.. _sec_tabularmultilabel:
Predicting Multiple Columns in a Table (Multi-Label Prediction)
===============================================================
In multi-label prediction, we wish to predict multiple columns of a
table (i.e. labels) based on the values in the remaining columns. Here
we present a simple strategy to do this with AutoGluon, which simply
maintains a separate
`TabularPredictor <../../api/autogluon.predictor.html#autogluon.tabular.TabularPredictor.fit>`__
object for each column being predicted. Correlations between labels can
be accounted for in predictions by imposing an order on the labels and
allowing the ``TabularPredictor`` for each label to condition on the
predicted values for labels that appeared earlier in the order.
MultilabelPredictor Class
~~~~~~~~~~~~~~~~~~~~~~~~~
We start by defining a custom ``MultilabelPredictor`` class to manage a
collection of ``TabularPredictor`` objects, one for each label. You can
use the ``MultilabelPredictor`` similarly to an individual
``TabularPredictor``, except it operates on multiple labels rather than
one.
.. code:: python
from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.common.utils.utils import setup_outputdir
from autogluon.core.utils.loaders import load_pkl
from autogluon.core.utils.savers import save_pkl
import os.path
class MultilabelPredictor():
""" Tabular Predictor for predicting multiple columns in table.
Creates multiple TabularPredictor objects which you can also use individually.
You can access the TabularPredictor for a particular label via: `multilabel_predictor.get_predictor(label_i)`
Parameters
----------
labels : List[str]
The ith element of this list is the column (i.e. `label`) predicted by the ith TabularPredictor stored in this object.
path : str, default = None
Path to directory where models and intermediate outputs should be saved.
If unspecified, a time-stamped folder called "AutogluonModels/ag-[TIMESTAMP]" will be created in the working directory to store all models.
Note: To call `fit()` twice and save all results of each fit, you must specify different `path` locations or don't specify `path` at all.
Otherwise files from first `fit()` will be overwritten by second `fit()`.
Caution: when predicting many labels, this directory may grow large as it needs to store many TabularPredictors.
problem_types : List[str], default = None
The ith element is the `problem_type` for the ith TabularPredictor stored in this object.
eval_metrics : List[str], default = None
The ith element is the `eval_metric` for the ith TabularPredictor stored in this object.
consider_labels_correlation : bool, default = True
Whether the predictions of multiple labels should account for label correlations or predict each label independently of the others.
If True, the ordering of `labels` may affect resulting accuracy as each label is predicted conditional on the previous labels appearing earlier in this list (i.e. in an auto-regressive fashion).
Set to False if during inference you may want to individually use just the ith TabularPredictor without predicting all the other labels.
kwargs :
Arguments passed into the initialization of each TabularPredictor.
"""
multi_predictor_file = 'multilabel_predictor.pkl'
def __init__(self, labels, path=None, problem_types=None, eval_metrics=None, consider_labels_correlation=True, **kwargs):
if len(labels) < 2:
raise ValueError("MultilabelPredictor is only intended for predicting MULTIPLE labels (columns), use TabularPredictor for predicting one label (column).")
if (problem_types is not None) and (len(problem_types) != len(labels)):
raise ValueError("If provided, `problem_types` must have same length as `labels`")
if (eval_metrics is not None) and (len(eval_metrics) != len(labels)):
raise ValueError("If provided, `eval_metrics` must have same length as `labels`")
self.path = setup_outputdir(path, warn_if_exist=False)
self.labels = labels
self.consider_labels_correlation = consider_labels_correlation
self.predictors = {} # key = label, value = TabularPredictor or str path to the TabularPredictor for this label
if eval_metrics is None:
self.eval_metrics = {}
else:
self.eval_metrics = {labels[i] : eval_metrics[i] for i in range(len(labels))}
problem_type = None
eval_metric = None
for i in range(len(labels)):
label = labels[i]
path_i = self.path + "Predictor_" + label
if problem_types is not None:
problem_type = problem_types[i]
if eval_metrics is not None:
eval_metric = eval_metrics[i]
self.predictors[label] = TabularPredictor(label=label, problem_type=problem_type, eval_metric=eval_metric, path=path_i, **kwargs)
def fit(self, train_data, tuning_data=None, **kwargs):
""" Fits a separate TabularPredictor to predict each of the labels.
Parameters
----------
train_data, tuning_data : str or autogluon.tabular.TabularDataset or pd.DataFrame
See documentation for `TabularPredictor.fit()`.
kwargs :
Arguments passed into the `fit()` call for each TabularPredictor.
"""
if isinstance(train_data, str):
train_data = TabularDataset(train_data)
if tuning_data is not None and isinstance(tuning_data, str):
tuning_data = TabularDataset(tuning_data)
train_data_og = train_data.copy()
if tuning_data is not None:
tuning_data_og = tuning_data.copy()
else:
tuning_data_og = None
save_metrics = len(self.eval_metrics) == 0
for i in range(len(self.labels)):
label = self.labels[i]
predictor = self.get_predictor(label)
if not self.consider_labels_correlation:
labels_to_drop = [l for l in self.labels if l != label]
else:
labels_to_drop = [self.labels[j] for j in range(i+1, len(self.labels))]
train_data = train_data_og.drop(labels_to_drop, axis=1)
if tuning_data is not None:
tuning_data = tuning_data_og.drop(labels_to_drop, axis=1)
print(f"Fitting TabularPredictor for label: {label} ...")
predictor.fit(train_data=train_data, tuning_data=tuning_data, **kwargs)
self.predictors[label] = predictor.path
if save_metrics:
self.eval_metrics[label] = predictor.eval_metric
self.save()
def predict(self, data, **kwargs):
""" Returns DataFrame with label columns containing predictions for each label.
Parameters
----------
data : str or autogluon.tabular.TabularDataset or pd.DataFrame
Data to make predictions for. If label columns are present in this data, they will be ignored. See documentation for `TabularPredictor.predict()`.
kwargs :
Arguments passed into the predict() call for each TabularPredictor.
"""
return self._predict(data, as_proba=False, **kwargs)
def predict_proba(self, data, **kwargs):
""" Returns dict where each key is a label and the corresponding value is the `predict_proba()` output for just that label.
Parameters
----------
data : str or autogluon.tabular.TabularDataset or pd.DataFrame
Data to make predictions for. See documentation for `TabularPredictor.predict()` and `TabularPredictor.predict_proba()`.
kwargs :
Arguments passed into the `predict_proba()` call for each TabularPredictor (also passed into a `predict()` call).
"""
return self._predict(data, as_proba=True, **kwargs)
def evaluate(self, data, **kwargs):
""" Returns dict where each key is a label and the corresponding value is the `evaluate()` output for just that label.
Parameters
----------
data : str or autogluon.tabular.TabularDataset or pd.DataFrame
Data to evalate predictions of all labels for, must contain all labels as columns. See documentation for `TabularPredictor.evaluate()`.
kwargs :
Arguments passed into the `evaluate()` call for each TabularPredictor (also passed into the `predict()` call).
"""
data = self._get_data(data)
eval_dict = {}
for label in self.labels:
print(f"Evaluating TabularPredictor for label: {label} ...")
predictor = self.get_predictor(label)
eval_dict[label] = predictor.evaluate(data, **kwargs)
if self.consider_labels_correlation:
data[label] = predictor.predict(data, **kwargs)
return eval_dict
def save(self):
""" Save MultilabelPredictor to disk. """
for label in self.labels:
if not isinstance(self.predictors[label], str):
self.predictors[label] = self.predictors[label].path
save_pkl.save(path=self.path+self.multi_predictor_file, object=self)
print(f"MultilabelPredictor saved to disk. Load with: MultilabelPredictor.load('{self.path}')")
@classmethod
def load(cls, path):
""" Load MultilabelPredictor from disk `path` previously specified when creating this MultilabelPredictor. """
path = os.path.expanduser(path)
if path[-1] != os.path.sep:
path = path + os.path.sep
return load_pkl.load(path=path+cls.multi_predictor_file)
def get_predictor(self, label):
""" Returns TabularPredictor which is used to predict this label. """
predictor = self.predictors[label]
if isinstance(predictor, str):
return TabularPredictor.load(path=predictor)
return predictor
def _get_data(self, data):
if isinstance(data, str):
return TabularDataset(data)
return data.copy()
def _predict(self, data, as_proba=False, **kwargs):
data = self._get_data(data)
if as_proba:
predproba_dict = {}
for label in self.labels:
print(f"Predicting with TabularPredictor for label: {label} ...")
predictor = self.get_predictor(label)
if as_proba:
predproba_dict[label] = predictor.predict_proba(data, as_multiclass=True, **kwargs)
data[label] = predictor.predict(data, **kwargs)
if not as_proba:
return data[self.labels]
else:
return predproba_dict
Training
~~~~~~~~
Let’s now apply our multi-label predictor to predict multiple columns in
a data table. We first train models to predict each of the labels.
.. code:: python
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
subsample_size = 500 # subsample subset of data for faster demo, try setting this to much larger values
train_data = train_data.sample(n=subsample_size, random_state=0)
train_data.head()
.. raw:: html
|
age |
workclass |
fnlwgt |
education |
education-num |
marital-status |
occupation |
relationship |
race |
sex |
capital-gain |
capital-loss |
hours-per-week |
native-country |
class |
| 6118 |
51 |
Private |
39264 |
Some-college |
10 |
Married-civ-spouse |
Exec-managerial |
Wife |
White |
Female |
0 |
0 |
40 |
United-States |
>50K |
| 23204 |
58 |
Private |
51662 |
10th |
6 |
Married-civ-spouse |
Other-service |
Wife |
White |
Female |
0 |
0 |
8 |
United-States |
<=50K |
| 29590 |
40 |
Private |
326310 |
Some-college |
10 |
Married-civ-spouse |
Craft-repair |
Husband |
White |
Male |
0 |
0 |
44 |
United-States |
<=50K |
| 18116 |
37 |
Private |
222450 |
HS-grad |
9 |
Never-married |
Sales |
Not-in-family |
White |
Male |
0 |
2339 |
40 |
El-Salvador |
<=50K |
| 33964 |
62 |
Private |
109190 |
Bachelors |
13 |
Married-civ-spouse |
Exec-managerial |
Husband |
White |
Male |
15024 |
0 |
40 |
United-States |
>50K |
.. code:: python
labels = ['education-num','education','class'] # which columns to predict based on the others
problem_types = ['regression','multiclass','binary'] # type of each prediction problem (optional)
eval_metrics = ['mean_absolute_error','accuracy','accuracy'] # metrics used to evaluate predictions for each label (optional)
save_path = 'agModels-predictEducationClass' # specifies folder to store trained models (optional)
time_limit = 5 # how many seconds to train the TabularPredictor for each label, set much larger in your applications!
.. code:: python
multi_predictor = MultilabelPredictor(labels=labels, problem_types=problem_types, eval_metrics=eval_metrics, path=save_path)
multi_predictor.fit(train_data, time_limit=time_limit)
.. parsed-literal::
:class: output
Beginning AutoGluon training ... Time limit = 5s
AutoGluon will save models to "agModels-predictEducationClass/Predictor_education-num/"
AutoGluon Version: 0.6.0b20221117
Python Version: 3.8.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Tue Nov 30 00:17:50 UTC 2021
Train Data Rows: 500
Train Data Columns: 12
Label Column: education-num
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 31606.47 MB
Train Data (Original) Memory Usage: 0.26 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 5 | ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']
('object', []) : 7 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 6 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
('int', []) : 5 | ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']
('int', ['bool']) : 1 | ['sex']
0.1s = Fit runtime
12 features in original data used to generate 12 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.07s ...
AutoGluon will gauge predictive performance using evaluation metric: 'mean_absolute_error'
This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting 11 L1 models ...
Fitting model: KNeighborsUnif ... Training model for up to 4.93s of the 4.93s of remaining time.
.. parsed-literal::
:class: output
Fitting TabularPredictor for label: education-num ...
.. parsed-literal::
:class: output
-2.086 = Validation score (-mean_absolute_error)
0.6s = Training runtime
0.01s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.32s of the 4.31s of remaining time.
-2.1856 = Validation score (-mean_absolute_error)
0.6s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 3.7s of the 3.7s of remaining time.
-1.7808 = Validation score (-mean_absolute_error)
1.25s = Training runtime
0.0s = Validation runtime
Fitting model: LightGBM ... Training model for up to 2.45s of the 2.45s of remaining time.
-1.7854 = Validation score (-mean_absolute_error)
0.81s = Training runtime
0.0s = Validation runtime
Fitting model: RandomForestMSE ... Training model for up to 1.62s of the 1.62s of remaining time.
-1.7082 = Validation score (-mean_absolute_error)
0.97s = Training runtime
0.04s = Validation runtime
Fitting model: CatBoost ... Training model for up to 0.59s of the 0.59s of remaining time.
Ran out of time, early stopping on iteration 310.
-1.7377 = Validation score (-mean_absolute_error)
1.2s = Training runtime
0.0s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.93s of the -0.63s of remaining time.
-1.6888 = Validation score (-mean_absolute_error)
0.14s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 6.38s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictEducationClass/Predictor_education-num/")
Beginning AutoGluon training ... Time limit = 5s
AutoGluon will save models to "agModels-predictEducationClass/Predictor_education/"
AutoGluon Version: 0.6.0b20221117
Python Version: 3.8.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Tue Nov 30 00:17:50 UTC 2021
Train Data Rows: 500
Train Data Columns: 13
Label Column: education
Preprocessing data ...
Warning: Some classes in the training set have fewer than 10 examples. AutoGluon will only keep 11 out of 15 classes for training and will not try to predict the rare classes. To keep more classes, increase the number of datapoints from these rare classes in the training data or reduce label_count_threshold.
Fraction of data from classes with at least 10 examples that will be kept for training models: 0.976
Train Data Class Count: 11
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 31516.22 MB
Train Data (Original) Memory Usage: 0.25 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('object', []) : 7 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 6 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('int', ['bool']) : 1 | ['sex']
0.1s = Fit runtime
13 features in original data used to generate 13 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.08s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 390, Val Rows: 98
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif ... Training model for up to 4.92s of the 4.92s of remaining time.
.. parsed-literal::
:class: output
Fitting TabularPredictor for label: education ...
.. parsed-literal::
:class: output
0.2653 = Validation score (accuracy)
0.6s = Training runtime
0.01s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.31s of the 4.31s of remaining time.
0.2347 = Validation score (accuracy)
0.6s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 3.7s of the 3.7s of remaining time.
0.8163 = Validation score (accuracy)
2.54s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 1.13s of the 1.13s of remaining time.
0.9694 = Validation score (accuracy)
0.87s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ... Training model for up to 0.22s of the 0.22s of remaining time.
Ran out of time, early stopping on iteration 47. Best iteration is:
[12] valid_set's multi_error: 0
1.0 = Validation score (accuracy)
0.24s = Training runtime
0.0s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.92s of the -0.04s of remaining time.
1.0 = Validation score (accuracy)
0.14s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 5.2s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictEducationClass/Predictor_education/")
Beginning AutoGluon training ... Time limit = 5s
AutoGluon will save models to "agModels-predictEducationClass/Predictor_class/"
AutoGluon Version: 0.6.0b20221117
Python Version: 3.8.10
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Tue Nov 30 00:17:50 UTC 2021
Train Data Rows: 500
Train Data Columns: 14
Label Column: class
Preprocessing data ...
Selected class <--> label mapping: class 1 = >50K, class 0 = <=50K
Note: For your binary classification, AutoGluon arbitrarily selected which label-value represents positive ( >50K) vs negative ( <=50K) class.
To explicitly set the positive_class, either rename classes to 1 and 0, or specify positive_class in Predictor init.
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 31358.43 MB
Train Data (Original) Memory Usage: 0.29 MB (0.0% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Fitting CategoryFeatureGenerator...
Fitting CategoryMemoryMinimizeFeatureGenerator...
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('object', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
Types of features in processed data (raw dtype, special dtypes):
('category', []) : 7 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
('int', ['bool']) : 1 | ['sex']
0.1s = Fit runtime
14 features in original data used to generate 14 features in processed data.
Train Data (Processed) Memory Usage: 0.03 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.09s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 400, Val Rows: 100
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif ... Training model for up to 4.91s of the 4.91s of remaining time.
0.73 = Validation score (accuracy)
0.01s = Training runtime
0.01s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.89s of the 4.89s of remaining time.
0.65 = Validation score (accuracy)
0.0s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 4.88s of the 4.88s of remaining time.
.. parsed-literal::
:class: output
Fitting TabularPredictor for label: class ...
.. parsed-literal::
:class: output
0.83 = Validation score (accuracy)
0.18s = Training runtime
0.0s = Validation runtime
Fitting model: LightGBM ... Training model for up to 4.69s of the 4.69s of remaining time.
0.85 = Validation score (accuracy)
0.24s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestGini ... Training model for up to 4.44s of the 4.44s of remaining time.
0.84 = Validation score (accuracy)
0.45s = Training runtime
0.06s = Validation runtime
Fitting model: RandomForestEntr ... Training model for up to 3.93s of the 3.93s of remaining time.
0.83 = Validation score (accuracy)
0.44s = Training runtime
0.06s = Validation runtime
Fitting model: CatBoost ... Training model for up to 3.42s of the 3.42s of remaining time.
0.85 = Validation score (accuracy)
0.72s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesGini ... Training model for up to 2.7s of the 2.7s of remaining time.
0.82 = Validation score (accuracy)
0.43s = Training runtime
0.06s = Validation runtime
Fitting model: ExtraTreesEntr ... Training model for up to 2.2s of the 2.2s of remaining time.
0.81 = Validation score (accuracy)
0.43s = Training runtime
0.06s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 1.7s of the 1.7s of remaining time.
0.82 = Validation score (accuracy)
0.49s = Training runtime
0.01s = Validation runtime
Fitting model: XGBoost ... Training model for up to 1.18s of the 1.18s of remaining time.
0.87 = Validation score (accuracy)
0.24s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetTorch ... Training model for up to 0.93s of the 0.93s of remaining time.
Ran out of time, stopping training early. (Stopping on epoch 21)
0.83 = Validation score (accuracy)
0.91s = Training runtime
0.01s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.91s of the -0.01s of remaining time.
0.87 = Validation score (accuracy)
0.28s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 5.31s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictEducationClass/Predictor_class/")
.. parsed-literal::
:class: output
MultilabelPredictor saved to disk. Load with: MultilabelPredictor.load('agModels-predictEducationClass/')
Inference and Evaluation
~~~~~~~~~~~~~~~~~~~~~~~~
After training, you can easily use the ``MultilabelPredictor`` to
predict all labels in new data:
.. code:: python
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')
test_data = test_data.sample(n=subsample_size, random_state=0)
test_data_nolab = test_data.drop(columns=labels) # unnecessary, just to demonstrate we're not cheating here
test_data_nolab.head()
.. parsed-literal::
:class: output
Loaded data from: https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv | Columns = 15 / 15 | Rows = 9769 -> 9769
.. raw:: html
|
age |
workclass |
fnlwgt |
marital-status |
occupation |
relationship |
race |
sex |
capital-gain |
capital-loss |
hours-per-week |
native-country |
| 5454 |
41 |
Self-emp-not-inc |
408498 |
Married-civ-spouse |
Exec-managerial |
Husband |
White |
Male |
0 |
0 |
50 |
United-States |
| 6111 |
39 |
Private |
746786 |
Married-civ-spouse |
Prof-specialty |
Husband |
White |
Male |
0 |
0 |
55 |
United-States |
| 5282 |
50 |
Private |
62593 |
Married-civ-spouse |
Farming-fishing |
Husband |
Asian-Pac-Islander |
Male |
0 |
0 |
40 |
United-States |
| 3046 |
31 |
Private |
248178 |
Married-civ-spouse |
Other-service |
Husband |
Black |
Male |
0 |
0 |
35 |
United-States |
| 2162 |
43 |
State-gov |
52849 |
Married-civ-spouse |
Prof-specialty |
Husband |
White |
Male |
0 |
0 |
40 |
United-States |
.. code:: python
multi_predictor = MultilabelPredictor.load(save_path) # unnecessary, just demonstrates how to load previously-trained multilabel predictor from file
predictions = multi_predictor.predict(test_data_nolab)
print("Predictions: \n", predictions)
.. parsed-literal::
:class: output
Predicting with TabularPredictor for label: education-num ...
Predicting with TabularPredictor for label: education ...
Predicting with TabularPredictor for label: class ...
Predictions:
education-num education class
5454 10.938680 Some-college >50K
6111 13.351671 Bachelors >50K
5282 9.274139 HS-grad >50K
3046 9.485075 HS-grad <=50K
2162 12.900052 HS-grad >50K
... ... ... ...
6965 10.324689 Some-college >50K
4762 9.260773 HS-grad <=50K
234 10.479671 Some-college <=50K
6291 10.427308 Some-college >50K
9575 9.882655 HS-grad >50K
[500 rows x 3 columns]
We can also easily evaluate the performance of our predictions if our
new data contain the ground truth labels:
.. code:: python
evaluations = multi_predictor.evaluate(test_data)
print(evaluations)
print("Evaluated using metrics:", multi_predictor.eval_metrics)
.. parsed-literal::
:class: output
Evaluating TabularPredictor for label: education-num ...
.. parsed-literal::
:class: output
Evaluation: mean_absolute_error on test data: -1.6700374813079835
Note: Scores are always higher_is_better. This metric score can be multiplied by -1 to get the metric value.
Evaluations on test data:
{
"mean_absolute_error": -1.6700374813079835,
"root_mean_squared_error": -2.2597730139300722,
"mean_squared_error": -5.106574074486603,
"r2": 0.33969811142219675,
"pearsonr": 0.6002090915537582,
"median_absolute_error": -1.2597825527191162
}
.. parsed-literal::
:class: output
Evaluating TabularPredictor for label: education ...
.. parsed-literal::
:class: output
Evaluation: accuracy on test data: 0.238
Evaluations on test data:
{
"accuracy": 0.238,
"balanced_accuracy": 0.08631333310271683,
"mcc": 0.051960705044082345
}
Evaluation: accuracy on test data: 0.814
Evaluations on test data:
{
"accuracy": 0.814,
"balanced_accuracy": 0.715382686642011,
"mcc": 0.4785158157497335,
"roc_auc": 0.8452746609833053,
"f1": 0.5866666666666667,
"precision": 0.6875,
"recall": 0.5116279069767442
}
.. parsed-literal::
:class: output
Evaluating TabularPredictor for label: class ...
{'education-num': {'mean_absolute_error': -1.6700374813079835, 'root_mean_squared_error': -2.2597730139300722, 'mean_squared_error': -5.106574074486603, 'r2': 0.33969811142219675, 'pearsonr': 0.6002090915537582, 'median_absolute_error': -1.2597825527191162}, 'education': {'accuracy': 0.238, 'balanced_accuracy': 0.08631333310271683, 'mcc': 0.051960705044082345}, 'class': {'accuracy': 0.814, 'balanced_accuracy': 0.715382686642011, 'mcc': 0.4785158157497335, 'roc_auc': 0.8452746609833053, 'f1': 0.5866666666666667, 'precision': 0.6875, 'recall': 0.5116279069767442}}
Evaluated using metrics: {'education-num': 'mean_absolute_error', 'education': 'accuracy', 'class': 'accuracy'}
Accessing the TabularPredictor for One Label
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We can also directly work with the ``TabularPredictor`` for any one of
the labels as follows. However we recommend you set
``consider_labels_correlation=False`` before training if you later plan
to use an individual ``TabularPredictor`` to predict just one label
rather than all of the labels predicted by the ``MultilabelPredictor``.
.. code:: python
predictor_class = multi_predictor.get_predictor('class')
predictor_class.leaderboard(silent=True)
.. raw:: html
|
model |
score_val |
pred_time_val |
fit_time |
pred_time_val_marginal |
fit_time_marginal |
stack_level |
can_infer |
fit_order |
| 0 |
XGBoost |
0.87 |
0.006277 |
0.239301 |
0.006277 |
0.239301 |
1 |
True |
11 |
| 1 |
WeightedEnsemble_L2 |
0.87 |
0.006862 |
0.523577 |
0.000585 |
0.284276 |
2 |
True |
13 |
| 2 |
LightGBM |
0.85 |
0.005125 |
0.238757 |
0.005125 |
0.238757 |
1 |
True |
4 |
| 3 |
CatBoost |
0.85 |
0.005419 |
0.716723 |
0.005419 |
0.716723 |
1 |
True |
7 |
| 4 |
RandomForestGini |
0.84 |
0.055817 |
0.445244 |
0.055817 |
0.445244 |
1 |
True |
5 |
| 5 |
LightGBMXT |
0.83 |
0.004931 |
0.177437 |
0.004931 |
0.177437 |
1 |
True |
3 |
| 6 |
NeuralNetTorch |
0.83 |
0.011323 |
0.912126 |
0.011323 |
0.912126 |
1 |
True |
12 |
| 7 |
RandomForestEntr |
0.83 |
0.055276 |
0.441456 |
0.055276 |
0.441456 |
1 |
True |
6 |
| 8 |
NeuralNetFastAI |
0.82 |
0.011197 |
0.494656 |
0.011197 |
0.494656 |
1 |
True |
10 |
| 9 |
ExtraTreesGini |
0.82 |
0.055579 |
0.431322 |
0.055579 |
0.431322 |
1 |
True |
8 |
| 10 |
ExtraTreesEntr |
0.81 |
0.055538 |
0.432667 |
0.055538 |
0.432667 |
1 |
True |
9 |
| 11 |
KNeighborsUnif |
0.73 |
0.006336 |
0.005891 |
0.006336 |
0.005891 |
1 |
True |
1 |
| 12 |
KNeighborsDist |
0.65 |
0.005465 |
0.004508 |
0.005465 |
0.004508 |
1 |
True |
2 |
Tips
~~~~
In order to obtain the best predictions, you should generally add the
following arguments to ``MultilabelPredictor.fit()``:
1) Specify ``eval_metrics`` to the metrics you will use to evaluate
predictions for each label
2) Specify ``presets='best_quality'`` to tell AutoGluon you care about
predictive performance more than latency/memory usage, which will
utilize stack ensembling when predicting each label.
If you find that too much memory/disk is being used, try calling
``MultilabelPredictor.fit()`` with additional arguments discussed under
`“If you encounter memory issues” in the In Depth
Tutorial `__ or
`“If you encounter disk space
issues” `__.
If you find inference too slow, you can try the strategies discussed
under `“Accelerating Inference” in the In Depth
Tutorial `__. In
particular, simply try specifying the following preset in
``MultilabelPredictor.fit()``:
``presets = ['good_quality', 'optimize_for_deployment']``