Data Engineering for Data Scientists

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Data Engineering for Data Scientists Max Humber

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When models and data applications are pushed to production, they become brittle black boxes that can and will break. In this talk you’ll learn how to one-up your data science workﬂow with a little engineering! Or more speciﬁcally, about how to improve the reliability and quality of your data applications... all so that your models won’t break (or at least won’t break as often)! Examples for this session will be in Python 3.6+ and will rely on: logging to allow us to debug and diagnose things while they’re running, Click to develop “beautiful” command line interfaces with minimal boiler-plating, and pytest to write short, elegant, and maintainable tests.

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you can't do this

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without this you can't do this

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#1 .py #2 defence #3 log #4 cli #5

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#1 .py #2 defence #3 log #4 cli #5

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#1 Lose the Notebook

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.ipynb exploratory analysis visualizing ideas prototyping messy bad at versioning not ideal for production ✅ ✅ ✅ ❌ ❌ ❌

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.ipynb exploratory analysis visualizing ideas prototyping messy bad at versioning not ideal for production ✅ ✅ ✅ ❌ ❌ ❌

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.ipynb .py

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$ jupyter nbconvert --to script [NOTEBOOK_NAME].ipynb

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$ jupyter nbconvert --to script [NOTEBOOK_NAME].ipynb

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$ jupyter nbconvert --to script [NOTEBOOK_NAME].ipynb

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cmd+enter

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lose the notebook not the kernel

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lose the notebook not the kernel

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lose the notebook not the kernel

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#2 Get Defensive

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$ pip install sklearn-pandas

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DataFrameMapper CategoricalImputer

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from sklearn_pandas import DataFrameMapper, CategoricalImputer mapper = DataFrameMapper([ ('time', None), ('pick_up', None), ('last_drop_off', CategoricalImputer()), ('last_pick_up', CategoricalImputer()) ]) mapper.fit(X_train)

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from sklearn.base import TransformerMixin class DateEncoder(TransformerMixin): def fit(self, X, y=None): return self def transform(self, X): dt = X.dt return pd.concat([dt.month, dt.dayofweek, dt.hour], axis=1)

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month, dayofweek, hour

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#3 LOG ALL THE THINGS

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#3 LOG ALL THE THINGS

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Cerberus is a lightweight and extensible data validation library for Python

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Cerberus is a lightweight and extensible data validation library for Python $ pip install cerberus

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from cerberus import Validator from copy import deepcopy class PandasValidator(Validator): def validate(self, document, schema, update=False, normalize=True): document = document.to_dict(orient='list') schema = self.transform_schema(schema) super().validate(document, schema, update=update, normalize=normalize) def transform_schema(self, schema): schema = deepcopy(schema) for k, v in schema.items(): schema[k] = {'type': 'list', 'schema': v} return schema

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78asd86d876ad8678sdadsa687d

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78asd86d876ad8678sdadsa687d

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#4 Learn how to CLI

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input output

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< refactor >

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$ python model.py predict --file=max_bike_data.csv

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$ python model.py predict --file=max_bike_data.csv

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$ python model.py predict --file=max_bike_data.csv

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$ python model.py predict my_bike_data.csv

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$ python model.py predict sunny_bike_data.csv

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$ python model.py predict sunny_bike_data.csv

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#5 mummify

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you suck at git and logging but it’s not your fault

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you suck at git and logging but it’s not your fault

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you suck at git and logging but it’s not your fault

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import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelBinarizer from sklearn.pipeline import make_pipeline from sklearn_pandas import DataFrameMapper, CategoricalImputer from helpers import DateEncoder df = pd.read_csv('../max_bike_data.csv') df['time'] = pd.to_datetime(df['time']) df = df[(df['pick_up'].notnull()) & (df['drop_off'].notnull())] TARGET = 'drop_off' y = df[TARGET].values X = df.drop(TARGET, axis=1) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42) mapper = DataFrameMapper([ ('time', DateEncoder(), {'input_df': True}), ('pick_up', LabelBinarizer()), ('last_drop_off', [CategoricalImputer(), LabelBinarizer()]), ('last_pick_up', [CategoricalImputer(), LabelBinarizer()]) ]) lb = LabelBinarizer() y_train = lb.fit_transform(y_train) model.py base

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model.py add from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier() pipe = make_pipeline(mapper, model) pipe.fit(X_train, y_train) acc_train = pipe.score(X_train, y_train) acc_test = pipe.score(X_test, lb.transform(y_test)) print(f'Training: {acc_train:.3f}, Testing: {acc_test:.3f}')

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model.py mummify import mummify mummify.log(f'Training: {acc_train:.3f}, Testing: {acc_test:.3f}’)

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from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier() model.py model swap 1

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from sklearn.neural_network import MLPClassifier model = MLPClassifier() model.py model swap 2

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from sklearn.neural_network import MLPClassifier model = MLPClassifier(max_iter=2000) model.py model swap 2 + max_iter

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mummify history mummify switch mummify history mummify command line

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git --git-dir=.mummify status mummify is just git

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from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier(n_neighbors=6) mummify adjust hypers on 1

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from sklearn.neighbors import KNeighborsClassifier model = KNeighborsClassifier(n_neighbors=4) mummify adjust hypers on 1

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from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=1000) mummify switch back to rf

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import pickle with open('rick.pkl', 'wb') as f: pickle.dump((pipe, lb), f) pickle model

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import pickle from fire import Fire import pandas as pd with open('rick.pkl', 'rb') as f: pipe, lb = pickle.load(f) def predict(file): df = pd.read_csv(file) df['time'] = pd.to_datetime(df['time']) y = pipe.predict(df) y = lb.inverse_transform(y)[0] return f'Max is probably going to {y}' if __name__ == '__main__': Fire(predict) predict.py $ git --git-dir=.mummify add . $ git --git-dir=.mummify commit -m 'add predict'

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time,pick_up,last_drop_off,last_pick_up 2018-04-09 9:15:52,home,other,home new_data.csv

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https://github.com/maxhumber/mummify pip install mummify

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https://github.com/maxhumber/mummify pip install mummify conda install -c maxhumber mummify

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#END

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hydrogen sklearn sklearn-pandas cerberus

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mummify https://leanpub.com/personal_finance_with_python/c/anaconda First 50 get it free!

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