jgoerner

BEGIN TRANSACTION;
DROP TABLE IF EXISTS "actors";
CREATE TABLE IF NOT EXISTS "actors" (
	"actor_id"	INTEGER,
	"name"	TEXT,
	PRIMARY KEY("actor_id")
);
DROP TABLE IF EXISTS "roles";
CREATE TABLE IF NOT EXISTS "roles" (
	"role_id"	INTEGER,

jgoerner

mkdir -p adapter/in adapter/out application/port/in application/port/out application/service domain; 

touch adapter/in/.gitkeep adapter/out/.gitkeep application/port/in/.gitkeep application/port/out/.gitkeep application/service/.gitkeep domain/.gitkeep

jgoerner

### IMPORTS ###
from datetime import datetime
from random import random, randint
import uuid

from faker import Faker
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tqdm import tqdm

jgoerner

version: "3"

services:
  jupyter:
    image: jupyter/scipy-notebook
    ports:
      - 8888:8888
    volumes:
      - jp_vol:/home/jovyan/work

jgoerner

# Import
import numpy as np
from matplotlib.colors import ListedColormap, to_rgba_array
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal
from sklearn.mixture import GaussianMixture

# Config
%config InlineBackend.figure_format = 'retina'
%matplotlib inline

jgoerner

import numpy as np

foo = np.array([
  [1, 2, 3],
  [1, 2, 3],
  [1, 2, 9],
])

bar = [1, 2, 3]

jgoerner

# A little snippet to show how to use sklearn transformer interface 
# to extract NLP features

# necessary imports
import pandas as pd
from sklearn.base import TransformerMixin
from sklearn.pipeline import FeatureUnion

# load your dataset
df_train = pd.read_csv("./data/train/cwi_training.txt", sep="\t", names=["sentence", "word", "index", "label"])