17_Using_a_huggingface_model

Using a Hugginface model¶

In this notebook we will show how to use an "external" Hugginface model along with any other model in the libray. In particular we will show how to combine it with a tabular DL model.

Since we are here, we will also compare the performance of a few models on a text classification problem.

The notebook will go as follows:

1. Text classification using tf-idf + LightGBM
2. Text classification using a basic RNN
3. Text classification using Distilbert

In all 3 cases we will add some tabular features to see if these help.

In general, I would not pay much attention to the results since I have placed no effort in getting the best possible results (i.e. no hyperparameter optimization or trying different architectures, for example).

Let's go

In [1]:

import numpy as np
import torch
import lightgbm as lgb
from lightgbm import Dataset as lgbDataset
from scipy.sparse import hstack, csr_matrix
from sklearn.metrics import (
    f1_score,
    recall_score,
    accuracy_score,
    precision_score,
    confusion_matrix,
)
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer

from torch import Tensor, nn
from transformers import DistilBertModel, DistilBertTokenizer
from pytorch_widedeep import Trainer
from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep
from pytorch_widedeep.metrics import F1Score, Accuracy
from pytorch_widedeep.utils import Tokenizer, LabelEncoder
from pytorch_widedeep.preprocessing import TextPreprocessor, TabPreprocessor
from pytorch_widedeep.datasets import load_womens_ecommerce
from pytorch_widedeep.utils.fastai_transforms import (
    fix_html,
    spec_add_spaces,
    rm_useless_spaces,
)

import numpy as np import torch import lightgbm as lgb from lightgbm import Dataset as lgbDataset from scipy.sparse import hstack, csr_matrix from sklearn.metrics import ( f1_score, recall_score, accuracy_score, precision_score, confusion_matrix, ) from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from torch import Tensor, nn from transformers import DistilBertModel, DistilBertTokenizer from pytorch_widedeep import Trainer from pytorch_widedeep.models import TabMlp, BasicRNN, WideDeep from pytorch_widedeep.metrics import F1Score, Accuracy from pytorch_widedeep.utils import Tokenizer, LabelEncoder from pytorch_widedeep.preprocessing import TextPreprocessor, TabPreprocessor from pytorch_widedeep.datasets import load_womens_ecommerce from pytorch_widedeep.utils.fastai_transforms import ( fix_html, spec_add_spaces, rm_useless_spaces, )

Let's load the data and have a look:

In [2]:

df = load_womens_ecommerce(as_frame=True)

df.columns = [c.replace(" ", "_").lower() for c in df.columns]

# classes from [0,num_class)
df["rating"] = (df["rating"] - 1).astype("int64")

# group reviews with 1 and 2 scores into one class
df.loc[df.rating == 0, "rating"] = 1

# and back again to [0,num_class)
df["rating"] = (df["rating"] - 1).astype("int64")

# drop short reviews
df = df[~df.review_text.isna()]
df["review_length"] = df.review_text.apply(lambda x: len(x.split(" ")))
df = df[df.review_length >= 5]
df = df.drop("review_length", axis=1).reset_index(drop=True)

df = load_womens_ecommerce(as_frame=True) df.columns = [c.replace(" ", "_").lower() for c in df.columns] # classes from [0,num_class) df["rating"] = (df["rating"] - 1).astype("int64") # group reviews with 1 and 2 scores into one class df.loc[df.rating == 0, "rating"] = 1 # and back again to [0,num_class) df["rating"] = (df["rating"] - 1).astype("int64") # drop short reviews df = df[~df.review_text.isna()] df["review_length"] = df.review_text.apply(lambda x: len(x.split(" "))) df = df[df.review_length >= 5] df = df.drop("review_length", axis=1).reset_index(drop=True)

In [3]:

df.head()

df.head()

Out[3]:

	clothing_id	age	title	review_text	rating	recommended_ind	positive_feedback_count	division_name	department_name	class_name
0	767	33	None	Absolutely wonderful - silky and sexy and comf...	2	1	0	Initmates	Intimate	Intimates
1	1080	34	None	Love this dress! it's sooo pretty. i happene...	3	1	4	General	Dresses	Dresses
2	1077	60	Some major design flaws	I had such high hopes for this dress and reall...	1	0	0	General	Dresses	Dresses
3	1049	50	My favorite buy!	I love, love, love this jumpsuit. it's fun, fl...	3	1	0	General Petite	Bottoms	Pants
4	847	47	Flattering shirt	This shirt is very flattering to all due to th...	3	1	6	General	Tops	Blouses

So, we will use the review_text column to predict the rating. Later on, we will try to combine it with some other columns (like division_name and age) see if these help.

Let's first have a look to the distribution of ratings

In [4]:

df.rating.value_counts()

df.rating.value_counts()

Out[4]:

rating
3    12515
2     4904
1     2820
0     2369
Name: count, dtype: int64

This shows that we could have perhaps grouped rating scores of 1, 2 and 3 into 1...but anyway, let's just move on with those 4 classes.

We are not going to carry any hyperparameter optimization here, so, we will only need a train and a test set (i.e. no need of a validation set for the example in this notebook)

In [5]:

train, test = train_test_split(df, train_size=0.8, random_state=1, stratify=df.rating)

train, test = train_test_split(df, train_size=0.8, random_state=1, stratify=df.rating)

Let's see what we have to beat. What metrics would we obtain if we always predict the most common rating (3)?

In [6]:

most_common_pred = [train.rating.value_counts().index[0]] * len(test)

most_common_acc = accuracy_score(test.rating, most_common_pred)
most_common_f1 = f1_score(test.rating, most_common_pred, average="weighted")

most_common_pred = [train.rating.value_counts().index[0]] * len(test) most_common_acc = accuracy_score(test.rating, most_common_pred) most_common_f1 = f1_score(test.rating, most_common_pred, average="weighted")

In [7]:

print(f"Accuracy: {most_common_acc}. F1 Score: {most_common_f1}")

print(f"Accuracy: {most_common_acc}. F1 Score: {most_common_f1}")

Accuracy: 0.553516143299425. F1 Score: 0.3944344218301668

ok, these are our "baseline" metrics.

Let's start by using simply tf-idf + lightGBM

1. Text classification using tf-idf + LightGBM¶

In [8]:

# ?Tokenizer

# ?Tokenizer

In [9]:

# this Tokenizer is part of our utils module but of course, any valid tokenizer can be used here.

# When using notebooks there seems to be an issue related with multiprocessing (and sometimes tqdm)
# that can only be solved by using only one CPU
tok = Tokenizer(n_cpus=1)
tok_reviews_tr = tok.process_all(train.review_text.tolist())
tok_reviews_te = tok.process_all(test.review_text.tolist())

# this Tokenizer is part of our utils module but of course, any valid tokenizer can be used here. # When using notebooks there seems to be an issue related with multiprocessing (and sometimes tqdm) # that can only be solved by using only one CPU tok = Tokenizer(n_cpus=1) tok_reviews_tr = tok.process_all(train.review_text.tolist()) tok_reviews_te = tok.process_all(test.review_text.tolist())

In [10]:

vectorizer = TfidfVectorizer(
    max_features=5000, preprocessor=lambda x: x, tokenizer=lambda x: x, min_df=5
)

X_text_tr = vectorizer.fit_transform(tok_reviews_tr)
X_text_te = vectorizer.transform(tok_reviews_te)

vectorizer = TfidfVectorizer( max_features=5000, preprocessor=lambda x: x, tokenizer=lambda x: x, min_df=5 ) X_text_tr = vectorizer.fit_transform(tok_reviews_tr) X_text_te = vectorizer.transform(tok_reviews_te)

/Users/javierrodriguezzaurin/.pyenv/versions/3.10.13/envs/widedeep310/lib/python3.10/site-packages/sklearn/feature_extraction/text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'
  warnings.warn(

In [11]:

X_text_tr

X_text_tr

Out[11]:

<18086x4566 sparse matrix of type '<class 'numpy.float64'>'
	with 884074 stored elements in Compressed Sparse Row format>

We now move our matrices to lightGBM Dataset format

In [12]:

lgbtrain_text = lgbDataset(
    X_text_tr,
    train.rating.values,
    free_raw_data=False,
)

lgbtest_text = lgbDataset(
    X_text_te,
    test.rating.values,
    reference=lgbtrain_text,
    free_raw_data=False,
)

lgbtrain_text = lgbDataset( X_text_tr, train.rating.values, free_raw_data=False, ) lgbtest_text = lgbDataset( X_text_te, test.rating.values, reference=lgbtrain_text, free_raw_data=False, )

and off we go. By the way, I think as we run the next cell, we should appreciate how fast lightGBM runs. Yes, the input is a sparse matrix, but still, trains on 18086x4566 in a matter of secs

In [ ]:

lgb_text_model = lgb.train(
    {"objective": "multiclass", "num_classes": 4},
    lgbtrain_text,
    valid_sets=[lgbtest_text, lgbtrain_text],
    valid_names=["test", "train"],
)

lgb_text_model = lgb.train( {"objective": "multiclass", "num_classes": 4}, lgbtrain_text, valid_sets=[lgbtest_text, lgbtrain_text], valid_names=["test", "train"], )

In [14]:

preds_text = lgb_text_model.predict(X_text_te)
pred_text_class = np.argmax(preds_text, 1)

preds_text = lgb_text_model.predict(X_text_te) pred_text_class = np.argmax(preds_text, 1)

In [15]:

acc_text = accuracy_score(lgbtest_text.label, pred_text_class)
f1_text = f1_score(lgbtest_text.label, pred_text_class, average="weighted")
cm_text = confusion_matrix(lgbtest_text.label, pred_text_class)

acc_text = accuracy_score(lgbtest_text.label, pred_text_class) f1_text = f1_score(lgbtest_text.label, pred_text_class, average="weighted") cm_text = confusion_matrix(lgbtest_text.label, pred_text_class)

In [16]:

print(f"LightGBM Accuracy: {acc_text}. LightGBM F1 Score: {f1_text}")

print(f"LightGBM Accuracy: {acc_text}. LightGBM F1 Score: {f1_text}")

LightGBM Accuracy: 0.6444051304732419. LightGBM F1 Score: 0.617154488246181

In [17]:

print(f"LightGBM Confusion Matrix: \n {cm_text}")

print(f"LightGBM Confusion Matrix: \n {cm_text}")

LightGBM Confusion Matrix: 
 [[ 199  135   61   79]
 [ 123  169  149  123]
 [  30   94  279  578]
 [  16   30  190 2267]]

Ok, so, with no hyperparameter optimization lightGBM gets an accuracy of 0.64 and a F1 score of 0.62. This is significantly better than predicting always the most popular.

Let's see if in this implementation, some additional features, like age or class_name are of any help

In [18]:

tab_cols = [
    "age",
    "division_name",
    "department_name",
    "class_name",
]

for tab_df in [train, test]:
    for c in ["division_name", "department_name", "class_name"]:
        tab_df[c] = tab_df[c].str.lower()
        tab_df[c].fillna("missing", inplace=True)

tab_cols = [ "age", "division_name", "department_name", "class_name", ] for tab_df in [train, test]: for c in ["division_name", "department_name", "class_name"]: tab_df[c] = tab_df[c].str.lower() tab_df[c].fillna("missing", inplace=True)

In [19]:

# This is our LabelEncoder. A class that is designed to work with the models in this library but
# can be used for general purposes
le = LabelEncoder(columns_to_encode=["division_name", "department_name", "class_name"])
train_tab_le = le.fit_transform(train)
test_tab_le = le.transform(test)

# This is our LabelEncoder. A class that is designed to work with the models in this library but # can be used for general purposes le = LabelEncoder(columns_to_encode=["division_name", "department_name", "class_name"]) train_tab_le = le.fit_transform(train) test_tab_le = le.transform(test)

In [20]:

train_tab_le.head()

train_tab_le.head()

Out[20]:

	clothing_id	age	title	review_text	rating	recommended_ind	positive_feedback_count	division_name	department_name	class_name
4541	836	35	None	Bought this on sale in my reg size- 10. im 5'9...	2	1	2	1	1	1
18573	1022	25	Look like "mom jeans"	Maybe i just have the wrong body type for thes...	1	0	0	2	2	2
1058	815	39	Ig brought me here	Love the way this top layers under my jackets ...	2	1	0	1	1	1
12132	984	47	Runs small especially the arms	I love this jacket. it's the prettiest and mos...	3	1	0	1	3	3
20756	1051	42	True red, true beauty.	These pants are gorgeous--the fabric has a sat...	3	1	0	2	2	4

let's for example have a look to the encodings for the categorical feature class_name

In [21]:

le.encoding_dict["class_name"]

le.encoding_dict["class_name"]

Out[21]:

{'blouses': 1,
 'jeans': 2,
 'jackets': 3,
 'pants': 4,
 'knits': 5,
 'dresses': 6,
 'skirts': 7,
 'sweaters': 8,
 'fine gauge': 9,
 'legwear': 10,
 'lounge': 11,
 'shorts': 12,
 'outerwear': 13,
 'intimates': 14,
 'swim': 15,
 'trend': 16,
 'sleep': 17,
 'layering': 18,
 'missing': 19,
 'casual bottoms': 20,
 'chemises': 21}

In [22]:

# tabular training and test sets
X_tab_tr = csr_matrix(train_tab_le[tab_cols].values)
X_tab_te = csr_matrix(test_tab_le[tab_cols].values)

# text + tabular training and test sets
X_tab_text_tr = hstack((X_tab_tr, X_text_tr))
X_tab_text_te = hstack((X_tab_te, X_text_te))

# tabular training and test sets X_tab_tr = csr_matrix(train_tab_le[tab_cols].values) X_tab_te = csr_matrix(test_tab_le[tab_cols].values) # text + tabular training and test sets X_tab_text_tr = hstack((X_tab_tr, X_text_tr)) X_tab_text_te = hstack((X_tab_te, X_text_te))

In [23]:

X_tab_tr

X_tab_tr

Out[23]:

<18086x4 sparse matrix of type '<class 'numpy.int64'>'
	with 72344 stored elements in Compressed Sparse Row format>

In [24]:

X_tab_text_tr

X_tab_text_tr

Out[24]:

<18086x4570 sparse matrix of type '<class 'numpy.float64'>'
	with 956418 stored elements in Compressed Sparse Row format>

In [25]:

lgbtrain_tab_text = lgbDataset(
    X_tab_text_tr,
    train.rating.values,
    categorical_feature=[0, 1, 2, 3],
    free_raw_data=False,
)

lgbtest_tab_text = lgbDataset(
    X_tab_text_te,
    test.rating.values,
    reference=lgbtrain_tab_text,
    free_raw_data=False,
)

lgbtrain_tab_text = lgbDataset( X_tab_text_tr, train.rating.values, categorical_feature=[0, 1, 2, 3], free_raw_data=False, ) lgbtest_tab_text = lgbDataset( X_tab_text_te, test.rating.values, reference=lgbtrain_tab_text, free_raw_data=False, )

In [26]:

lgb_tab_text_model = lgb.train(
    {"objective": "multiclass", "num_classes": 4},
    lgbtrain_tab_text,
    valid_sets=[lgbtrain_tab_text, lgbtest_tab_text],
    valid_names=["test", "train"],
    verbose_eval=False,
)

lgb_tab_text_model = lgb.train( {"objective": "multiclass", "num_classes": 4}, lgbtrain_tab_text, valid_sets=[lgbtrain_tab_text, lgbtest_tab_text], valid_names=["test", "train"], verbose_eval=False, )

/opt/conda/envs/wd38/lib/python3.8/site-packages/lightgbm/basic.py:2065: UserWarning: Using categorical_feature in Dataset.
  _log_warning('Using categorical_feature in Dataset.')
/opt/conda/envs/wd38/lib/python3.8/site-packages/lightgbm/basic.py:2068: UserWarning: categorical_feature in Dataset is overridden.
New categorical_feature is [0, 1, 2, 3]
  _log_warning('categorical_feature in Dataset is overridden.\n'
/opt/conda/envs/wd38/lib/python3.8/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.
  _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "

[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.138280 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 143432
[LightGBM] [Info] Number of data points in the train set: 18086, number of used features: 2289
[LightGBM] [Info] Start training from score -2.255919
[LightGBM] [Info] Start training from score -2.081545
[LightGBM] [Info] Start training from score -1.528281
[LightGBM] [Info] Start training from score -0.591354

/opt/conda/envs/wd38/lib/python3.8/site-packages/lightgbm/basic.py:1780: UserWarning: Overriding the parameters from Reference Dataset.
  _log_warning('Overriding the parameters from Reference Dataset.')
/opt/conda/envs/wd38/lib/python3.8/site-packages/lightgbm/basic.py:1513: UserWarning: categorical_column in param dict is overridden.
  _log_warning(f'{cat_alias} in param dict is overridden.')

In [27]:

preds_tab_text = lgb_tab_text_model.predict(X_tab_text_te)
preds_tab_text_class = np.argmax(preds_tab_text, 1)

acc_tab_text = accuracy_score(lgbtest_tab_text.label, preds_tab_text_class)
f1_tab_text = f1_score(lgbtest_tab_text.label, preds_tab_text_class, average="weighted")
cm_tab_text = confusion_matrix(lgbtest_tab_text.label, preds_tab_text_class)

preds_tab_text = lgb_tab_text_model.predict(X_tab_text_te) preds_tab_text_class = np.argmax(preds_tab_text, 1) acc_tab_text = accuracy_score(lgbtest_tab_text.label, preds_tab_text_class) f1_tab_text = f1_score(lgbtest_tab_text.label, preds_tab_text_class, average="weighted") cm_tab_text = confusion_matrix(lgbtest_tab_text.label, preds_tab_text_class)

In [28]:

print(
    f"LightGBM text + tabular Accuracy: {acc_tab_text}. LightGBM text + tabular F1 Score: {f1_tab_text}"
)

print( f"LightGBM text + tabular Accuracy: {acc_tab_text}. LightGBM text + tabular F1 Score: {f1_tab_text}" )

LightGBM text + tabular Accuracy: 0.6382131800088456. LightGBM text + tabular F1 Score: 0.6080251307242649

In [29]:

print(f"LightGBM text + tabular Confusion Matrix:\n {cm_tab_text}")

print(f"LightGBM text + tabular Confusion Matrix:\n {cm_tab_text}")

LightGBM text + tabular Confusion Matrix:
 [[ 193  123   68   90]
 [ 123  146  157  138]
 [  37   90  272  582]
 [  16   37  175 2275]]

So, in this set up, the addition tabular columns do not help performance.

2. Text classification using pytorch-widedeep's built-in models (a basic RNN)¶

Moving on now to fully using pytorch-widedeep in this dataset, let's have a look on how one could use a simple RNN to predict the ratings with the library.

In [30]:

text_preprocessor = TextPreprocessor(
    text_col="review_text", max_vocab=5000, min_freq=5, maxlen=90, n_cpus=1
)

wd_X_text_tr = text_preprocessor.fit_transform(train)
wd_X_text_te = text_preprocessor.transform(test)

text_preprocessor = TextPreprocessor( text_col="review_text", max_vocab=5000, min_freq=5, maxlen=90, n_cpus=1 ) wd_X_text_tr = text_preprocessor.fit_transform(train) wd_X_text_te = text_preprocessor.transform(test)

The vocabulary contains 4328 tokens

In [31]:

basic_rnn = BasicRNN(
    vocab_size=len(text_preprocessor.vocab.itos),
    embed_dim=300,
    hidden_dim=64,
    n_layers=3,
    rnn_dropout=0.2,
    head_hidden_dims=[32],
)


wd_text_model = WideDeep(deeptext=basic_rnn, pred_dim=4)

basic_rnn = BasicRNN( vocab_size=len(text_preprocessor.vocab.itos), embed_dim=300, hidden_dim=64, n_layers=3, rnn_dropout=0.2, head_hidden_dims=[32], ) wd_text_model = WideDeep(deeptext=basic_rnn, pred_dim=4)

In [32]:

wd_text_model

wd_text_model

Out[32]:

WideDeep(
  (deeptext): Sequential(
    (0): BasicRNN(
      (word_embed): Embedding(4328, 300, padding_idx=1)
      (rnn): LSTM(300, 64, num_layers=3, batch_first=True, dropout=0.2)
      (rnn_mlp): MLP(
        (mlp): Sequential(
          (dense_layer_0): Sequential(
            (0): Linear(in_features=64, out_features=32, bias=True)
            (1): ReLU(inplace=True)
          )
        )
      )
    )
    (1): Linear(in_features=32, out_features=4, bias=True)
  )
)

In [33]:

text_trainer = Trainer(
    wd_text_model,
    objective="multiclass",
    metrics=[Accuracy, F1Score(average=True)],
    num_workers=0,  # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work
)

text_trainer = Trainer( wd_text_model, objective="multiclass", metrics=[Accuracy, F1Score(average=True)], num_workers=0, # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work )

In [34]:

text_trainer.fit(
    X_text=wd_X_text_tr,
    target=train.rating.values,
    n_epochs=5,
    batch_size=256,
)

text_trainer.fit( X_text=wd_X_text_tr, target=train.rating.values, n_epochs=5, batch_size=256, )

epoch 1: 100%|███████████████████████████████████████████████| 71/71 [00:01<00:00, 52.39it/s, loss=1.16, metrics={'acc': 0.5349, 'f1': 0.2011}]
epoch 2: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 70.35it/s, loss=0.964, metrics={'acc': 0.5827, 'f1': 0.3005}]
epoch 3: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 70.33it/s, loss=0.845, metrics={'acc': 0.6252, 'f1': 0.4133}]
epoch 4: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 69.99it/s, loss=0.765, metrics={'acc': 0.6575, 'f1': 0.4875}]
epoch 5: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 69.55it/s, loss=0.709, metrics={'acc': 0.6879, 'f1': 0.5423}]

In [35]:

wd_pred_text = text_trainer.predict_proba(X_text=wd_X_text_te)
wd_pred_text_class = np.argmax(wd_pred_text, 1)

wd_pred_text = text_trainer.predict_proba(X_text=wd_X_text_te) wd_pred_text_class = np.argmax(wd_pred_text, 1)

predict: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 211.51it/s]

In [36]:

wd_acc_text = accuracy_score(test.rating, wd_pred_text_class)
wd_f1_text = f1_score(test.rating, wd_pred_text_class, average="weighted")
wd_cm_text = confusion_matrix(test.rating, wd_pred_text_class)

wd_acc_text = accuracy_score(test.rating, wd_pred_text_class) wd_f1_text = f1_score(test.rating, wd_pred_text_class, average="weighted") wd_cm_text = confusion_matrix(test.rating, wd_pred_text_class)

In [37]:

print(f"Basic RNN Accuracy: {wd_acc_text}. Basic RNN F1 Score: {wd_f1_text}")

print(f"Basic RNN Accuracy: {wd_acc_text}. Basic RNN F1 Score: {wd_f1_text}")

Basic RNN Accuracy: 0.6076957098628926. Basic RNN F1 Score: 0.6017335854471788

In [38]:

print(f"Basic RNN Confusion Matrix:\n {wd_cm_text}")

print(f"Basic RNN Confusion Matrix:\n {wd_cm_text}")

Basic RNN Confusion Matrix:
 [[ 327   76   62    9]
 [ 285  115  117   47]
 [ 131  122  315  413]
 [  42   69  401 1991]]

The performance is very similar to that of using simply tf-idf and lightgbm. Let see if adding tabular features helps when using pytorch-widedeep

In [39]:

# ?TabPreprocessor

# ?TabPreprocessor

In [40]:

tab_preprocessor = TabPreprocessor(cat_embed_cols=tab_cols)

wd_X_tab_tr = tab_preprocessor.fit_transform(train)
wd_X_tab_te = tab_preprocessor.transform(test)

tab_preprocessor = TabPreprocessor(cat_embed_cols=tab_cols) wd_X_tab_tr = tab_preprocessor.fit_transform(train) wd_X_tab_te = tab_preprocessor.transform(test)

In [41]:

# ?TabMlp

# ?TabMlp

In [42]:

tab_model = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    mlp_hidden_dims=[100, 50],
)

tab_model = TabMlp( column_idx=tab_preprocessor.column_idx, cat_embed_input=tab_preprocessor.cat_embed_input, mlp_hidden_dims=[100, 50], )

In [43]:

tab_model

tab_model

Out[43]:

TabMlp(
  (cat_and_cont_embed): DiffSizeCatAndContEmbeddings(
    (cat_embed): DiffSizeCatEmbeddings(
      (embed_layers): ModuleDict(
        (emb_layer_age): Embedding(78, 18, padding_idx=0)
        (emb_layer_division_name): Embedding(5, 3, padding_idx=0)
        (emb_layer_department_name): Embedding(8, 5, padding_idx=0)
        (emb_layer_class_name): Embedding(22, 9, padding_idx=0)
      )
      (embedding_dropout): Dropout(p=0.1, inplace=False)
    )
  )
  (encoder): MLP(
    (mlp): Sequential(
      (dense_layer_0): Sequential(
        (0): Dropout(p=0.1, inplace=False)
        (1): Linear(in_features=35, out_features=100, bias=True)
        (2): ReLU(inplace=True)
      )
      (dense_layer_1): Sequential(
        (0): Dropout(p=0.1, inplace=False)
        (1): Linear(in_features=100, out_features=50, bias=True)
        (2): ReLU(inplace=True)
      )
    )
  )
)

In [44]:

text_model = BasicRNN(
    vocab_size=len(text_preprocessor.vocab.itos),
    embed_dim=300,
    hidden_dim=64,
    n_layers=3,
    rnn_dropout=0.2,
    head_hidden_dims=[32],
)

text_model = BasicRNN( vocab_size=len(text_preprocessor.vocab.itos), embed_dim=300, hidden_dim=64, n_layers=3, rnn_dropout=0.2, head_hidden_dims=[32], )

In [45]:

wd_tab_and_text_model = WideDeep(deeptabular=tab_model, deeptext=text_model, pred_dim=4)

wd_tab_and_text_model = WideDeep(deeptabular=tab_model, deeptext=text_model, pred_dim=4)

In [46]:

wd_tab_and_text_model

wd_tab_and_text_model

Out[46]:

WideDeep(
  (deeptabular): Sequential(
    (0): TabMlp(
      (cat_and_cont_embed): DiffSizeCatAndContEmbeddings(
        (cat_embed): DiffSizeCatEmbeddings(
          (embed_layers): ModuleDict(
            (emb_layer_age): Embedding(78, 18, padding_idx=0)
            (emb_layer_division_name): Embedding(5, 3, padding_idx=0)
            (emb_layer_department_name): Embedding(8, 5, padding_idx=0)
            (emb_layer_class_name): Embedding(22, 9, padding_idx=0)
          )
          (embedding_dropout): Dropout(p=0.1, inplace=False)
        )
      )
      (encoder): MLP(
        (mlp): Sequential(
          (dense_layer_0): Sequential(
            (0): Dropout(p=0.1, inplace=False)
            (1): Linear(in_features=35, out_features=100, bias=True)
            (2): ReLU(inplace=True)
          )
          (dense_layer_1): Sequential(
            (0): Dropout(p=0.1, inplace=False)
            (1): Linear(in_features=100, out_features=50, bias=True)
            (2): ReLU(inplace=True)
          )
        )
      )
    )
    (1): Linear(in_features=50, out_features=4, bias=True)
  )
  (deeptext): Sequential(
    (0): BasicRNN(
      (word_embed): Embedding(4328, 300, padding_idx=1)
      (rnn): LSTM(300, 64, num_layers=3, batch_first=True, dropout=0.2)
      (rnn_mlp): MLP(
        (mlp): Sequential(
          (dense_layer_0): Sequential(
            (0): Linear(in_features=64, out_features=32, bias=True)
            (1): ReLU(inplace=True)
          )
        )
      )
    )
    (1): Linear(in_features=32, out_features=4, bias=True)
  )
)

In [47]:

tab_and_text_trainer = Trainer(
    wd_tab_and_text_model,
    objective="multiclass",
    metrics=[Accuracy, F1Score(average=True)],
    num_workers=0,  # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work
)

tab_and_text_trainer = Trainer( wd_tab_and_text_model, objective="multiclass", metrics=[Accuracy, F1Score(average=True)], num_workers=0, # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work )

In [48]:

tab_and_text_trainer.fit(
    X_tab=wd_X_tab_tr,
    X_text=wd_X_text_tr,
    target=train.rating.values,
    n_epochs=5,
    batch_size=256,
)

tab_and_text_trainer.fit( X_tab=wd_X_tab_tr, X_text=wd_X_text_tr, target=train.rating.values, n_epochs=5, batch_size=256, )

epoch 1: 100%|████████████████████████████████████████████████| 71/71 [00:01<00:00, 52.04it/s, loss=1.13, metrics={'acc': 0.538, 'f1': 0.1911}]
epoch 2: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 52.28it/s, loss=0.936, metrics={'acc': 0.5887, 'f1': 0.3507}]
epoch 3: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 52.26it/s, loss=0.825, metrics={'acc': 0.6394, 'f1': 0.4545}]
epoch 4: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 51.33it/s, loss=0.757, metrics={'acc': 0.6696, 'f1': 0.5214}]
epoch 5: 100%|██████████████████████████████████████████████| 71/71 [00:01<00:00, 50.39it/s, loss=0.702, metrics={'acc': 0.6963, 'f1': 0.5654}]

In [49]:

wd_pred_tab_and_text = tab_and_text_trainer.predict_proba(
    X_tab=wd_X_tab_te, X_text=wd_X_text_te
)
wd_pred_tab_and_text_class = np.argmax(wd_pred_tab_and_text, 1)

wd_pred_tab_and_text = tab_and_text_trainer.predict_proba( X_tab=wd_X_tab_te, X_text=wd_X_text_te ) wd_pred_tab_and_text_class = np.argmax(wd_pred_tab_and_text, 1)

predict: 100%|████████████████████████████████████████████████████████████████████████████████████████████████| 18/18 [00:00<00:00, 136.94it/s]

In [50]:

wd_acc_tab_and_text = accuracy_score(test.rating, wd_pred_tab_and_text_class)
wd_f1_tab_and_text = f1_score(
    test.rating, wd_pred_tab_and_text_class, average="weighted"
)
wd_cm_tab_and_text = confusion_matrix(test.rating, wd_pred_tab_and_text_class)

wd_acc_tab_and_text = accuracy_score(test.rating, wd_pred_tab_and_text_class) wd_f1_tab_and_text = f1_score( test.rating, wd_pred_tab_and_text_class, average="weighted" ) wd_cm_tab_and_text = confusion_matrix(test.rating, wd_pred_tab_and_text_class)

In [51]:

print(
    f"Basic RNN + Tabular  Accuracy: {wd_acc_tab_and_text}. Basic RNN + TabularF1 Score: {wd_f1_tab_and_text}"
)
print(f"Basic RNN + Tabular  Confusion Matrix:\n {wd_cm_tab_and_text}")

print( f"Basic RNN + Tabular Accuracy: {wd_acc_tab_and_text}. Basic RNN + TabularF1 Score: {wd_f1_tab_and_text}" ) print(f"Basic RNN + Tabular Confusion Matrix:\n {wd_cm_tab_and_text}")

Basic RNN + Tabular  Accuracy: 0.6333480760725343. Basic RNN + TabularF1 Score: 0.6332310089593208
Basic RNN + Tabular  Confusion Matrix:
 [[ 267  132   65   10]
 [ 198  168  159   39]
 [  57  113  410  401]
 [  12   58  414 2019]]

3. Text classification using a Hugginface model as a custom model in pytorch-widedeep's¶

We are going to "manually" code the Tokenizer and the model and see how they can be used as part of the process along with the pytorch-widedeep library.

Tokenizer:

In [52]:

class BertTokenizer(object):
    def __init__(
        self,
        pretrained_tokenizer="distilbert-base-uncased",
        do_lower_case=True,
        max_length=90,
    ):
        super(BertTokenizer, self).__init__()
        self.pretrained_tokenizer = pretrained_tokenizer
        self.do_lower_case = do_lower_case
        self.max_length = max_length

    def fit(self, texts):
        self.tokenizer = DistilBertTokenizer.from_pretrained(
            self.pretrained_tokenizer, do_lower_case=self.do_lower_case
        )

        return self

    def transform(self, texts):
        input_ids = []
        for text in texts:
            encoded_sent = self.tokenizer.encode_plus(
                text=self._pre_rules(text),
                add_special_tokens=True,
                max_length=self.max_length,
                padding="max_length",
                truncation=True,
            )

            input_ids.append(encoded_sent.get("input_ids"))
        return np.stack(input_ids)

    def fit_transform(self, texts):
        return self.fit(texts).transform(texts)

    @staticmethod
    def _pre_rules(text):
        return fix_html(rm_useless_spaces(spec_add_spaces(text)))

class BertTokenizer(object): def __init__( self, pretrained_tokenizer="distilbert-base-uncased", do_lower_case=True, max_length=90, ): super(BertTokenizer, self).__init__() self.pretrained_tokenizer = pretrained_tokenizer self.do_lower_case = do_lower_case self.max_length = max_length def fit(self, texts): self.tokenizer = DistilBertTokenizer.from_pretrained( self.pretrained_tokenizer, do_lower_case=self.do_lower_case ) return self def transform(self, texts): input_ids = [] for text in texts: encoded_sent = self.tokenizer.encode_plus( text=self._pre_rules(text), add_special_tokens=True, max_length=self.max_length, padding="max_length", truncation=True, ) input_ids.append(encoded_sent.get("input_ids")) return np.stack(input_ids) def fit_transform(self, texts): return self.fit(texts).transform(texts) @staticmethod def _pre_rules(text): return fix_html(rm_useless_spaces(spec_add_spaces(text)))

Model:

In [53]:

class BertModel(nn.Module):
    def __init__(
        self,
        model_name: str = "distilbert-base-uncased",
        freeze_bert: bool = False,
    ):
        super(BertModel, self).__init__()

        self.bert = DistilBertModel.from_pretrained(
            model_name,
        )

        if freeze_bert:
            for param in self.bert.parameters():
                param.requires_grad = False

    def forward(self, X_inp: Tensor) -> Tensor:
        attn_mask = (X_inp != 0).type(torch.int8)
        outputs = self.bert(input_ids=X_inp, attention_mask=attn_mask)
        return outputs[0][:, 0, :]

    @property
    def output_dim(self) -> int:
        # This is THE ONLY requirement for any model to work with pytorch-widedeep. Must
        # have a 'output_dim' property so the WideDeep class knows the incoming dims
        # from the custom model. in this case, I hardcoded it
        return 768

class BertModel(nn.Module): def __init__( self, model_name: str = "distilbert-base-uncased", freeze_bert: bool = False, ): super(BertModel, self).__init__() self.bert = DistilBertModel.from_pretrained( model_name, ) if freeze_bert: for param in self.bert.parameters(): param.requires_grad = False def forward(self, X_inp: Tensor) -> Tensor: attn_mask = (X_inp != 0).type(torch.int8) outputs = self.bert(input_ids=X_inp, attention_mask=attn_mask) return outputs[0][:, 0, :] @property def output_dim(self) -> int: # This is THE ONLY requirement for any model to work with pytorch-widedeep. Must # have a 'output_dim' property so the WideDeep class knows the incoming dims # from the custom model. in this case, I hardcoded it return 768

In [54]:

bert_tokenizer = BertTokenizer()
X_bert_tr = bert_tokenizer.fit_transform(train["review_text"].tolist())
X_bert_te = bert_tokenizer.transform(test["review_text"].tolist())

bert_tokenizer = BertTokenizer() X_bert_tr = bert_tokenizer.fit_transform(train["review_text"].tolist()) X_bert_te = bert_tokenizer.transform(test["review_text"].tolist())

As I mentioned a number of times in the documentation and examples, pytorch-widedeep is designed for flexibility. For any of the data modes (tabular, text and images) there are available components/models in the library. However, the user can choose to use any model they want with the only requirement that such model must have a output_dim property.

With that in mind, the BertModel class defined above can be used by pytorch-widedeep as any other of the internal components. In other words, simply...pass it to the WideDeep class. In this case we are going to add a FC-head as part of the classifier.

In [55]:

bert_model = BertModel(freeze_bert=True)
wd_bert_model = WideDeep(
    deeptext=bert_model,
    head_hidden_dims=[256, 128, 64],
    pred_dim=4,
)

bert_model = BertModel(freeze_bert=True) wd_bert_model = WideDeep( deeptext=bert_model, head_hidden_dims=[256, 128, 64], pred_dim=4, )

Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

In [56]:

wd_bert_model

wd_bert_model

Out[56]:

WideDeep(
  (deeptext): BertModel(
    (bert): DistilBertModel(
      (embeddings): Embeddings(
        (word_embeddings): Embedding(30522, 768, padding_idx=0)
        (position_embeddings): Embedding(512, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (transformer): Transformer(
        (layer): ModuleList(
          (0): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
          (1): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
          (2): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
          (3): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
          (4): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
          (5): TransformerBlock(
            (attention): MultiHeadSelfAttention(
              (dropout): Dropout(p=0.1, inplace=False)
              (q_lin): Linear(in_features=768, out_features=768, bias=True)
              (k_lin): Linear(in_features=768, out_features=768, bias=True)
              (v_lin): Linear(in_features=768, out_features=768, bias=True)
              (out_lin): Linear(in_features=768, out_features=768, bias=True)
            )
            (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (ffn): FFN(
              (dropout): Dropout(p=0.1, inplace=False)
              (lin1): Linear(in_features=768, out_features=3072, bias=True)
              (lin2): Linear(in_features=3072, out_features=768, bias=True)
              (activation): GELUActivation()
            )
            (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
          )
        )
      )
    )
  )
  (deephead): Sequential(
    (0): MLP(
      (mlp): Sequential(
        (dense_layer_0): Sequential(
          (0): Linear(in_features=768, out_features=256, bias=True)
          (1): ReLU(inplace=True)
          (2): Dropout(p=0.1, inplace=False)
        )
        (dense_layer_1): Sequential(
          (0): Linear(in_features=256, out_features=128, bias=True)
          (1): ReLU(inplace=True)
          (2): Dropout(p=0.1, inplace=False)
        )
        (dense_layer_2): Sequential(
          (0): Linear(in_features=128, out_features=64, bias=True)
          (1): ReLU(inplace=True)
          (2): Dropout(p=0.1, inplace=False)
        )
      )
    )
    (1): Linear(in_features=64, out_features=4, bias=True)
  )
)

In [57]:

wd_bert_trainer = Trainer(
    wd_bert_model,
    objective="multiclass",
    metrics=[Accuracy, F1Score(average=True)],
    num_workers=0,  # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work
)

wd_bert_trainer.fit(
    X_text=X_bert_tr,
    target=train.rating.values,
    n_epochs=3,
    batch_size=64,
)

wd_bert_trainer = Trainer( wd_bert_model, objective="multiclass", metrics=[Accuracy, F1Score(average=True)], num_workers=0, # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work ) wd_bert_trainer.fit( X_text=X_bert_tr, target=train.rating.values, n_epochs=3, batch_size=64, )

epoch 1: 100%|████████████████████████████████████████████| 283/283 [00:14<00:00, 19.68it/s, loss=0.968, metrics={'acc': 0.5879, 'f1': 0.3591}]
epoch 2: 100%|████████████████████████████████████████████| 283/283 [00:14<00:00, 19.63it/s, loss=0.884, metrics={'acc': 0.6178, 'f1': 0.4399}]
epoch 3: 100%|█████████████████████████████████████████████| 283/283 [00:14<00:00, 19.55it/s, loss=0.87, metrics={'acc': 0.6234, 'f1': 0.4527}]

In [58]:

wd_bert_pred_text = wd_bert_trainer.predict_proba(X_text=X_bert_te)
wd_bert_pred_text_class = np.argmax(wd_bert_pred_text, 1)

wd_bert_pred_text = wd_bert_trainer.predict_proba(X_text=X_bert_te) wd_bert_pred_text_class = np.argmax(wd_bert_pred_text, 1)

predict: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 71/71 [00:03<00:00, 21.97it/s]

In [59]:

wd_bert_acc = accuracy_score(test.rating, wd_bert_pred_text_class)
wd_bert_f1 = f1_score(test.rating, wd_bert_pred_text_class, average="weighted")
wd_bert_cm = confusion_matrix(test.rating, wd_bert_pred_text_class)

wd_bert_acc = accuracy_score(test.rating, wd_bert_pred_text_class) wd_bert_f1 = f1_score(test.rating, wd_bert_pred_text_class, average="weighted") wd_bert_cm = confusion_matrix(test.rating, wd_bert_pred_text_class)

In [60]:

print(f"Distilbert Accuracy: {wd_bert_acc}. Distilbert F1 Score: {wd_bert_f1}")
print(f"Distilbert Confusion Matrix:\n {wd_bert_cm}")

print(f"Distilbert Accuracy: {wd_bert_acc}. Distilbert F1 Score: {wd_bert_f1}") print(f"Distilbert Confusion Matrix:\n {wd_bert_cm}")

Distilbert Accuracy: 0.6326846528084918. Distilbert F1 Score: 0.5796652991272998
Distilbert Confusion Matrix:
 [[ 287   75   22   90]
 [ 197  136   62  169]
 [  68  119  123  671]
 [  40   64   84 2315]]

Now, adding a tabular model follows the exact same process as the one described in section 2.

In [61]:

tab_model = TabMlp(
    column_idx=tab_preprocessor.column_idx,
    cat_embed_input=tab_preprocessor.cat_embed_input,
    mlp_hidden_dims=[100, 50],
)

tab_model = TabMlp( column_idx=tab_preprocessor.column_idx, cat_embed_input=tab_preprocessor.cat_embed_input, mlp_hidden_dims=[100, 50], )

In [62]:

wd_tab_bert_model = WideDeep(
    deeptabular=tab_model,
    deeptext=bert_model,
    head_hidden_dims=[256, 128, 64],
    pred_dim=4,
)

wd_tab_bert_model = WideDeep( deeptabular=tab_model, deeptext=bert_model, head_hidden_dims=[256, 128, 64], pred_dim=4, )

In [63]:

wd_tab_bert_trainer = Trainer(
    wd_tab_bert_model,
    objective="multiclass",
    metrics=[Accuracy, F1Score(average=True)],
    num_workers=0,  # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work
)

wd_tab_bert_trainer = Trainer( wd_tab_bert_model, objective="multiclass", metrics=[Accuracy, F1Score(average=True)], num_workers=0, # As in the case of the tokenizer, in notebook I need to set this to 0 for the Trainer to work )

In [64]:

wd_tab_bert_trainer.fit(
    X_tab=wd_X_tab_tr,
    X_text=X_bert_tr,
    target=train.rating.values,
    n_epochs=3,
    batch_size=64,
)

wd_tab_bert_trainer.fit( X_tab=wd_X_tab_tr, X_text=X_bert_tr, target=train.rating.values, n_epochs=3, batch_size=64, )

epoch 1: 100%|████████████████████████████████████████████| 283/283 [00:15<00:00, 18.15it/s, loss=0.974, metrics={'acc': 0.5838, 'f1': 0.3404}]
epoch 2: 100%|█████████████████████████████████████████████| 283/283 [00:15<00:00, 18.38it/s, loss=0.885, metrics={'acc': 0.618, 'f1': 0.4378}]
epoch 3: 100%|████████████████████████████████████████████| 283/283 [00:15<00:00, 18.40it/s, loss=0.868, metrics={'acc': 0.6252, 'f1': 0.4575}]

In [65]:

wd_tab_bert_pred_text = wd_tab_bert_trainer.predict_proba(
    X_tab=wd_X_tab_te, X_text=X_bert_te
)
wd_tab_bert_pred_text_class = np.argmax(wd_tab_bert_pred_text, 1)

wd_tab_bert_pred_text = wd_tab_bert_trainer.predict_proba( X_tab=wd_X_tab_te, X_text=X_bert_te ) wd_tab_bert_pred_text_class = np.argmax(wd_tab_bert_pred_text, 1)

predict: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 71/71 [00:03<00:00, 21.32it/s]

In [66]:

wd_tab_bert_acc = accuracy_score(test.rating, wd_tab_bert_pred_text_class)
wd_tab_bert_f1 = f1_score(test.rating, wd_tab_bert_pred_text_class, average="weighted")
wd_tab_bert_cm = confusion_matrix(test.rating, wd_tab_bert_pred_text_class)

wd_tab_bert_acc = accuracy_score(test.rating, wd_tab_bert_pred_text_class) wd_tab_bert_f1 = f1_score(test.rating, wd_tab_bert_pred_text_class, average="weighted") wd_tab_bert_cm = confusion_matrix(test.rating, wd_tab_bert_pred_text_class)

In [67]:

print(
    f"Distilbert + Tabular Accuracy: {wd_tab_bert_acc}. Distilbert+ Tabular F1 Score: {wd_tab_bert_f1}"
)
print(f"Distilbert + Tabular Confusion Matrix:\n {wd_tab_bert_cm}")

print( f"Distilbert + Tabular Accuracy: {wd_tab_bert_acc}. Distilbert+ Tabular F1 Score: {wd_tab_bert_f1}" ) print(f"Distilbert + Tabular Confusion Matrix:\n {wd_tab_bert_cm}")

Distilbert + Tabular Accuracy: 0.6242812914639541. Distilbert+ Tabular F1 Score: 0.5508351761564895
Distilbert + Tabular Confusion Matrix:
 [[ 297   56   11  110]
 [ 229   91   38  206]
 [  86   90   71  734]
 [  49   48   42 2364]]