mirror of https://github.com/microsoft/autogen.git
605 lines
23 KiB
Python
605 lines
23 KiB
Python
import unittest
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import numpy as np
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import scipy.sparse
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from sklearn.datasets import load_boston, load_iris, load_wine
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import pandas as pd
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from datetime import datetime
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from flaml import AutoML
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from flaml.data import get_output_from_log
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from flaml.model import SKLearnEstimator, XGBoostEstimator
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from rgf.sklearn import RGFClassifier, RGFRegressor
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from flaml import tune
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class MyRegularizedGreedyForest(SKLearnEstimator):
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def __init__(self, task='binary:logistic', n_jobs=1, max_leaf=4,
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n_iter=1, n_tree_search=1, opt_interval=1, learning_rate=1.0,
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min_samples_leaf=1, **params):
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super().__init__(task, **params)
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if 'regression' in task:
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self.estimator_class = RGFRegressor
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else:
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self.estimator_class = RGFClassifier
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# round integer hyperparameters
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self.params = {
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"n_jobs": n_jobs,
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'max_leaf': int(round(max_leaf)),
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'n_iter': int(round(n_iter)),
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'n_tree_search': int(round(n_tree_search)),
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'opt_interval': int(round(opt_interval)),
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'learning_rate': learning_rate,
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'min_samples_leaf': int(round(min_samples_leaf))
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}
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@classmethod
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def search_space(cls, data_size, task):
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space = {
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'max_leaf': {'domain': tune.qloguniform(
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lower=4, upper=data_size, q=1), 'init_value': 4},
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'n_iter': {'domain': tune.qloguniform(
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lower=1, upper=data_size, q=1), 'init_value': 1},
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'n_tree_search': {'domain': tune.qloguniform(
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lower=1, upper=32768, q=1), 'init_value': 1},
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'opt_interval': {'domain': tune.qloguniform(
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lower=1, upper=10000, q=1), 'init_value': 100},
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'learning_rate': {'domain': tune.loguniform(
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lower=0.01, upper=20.0)},
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'min_samples_leaf': {'domain': tune.qloguniform(
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lower=1, upper=20, q=1), 'init_value': 20},
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}
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return space
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@classmethod
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def size(cls, config):
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max_leaves = int(round(config['max_leaf']))
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n_estimators = int(round(config['n_iter']))
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return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8
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@classmethod
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def cost_relative2lgbm(cls):
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return 1.0
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight
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grad = preds - labels
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hess = preds * (1.0 - preds)
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return grad, hess
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class MyXGB1(XGBoostEstimator):
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'''XGBoostEstimator with logregobj as the objective function
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'''
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def __init__(self, **params):
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super().__init__(objective=logregobj, **params)
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class MyXGB2(XGBoostEstimator):
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'''XGBoostEstimator with 'reg:squarederror' as the objective function
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'''
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def __init__(self, **params):
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super().__init__(objective='reg:squarederror', **params)
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def custom_metric(X_test, y_test, estimator, labels, X_train, y_train,
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weight_test=None, weight_train=None):
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from sklearn.metrics import log_loss
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import time
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start = time.time()
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y_pred = estimator.predict_proba(X_test)
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pred_time = (time.time() - start) / len(X_test)
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test_loss = log_loss(y_test, y_pred, labels=labels,
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sample_weight=weight_test)
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y_pred = estimator.predict_proba(X_train)
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train_loss = log_loss(y_train, y_pred, labels=labels,
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sample_weight=weight_train)
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alpha = 0.5
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return test_loss * (1 + alpha) - alpha * train_loss, {
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"test_loss": test_loss, "train_loss": train_loss, "pred_time": pred_time
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}
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class TestAutoML(unittest.TestCase):
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def test_custom_learner(self):
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automl = AutoML()
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automl.add_learner(learner_name='RGF',
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learner_class=MyRegularizedGreedyForest)
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X_train, y_train = load_wine(return_X_y=True)
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settings = {
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"time_budget": 10, # total running time in seconds
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"estimator_list": ['RGF', 'lgbm', 'rf', 'xgboost'],
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"task": 'classification', # task type
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"sample": True, # whether to subsample training data
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"log_file_name": "test/wine.log",
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"log_training_metric": True, # whether to log training metric
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"n_jobs": 1,
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}
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'''The main flaml automl API'''
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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# print the best model found for RGF
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print(automl.best_model_for_estimator("RGF"))
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def test_ensemble(self):
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automl = AutoML()
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automl.add_learner(learner_name='RGF',
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learner_class=MyRegularizedGreedyForest)
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X_train, y_train = load_wine(return_X_y=True)
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settings = {
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"time_budget": 5, # total running time in seconds
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"estimator_list": ['rf', 'xgboost', 'catboost'],
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"task": 'classification', # task type
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"sample": True, # whether to subsample training data
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"log_file_name": "test/wine.log",
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"log_training_metric": True, # whether to log training metric
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"ensemble": True,
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"n_jobs": 1,
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}
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'''The main flaml automl API'''
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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def test_preprocess(self):
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automl = AutoML()
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X = pd.DataFrame({
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'f1': [1, -2, 3, -4, 5, -6, -7, 8, -9, -10, -11, -12, -13, -14],
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'f2': [3., 16., 10., 12., 3., 14., 11., 12., 5., 14., 20., 16., 15., 11.],
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'f3': ['a', 'b', 'a', 'c', 'c', 'b', 'b', 'b', 'b', 'a', 'b', 1.0, 1.0, 'a'],
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'f4': [True, True, False, True, True, False, False, False, True, True, False, False, True, True],
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})
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y = pd.Series([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
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automl = AutoML()
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automl_settings = {
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"time_budget": 6,
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"task": 'classification',
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"n_jobs": 1,
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"estimator_list": ['catboost', 'lrl2'],
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"eval_method": "cv",
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"n_splits": 3,
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"metric": "accuracy",
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"log_training_metric": True,
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"verbose": 1,
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"ensemble": True,
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}
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automl.fit(X, y, **automl_settings)
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automl = AutoML()
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automl_settings = {
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"time_budget": 2,
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"task": 'classification',
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"n_jobs": 1,
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"estimator_list": ['lrl2', 'kneighbor'],
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"eval_method": "cv",
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"n_splits": 3,
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"metric": "accuracy",
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"log_training_metric": True,
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"verbose": 1,
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"ensemble": True,
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}
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automl.fit(X, y, **automl_settings)
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automl = AutoML()
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automl_settings = {
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"time_budget": 3,
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"task": 'classification',
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"n_jobs": 1,
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"estimator_list": ['xgboost', 'catboost', 'kneighbor'],
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"eval_method": "cv",
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"n_splits": 3,
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"metric": "accuracy",
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"log_training_metric": True,
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"verbose": 1,
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"ensemble": True,
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}
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automl.fit(X, y, **automl_settings)
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automl = AutoML()
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automl_settings = {
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"time_budget": 3,
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"task": 'classification',
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"n_jobs": 1,
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"estimator_list": ['lgbm', 'catboost', 'kneighbor'],
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"eval_method": "cv",
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"n_splits": 3,
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"metric": "accuracy",
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"log_training_metric": True,
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"verbose": 1,
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"ensemble": True,
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}
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automl.fit(X, y, **automl_settings)
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def test_dataframe(self):
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self.test_classification(True)
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def test_custom_metric(self):
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df, y = load_iris(return_X_y=True, as_frame=True)
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df['label'] = y
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automl_experiment = AutoML()
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automl_settings = {
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"dataframe": df,
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"label": 'label',
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"time_budget": 5,
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'eval_method': 'cv',
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"metric": custom_metric,
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"task": 'classification',
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"log_file_name": "test/iris_custom.log",
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"log_training_metric": True,
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'log_type': 'all',
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"n_jobs": 1,
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"model_history": True,
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"sample_weight": np.ones(len(y)),
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"pred_time_limit": 1e-5,
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}
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automl_experiment.fit(**automl_settings)
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print(automl_experiment.classes_)
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print(automl_experiment.model)
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print(automl_experiment.config_history)
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print(automl_experiment.model_history)
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print(automl_experiment.best_iteration)
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print(automl_experiment.best_estimator)
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automl_experiment = AutoML()
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estimator = automl_experiment.get_estimator_from_log(
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automl_settings["log_file_name"], record_id=0,
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task='multi')
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print(estimator)
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time_history, best_valid_loss_history, valid_loss_history, \
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config_history, train_loss_history = get_output_from_log(
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filename=automl_settings['log_file_name'], time_budget=6)
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print(train_loss_history)
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def test_classification(self, as_frame=False):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 4,
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"metric": 'accuracy',
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"task": 'classification',
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"log_file_name": "test/iris.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True
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}
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X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
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if as_frame:
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# test drop column
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X_train.columns = range(X_train.shape[1])
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X_train[X_train.shape[1]] = np.zeros(len(y_train))
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automl_experiment.fit(X_train=X_train, y_train=y_train,
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**automl_settings)
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print(automl_experiment.classes_)
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print(automl_experiment.predict(X_train)[:5])
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print(automl_experiment.model)
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print(automl_experiment.config_history)
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print(automl_experiment.model_history)
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print(automl_experiment.best_iteration)
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print(automl_experiment.best_estimator)
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del automl_settings["metric"]
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del automl_settings["model_history"]
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del automl_settings["log_training_metric"]
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automl_experiment = AutoML()
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duration = automl_experiment.retrain_from_log(
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log_file_name=automl_settings["log_file_name"],
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X_train=X_train, y_train=y_train,
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train_full=True, record_id=0)
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print(duration)
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print(automl_experiment.model)
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print(automl_experiment.predict_proba(X_train)[:5])
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def test_datetime_columns(self):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"log_file_name": "test/datetime_columns.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True,
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}
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fake_df = pd.DataFrame({'A': [datetime(1900, 2, 3), datetime(1900, 3, 4),
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datetime(1900, 3, 4), datetime(1900, 3, 4),
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datetime(1900, 7, 2), datetime(1900, 8, 9)],
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'B': [datetime(1900, 1, 1), datetime(1900, 1, 1),
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datetime(1900, 1, 1), datetime(1900, 1, 1),
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datetime(1900, 1, 1), datetime(1900, 1, 1)],
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'year_A': [datetime(1900, 1, 2), datetime(1900, 8, 1),
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datetime(1900, 1, 4), datetime(1900, 6, 1),
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datetime(1900, 1, 5), datetime(1900, 4, 1)]})
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y = np.array([0, 1, 0, 1, 0, 0])
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automl_experiment.fit(X_train=fake_df, y_train=y, **automl_settings)
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_ = automl_experiment.predict(fake_df)
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def test_micro_macro_f1(self):
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automl_experiment_micro = AutoML()
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automl_experiment_macro = AutoML()
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automl_settings = {
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"time_budget": 2,
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"task": 'classification',
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"log_file_name": "test/micro_macro_f1.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True
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}
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X_train, y_train = load_iris(return_X_y=True)
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automl_experiment_micro.fit(
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X_train=X_train, y_train=y_train, metric='micro_f1', **automl_settings)
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automl_experiment_macro.fit(
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X_train=X_train, y_train=y_train, metric='macro_f1', **automl_settings)
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estimator = automl_experiment_macro.model
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y_pred = estimator.predict(X_train)
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y_pred_proba = estimator.predict_proba(X_train)
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from flaml.ml import norm_confusion_matrix, multi_class_curves
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print(norm_confusion_matrix(y_train, y_pred))
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from sklearn.metrics import roc_curve, precision_recall_curve
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print(multi_class_curves(y_train, y_pred_proba, roc_curve))
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print(multi_class_curves(y_train, y_pred_proba, precision_recall_curve))
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def test_roc_auc_ovr(self):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"metric": "roc_auc_ovr",
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"task": "classification",
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"log_file_name": "test/roc_auc_ovr.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True
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}
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X_train, y_train = load_iris(return_X_y=True)
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automl_experiment.fit(
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X_train=X_train, y_train=y_train, **automl_settings)
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def test_roc_auc_ovo(self):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"metric": "roc_auc_ovo",
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"task": "classification",
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"log_file_name": "test/roc_auc_ovo.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True
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}
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X_train, y_train = load_iris(return_X_y=True)
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automl_experiment.fit(
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X_train=X_train, y_train=y_train, **automl_settings)
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def test_regression(self):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"task": 'regression',
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"log_file_name": "test/boston.log",
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"log_training_metric": True,
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"n_jobs": 1,
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"model_history": True
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}
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X_train, y_train = load_boston(return_X_y=True)
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n = int(len(y_train) * 9 // 10)
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automl_experiment.fit(X_train=X_train[:n], y_train=y_train[:n],
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X_val=X_train[n:], y_val=y_train[n:],
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**automl_settings)
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assert automl_experiment._state.eval_method == 'holdout'
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print(automl_experiment.predict(X_train))
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print(automl_experiment.model)
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print(automl_experiment.config_history)
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print(automl_experiment.model_history)
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print(automl_experiment.best_iteration)
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print(automl_experiment.best_estimator)
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print(get_output_from_log(automl_settings["log_file_name"], 1))
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automl_experiment.retrain_from_log(
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task="regression",
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log_file_name=automl_settings["log_file_name"],
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X_train=X_train, y_train=y_train,
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train_full=True, time_budget=1)
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def test_sparse_matrix_classification(self):
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"metric": 'auto',
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"task": 'classification',
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"log_file_name": "test/sparse_classification.log",
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"split_type": "uniform",
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"n_jobs": 1,
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"model_history": True
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}
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X_train = scipy.sparse.random(1554, 21, dtype=int)
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y_train = np.random.randint(3, size=1554)
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automl_experiment.fit(X_train=X_train, y_train=y_train,
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**automl_settings)
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print(automl_experiment.classes_)
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print(automl_experiment.predict_proba(X_train))
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print(automl_experiment.model)
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print(automl_experiment.config_history)
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print(automl_experiment.model_history)
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print(automl_experiment.best_iteration)
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print(automl_experiment.best_estimator)
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def test_sparse_matrix_regression(self):
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X_train = scipy.sparse.random(300, 900, density=0.0001)
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y_train = np.random.uniform(size=300)
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X_val = scipy.sparse.random(100, 900, density=0.0001)
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y_val = np.random.uniform(size=100)
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automl_experiment = AutoML()
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automl_settings = {
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"time_budget": 2,
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"metric": 'mae',
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"task": 'regression',
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"log_file_name": "test/sparse_regression.log",
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"n_jobs": 1,
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"model_history": True,
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"verbose": 0,
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}
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automl_experiment.fit(X_train=X_train, y_train=y_train,
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X_val=X_val, y_val=y_val,
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**automl_settings)
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assert automl_experiment._state.X_val.shape == X_val.shape
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print(automl_experiment.predict(X_train))
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print(automl_experiment.model)
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print(automl_experiment.config_history)
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|
print(automl_experiment.model_history)
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
print(automl_experiment.best_config)
|
|
print(automl_experiment.best_loss)
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|
print(automl_experiment.best_config_train_time)
|
|
|
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def test_sparse_matrix_xgboost(self):
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automl_experiment = AutoML()
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|
automl_settings = {
|
|
"time_budget": 3,
|
|
"metric": 'ap',
|
|
"task": 'classification',
|
|
"log_file_name": "test/sparse_classification.log",
|
|
"estimator_list": ["xgboost"],
|
|
"log_type": "all",
|
|
"n_jobs": 1,
|
|
}
|
|
X_train = scipy.sparse.eye(900000)
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|
y_train = np.random.randint(2, size=900000)
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|
automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
**automl_settings)
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.model_history)
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
|
|
def test_sparse_matrix_lr(self):
|
|
automl_experiment = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 2,
|
|
"metric": 'f1',
|
|
"task": 'classification',
|
|
"log_file_name": "test/sparse_classification.log",
|
|
"estimator_list": ["lrl1", "lrl2"],
|
|
"log_type": "all",
|
|
"n_jobs": 1,
|
|
}
|
|
X_train = scipy.sparse.random(3000, 900, density=0.1)
|
|
y_train = np.random.randint(2, size=3000)
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
**automl_settings)
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.model_history)
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
|
|
def test_sparse_matrix_regression_cv(self):
|
|
X_train = scipy.sparse.random(8, 100)
|
|
y_train = np.random.uniform(size=8)
|
|
automl_experiment = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 2,
|
|
'eval_method': 'cv',
|
|
"task": 'regression',
|
|
"log_file_name": "test/sparse_regression.log",
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
"metric": "mse",
|
|
"sample_weight": np.ones(len(y_train)),
|
|
}
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
**automl_settings)
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.model_history)
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
|
|
def test_regression_xgboost(self):
|
|
X_train = scipy.sparse.random(300, 900, density=0.0001)
|
|
y_train = np.random.uniform(size=300)
|
|
X_val = scipy.sparse.random(100, 900, density=0.0001)
|
|
y_val = np.random.uniform(size=100)
|
|
automl_experiment = AutoML()
|
|
automl_experiment.add_learner(learner_name='my_xgb1', learner_class=MyXGB1)
|
|
automl_experiment.add_learner(learner_name='my_xgb2', learner_class=MyXGB2)
|
|
automl_settings = {
|
|
"time_budget": 2,
|
|
"estimator_list": ['my_xgb1', 'my_xgb2'],
|
|
"task": 'regression',
|
|
"log_file_name": 'test/regression_xgboost.log',
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
}
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
X_val=X_val, y_val=y_val,
|
|
**automl_settings)
|
|
assert automl_experiment._state.X_val.shape == X_val.shape
|
|
print(automl_experiment.predict(X_train))
|
|
print(automl_experiment.model)
|
|
print(automl_experiment.config_history)
|
|
print(automl_experiment.model_history)
|
|
print(automl_experiment.best_iteration)
|
|
print(automl_experiment.best_estimator)
|
|
print(automl_experiment.best_config)
|
|
print(automl_experiment.best_loss)
|
|
print(automl_experiment.best_config_train_time)
|
|
|
|
def test_fit_w_starting_point(self, as_frame=True):
|
|
automl_experiment = AutoML()
|
|
automl_settings = {
|
|
"time_budget": 3,
|
|
"metric": 'accuracy',
|
|
"task": 'classification',
|
|
"log_file_name": "test/iris.log",
|
|
"log_training_metric": True,
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
}
|
|
X_train, y_train = load_iris(return_X_y=True, as_frame=as_frame)
|
|
if as_frame:
|
|
# test drop column
|
|
X_train.columns = range(X_train.shape[1])
|
|
X_train[X_train.shape[1]] = np.zeros(len(y_train))
|
|
automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
**automl_settings)
|
|
automl_val_accuracy = 1.0 - automl_experiment.best_loss
|
|
print('Best ML leaner:', automl_experiment.best_estimator)
|
|
print('Best hyperparmeter config:', automl_experiment.best_config)
|
|
print('Best accuracy on validation data: {0:.4g}'.format(automl_val_accuracy))
|
|
print('Training duration of best run: {0:.4g} s'.format(automl_experiment.best_config_train_time))
|
|
|
|
starting_points = automl_experiment.best_config_per_estimator
|
|
print('starting_points', starting_points)
|
|
automl_settings_resume = {
|
|
"time_budget": 2,
|
|
"metric": 'accuracy',
|
|
"task": 'classification',
|
|
"log_file_name": "test/iris_resume.log",
|
|
"log_training_metric": True,
|
|
"n_jobs": 1,
|
|
"model_history": True,
|
|
"log_type": 'all',
|
|
"starting_points": starting_points,
|
|
}
|
|
new_automl_experiment = AutoML()
|
|
new_automl_experiment.fit(X_train=X_train, y_train=y_train,
|
|
**automl_settings_resume)
|
|
|
|
new_automl_val_accuracy = 1.0 - new_automl_experiment.best_loss
|
|
print('Best ML leaner:', new_automl_experiment.best_estimator)
|
|
print('Best hyperparmeter config:', new_automl_experiment.best_config)
|
|
print('Best accuracy on validation data: {0:.4g}'.format(new_automl_val_accuracy))
|
|
print('Training duration of best run: {0:.4g} s'.format(new_automl_experiment.best_config_train_time))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|