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
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.
from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.core.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
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]
The ith element is the `problem_type` for the ith TabularPredictor stored in this object.
eval_metrics : List[str]
The ith element is the `eval_metric` for the ith TabularPredictor stored in this object.
consider_labels_correlation : bool
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, 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).")
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 = self.eval_metrics[label]
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.
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()
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 |
labels = ['education-num','education','class'] # which columns to predict based on the others
problem_types = ['regression','multiclass','binary'] # type of each prediction problem
save_path = 'agModels-predictEducationClass' # specifies folder to store trained models
time_limit = 5 # how many seconds to train the TabularPredictor for each label, set much larger in your applications!
multi_predictor = MultilabelPredictor(labels=labels, problem_types=problem_types, path=save_path)
multi_predictor.fit(train_data, time_limit=time_limit)
Beginning AutoGluon training ... Time limit = 5s
AutoGluon will save models to "agModels-predictEducationClass/Predictor_education-num/"
AutoGluon Version: 0.3.0b20210827
Train Data Rows: 500
Train Data Columns: 12
Preprocessing data ...
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 22173.95 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...
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', []) : 7 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
('int', []) : 5 | ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']
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: 'root_mean_squared_error'
To change this, specify the eval_metric argument of fit()
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.
-2.703 = Validation score (root_mean_squared_error)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.82s of the 4.82s of remaining time.
Fitting TabularPredictor for label: education-num ...
-2.7447 = Validation score (root_mean_squared_error)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 4.71s of the 4.71s of remaining time.
-2.2917 = Validation score (root_mean_squared_error)
0.44s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ... Training model for up to 4.26s of the 4.26s of remaining time.
-2.3176 = Validation score (root_mean_squared_error)
0.15s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestMSE ... Training model for up to 4.1s of the 4.1s of remaining time.
-2.2527 = Validation score (root_mean_squared_error)
0.51s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ... Training model for up to 3.47s of the 3.46s of remaining time.
-2.1162 = Validation score (root_mean_squared_error)
0.81s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesMSE ... Training model for up to 2.64s of the 2.64s of remaining time.
-2.3301 = Validation score (root_mean_squared_error)
0.5s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 2.01s of the 2.01s of remaining time.
Ran out of time, stopping training early.
-2.6124 = Validation score (root_mean_squared_error)
4.95s = Training runtime
0.02s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.93s of the -3.45s of remaining time.
-2.1055 = Validation score (root_mean_squared_error)
0.22s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 8.68s ...
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: 0.3.0b20210827
Train Data Rows: 500
Train Data Columns: 13
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: 18905.87 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...
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', []) : 7 | ['workclass', 'marital-status', 'occupation', 'relationship', 'race', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
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 argument of fit()
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.
0.2653 = Validation score (accuracy)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.82s of the 4.81s of remaining time.
Fitting TabularPredictor for label: education ...
0.2347 = Validation score (accuracy)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 4.71s of the 4.71s of remaining time.
0.8265 = Validation score (accuracy)
0.57s = Training runtime
0.02s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 4.11s of the 4.11s of remaining time.
0.9796 = Validation score (accuracy)
0.62s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ... Training model for up to 3.45s of the 3.45s of remaining time.
1.0 = Validation score (accuracy)
0.43s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestGini ... Training model for up to 3.01s of the 3.0s of remaining time.
Warning: Reducing model 'n_estimators' from 300 -> 115 due to low time. Expected time usage reduced from 7.8s -> 3.0s...
0.9286 = Validation score (accuracy)
0.35s = Training runtime
0.11s = Validation runtime
Fitting model: RandomForestEntr ... Training model for up to 2.54s of the 2.54s of remaining time.
Warning: Reducing model 'n_estimators' from 300 -> 98 due to low time. Expected time usage reduced from 7.8s -> 2.5s...
0.8571 = Validation score (accuracy)
0.35s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ... Training model for up to 2.08s of the 2.08s of remaining time.
1.0 = Validation score (accuracy)
4.33s = Training runtime
0.01s = Validation runtime
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.92s of the -2.77s of remaining time.
1.0 = Validation score (accuracy)
0.18s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 7.96s ...
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: 0.3.0b20210827
Train Data Rows: 500
Train Data Columns: 14
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: 18876.19 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...
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', []) : 8 | ['workclass', 'education', 'marital-status', 'occupation', 'relationship', ...]
('int', []) : 6 | ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', ...]
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 = 0.08s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
To change this, specify the eval_metric argument of fit()
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 4.92s of the 4.92s of remaining time.
0.73 = Validation score (accuracy)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: KNeighborsDist ... Training model for up to 4.81s of the 4.81s of remaining time.
Fitting TabularPredictor for label: class ...
0.65 = Validation score (accuracy)
0.0s = Training runtime
0.1s = Validation runtime
Fitting model: LightGBMXT ... Training model for up to 4.7s of the 4.7s of remaining time.
0.83 = Validation score (accuracy)
0.15s = Training runtime
0.01s = Validation runtime
Fitting model: LightGBM ... Training model for up to 4.54s of the 4.54s of remaining time.
0.85 = Validation score (accuracy)
0.18s = Training runtime
0.01s = Validation runtime
Fitting model: RandomForestGini ... Training model for up to 4.35s of the 4.35s of remaining time.
0.84 = Validation score (accuracy)
0.51s = Training runtime
0.11s = Validation runtime
Fitting model: RandomForestEntr ... Training model for up to 3.72s of the 3.72s of remaining time.
0.82 = Validation score (accuracy)
0.61s = Training runtime
0.11s = Validation runtime
Fitting model: CatBoost ... Training model for up to 2.99s of the 2.99s of remaining time.
0.84 = Validation score (accuracy)
0.43s = Training runtime
0.01s = Validation runtime
Fitting model: ExtraTreesGini ... Training model for up to 2.55s of the 2.55s of remaining time.
0.82 = Validation score (accuracy)
0.61s = Training runtime
0.11s = Validation runtime
Fitting model: ExtraTreesEntr ... Training model for up to 1.82s of the 1.82s of remaining time.
0.82 = Validation score (accuracy)
0.61s = Training runtime
0.11s = Validation runtime
Fitting model: NeuralNetFastAI ... Training model for up to 1.1s of the 1.1s of remaining time.
0.83 = Validation score (accuracy)
0.56s = Training runtime
0.02s = Validation runtime
Fitting model: XGBoost ... Training model for up to 0.51s of the 0.51s of remaining time.
0.87 = Validation score (accuracy)
0.18s = Training runtime
0.01s = Validation runtime
Fitting model: NeuralNetMXNet ... Training model for up to 0.3s of the 0.3s of remaining time.
Time limit exceeded... Skipping NeuralNetMXNet.
Fitting model: WeightedEnsemble_L2 ... Training model for up to 4.92s of the -1.65s of remaining time.
0.87 = Validation score (accuracy)
0.24s = Training runtime
0.0s = Validation runtime
AutoGluon training complete, total runtime = 6.9s ...
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("agModels-predictEducationClass/Predictor_class/")
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:
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()
Loaded data from: https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv | Columns = 15 / 15 | Rows = 9769 -> 9769
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 |
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)
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 11.055421 Assoc-voc >50K
6111 12.239116 HS-grad >50K
5282 9.289109 HS-grad >50K
3046 8.937429 11th <=50K
2162 12.659792 HS-grad >50K
... ... ... ...
6965 9.956499 HS-grad >50K
4762 8.792459 11th <=50K
234 10.649448 Some-college <=50K
6291 10.558071 Some-college >50K
9575 9.509919 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:
evaluations = multi_predictor.evaluate(test_data)
print(evaluations)
print("Evaluated using metrics:", multi_predictor.eval_metrics)
Evaluating TabularPredictor for label: education-num ...
Evaluation: root_mean_squared_error on test data: -2.1659743554441215
Note: Scores are always higher_is_better. This metric score can be multiplied by -1 to get the metric value.
Evaluations on test data:
{
"root_mean_squared_error": -2.1659743554441215,
"mean_squared_error": -4.691444908441578,
"mean_absolute_error": -1.6176969652175903,
"r2": 0.39337608971938165,
"pearsonr": 0.641863702661109,
"median_absolute_error": -1.2806296348571777
}
Evaluation: accuracy on test data: 0.214
Evaluations on test data:
{
"accuracy": 0.214,
"balanced_accuracy": 0.08723911014150755,
"mcc": 0.03847538003426249
}
Evaluation: accuracy on test data: 0.814
Evaluations on test data:
{
"accuracy": 0.814,
"balanced_accuracy": 0.7077979063499028,
"mcc": 0.4733035807264,
"roc_auc": 0.8485446833406465,
"f1": 0.5753424657534246,
"precision": 0.7,
"recall": 0.4883720930232558
}
Evaluating TabularPredictor for label: education ...
Evaluating TabularPredictor for label: class ...
{'education-num': {'root_mean_squared_error': -2.1659743554441215, 'mean_squared_error': -4.691444908441578, 'mean_absolute_error': -1.6176969652175903, 'r2': 0.39337608971938165, 'pearsonr': 0.641863702661109, 'median_absolute_error': -1.2806296348571777}, 'education': {'accuracy': 0.214, 'balanced_accuracy': 0.08723911014150755, 'mcc': 0.03847538003426249}, 'class': {'accuracy': 0.814, 'balanced_accuracy': 0.7077979063499028, 'mcc': 0.4733035807264, 'roc_auc': 0.8485446833406465, 'f1': 0.5753424657534246, 'precision': 0.7, 'recall': 0.4883720930232558}}
Evaluated using metrics: {'education-num': root_mean_squared_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
.
predictor_class = multi_predictor.get_predictor('class')
predictor_class.leaderboard(silent=True)
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 | 0.006115 | 0.181115 | 0.006115 | 0.181115 | 1 | True | 11 |
1 | WeightedEnsemble_L2 | 0.87 | 0.006654 | 0.416689 | 0.000539 | 0.235574 | 2 | True | 12 |
2 | LightGBM | 0.85 | 0.007534 | 0.178237 | 0.007534 | 0.178237 | 1 | True | 4 |
3 | CatBoost | 0.84 | 0.009202 | 0.428102 | 0.009202 | 0.428102 | 1 | True | 7 |
4 | RandomForestGini | 0.84 | 0.106746 | 0.508887 | 0.106746 | 0.508887 | 1 | True | 5 |
5 | LightGBMXT | 0.83 | 0.007622 | 0.149821 | 0.007622 | 0.149821 | 1 | True | 3 |
6 | NeuralNetFastAI | 0.83 | 0.015927 | 0.560520 | 0.015927 | 0.560520 | 1 | True | 10 |
7 | RandomForestEntr | 0.82 | 0.106828 | 0.609127 | 0.106828 | 0.609127 | 1 | True | 6 |
8 | ExtraTreesEntr | 0.82 | 0.106994 | 0.606519 | 0.106994 | 0.606519 | 1 | True | 9 |
9 | ExtraTreesGini | 0.82 | 0.107108 | 0.606220 | 0.107108 | 0.606220 | 1 | True | 8 |
10 | KNeighborsUnif | 0.73 | 0.102892 | 0.003825 | 0.102892 | 0.003825 | 1 | True | 1 |
11 | KNeighborsDist | 0.65 | 0.103310 | 0.003794 | 0.103310 | 0.003794 | 1 | True | 2 |
Tips¶
In order to obtain the best predictions, you should generally add the
following arguments to MultilabelPredictor.fit()
:
Specify
eval_metrics
to the metrics you will use to evaluate predictions for each labelSpecify
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_faster_inference_only_refit', 'optimize_for_deployment']