asbe 0.1.4

Description:

asbe 0.1.4

Automatic Stopping for Batch-mode Experimentation

Created with nbdev by Zoltan Puha
Install
python -m pip install git+https://github.com/puhazoli/asbe
How to use
ASBE builds on the functional views of modAL, where an AL algorithm can
be run by putting together pieces. You need the following ingredients: -
an ITE estimator (ITEEstimator()), - an acquisition function, - and an
assignment function. - Additionaly, you can add a stopping criteria to
your model. If all the above are defined, you can construct an
ASLearner, which will help you in the active learning process.
from asbe.base import *
from asbe.models import *
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
import numpy as np

N = 1000
X = np.random.normal(size = N*2).reshape((-1,2))
t = np.random.binomial(n = 1, p = 0.5, size = N)
y = np.random.binomial(n = 1, p = 1/(1+np.exp(X[:, 1]*2 + t*3)))
ite = 1/(1+np.exp(X[:, 1]*2 + t*3)) - 1/(1+np.exp(X[:, 1]*2))
a = BaseITEEstimator(LogisticRegression(solver="lbfgs"))
a.fit(X_training=X, t_training=t, y_training=y)

Learning actively
Similarly, you can create an
BaseActiveLearner,
for which you will initialize the dataset and set the preferred modeling
options. Let’s see how it works: - we will use XBART to model the
treatment effect with a one-model approach - we will use expected model
change maximization - for that, we need an approximate model, we will
use the SGDRegressor
You can call .fit() on the
BaseActiveLearner,
which will by default fit the training data supplied. To select new
units from the pool, you just need to call the query() method, which
will return the selected X and the query_ix of these units.
BaseActiveLearner
expects the n2 argument, which tells how many units are queried at
once. For sequential AL, we can set this to 1. Additionally, some query
strategies can require different treatment effect estimates - EMCM needs
uncertainty around the ITE. We can explicitly tell the the
BaseITEEstimator
to return all the predicted treatment effects. Then, we can teach the
newly acquired units to the learner, by calling the teach function.
The score function provides an evaluation of the given learner.
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDRegressor
from copy import deepcopy
import pandas as pd

X_train, X_test, t_train, t_test, y_train, y_test, ite_train, ite_test = train_test_split(
X, t, y, ite, test_size=0.8, random_state=1005)
ds = {"X_training": X_train,
"y_training": y_train,
"t_training": t_train,
"ite_training": np.zeros_like(y_train),
"X_pool": deepcopy(X_test),
"y_pool": deepcopy(y_test),
"t_pool": deepcopy(t_test),
"ite_pool" : np.zeros_like(y_test),
"X_test": X_test,
"y_test": y_test,
"t_test": t_test,
"ite_test": ite_test
}
asl = BaseActiveLearner(estimator = BaseITEEstimator(model = RandomForestClassifier(),
two_model=False),
acquisition_function=BaseAcquisitionFunction(),
assignment_function=BaseAssignmentFunction(),
stopping_function = None,
dataset=ds)
asl.fit()
X_new, query_idx = asl.query(no_query=10)
asl.teach(query_idx)
preds = asl.predict(asl.dataset["X_test"])
asl.score()

0.34842037641629464

asl = BaseActiveLearner(estimator = BaseITEEstimator(model = RandomForestClassifier(),
two_model=True),
acquisition_function=[BaseAcquisitionFunction(),
BaseAcquisitionFunction(no_query=20)],
assignment_function=BaseAssignmentFunction(),
stopping_function = None,
dataset=ds,
al_steps = 3)
resd = pd.DataFrame(asl.simulate(metric="decision"))

resd.plot()

<AxesSubplot:>