':['uniform','distance'],
         'classifier__leaf_size':[10,30,50,100,200],
         'classifier__p':[1,2]}

ryanbbrown

from sklearn.model_selection import GroupKFold, cross_val_score
from sklearn.linear_model import LogisticRegression, RidgeClassifier, SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC

models = [LogisticRegression(solver='liblinear', max_iter=300),
          SVC(C=1.0, kernel='linear', degree=3, gamma='auto'), MultinomialNB(),
          KNeighborsClassifier(), RidgeClassifier(),
          SGDClassifier(loss='hinge', penalty='l2', alpha=1e-3)]

ryanbbrown

from sklearn.preprocessing import LabelBinarizer

lb = LabelBinarizer()
X, y = df.text, df.label
y = lb.fit_transform(y).reshape(len(y),)
titles = df.title.tolist()

ryanbbrown

def clean_text(text):
    return text.strip().lower()

class clean_transformer(TransformerMixin):
    def transform(self, X, **transform_params):
        return [clean_text(text) for text in X]

    def fit(self, X, y=None, **fit_params):
        return self

ryanbbrown

bow_vector = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1))

tfidf_vector = TfidfVectorizer(tokenizer = spacy_tokenizer)

ryanbbrown

def spacy_tokenizer(sentence):
    mytokens = parser(sentence)
    mytokens = [ word.lemma_.lower().strip() if word.lemma_ != "-PRON-" else word.lower_ for word in mytokens ]
    mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]

    return mytokens

ryanbbrown

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.base import TransformerMixin
from sklearn.pipeline import Pipeline

import spacy
from spacy.lang.en import English
parser = English()
from spacy.lang.en.stop_words import STOP_WORDS
stop_words = spacy.lang.en.stop_words.STOP_WORDS
import string

ryanbbrown

df1, df2, df3, df4, df5 = df.copy(), df.copy(), df.copy(), df.copy(), df.copy()

dflist = [df1, df2, df3, df4, df5]
ranges = [0,20000,40000,60000,80000,100000]

for df, i in zip(dflist, range(1,7)):
    df['text'] = df['text'].apply(lambda x: x[ranges[i-1]:ranges[i]])
    
df = df1.append([df2, df3, df4, df5])
df.insert(4, 'section_number', [1]*40+[2]*40+[3]*40+[4]*40+[5]*40)

ryanbbrown

import os
import codecs

files = sorted([i for i in os.listdir('book_text/')])

booklist = []

for file in files:
    with codecs.open('book_text/'+file, 'r', encoding='utf8',) as book:
        booklist.append(book.read()[0:100000])