Create MLPrimitive for feature engineering pipeline

[micahjsmith]

Creating a primitive for Ballet feature engineering pipelines will allow these pipelines to be included in an MLPipeline

• primitive is generic and will work for any ballet project
• primitive takes optional init param that gives the fully-qualified name of the Ballet project
• implement adapter:
  • adapter takes the init param
  • initializes a ballet.client.Client
  • loads the desired project
  • accesses the feature engineering pipeline instances
  • creates a function that returns a deepcopy of the pipeline
  • returns the function
• if the init param is not given, then a project is detected from cwd
• expose primitive in new ballet entry points

Prototype (that is not generic)

{
    "name": "predict_census_income.engineer_features",
    "contributors": [
        "Micah Smith <micahs@mit.edu>"
    ],
    "documentation": "",
    "description": "Applies the feature engineering pipeline from the predict_census_income project",
    "classifiers": {
        "type": "preprocessor",
        "subtype": "transformer"
    },
    "modalities": [],
    "primitive": "predict_census_income.api.make_feature_engineering_pipeline",
    "fit": {
        "method": "fit",
        "args": [
            {
                "name": "X",
                "type": "pandas.DataFrame"
            },
            {
                "name": "y",
                "type": "pandas.DataFrame"
            }
        ]
    },
    "produce": {
        "method": "transform",
        "args": [
            {
                "name": "X",
                "type": "pandas.DataFrame"
            }
        ],
        "output": [
            {
                "name": "X",
                "type": "pandas.DataFrame"
            }
        ]
    },
    "hyperparameters": {}
}

[micahjsmith]

Something like this allows the feature engineering pipeline to have access to the unencoded targets for supervised transformations

import mlblocks
from ballet import b
from sklearn.metrics import classification_report

X_df, y_df = b.api.load_data()
X_df_te, y_df_te = b.api.load_data(input_dir='data/val')

encoder = b.api.encoder
y = encoder.fit_transform(y_df)
y_te = encoder.transform(y_df_te)

pipeline = mlblocks.MLPipeline(
    primitives=[
        'predict_census_income.engineer_features',
        'sklearn.ensemble.RandomForestClassifier',
    ],
    input_names={
        'predict_census_income.engineer_features#1': {
            'y': 'y_df',
        }
    },
)
pipeline.fit(X_df, y, y_df=y_df)
y_pred = pipeline.predict(X_df)
report = classification_report(y, y_pred, output_dict=True)

y_pred_te = pipeline.predict(X_df_te)
report_te = classification_report(y_te, y_pred_te, output_dict=True)

[micahjsmith]

Added in #86