simplifies basic example

README.md

@@ -14,25 +14,35 @@
 
 ## Getting started
 
+```python
+from auto_ml import Predictor
+from auto_ml.utils import get_boston_dataset
+
+df_train, df_test = get_boston_dataset()
+
+column_descriptions = {
+    'MEDV': 'output'
+    , 'CHAS': 'categorical'
+}
+
+ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
+
+ml_predictor.train(df_train)
+
+ml_predictor.score(df_test, df_test.MEDV)
+```
 
 ## Show off some more features!
 
 auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model.
 
 ```python
 import dill
-import pandas as pd
-from sklearn.datasets import load_boston
-from sklearn.model_selection import train_test_split
-
 from auto_ml import Predictor
+from auto_ml.utils import get_boston_dataset
 
 # Load data
-boston = load_boston()
-df_boston = pd.DataFrame(boston.data)
-df_boston.columns = boston.feature_names
-df_boston['MEDV'] = boston['target']
-df_boston_train, df_boston_test = train_test_split(df_boston, test_size=0.2, random_state=42)
+df_train, df_test = get_boston_dataset()
 
 # Tell auto_ml which column is 'output'
 # Also note columns that aren't purely numerical
@@ -44,10 +54,10 @@ column_descriptions = {
 
 ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
 
-ml_predictor.train(df_boston_train)
+ml_predictor.train(df_train)
 
 # Score the model on test data
-test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV)
+test_score = ml_predictor.score(df_test, df_test.MEDV)
 
 # auto_ml is specifically tuned for running in production
 # It can get predictions on an individual row (passed in as a dictionary)
@@ -63,7 +73,7 @@ with open (file_name, 'rb') as read_file:
 # A pandas DataFrame
 # A list of dictionaries
 # A single dictionary (optimized for speed in production evironments)
-predictions = trained_model.predict(df_boston_test)
+predictions = trained_model.predict(df_test)
 print(predictions)
 ```
 
@@ -94,7 +104,7 @@ A quick overview of buzzwords, this project automates:
 - Data formatting (turning a DataFrame or a list of dictionaries into a sparse matrix, one-hot encoding categorical variables, taking the natural log of y for regression problems, etc).
 - Model Selection (which model works best for your problem- we try roughly a dozen apiece for classification and regression problems, including favorites like XGBoost if it's installed on your machine).
 - Hyperparameter Optimization (what hyperparameters work best for that model).
-- Ensembling (Train up a bunch of different estimators, then train a final estimator to intelligently aggregate them together. Also useful if you're just trying to compare many different models and see what works best.)
+<!-- - Ensembling (Train up a bunch of different estimators, then train a final estimator to intelligently aggregate them together. Also useful if you're just trying to compare many different models and see what works best.) -->
 - Big Data (feed it lots of data- it's fairly efficient with resources).
 - Unicorns (you could conceivably train it to predict what is a unicorn and what is not).
 - Ice Cream (mmm, tasty...).