Jonathan Loscalzo JonathanLoscalzo

Metrics for: Test

accuracy: 0.8068054280175492

Classification Report

target	precision	recall	f1-score	support
functional	0.90	0.80	0.85	12051
functional needs repair	0.29	0.63	0.40	665
non functional	0.77	0.84	0.80	6886

Directory structure for JavaScript/Node Projects

While the following structure is not an absolute requirement or enforced by the tools, it is a recommendation based on what the JavaScript and in particular Node community at large have been following by convention.

Beyond a suggested structure, no tooling recommendations, or sub-module structure is outlined here.

Directories

lib/ is intended for code that can run as-is
src/ is intended for code that needs to be manipulated before it can be used

	import numpy as np
	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.layers import Input, Dense
	from keras.models import Model


	def write_log(callback, names, logs, batch_no):
	for name, value in zip(names, logs):
	summary = tf.Summary()

	params_rf = {
	'clf__n_estimators':[
	100,
	300,
	500
	],
	'clf__min_samples_leaf': [
	1,
	2,
	4

	@utils.timeit
	def evaluate_classifier(clf, X, y, scoring='accuracy'):
	scores = cross_val_score(clf, X, y, scoring='accuracy', cv=StratifiedKFold(3), verbose=0)
	name = type(clf).__name__ if (type(clf).__name__ != 'Pipeline') else type(clf.steps[-1][1]).__name__
	print(f'{name} : {scores.mean()} +/- {scores.std()}')

	def get_pipeline_model(clf):
	return Pipeline([
	('preprocess', preprocess_pipeline()),
	('clf', clf)
	])

	get_numeric_data = FunctionTransformer(lambda df: df[NUMERIC_COLUMNS])
	get_text_data = FunctionTransformer(lambda df: df[TEXT_COLUMNS])
	get_date_data = FunctionTransformer(lambda df: df[['date_recorded']])

	transform_date_data = FunctionTransformer(lambda df: df.assign(
	date_recorded_day=df.date_recorded.dt.day,
	date_recorded_month=df.date_recorded.dt.month,
	date_recorded_year=df.date_recorded.dt.year
	).drop('date_recorded', axis=1))

	def preprocess_pipeline():
	return FeatureUnion(
	transformer_list = [
	('date_features', Pipeline([
	('selector', get_date_data),
	('transform', transform_date_data)
	])),
	('numeric_features', Pipeline([
	('selector', get_numeric_data),
	('imputer', SimpleImputer()),

	{
	"name": "node-ts-barebones",
	"version": "0.0.1",
	"description": "Barebones NodeJS setup with TypeScript, Jest, TSLint and Prettier",
	"author": "Tiago Pina - <tiagoaspina@gmail.com>",
	"license": "MIT",
	"scripts": {
	"build": "tsc -p tsconfig.build.json",
	"format": "prettier --write \"src/*/.ts\"",
	"start": "ts-node -r tsconfig-paths/register src/main.ts",