Source code for autogluon.tabular.models.tabular_nn.tabular_nn_model
""" MXNet neural networks for tabular data containing numerical, categorical, and text fields.
First performs neural network specific pre-processing of the data.
Contains separate input modules which are applied to different columns of the data depending on the type of values they contain:
- Numeric columns are pased through single Dense layer (binary categorical variables are treated as numeric)
- Categorical columns are passed through separate Embedding layers
- Text columns are passed through separate LanguageModel layers
Vectors produced by different input layers are then concatenated and passed to multi-layer MLP model with problem_type determined output layer.
Hyperparameters are passed as dict params, including options for preprocessing stages.
"""
import json
import logging
import os
import random
import time
import warnings
from collections import OrderedDict
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, QuantileTransformer, FunctionTransformer # PowerTransformer
from autogluon.core import Space
from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION, SOFTCLASS
from autogluon.core.features.types import R_OBJECT, S_TEXT_NGRAM, S_TEXT_AS_CATEGORY
from autogluon.core.utils import try_import_mxboard, try_import_mxnet
from autogluon.core.utils.exceptions import TimeLimitExceeded
from .categorical_encoders import OneHotMergeRaresHandleUnknownEncoder, OrdinalMergeRaresHandleUnknownEncoder
from .hyperparameters.parameters import get_default_param
from .hyperparameters.searchspaces import get_default_searchspace
from autogluon.core.models.abstract.abstract_model import AbstractNeuralNetworkModel
from ..utils import fixedvals_from_searchspaces
warnings.filterwarnings("ignore", module='sklearn.preprocessing') # sklearn processing n_quantiles warning
logger = logging.getLogger(__name__)
EPS = 1e-10 # small number
# TODO: Gets stuck after infering feature types near infinitely in nyc-jiashenliu-515k-hotel-reviews-data-in-europe dataset, 70 GB of memory, c5.9xlarge
# Suspect issue is coming from embeddings due to text features with extremely large categorical counts.
[docs]class TabularNeuralNetModel(AbstractNeuralNetworkModel):
""" Class for neural network models that operate on tabular data.
These networks use different types of input layers to process different types of data in various columns.
Attributes:
_types_of_features (dict): keys = 'continuous', 'skewed', 'onehot', 'embed', 'language'; values = column-names of Dataframe corresponding to the features of this type
feature_arraycol_map (OrderedDict): maps feature-name -> list of column-indices in df corresponding to this feature
self.feature_type_map (OrderedDict): maps feature-name -> feature_type string (options: 'vector', 'embed', 'language')
processor (sklearn.ColumnTransformer): scikit-learn preprocessor object.
Note: This model always assumes higher values of self.eval_metric indicate better performance.
"""
# Constants used throughout this class:
# model_internals_file_name = 'model-internals.pkl' # store model internals here
unique_category_str = '!missing!' # string used to represent missing values and unknown categories for categorical features. Should not appear in the dataset
params_file_name = 'net.params' # Stores parameters of final network
temp_file_name = 'temp_net.params' # Stores temporary network parameters (eg. during the course of training)
def __init__(self, **kwargs):
super().__init__(**kwargs)
"""
TabularNeuralNetModel object.
Parameters
----------
path (str): file-path to directory where to save files associated with this model
name (str): name used to refer to this model
problem_type (str): what type of prediction problem is this model used for
eval_metric (func): function used to evaluate performance (Note: we assume higher = better)
hyperparameters (dict): various hyperparameters for neural network and the NN-specific data processing
features (list): List of predictive features to use, other features are ignored by the model.
"""
self.feature_arraycol_map = None
self.feature_type_map = None
self.features_to_drop = [] # may change between different bagging folds. TODO: consider just removing these from self.features if it works with bagging
self.processor = None # data processor
self.summary_writer = None
self.ctx = None
self.batch_size = None
self.num_dataloading_workers = None
self.num_dataloading_workers_inference = 0
self.params_post_fit = None
self.num_net_outputs = None
self._architecture_desc = None
self.optimizer = None
self.verbosity = None
def _set_default_params(self):
""" Specifies hyperparameter values to use by default """
default_params = get_default_param(self.problem_type)
for param, val in default_params.items():
self._set_default_param_value(param, val)
def _get_default_auxiliary_params(self) -> dict:
default_auxiliary_params = super()._get_default_auxiliary_params()
extra_auxiliary_params = dict(
ignored_type_group_raw=[R_OBJECT],
ignored_type_group_special=[S_TEXT_NGRAM, S_TEXT_AS_CATEGORY],
)
default_auxiliary_params.update(extra_auxiliary_params)
return default_auxiliary_params
def _get_default_searchspace(self):
return get_default_searchspace(self.problem_type, num_classes=None)
def set_net_defaults(self, train_dataset, params):
""" Sets dataset-adaptive default values to use for our neural network """
if (self.problem_type == MULTICLASS) or (self.problem_type == SOFTCLASS):
self.num_net_outputs = train_dataset.num_classes
elif self.problem_type == REGRESSION:
self.num_net_outputs = 1
if params['y_range'] is None: # Infer default y-range
y_vals = train_dataset.dataset._data[train_dataset.label_index].asnumpy()
min_y = float(min(y_vals))
max_y = float(max(y_vals))
std_y = np.std(y_vals)
y_ext = params['y_range_extend'] * std_y
if min_y >= 0: # infer y must be nonnegative
min_y = max(0, min_y-y_ext)
else:
min_y = min_y-y_ext
if max_y <= 0: # infer y must be non-positive
max_y = min(0, max_y+y_ext)
else:
max_y = max_y+y_ext
params['y_range'] = (min_y, max_y)
elif self.problem_type == BINARY:
self.num_net_outputs = 2
else:
raise ValueError("unknown problem_type specified: %s" % self.problem_type)
if params['layers'] is None: # Use default choices for MLP architecture
if self.problem_type == REGRESSION:
default_layer_sizes = [256, 128] # overall network will have 4 layers. Input layer, 256-unit hidden layer, 128-unit hidden layer, output layer.
else:
default_sizes = [256, 128] # will be scaled adaptively
# base_size = max(1, min(self.num_net_outputs, 20)/2.0) # scale layer width based on number of classes
base_size = max(1, min(self.num_net_outputs, 100) / 50) # TODO: Updated because it improved model quality and made training far faster
default_layer_sizes = [defaultsize*base_size for defaultsize in default_sizes]
layer_expansion_factor = 1 # TODO: consider scaling based on num_rows, eg: layer_expansion_factor = 2-np.exp(-max(0,train_dataset.num_examples-10000))
max_layer_width = params['max_layer_width']
params['layers'] = [int(min(max_layer_width, layer_expansion_factor*defaultsize)) for defaultsize in default_layer_sizes]
if train_dataset.has_vector_features() and params['numeric_embed_dim'] is None: # Use default choices for numeric embedding size
vector_dim = train_dataset.dataset._data[train_dataset.vectordata_index].shape[1] # total dimensionality of vector features
prop_vector_features = train_dataset.num_vector_features() / float(train_dataset.num_features) # Fraction of features that are numeric
min_numeric_embed_dim = 32
max_numeric_embed_dim = params['max_layer_width']
params['numeric_embed_dim'] = int(min(max_numeric_embed_dim, max(min_numeric_embed_dim,
params['layers'][0]*prop_vector_features*np.log10(vector_dim+10) )))
return
def _fit(self,
X,
y,
X_val=None,
y_val=None,
time_limit=None,
sample_weight=None,
num_cpus=1,
num_gpus=0,
reporter=None,
**kwargs):
""" X (pd.DataFrame): training data features (not necessarily preprocessed yet)
X_val (pd.DataFrame): test data features (should have same column names as Xtrain)
y (pd.Series):
y_val (pd.Series): are pandas Series
kwargs: Can specify amount of compute resources to utilize (num_cpus, num_gpus).
"""
start_time = time.time()
try_import_mxnet()
import mxnet as mx
self.verbosity = kwargs.get('verbosity', 2)
if sample_weight is not None: # TODO: support
logger.log(15, "sample_weight not yet supported for TabularNeuralNetModel, this model will ignore them in training.")
params = self._get_model_params()
params = fixedvals_from_searchspaces(params)
if self.feature_metadata is None:
raise ValueError("Trainer class must set feature_metadata for this model")
if num_cpus is not None:
self.num_dataloading_workers = max(1, int(num_cpus/2.0))
else:
self.num_dataloading_workers = 1
if self.num_dataloading_workers == 1:
self.num_dataloading_workers = 0 # 0 is always faster and uses less memory than 1
self.batch_size = params['batch_size']
train_dataset, val_dataset = self.generate_datasets(X=X, y=y, params=params, X_val=X_val, y_val=y_val)
logger.log(15, "Training data for neural network has: %d examples, %d features (%d vector, %d embedding, %d language)" %
(train_dataset.num_examples, train_dataset.num_features,
len(train_dataset.feature_groups['vector']), len(train_dataset.feature_groups['embed']),
len(train_dataset.feature_groups['language']) ))
# self._save_preprocessor() # TODO: should save these things for hyperparam tunning. Need one HP tuner for network-specific HPs, another for preprocessing HPs.
if num_gpus is not None and num_gpus >= 1:
self.ctx = mx.gpu() # Currently cannot use more than 1 GPU
else:
self.ctx = mx.cpu()
self.get_net(train_dataset, params=params)
if time_limit is not None:
time_elapsed = time.time() - start_time
time_limit_orig = time_limit
time_limit = time_limit - time_elapsed
if time_limit <= time_limit_orig * 0.4: # if 60% of time was spent preprocessing, likely not enough time to train model
raise TimeLimitExceeded
self.train_net(train_dataset=train_dataset, params=params, val_dataset=val_dataset, initialize=True, setup_trainer=True, time_limit=time_limit, reporter=reporter)
self.params_post_fit = params
"""
# TODO: if we don't want to save intermediate network parameters, need to do something like saving in temp directory to clean up after training:
with make_temp_directory() as temp_dir:
save_callback = SaveModelCallback(self.model, monitor=self.metric, mode=save_callback_mode, name=self.name)
with progress_disabled_ctx(self.model) as model:
original_path = model.path
model.path = Path(temp_dir)
model.fit_one_cycle(self.epochs, self.lr, callbacks=save_callback)
# Load the best one and export it
model.load(self.name)
print(f'Model validation metrics: {model.validate()}')
model.path = original_path
"""
def get_net(self, train_dataset, params):
""" Creates a Gluon neural net and context for this dataset.
Also sets up trainer/optimizer as necessary.
"""
from .embednet import EmbedNet
self.set_net_defaults(train_dataset, params)
self.model = EmbedNet(train_dataset=train_dataset, params=params, num_net_outputs=self.num_net_outputs, ctx=self.ctx)
# TODO: Below should not occur until at time of saving
if not os.path.exists(self.path):
os.makedirs(self.path)
def train_net(self, train_dataset, params, val_dataset=None, initialize=True, setup_trainer=True, time_limit=None, reporter=None):
""" Trains neural net on given train dataset, early stops based on test_dataset.
Args:
train_dataset (TabularNNDataset): training data used to learn network weights
val_dataset (TabularNNDataset): validation data used for hyperparameter tuning
initialize (bool): set = False to continue training of a previously trained model, otherwise initializes network weights randomly
setup_trainer (bool): set = False to reuse the same trainer from a previous training run, otherwise creates new trainer from scratch
"""
start_time = time.time()
import mxnet as mx
logger.log(15, "Training neural network for up to %s epochs..." % params['num_epochs'])
seed_value = params.get('seed_value')
if seed_value is not None: # Set seed
random.seed(seed_value)
np.random.seed(seed_value)
mx.random.seed(seed_value)
if initialize: # Initialize the weights of network
logging.debug("initializing neural network...")
self.model.collect_params().initialize(ctx=self.ctx)
self.model.hybridize()
logging.debug("initialized")
if setup_trainer:
# Also setup mxboard to monitor training if visualizer has been specified:
visualizer = self.params_aux.get('visualizer', 'none')
if visualizer == 'tensorboard' or visualizer == 'mxboard':
try_import_mxboard()
from mxboard import SummaryWriter
self.summary_writer = SummaryWriter(logdir=self.path, flush_secs=5, verbose=False)
self.optimizer = self.setup_trainer(params=params, train_dataset=train_dataset)
best_val_metric = -np.inf # higher = better
val_metric = None
best_val_epoch = 0
val_improve_epoch = 0 # most recent epoch where validation-score strictly improved
num_epochs = params['num_epochs']
if val_dataset is not None:
y_val = val_dataset.get_labels()
else:
y_val = None
if params['loss_function'] is None:
if self.problem_type == REGRESSION:
params['loss_function'] = mx.gluon.loss.L1Loss()
elif self.problem_type == SOFTCLASS:
params['loss_function'] = mx.gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False, from_logits=self.model.from_logits)
else:
params['loss_function'] = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=self.model.from_logits)
loss_func = params['loss_function']
epochs_wo_improve = params['epochs_wo_improve']
loss_scaling_factor = 1.0 # we divide loss by this quantity to stabilize gradients
rescale_losses = {mx.gluon.loss.L1Loss: 'std', mx.gluon.loss.HuberLoss: 'std', mx.gluon.loss.L2Loss: 'var'} # dict of loss names where we should rescale loss, value indicates how to rescale.
loss_torescale = [key for key in rescale_losses if isinstance(loss_func, key)]
if loss_torescale:
loss_torescale = loss_torescale[0]
if rescale_losses[loss_torescale] == 'std':
loss_scaling_factor = np.std(train_dataset.get_labels())/5.0 + EPS # std-dev of labels
elif rescale_losses[loss_torescale] == 'var':
loss_scaling_factor = np.var(train_dataset.get_labels())/5.0 + EPS # variance of labels
else:
raise ValueError("Unknown loss-rescaling type %s specified for loss_func==%s" % (rescale_losses[loss_torescale], loss_func))
if self.verbosity <= 1:
verbose_eval = -1 # Print losses every verbose epochs, Never if -1
elif self.verbosity == 2:
verbose_eval = 50
elif self.verbosity == 3:
verbose_eval = 10
else:
verbose_eval = 1
net_filename = self.path + self.temp_file_name
if num_epochs == 0: # use dummy training loop that stops immediately (useful for using NN just for data preprocessing / debugging)
logger.log(20, "Not training Neural Net since num_epochs == 0. Neural network architecture is:")
for batch_idx, data_batch in enumerate(train_dataset.dataloader):
data_batch = train_dataset.format_batch_data(data_batch, self.ctx)
with mx.autograd.record():
output = self.model(data_batch)
labels = data_batch['label']
loss = loss_func(output, labels) / loss_scaling_factor
# print(str(mx.nd.mean(loss).asscalar()), end="\r") # prints per-batch losses
loss.backward()
self.optimizer.step(labels.shape[0])
if batch_idx > 0:
break
self.model.save_parameters(net_filename)
logger.log(15, "untrained Neural Net saved to file")
return
start_fit_time = time.time()
if time_limit is not None:
time_limit = time_limit - (start_fit_time - start_time)
# Training Loop:
for e in range(num_epochs):
if e == 0: # special actions during first epoch:
logger.log(15, "Neural network architecture:")
logger.log(15, str(self.model))
cumulative_loss = 0
for batch_idx, data_batch in enumerate(train_dataset.dataloader):
data_batch = train_dataset.format_batch_data(data_batch, self.ctx)
with mx.autograd.record():
output = self.model(data_batch)
labels = data_batch['label']
loss = loss_func(output, labels) / loss_scaling_factor
# print(str(mx.nd.mean(loss).asscalar()), end="\r") # prints per-batch losses
loss.backward()
self.optimizer.step(labels.shape[0])
cumulative_loss += loss.sum()
train_loss = cumulative_loss/float(train_dataset.num_examples) # training loss this epoch
if val_dataset is not None:
val_metric = self.score(X=val_dataset, y=y_val, metric=self.stopping_metric)
if np.isnan(val_metric):
if e == 0:
raise RuntimeError("NaNs encountered in TabularNeuralNetModel training. Features/labels may be improperly formatted or NN weights may have diverged.")
else:
logger.warning("Warning: NaNs encountered in TabularNeuralNetModel training. Reverting model to last checkpoint without NaNs.")
break
if (val_metric >= best_val_metric) or (e == 0):
if val_metric > best_val_metric:
val_improve_epoch = e
best_val_metric = val_metric
best_val_epoch = e
# Until functionality is added to restart training from a particular epoch, there is no point in saving params without test_dataset
self.model.save_parameters(net_filename)
else:
best_val_epoch = e
if val_dataset is not None:
if verbose_eval > 0 and e % verbose_eval == 0:
logger.log(15, "Epoch %s. Train loss: %s, Val %s: %s" %
(e, train_loss.asscalar(), self.stopping_metric.name, val_metric))
if self.summary_writer is not None:
self.summary_writer.add_scalar(tag='val_'+self.stopping_metric.name,
value=val_metric, global_step=e)
else:
if verbose_eval > 0 and e % verbose_eval == 0:
logger.log(15, "Epoch %s. Train loss: %s" % (e, train_loss.asscalar()))
if self.summary_writer is not None:
self.summary_writer.add_scalar(tag='train_loss', value=train_loss.asscalar(), global_step=e) # TODO: do we want to keep mxboard support?
if reporter is not None:
# TODO: Ensure reporter/scheduler properly handle None/nan values after refactor
if val_dataset is not None and (not np.isnan(val_metric)): # TODO: This might work without the if statement
# epoch must be number of epochs done (starting at 1)
reporter(epoch=e + 1,
validation_performance=val_metric, # Higher val_metric = better
train_loss=float(train_loss.asscalar()),
eval_metric=self.eval_metric.name,
greater_is_better=self.eval_metric.greater_is_better)
if e - val_improve_epoch > epochs_wo_improve:
break # early-stop if validation-score hasn't strictly improved in `epochs_wo_improve` consecutive epochs
if time_limit is not None:
time_elapsed = time.time() - start_fit_time
time_epoch_average = time_elapsed / (e+1)
time_left = time_limit - time_elapsed
if time_left < time_epoch_average:
logger.log(20, f"\tRan out of time, stopping training early. (Stopping on epoch {e})")
break
if val_dataset is not None:
self.model.load_parameters(net_filename) # Revert back to best model
try:
os.remove(net_filename)
except FileNotFoundError:
pass
if val_dataset is None:
logger.log(15, "Best model found in epoch %d" % best_val_epoch)
else: # evaluate one final time:
final_val_metric = self.score(X=val_dataset, y=y_val, metric=self.stopping_metric)
if np.isnan(final_val_metric):
final_val_metric = -np.inf
logger.log(15, "Best model found in epoch %d. Val %s: %s" %
(best_val_epoch, self.stopping_metric.name, final_val_metric))
self.params_trained['num_epochs'] = best_val_epoch + 1
return
def _predict_proba(self, X, **kwargs):
""" To align predict with abstract_model API.
Preprocess here only refers to feature processing steps done by all AbstractModel objects,
not tabularNN-specific preprocessing steps.
If X is not DataFrame but instead TabularNNDataset object, we can still produce predictions,
but cannot use preprocess in this case (needs to be already processed).
"""
from .tabular_nn_dataset import TabularNNDataset
if isinstance(X, TabularNNDataset):
return self._predict_tabular_data(new_data=X, process=False, predict_proba=True)
elif isinstance(X, pd.DataFrame):
X = self.preprocess(X, **kwargs)
return self._predict_tabular_data(new_data=X, process=True, predict_proba=True)
else:
raise ValueError("X must be of type pd.DataFrame or TabularNNDataset, not type: %s" % type(X))
def _predict_tabular_data(self, new_data, process=True, predict_proba=True): # TODO ensure API lines up with tabular.Model class.
""" Specific TabularNN method to produce predictions on new (unprocessed) data.
Returns 1D numpy array unless predict_proba=True and task is multi-class classification (not binary).
Args:
new_data (pd.Dataframe or TabularNNDataset): new data to make predictions on.
If you want to make prediction for just a single row of new_data, pass in: new_data.iloc[[row_index]]
process (bool): should new data be processed (if False, new_data must be TabularNNDataset)
predict_proba (bool): should we output class-probabilities (not used for regression)
"""
from .tabular_nn_dataset import TabularNNDataset
import mxnet as mx
if process:
new_data = self.process_test_data(new_data, batch_size=self.batch_size, num_dataloading_workers=self.num_dataloading_workers_inference, labels=None)
if not isinstance(new_data, TabularNNDataset):
raise ValueError("new_data must of of type TabularNNDataset if process=False")
if self.problem_type == REGRESSION or not predict_proba:
preds = mx.nd.zeros((new_data.num_examples,1))
else:
preds = mx.nd.zeros((new_data.num_examples, self.num_net_outputs))
i = 0
for batch_idx, data_batch in enumerate(new_data.dataloader):
data_batch = new_data.format_batch_data(data_batch, self.ctx)
preds_batch = self.model(data_batch)
batch_size = len(preds_batch)
if self.problem_type != REGRESSION:
if not predict_proba: # need to take argmax
preds_batch = mx.nd.argmax(preds_batch, axis=1, keepdims=True)
else: # need to take softmax
preds_batch = mx.nd.softmax(preds_batch, axis=1)
preds[i:(i+batch_size)] = preds_batch
i = i+batch_size
if self.problem_type == REGRESSION or not predict_proba:
return preds.asnumpy().flatten() # return 1D numpy array
elif self.problem_type == BINARY and predict_proba:
return preds[:,1].asnumpy() # for binary problems, only return P(Y==+1)
return preds.asnumpy() # return 2D numpy array
def generate_datasets(self, X, y, params, X_val=None, y_val=None):
impute_strategy = params['proc.impute_strategy']
max_category_levels = params['proc.max_category_levels']
skew_threshold = params['proc.skew_threshold']
embed_min_categories = params['proc.embed_min_categories']
use_ngram_features = params['use_ngram_features']
from .tabular_nn_dataset import TabularNNDataset
if isinstance(X, TabularNNDataset):
train_dataset = X
else:
X = self.preprocess(X)
if self.features is None:
self.features = list(X.columns)
train_dataset = self.process_train_data(
df=X, labels=y, batch_size=self.batch_size, num_dataloading_workers=self.num_dataloading_workers,
impute_strategy=impute_strategy, max_category_levels=max_category_levels, skew_threshold=skew_threshold, embed_min_categories=embed_min_categories, use_ngram_features=use_ngram_features,
)
if X_val is not None:
if isinstance(X_val, TabularNNDataset):
val_dataset = X_val
else:
X_val = self.preprocess(X_val)
val_dataset = self.process_test_data(df=X_val, labels=y_val, batch_size=self.batch_size, num_dataloading_workers=self.num_dataloading_workers_inference)
else:
val_dataset = None
return train_dataset, val_dataset
def process_test_data(self, df, batch_size, num_dataloading_workers, labels=None):
""" Process train or test DataFrame into a form fit for neural network models.
Args:
df (pd.DataFrame): Data to be processed (X)
labels (pd.Series): labels to be processed (y)
test (bool): Is this test data where each datapoint should be processed separately using predetermined preprocessing steps.
Otherwise preprocessor uses all data to determine propreties like best scaling factors, number of categories, etc.
Returns:
Dataset object
"""
from .tabular_nn_dataset import TabularNNDataset
warnings.filterwarnings("ignore", module='sklearn.preprocessing') # sklearn processing n_quantiles warning
if labels is not None and len(labels) != len(df):
raise ValueError("Number of examples in Dataframe does not match number of labels")
if (self.processor is None or self._types_of_features is None
or self.feature_arraycol_map is None or self.feature_type_map is None):
raise ValueError("Need to process training data before test data")
if self.features_to_drop:
drop_cols = [col for col in df.columns if col in self.features_to_drop]
if drop_cols:
df = df.drop(columns=drop_cols)
df = self.processor.transform(df) # 2D numpy array. self.feature_arraycol_map, self.feature_type_map have been previously set while processing training data.
return TabularNNDataset(df, self.feature_arraycol_map, self.feature_type_map,
batch_size=batch_size, num_dataloading_workers=num_dataloading_workers,
problem_type=self.problem_type, labels=labels, is_test=True)
def process_train_data(self, df, batch_size, num_dataloading_workers, impute_strategy, max_category_levels, skew_threshold, embed_min_categories, use_ngram_features, labels):
""" Preprocess training data and create self.processor object that can be used to process future data.
This method should only be used once per TabularNeuralNetModel object, otherwise will produce Warning.
# TODO no label processing for now
# TODO: language features are ignored for now
# TODO: add time/ngram features
# TODO: no filtering of data-frame columns based on statistics, e.g. categorical columns with all unique variables or zero-variance features.
This should be done in default_learner class for all models not just TabularNeuralNetModel...
"""
from .tabular_nn_dataset import TabularNNDataset
warnings.filterwarnings("ignore", module='sklearn.preprocessing') # sklearn processing n_quantiles warning
if set(df.columns) != set(self.features):
raise ValueError("Column names in provided Dataframe do not match self.features")
if labels is None:
raise ValueError("Attempting process training data without labels")
if len(labels) != len(df):
raise ValueError("Number of examples in Dataframe does not match number of labels")
self._types_of_features, df = self._get_types_of_features(df, skew_threshold=skew_threshold, embed_min_categories=embed_min_categories, use_ngram_features=use_ngram_features) # dict with keys: : 'continuous', 'skewed', 'onehot', 'embed', 'language', values = column-names of df
logger.log(15, "AutoGluon Neural Network infers features are of the following types:")
logger.log(15, json.dumps(self._types_of_features, indent=4))
logger.log(15, "\n")
self.processor = self._create_preprocessor(impute_strategy=impute_strategy, max_category_levels=max_category_levels)
df = self.processor.fit_transform(df) # 2D numpy array
self.feature_arraycol_map = self._get_feature_arraycol_map(max_category_levels=max_category_levels) # OrderedDict of feature-name -> list of column-indices in df corresponding to this feature
num_array_cols = np.sum([len(self.feature_arraycol_map[key]) for key in self.feature_arraycol_map]) # should match number of columns in processed array
if num_array_cols != df.shape[1]:
raise ValueError("Error during one-hot encoding data processing for neural network. Number of columns in df array does not match feature_arraycol_map.")
self.feature_type_map = self._get_feature_type_map() # OrderedDict of feature-name -> feature_type string (options: 'vector', 'embed', 'language')
return TabularNNDataset(df, self.feature_arraycol_map, self.feature_type_map,
batch_size=batch_size, num_dataloading_workers=num_dataloading_workers,
problem_type=self.problem_type, labels=labels, is_test=False)
def setup_trainer(self, params, train_dataset=None):
""" Set up optimizer needed for training.
Network must first be initialized before this.
"""
import mxnet as mx
optimizer_opts = {'learning_rate': params['learning_rate'], 'wd': params['weight_decay'], 'clip_gradient': params['clip_gradient']}
if 'lr_scheduler' in params and params['lr_scheduler'] is not None:
if train_dataset is None:
raise ValueError("train_dataset cannot be None when lr_scheduler is specified.")
base_lr = params.get('base_lr', 1e-6)
target_lr = params.get('target_lr', 1.0)
warmup_epochs = params.get('warmup_epochs', 10)
lr_decay = params.get('lr_decay', 0.1)
lr_mode = params['lr_scheduler']
num_batches = train_dataset.num_examples // params['batch_size']
lr_decay_epoch = [max(warmup_epochs, int(params['num_epochs']/3)), max(warmup_epochs+1, int(params['num_epochs']/2)),
max(warmup_epochs+2, int(2*params['num_epochs']/3))]
from .utils.lr_scheduler import LRSequential, LRScheduler
lr_scheduler = LRSequential([
LRScheduler('linear', base_lr=base_lr, target_lr=target_lr, nepochs=warmup_epochs, iters_per_epoch=num_batches),
LRScheduler(lr_mode, base_lr=target_lr, target_lr=base_lr, nepochs=params['num_epochs'] - warmup_epochs,
iters_per_epoch=num_batches, step_epoch=lr_decay_epoch, step_factor=lr_decay, power=2)
])
optimizer_opts['lr_scheduler'] = lr_scheduler
if params['optimizer'] == 'sgd':
if 'momentum' in params:
optimizer_opts['momentum'] = params['momentum']
optimizer = mx.gluon.Trainer(self.model.collect_params(), 'sgd', optimizer_opts)
elif params['optimizer'] == 'adam': # TODO: Can we try AdamW?
optimizer = mx.gluon.Trainer(self.model.collect_params(), 'adam', optimizer_opts)
else:
raise ValueError("Unknown optimizer specified: %s" % params['optimizer'])
return optimizer
def _get_feature_arraycol_map(self, max_category_levels):
""" Returns OrderedDict of feature-name -> list of column-indices in processed data array corresponding to this feature """
feature_preserving_transforms = set(['continuous','skewed', 'ordinal', 'language']) # these transforms do not alter dimensionality of feature
feature_arraycol_map = {} # unordered version
current_colindex = 0
for transformer in self.processor.transformers_:
transformer_name = transformer[0]
transformed_features = transformer[2]
if transformer_name in feature_preserving_transforms:
for feature in transformed_features:
if feature in feature_arraycol_map:
raise ValueError("same feature is processed by two different column transformers: %s" % feature)
feature_arraycol_map[feature] = [current_colindex]
current_colindex += 1
elif transformer_name == 'onehot':
oh_encoder = [step for (name, step) in transformer[1].steps if name == 'onehot'][0]
for i in range(len(transformed_features)):
feature = transformed_features[i]
if feature in feature_arraycol_map:
raise ValueError("same feature is processed by two different column transformers: %s" % feature)
oh_dimensionality = min(len(oh_encoder.categories_[i]), max_category_levels+1)
feature_arraycol_map[feature] = list(range(current_colindex, current_colindex+oh_dimensionality))
current_colindex += oh_dimensionality
else:
raise ValueError("unknown transformer encountered: %s" % transformer_name)
return OrderedDict([(key, feature_arraycol_map[key]) for key in feature_arraycol_map])
def _get_feature_type_map(self):
""" Returns OrderedDict of feature-name -> feature_type string (options: 'vector', 'embed', 'language') """
if self.feature_arraycol_map is None:
raise ValueError("must first call _get_feature_arraycol_map() before _get_feature_type_map()")
vector_features = self._types_of_features['continuous'] + self._types_of_features['skewed'] + self._types_of_features['onehot']
feature_type_map = OrderedDict()
for feature_name in self.feature_arraycol_map:
if feature_name in vector_features:
feature_type_map[feature_name] = 'vector'
elif feature_name in self._types_of_features['embed']:
feature_type_map[feature_name] = 'embed'
elif feature_name in self._types_of_features['language']:
feature_type_map[feature_name] = 'language'
else:
raise ValueError("unknown feature type encountered")
return feature_type_map
def _create_preprocessor(self, impute_strategy, max_category_levels):
""" Defines data encoders used to preprocess different data types and creates instance variable which is sklearn ColumnTransformer object """
if self.processor is not None:
Warning("Attempting to process training data for TabularNeuralNetModel, but previously already did this.")
continuous_features = self._types_of_features['continuous']
skewed_features = self._types_of_features['skewed']
onehot_features = self._types_of_features['onehot']
embed_features = self._types_of_features['embed']
language_features = self._types_of_features['language']
transformers = [] # order of various column transformers in this list is important!
if continuous_features:
continuous_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy=impute_strategy)),
('scaler', StandardScaler())])
transformers.append( ('continuous', continuous_transformer, continuous_features) )
if skewed_features:
power_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy=impute_strategy)),
('quantile', QuantileTransformer(output_distribution='normal')) ]) # Or output_distribution = 'uniform'
transformers.append( ('skewed', power_transformer, skewed_features) )
if onehot_features:
onehot_transformer = Pipeline(steps=[
# TODO: Consider avoiding converting to string for improved memory efficiency
('to_str', FunctionTransformer(convert_df_dtype_to_str)),
('imputer', SimpleImputer(strategy='constant', fill_value=self.unique_category_str)),
('onehot', OneHotMergeRaresHandleUnknownEncoder(max_levels=max_category_levels, sparse=False))]) # test-time unknown values will be encoded as all zeros vector
transformers.append( ('onehot', onehot_transformer, onehot_features) )
if embed_features: # Ordinal transformer applied to convert to-be-embedded categorical features to integer levels
ordinal_transformer = Pipeline(steps=[
('to_str', FunctionTransformer(convert_df_dtype_to_str)),
('imputer', SimpleImputer(strategy='constant', fill_value=self.unique_category_str)),
('ordinal', OrdinalMergeRaresHandleUnknownEncoder(max_levels=max_category_levels))]) # returns 0-n when max_category_levels = n-1. category n is reserved for unknown test-time categories.
transformers.append( ('ordinal', ordinal_transformer, embed_features) )
if language_features:
raise NotImplementedError("language_features cannot be used at the moment")
return ColumnTransformer(transformers=transformers) # numeric features are processed in the same order as in numeric_features vector, so feature-names remain the same.
def save(self, path: str = None, verbose=True) -> str:
if self.model is not None:
self._architecture_desc = self.model.architecture_desc
temp_model = self.model
temp_sw = self.summary_writer
self.model = None
self.summary_writer = None
path_final = super().save(path=path, verbose=verbose)
self.model = temp_model
self.summary_writer = temp_sw
self._architecture_desc = None
# Export model
if self.model is not None:
params_filepath = path_final + self.params_file_name
# TODO: Don't use os.makedirs here, have save_parameters function in tabular_nn_model that checks if local path or S3 path
os.makedirs(os.path.dirname(path_final), exist_ok=True)
self.model.save_parameters(params_filepath)
return path_final
@classmethod
def load(cls, path: str, reset_paths=True, verbose=True):
model: TabularNeuralNetModel = super().load(path=path, reset_paths=reset_paths, verbose=verbose)
if model._architecture_desc is not None:
from .embednet import EmbedNet
model.model = EmbedNet(architecture_desc=model._architecture_desc, ctx=model.ctx) # recreate network from architecture description
model._architecture_desc = None
# TODO: maybe need to initialize/hybridize?
model.model.load_parameters(model.path + model.params_file_name, ctx=model.ctx)
model.summary_writer = None
return model
def _hyperparameter_tune(self, X, y, X_val, y_val, scheduler_options, **kwargs):
""" Performs HPO and sets self.params to best hyperparameter values """
try_import_mxnet()
from .tabular_nn_trial import tabular_nn_trial
from .tabular_nn_dataset import TabularNNDataset
time_start = time.time()
self.verbosity = kwargs.get('verbosity', 2)
logger.log(15, "Beginning hyperparameter tuning for Neural Network...")
self._set_default_searchspace() # changes non-specified default hyperparams from fixed values to search-spaces.
if self.feature_metadata is None:
raise ValueError("Trainer class must set feature_metadata for this model")
scheduler_cls, scheduler_params = scheduler_options # Unpack tuple
if scheduler_cls is None or scheduler_params is None:
raise ValueError("scheduler_cls and scheduler_params cannot be None for hyperparameter tuning")
num_cpus = scheduler_params['resource']['num_cpus']
params_copy = self._get_params()
self.num_dataloading_workers = max(1, int(num_cpus/2.0))
self.batch_size = params_copy['batch_size']
train_dataset, val_dataset = self.generate_datasets(X=X, y=y, params=params_copy, X_val=X_val, y_val=y_val)
train_path = self.path + "train"
val_path = self.path + "validation"
train_dataset.save(file_prefix=train_path)
val_dataset.save(file_prefix=val_path)
if not np.any([isinstance(params_copy[hyperparam], Space) for hyperparam in params_copy]):
logger.warning("Warning: Attempting to do hyperparameter optimization without any search space (all hyperparameters are already fixed values)")
else:
logger.log(15, "Hyperparameter search space for Neural Network: ")
for hyperparam in params_copy:
if isinstance(params_copy[hyperparam], Space):
logger.log(15, str(hyperparam)+ ": "+str(params_copy[hyperparam]))
util_args = dict(
train_path=train_path,
val_path=val_path,
model=self,
time_start=time_start,
time_limit=scheduler_params['time_out'],
fit_kwargs=scheduler_params['resource'],
)
tabular_nn_trial.register_args(util_args=util_args, **params_copy)
scheduler = scheduler_cls(tabular_nn_trial, **scheduler_params)
if ('dist_ip_addrs' in scheduler_params) and (len(scheduler_params['dist_ip_addrs']) > 0):
# TODO: Ensure proper working directory setup on remote machines
# This is multi-machine setting, so need to copy dataset to workers:
logger.log(15, "Uploading preprocessed data to remote workers...")
scheduler.upload_files([
train_path + TabularNNDataset.DATAOBJ_SUFFIX,
train_path + TabularNNDataset.DATAVALUES_SUFFIX,
val_path + TabularNNDataset.DATAOBJ_SUFFIX,
val_path + TabularNNDataset.DATAVALUES_SUFFIX
]) # TODO: currently does not work.
logger.log(15, "uploaded")
scheduler.run()
scheduler.join_jobs()
return self._get_hpo_results(scheduler=scheduler, scheduler_params=scheduler_params, time_start=time_start)
def get_info(self):
info = super().get_info()
info['hyperparameters_post_fit'] = self.params_post_fit
return info
def reduce_memory_size(self, remove_fit=True, requires_save=True, **kwargs):
super().reduce_memory_size(remove_fit=remove_fit, requires_save=requires_save, **kwargs)
if remove_fit and requires_save:
self.optimizer = None
def _get_default_stopping_metric(self):
return self.eval_metric
def convert_df_dtype_to_str(df):
return df.astype(str)
""" General TODOs:
- Automatically decrease batch-size if memory issue arises
- Retrain final NN on full dataset (train+val). How to ensure stability here?
- OrdinalEncoder class in sklearn currently cannot handle rare categories or unknown ones at test-time, so we have created our own Encoder in category_encoders.py
There is open PR in sklearn to address this: https://github.com/scikit-learn/scikit-learn/pull/13833/files
Currently, our code uses category_encoders package (BSD license) instead: https://github.com/scikit-learn-contrib/categorical-encoding
Once PR is merged into sklearn, may want to switch: category_encoders.Ordinal -> sklearn.preprocessing.OrdinalEncoder in preprocess_train_data()
- Save preprocessed data so that we can do HPO of neural net hyperparameters more efficiently, while also doing HPO of preprocessing hyperparameters?
Naive full HPO method requires redoing preprocessing in each trial even if we did not change preprocessing hyperparameters.
Alternative is we save each proprocessed dataset & corresponding TabularNeuralNetModel object with its unique param names in the file. Then when we try a new HP-config, we first try loading from file if one exists.
"""