sentiment analist

README.md

@@ -1,6 +1,6 @@
 ---
 title: Analisis De Sentimientos
-emoji: 🐨
+emoji: 🥺😡
 colorFrom: yellow
 colorTo: indigo
 sdk: streamlit
@@ -9,4 +9,57 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Modelo Clasificatorio de sentiminentos
+
+Mi proyecto es un modelo multi-label que clasifica sentimientos en tristeza (sadness), enojo(anger), alegria(joy), miedo(fear) y sorpresa(surprise) fue entrenado y validado a partir de una tabla de texto y fue modelado con BERT y PyTorch Lightning.
+
+## Decisions
+
+`1` Clasificar sentimientos (anger, sadness, joy, fear, surprise, neutral).
+
+`2` Usar de referencia estos modelos:
+
+- https://github.com/curiousily/Getting-Things-Done-with-Pytorch/blob/master/11.multi-label-text-classification-with-bert.ipynb
+- https://www.youtube.com/watch?v=UJGxCsZgalA&ab_channel=VenelinValkov
+- https://github.com/theartificialguy/NLP-with-Deep-Learning/blob/master/BERT/Multi%20Label%20Text%20Classification%20using%20BERT%20PyTorch/bert_multilabel_pytorch_standard.ipynb
+
+`3` Añadir un dropout en las capas para que el modelo no se sobreajuste.
+
+`4` Anadir checkpoints en el entrenamiento para evitar que la ram se ocupe totalmente.
+
+`5` Utilizar la funcion de activacion sigmoid en al ultima capa para un mejor resultado final.
+
+`6` Utilizar optimizador AdamW y funcion de perdida binary_crossentropy con BCELoss como criterio, que son de los mas recomendados y usados para estos modelos.
+
+`7` Utilizar 1625 ensayos de entrenamiento y 86 ensayos de validacion.
+
+## Data Sources
+
+Use la tabla de entrenamiento y de validacion sobre sentimientos de esta fuente [messages_train_ready_for_WS.tsv](https://github.com/caisa-lab/wassa-empathy-adapters/tree/main/data).
+
+## Features
+
+La entrada es texto a trave de un campo de texto, para predecir y/o clasificar los sentimientos que mas se asemejan al texto.
+
+## Data Collection
+
+Decidi utilizar esta coleccion de datos porque tiene una gran variedad de texto en diferentes contextos y con una gran variedad de sentimientos que se midieron a aprtir de varias columnas de metricas.
+
+## Value Proposition
+
+Puede ser utilizado en aplicaciones de reconocimiento de sentimientos, como filtro de textos empresariales por ejemplo, en programas psicologicos que permitan ayudar a personas que tengan algun problema en entender los sentimientos de otros o personas con discapacidad en general, por ejemplo con discapacidad en el habla que se comunica a traves de texto como entrada a un sistema que genera sonidos, podria modificar el tono de voz a partir de esta prediccion y reflejar los sentimientos de la persona.
+
+# Environment requirements to run the model
+
+Transformers 4.5.1
+
+Pytorch lightning 1.2.8
+
+Numpy
+
+Pandas
+
+Torch
+
+Sklearn

app.py

@@ -0,0 +1,20 @@
+import streamlit as st
+from transformers import pipeline
+import run_sentiment_analysis from model.py
+
+st.title("Analisis de Sentimientos")
+
+txt = st.text_area(label="Please write what you want to analyze...")
+predictions = run_sentiment_analysis(txt)
+
+for prediction in predictions:
+    st.write(prediction)
+    col1, col2 = st.columns(2)
+
+    image = Image.open(file_name)
+    col1.image(image, use_column_width=True)
+    predictions = pipeline(image)
+
+    col2.header("Probabilities")
+    for p in predictions:
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")

model.py

@@ -0,0 +1,361 @@
+# -*- coding: utf-8 -*-
+"""
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/193Qwk9yyPHgI0H84JJOchTovg_CELJuw
+"""
+
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+from transformers import BertTokenizer, BertModel, AdamW, get_linear_schedule_with_warmup
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+from sklearn.model_selection import train_test_split
+
+RANDOM_SEED = 42
+np.random.seed(RANDOM_SEED)
+torch.manual_seed(RANDOM_SEED)
+
+# Preparing training data
+train_file_path = '/content/sample_data/train_data.csv'
+train_data = pd.read_csv(train_file_path) 
+
+filtro = (train_data['emotion'] == 'anger') | (train_data['emotion'] == 'fear') | (train_data['emotion'] == 'joy') | (train_data['emotion'] == 'sadness') | (train_data['emotion'] == 'neutral') | (train_data['emotion'] == 'surprise')
+df = train_data[filtro]
+
+angerColumn = []
+fearColumn = []
+surpriseColumn = []
+sadnessColumn = []
+joyColumn = []
+neutralColumn = []
+
+for e in df['emotion']:
+  if e == 'anger':
+    angerColumn.append(1)
+    joyColumn.append(0)
+    sadnessColumn.append(0)
+    fearColumn.append(0)
+    surpriseColumn.append(0)
+    neutralColumn.append(0)
+  elif e == 'joy':
+    joyColumn.append(1)
+    angerColumn.append(0)
+    sadnessColumn.append(0)
+    fearColumn.append(0)
+    surpriseColumn.append(0)
+    neutralColumn.append(0)
+  elif e == 'sadness':
+    sadnessColumn.append(1)
+    angerColumn.append(0)
+    joyColumn.append(0)
+    fearColumn.append(0)
+    surpriseColumn.append(0)
+    neutralColumn.append(0)
+  elif e == 'fear':
+    fearColumn.append(1)
+    angerColumn.append(0)
+    joyColumn.append(0)
+    sadnessColumn.append(0)
+    surpriseColumn.append(0)
+    neutralColumn.append(0)
+  elif e == 'surprise':
+    surpriseColumn.append(1)
+    angerColumn.append(0)
+    joyColumn.append(0)
+    sadnessColumn.append(0)
+    fearColumn.append(0)
+    neutralColumn.append(0)
+  elif e == 'neutral':
+    neutralColumn.append(1)
+    surpriseColumn.append(0)
+    angerColumn.append(0)
+    joyColumn.append(0)
+    sadnessColumn.append(0)
+    fearColumn.append(0)
+
+df['anger'] = angerColumn
+df['fear'] = fearColumn
+df['surprise'] = surpriseColumn
+df['joy'] = joyColumn
+df['sadness'] = sadnessColumn
+df['neutral'] = neutralColumn
+
+df.drop(['emotion', 'message_id', 'response_id', 'article_id', 'empathy', 'distress', 
+               'empathy_bin', 'distress_bin', 'gender', 'education','race', 'age','income','personality_conscientiousness',
+               'personality_openess','personality_extraversion','personality_agreeableness','personality_stability',
+               'iri_perspective_taking','iri_personal_distress', 'iri_fantasy', 'iri_empathatic_concern','raw_input_emotions'], 
+                axis=1, inplace=True)
+
+print(df.head())
+
+train_df, val_df = train_test_split(df, test_size=0.05)
+train_df.shape, val_df.shape
+
+LABEL_COLUMNS = ['anger','joy','fear','surprise','sadness', 'neutral']
+
+sample_row = train_df.iloc[16]
+sample_comment = sample_row.essay
+sample_labels = sample_row[LABEL_COLUMNS]
+print(sample_comment)
+print(sample_labels.to_dict())
+
+BERT_MODEL_NAME = 'bert-base-cased'
+tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME)
+
+encoding = tokenizer.encode_plus(
+  sample_comment,
+  add_special_tokens=True,
+  max_length=512,
+  return_token_type_ids=False,
+  padding="max_length",
+  return_attention_mask=True,
+  return_tensors='pt',
+)
+
+encoding.keys()
+
+encoding["input_ids"].shape, encoding["attention_mask"].shape
+
+encoding["input_ids"].squeeze()[:20]
+
+encoding["attention_mask"].squeeze()[:20]
+
+print(tokenizer.convert_ids_to_tokens(encoding["input_ids"].squeeze())[:20])
+
+class EmotionDataset(Dataset):
+  def __init__(
+    self, 
+    data: pd.DataFrame, 
+    tokenizer: BertTokenizer, 
+    max_token_len: int = 128
+  ):
+    self.tokenizer = tokenizer
+    self.data = data
+    self.max_token_len = max_token_len
+    
+  def __len__(self):
+    return len(self.data)
+
+  def __getitem__(self, index: int):
+    data_row = self.data.iloc[index]
+
+    comment_text = data_row.essay
+    labels = data_row[LABEL_COLUMNS]
+
+    encoding = self.tokenizer.encode_plus(
+      comment_text,
+      add_special_tokens=True,
+      max_length=self.max_token_len,
+      return_token_type_ids=False,
+      padding="max_length",
+      truncation=True,
+      return_attention_mask=True,
+      return_tensors='pt',
+    )
+
+    return dict(
+      comment_text=comment_text,
+      input_ids=encoding["input_ids"].flatten(),
+      attention_mask=encoding["attention_mask"].flatten(),
+      labels=torch.FloatTensor(labels)
+    )
+
+bert_model = BertModel.from_pretrained(BERT_MODEL_NAME, return_dict=True)
+
+train_dataset = EmotionDataset(train_df,tokenizer)
+sample_item = train_dataset[0]
+sample_item.keys()
+
+sample_batch = next(iter(DataLoader(train_dataset, batch_size=8, num_workers=2)))
+sample_batch["input_ids"].shape, sample_batch["attention_mask"].shape
+
+output = bert_model(sample_batch["input_ids"], sample_batch["attention_mask"])
+
+output.last_hidden_state.shape, output.pooler_output.shape
+
+class EmotionDataModule(pl.LightningDataModule):
+
+  def __init__(self, train_df, test_df, tokenizer, batch_size=8, max_token_len=128):
+    super().__init__()
+    self.batch_size = batch_size
+    self.train_df = train_df
+    self.test_df = test_df
+    self.tokenizer = tokenizer
+    self.max_token_len = max_token_len
+
+  def setup(self, stage=None):
+    self.train_dataset = EmotionDataset(
+      self.train_df,
+      self.tokenizer,
+      self.max_token_len
+    )
+
+    self.test_dataset = EmotionDataset(
+      self.test_df,
+      self.tokenizer,
+      self.max_token_len
+    )
+
+  def train_dataloader(self):
+    return DataLoader(
+      self.train_dataset,
+      batch_size=self.batch_size,
+      shuffle=True,
+      num_workers=2
+    )
+
+  def val_dataloader(self):
+    return DataLoader(
+      self.test_dataset,
+      batch_size=self.batch_size,
+      num_workers=2
+    )
+
+  def test_dataloader(self):
+    return DataLoader(
+      self.test_dataset,
+      batch_size=self.batch_size,
+      num_workers=2
+    )
+
+N_EPOCHS = 10
+BATCH_SIZE = 12
+MAX_TOKEN_COUNT = 512
+
+data_module = EmotionDataModule(
+  train_df,
+  val_df,
+  tokenizer,
+  batch_size=BATCH_SIZE,
+  max_token_len=MAX_TOKEN_COUNT
+)
+
+class EmotionTagger(pl.LightningModule):
+  def __init__(self, n_classes: int, n_training_steps=None, n_warmup_steps=None):
+    super().__init__()
+    self.bert = BertModel.from_pretrained(BERT_MODEL_NAME, return_dict=True)
+    self.classifier = nn.Linear(self.bert.config.hidden_size, n_classes)
+    self.n_training_steps = n_training_steps
+    self.n_warmup_steps = n_warmup_steps
+    self.criterion = nn.BCELoss()
+
+  def forward(self, input_ids, attention_mask, labels=None):
+    output = self.bert(input_ids, attention_mask=attention_mask)
+    output = self.classifier(output.pooler_output)
+    output = torch.sigmoid(output)    
+    loss = 0
+    if labels is not None:
+        loss = self.criterion(output, labels)
+    return loss, output
+
+  def training_step(self, batch, batch_idx):
+    input_ids = batch["input_ids"]
+    attention_mask = batch["attention_mask"]
+    labels = batch["labels"]
+    loss, outputs = self(input_ids, attention_mask, labels)
+    self.log("train_loss", loss, prog_bar=True, logger=True)
+    return {"loss": loss, "predictions": outputs, "labels": labels}
+
+  def validation_step(self, batch, batch_idx):
+    input_ids = batch["input_ids"]
+    attention_mask = batch["attention_mask"]
+    labels = batch["labels"]
+    loss, outputs = self(input_ids, attention_mask, labels)
+    self.log("val_loss", loss, prog_bar=True, logger=True)
+    return loss
+
+  def test_step(self, batch, batch_idx):
+    input_ids = batch["input_ids"]
+    attention_mask = batch["attention_mask"]
+    labels = batch["labels"]
+    loss, outputs = self(input_ids, attention_mask, labels)
+    self.log("test_loss", loss, prog_bar=True, logger=True)
+    return loss
+
+    for i, name in enumerate(LABEL_COLUMNS):
+      class_roc_auc = pytorch_lightning.metrics.functional.auroc(predictions[:, i], labels[:, i])
+      self.logger.experiment.add_scalar(f"{name}_roc_auc/Train", class_roc_auc, self.current_epoch)
+
+  def configure_optimizers(self):
+    optimizer = AdamW(self.parameters(), lr=2e-5)
+
+    scheduler = get_linear_schedule_with_warmup(
+      optimizer,
+      num_warmup_steps=self.n_warmup_steps,
+      num_training_steps=self.n_training_steps
+    )
+
+    return dict(
+      optimizer=optimizer,
+      lr_scheduler=dict(
+        scheduler=scheduler,
+        interval='step'
+      )
+    )
+
+steps_per_epoch=len(train_df) // BATCH_SIZE
+total_training_steps = steps_per_epoch * N_EPOCHS
+warmup_steps = total_training_steps // 5
+
+model = EmotionTagger(
+  n_classes=len(LABEL_COLUMNS),
+  n_warmup_steps=warmup_steps,
+  n_training_steps=total_training_steps
+)
+
+!rm -rf lightning_logs/
+!rm -rf checkpoints/
+
+checkpoint_callback = ModelCheckpoint(
+  dirpath="checkpoints",
+  filename="best-checkpoint",
+  save_top_k=1,
+  verbose=True,
+  monitor="val_loss",
+  mode="min"
+)
+
+early_stopping_callback = EarlyStopping(monitor='val_loss', patience=2)
+
+trainer = pl.Trainer( 
+  max_epochs=N_EPOCHS,
+  callbacks=[early_stopping_callback,checkpoint_callback],)
+
+trainer.fit(model, data_module)
+
+trained_model = EmotionTagger.load_from_checkpoint(
+  trainer.checkpoint_callback.best_model_path,
+  n_classes=len(LABEL_COLUMNS)
+)
+trained_model.eval()
+trained_model.freeze()
+
+
+def run_sentiment_analysis (txt) :
+    THRESHOLD = 0.5
+    
+    encoding = tokenizer.encode_plus(
+        txt,
+        add_special_tokens=True,
+        max_length=512,
+        return_token_type_ids=False,
+        padding="max_length",
+        return_attention_mask=True,
+        return_tensors='pt',
+    )
+
+    _, test_prediction = trained_model(encoding["input_ids"], encoding["attention_mask"])
+    test_prediction = test_prediction.flatten().numpy()
+
+    predictions = []
+
+    for label, prediction in zip(LABEL_COLUMNS, test_prediction):
+        if prediction < THRESHOLD:
+            continue
+        predictions.append("{label}: {prediction}")
+        return predictions

requirements.txt

@@ -0,0 +1,6 @@
+transformers==4.5.1
+pytorch-lightning==1.2.8
+torch
+pandas
+numpy
+sklearn