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MAP_EXP_18.py

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+import torch
+import torch.nn as nn
+import shutil
+import numpy as np
+import pandas as pd
+import mlflow
+from collections import Counter
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from datasets import Dataset
+from transformers import (
+    AutoTokenizer, 
+    TrainingArguments, 
+    Trainer, 
+    DataCollatorWithPadding,
+    BitsAndBytesConfig,
+    AutoModel
+)
+from peft import (
+    LoraConfig,
+    TaskType,
+    get_peft_model,
+    prepare_model_for_kbit_training,
+)
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+model_name = "MathGenie/MathCoder2-DeepSeekMath-7B"
+MAX_LEN    = 256
+
+mlflow.set_tracking_uri("http://127.0.0.1:8081")
+
+
+
+############################################################<-DATA->###########################################################
+le_category         = LabelEncoder()
+le_misconception    = LabelEncoder()
+
+train               = pd.read_csv('category_misconception_folds.csv')
+train.Misconception = train.Misconception.fillna('NA')
+
+train['category_label']      = le_category.fit_transform(train['Category'])
+train['misconception_label'] = le_misconception.fit_transform(train['Misconception'])
+
+train.to_excel("train_text.xlsx")
+
+n_category_classes           = len(le_category.classes_)
+n_misconception_classes      = len(le_misconception.classes_)
+
+print(f"Train shape          : {train.shape}")
+print(f"Category classes     : {n_category_classes}")
+print(f"Misconception classes: {n_misconception_classes}")
+print(f"Category classes names : {le_category.classes_}")
+
+print(train[['Category', 'category_label', 'Misconception', 'misconception_label']].head())
+
+
+idx                   = train.apply(lambda row: row.Category.split('_')[0], axis=1) == 'True'
+correct               = train.loc[idx].copy()
+correct['c']          = correct.groupby(['QuestionId', 'MC_Answer']).MC_Answer.transform('count')
+correct               = correct.sort_values('c', ascending=False)
+correct               = correct.drop_duplicates(['QuestionId'])
+correct               = correct[['QuestionId', 'MC_Answer']]
+correct['is_correct'] = 1
+
+train                 = train.merge(correct, on=['QuestionId', 'MC_Answer'], how='left')
+train.is_correct      = train.is_correct.fillna(0)
+
+
+def format_input(row):
+    x = "This answer is correct."
+    if not row['is_correct']:
+        x = "This is answer is incorrect."
+    return (
+        f"Question: {row['QuestionText']}\n"
+        f"Answer: {row['MC_Answer']}\n"
+        f"{x}\n"
+        f"Student Explanation: {row['StudentExplanation']}"
+    )
+
+train['text'] = train.apply(format_input, axis=1)
+train_df      = train[train["fold"]==0]
+val_df        = train[train["fold"]==1]
+
+
+COLS     = ['text', 'category_label', 'misconception_label']
+train_ds = Dataset.from_pandas(train_df[COLS])
+val_ds   = Dataset.from_pandas(val_df[COLS])
+
+tokenizer              = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token    = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+def tokenize_func(examples):
+    tokenized = tokenizer(
+        examples["text"],
+        add_special_tokens   = True,
+        truncation           = True,
+        max_length           = MAX_LEN,
+        padding              = False,  
+    )
+
+    tokenized['category_label']      = examples['category_label']
+    tokenized['misconception_label'] = examples['misconception_label']
+    
+    return tokenized
+
+train_ds = train_ds.map(tokenize_func, batched=True, desc="Tokenizing train data")
+val_ds = val_ds.map(tokenize_func, batched=True, desc="Tokenizing train data")
+##########################################################<-END->###############################################################
+
+
+############################################################<-MODEL->###########################################################
+class MultiHeadClassificationModel(nn.Module):
+    def __init__(self, model_name, n_category_classes, n_misconception_classes, **model_kwargs):
+        super().__init__()
+        
+        self.base_model              = AutoModel.from_pretrained(model_name, **model_kwargs)
+        self.base_model.config.use_cache             = False  # Disable KV cache for training
+        self.base_model.config.output_hidden_states  = False
+        self.base_model.config.output_attentions     = False
+        self.config                  = self.base_model.config
+    
+        hidden_size                  = self.base_model.config.hidden_size
+        
+        self.category_head           = nn.Linear(hidden_size, n_category_classes)
+        self.misconception_head      = nn.Linear(hidden_size, n_misconception_classes)
+        
+        self.n_category_classes      = n_category_classes
+        self.n_misconception_classes = n_misconception_classes
+
+        self.alpha                   = 0.6
+        self.beta                    = 0.4
+        
+    def forward(self, input_ids, attention_mask=None, category_label=None, misconception_label=None, combined_label=None, **kwargs):
+
+        
+        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
+        pooled  = outputs.last_hidden_state.mean(dim=1)
+    
+        category_logits = self.category_head(pooled)
+        misconception_logits = self.misconception_head(pooled)
+        
+        loss = None
+        if category_label is not None and misconception_label is not None:
+            loss_fct                     = nn.CrossEntropyLoss(reduction='none')
+            
+            category_loss_unreduced      = loss_fct(category_logits, category_label)
+            misconception_loss_unreduced = loss_fct(misconception_logits, misconception_label)
+
+            # categories_with_subclasses   = torch.tensor([1, 4], device=category_label.device)
+            # mask                         = torch.isin(category_label, categories_with_subclasses).float()
+            
+            # misconception_loss_masked    = misconception_loss_unreduced * mask
+            
+            category_loss                = torch.mean(category_loss_unreduced)
+            misconception_loss           = torch.mean(misconception_loss_unreduced) 
+            
+            loss                         = self.alpha * category_loss + self.beta * misconception_loss
+
+            # if mask.any():
+            #     print(f"got the samples of misconception. so misco loss is : {misconception_loss} and cat loss is : {category_loss} and final loss is : {loss}")
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=(category_logits, misconception_logits)
+        )
+
+
+model_kwargs = dict(
+    trust_remote_code = True,
+    torch_dtype       = torch.float16
+)
+
+model_kwargs["quantization_config"] = BitsAndBytesConfig(
+    load_in_4bit              = True,
+    bnb_4bit_quant_type       = "nf4",
+    bnb_4bit_use_double_quant = True,
+    bnb_4bit_compute_dtype    = "float16",
+)
+
+print(f"Loading model : {model_name}")
+model = MultiHeadClassificationModel(
+    model_name, 
+    n_category_classes      = n_category_classes, 
+    n_misconception_classes = n_misconception_classes,
+    **model_kwargs
+)
+
+model.base_model.config.pad_token_id = tokenizer.pad_token_id
+
+lora_config = LoraConfig(
+    r                = 64,
+    lora_alpha       = 64,
+    target_modules   = "all-linear",
+    lora_dropout     = 0.05,
+    bias             = "none",
+    task_type        = TaskType.SEQ_CLS,
+    modules_to_save  = ["category_head", "misconception_head"],
+)
+
+model = prepare_model_for_kbit_training(model)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+print(f"Model Architecture : {model}")
+
+##########################################################<-END->###############################################################
+
+############################################################<-METRICS->###########################################################
+
+
+def compute_multi_map(eval_pred, ks=[3, 5, 10]):
+    """
+    Computes MAP@k and a detailed rank distribution for both category and misconception predictions.
+    
+    This includes:
+    - Rank counts for rank 1, 2-3, and above 3.
+    - For rank groups 2-3 and above 3, it finds the top 3 most frequent
+      classes and calculates their average probability score.
+    """
+    # 1. Unpack logits and labels
+    category_logits, misconception_logits = eval_pred.predictions
+    category_labels, misconception_labels = eval_pred.label_ids
+
+    category_labels = np.array(category_labels)
+    misconception_labels = np.array(misconception_labels)
+
+    # 2. Convert logits to probabilities
+    # The `probs` array has shape: (num_samples, num_classes)
+    category_probs = torch.nn.functional.softmax(torch.tensor(category_logits), dim=-1).numpy()
+    misconception_probs = torch.nn.functional.softmax(torch.tensor(misconception_logits), dim=-1).numpy()
+
+    print(f"category_probs : {category_probs}")
+    print(f"category_labels : {category_labels}")
+    print(f"misconception_probs : {misconception_probs}")
+    print(f"misconception_labels : {misconception_labels}")
+
+    # 3. Get top-k predictions
+    max_k = max(ks)
+    category_top_k_preds = np.argsort(-category_probs, axis=1)[:, :max_k]
+    misconception_top_k_preds = np.argsort(-misconception_probs, axis=1)[:, :max_k]
+
+    # 4. Create a boolean match array
+    category_match_array = (category_top_k_preds == category_labels[:, None])
+    misconception_match_array = (misconception_top_k_preds == misconception_labels[:, None])
+
+    # 5. Compute MAP@k for each specified k
+    metrics = {}
+    
+    # Category MAP@k
+    for k in ks:
+        match_at_k = category_match_array[:, :k]
+        ranks = np.argmax(match_at_k, axis=1) + 1
+        has_match_at_k = np.any(match_at_k, axis=1)
+        scores = has_match_at_k * (1.0 / ranks)
+        metrics[f"map@{k}_category"] = np.mean(scores)
+
+    # Misconception MAP@k
+    for k in ks:
+        match_at_k = misconception_match_array[:, :k]
+        ranks = np.argmax(match_at_k, axis=1) + 1
+        has_match_at_k = np.any(match_at_k, axis=1)
+        scores = has_match_at_k * (1.0 / ranks)
+        metrics[f"map@{k}_misconception"] = np.mean(scores)
+
+    # 6. Calculate detailed rank position breakdown for CATEGORY
+    category_ranks_with_indices = [np.where(row)[0] for row in category_match_array]
+    category_correct_ranks = np.array([r[0] + 1 if len(r) > 0 else max_k + 1 for r in category_ranks_with_indices])
+
+    total = category_labels.shape[0]
+    metrics["category_rank_1"] = np.sum(category_correct_ranks == 1)
+    metrics["category_rank_2_to_3"] = np.sum((category_correct_ranks >= 2) & (category_correct_ranks <= 3))
+    metrics["category_rank_above_3"] = np.sum((category_correct_ranks > 3) & (category_correct_ranks <= max_k))
+    metrics["category_no_match_in_top_k"] = np.sum(category_correct_ranks > max_k)
+    metrics["category_total"] = total
+
+    # 7. Find top 3 classes for rank groups and their average probability - CATEGORY
+    
+    # --- For category ranks 2 to 3 ---
+    category_rank_2_to_3_mask = (category_correct_ranks >= 2) & (category_correct_ranks <= 3)
+    category_rank_2_to_3_labels = category_labels[category_rank_2_to_3_mask]
+
+    if len(category_rank_2_to_3_labels) > 0:
+        top_classes = Counter(category_rank_2_to_3_labels).most_common(3)
+        augmented_top_classes = []
+        for cls, count in top_classes:
+            class_in_group_mask = (category_labels == cls) & category_rank_2_to_3_mask
+            class_probs = category_probs[class_in_group_mask, cls]
+            avg_prob = np.mean(class_probs)
+            augmented_top_classes.append((cls, count, round(float(avg_prob), 4)))
+    #     metrics["category_rank_2_to_3_details"] = augmented_top_classes
+    # else:
+    #     metrics["category_rank_2_to_3_details"] = []
+
+    # --- For category ranks above 3 (up to max_k) ---
+    category_rank_above_3_mask = (category_correct_ranks > 3) & (category_correct_ranks <= max_k)
+    category_rank_above_3_labels = category_labels[category_rank_above_3_mask]
+
+    if len(category_rank_above_3_labels) > 0:
+        top_classes = Counter(category_rank_above_3_labels).most_common(3)
+        augmented_top_classes = []
+        for cls, count in top_classes:
+            class_in_group_mask = (category_labels == cls) & category_rank_above_3_mask
+            class_probs = category_probs[class_in_group_mask, cls]
+            avg_prob = np.mean(class_probs)
+            augmented_top_classes.append((cls, count, round(float(avg_prob), 4)))
+    #     metrics["category_rank_above_3_details"] = augmented_top_classes
+    # else:
+    #     metrics["category_rank_above_3_details"] = []
+
+    # 8. Calculate detailed rank position breakdown for MISCONCEPTION
+    misconception_ranks_with_indices = [np.where(row)[0] for row in misconception_match_array]
+    misconception_correct_ranks = np.array([r[0] + 1 if len(r) > 0 else max_k + 1 for r in misconception_ranks_with_indices])
+
+    total = misconception_labels.shape[0]
+    metrics["misconception_rank_1"] = np.sum(misconception_correct_ranks == 1)
+    metrics["misconception_rank_2_to_3"] = np.sum((misconception_correct_ranks >= 2) & (misconception_correct_ranks <= 3))
+    metrics["misconception_rank_above_3"] = np.sum((misconception_correct_ranks > 3) & (misconception_correct_ranks <= max_k))
+    metrics["misconception_no_match_in_top_k"] = np.sum(misconception_correct_ranks > max_k)
+    metrics["misconception_total"] = total
+
+    # 9. Find top 3 classes for rank groups and their average probability - MISCONCEPTION
+    
+    # --- For misconception ranks 2 to 3 ---
+    misconception_rank_2_to_3_mask = (misconception_correct_ranks >= 2) & (misconception_correct_ranks <= 3)
+    misconception_rank_2_to_3_labels = misconception_labels[misconception_rank_2_to_3_mask]
+
+    if len(misconception_rank_2_to_3_labels) > 0:
+        top_classes = Counter(misconception_rank_2_to_3_labels).most_common(3)
+        augmented_top_classes = []
+        for cls, count in top_classes:
+            class_in_group_mask = (misconception_labels == cls) & misconception_rank_2_to_3_mask
+            class_probs = misconception_probs[class_in_group_mask, cls]
+            avg_prob = np.mean(class_probs)
+            augmented_top_classes.append((cls, count, round(float(avg_prob), 4)))
+#        metrics["misconception_rank_2_to_3_details"] = augmented_top_classes
+    # else:
+    #     metrics["misconception_rank_2_to_3_details"] = []
+
+    # --- For misconception ranks above 3 (up to max_k) ---
+    misconception_rank_above_3_mask = (misconception_correct_ranks > 3) & (misconception_correct_ranks <= max_k)
+    misconception_rank_above_3_labels = misconception_labels[misconception_rank_above_3_mask]
+
+    if len(misconception_rank_above_3_labels) > 0:
+        top_classes = Counter(misconception_rank_above_3_labels).most_common(3)
+        augmented_top_classes = []
+        for cls, count in top_classes:
+            class_in_group_mask = (misconception_labels == cls) & misconception_rank_above_3_mask
+            class_probs = misconception_probs[class_in_group_mask, cls]
+            avg_prob = np.mean(class_probs)
+            augmented_top_classes.append((cls, count, round(float(avg_prob), 4)))
+        #metrics["misconception_rank_above_3_details"] = augmented_top_classes
+    # else:
+    #     metrics["misconception_rank_above_3_details"] = []
+
+    # 10. Log metrics to MLflow for both category and misconception
+    # Category metrics
+    mlflow.log_metric("category_rank_1", metrics["category_rank_1"])
+    mlflow.log_metric("category_rank_2_to_3", metrics["category_rank_2_to_3"])
+    mlflow.log_metric("category_rank_above_3", metrics["category_rank_above_3"])
+    mlflow.log_metric("category_no_match_in_top_k", metrics["category_no_match_in_top_k"])
+    
+    # Misconception metrics
+    mlflow.log_metric("misconception_rank_1", metrics["misconception_rank_1"])
+    mlflow.log_metric("misconception_rank_2_to_3", metrics["misconception_rank_2_to_3"])
+    mlflow.log_metric("misconception_rank_above_3", metrics["misconception_rank_above_3"])
+    mlflow.log_metric("misconception_no_match_in_top_k", metrics["misconception_no_match_in_top_k"])
+
+    return metrics
+
+
+
+##########################################################<-END->###############################################################
+
+############################################################<-TRAINER->###########################################################
+training_args = TrainingArguments(
+    output_dir                   = "MAP_EXP_18",
+    eval_strategy                = "steps",
+    save_strategy                = "no",
+    logging_strategy             = "steps",
+    logging_steps                = 100,
+    eval_steps                   = 500,
+    learning_rate                = 1e-4,
+    per_device_train_batch_size  = 16,
+    per_device_eval_batch_size   = 32,
+    lr_scheduler_type            = "cosine",
+    warmup_ratio                 = 0.05,
+    report_to                    = "mlflow",
+    group_by_length              = True,
+    max_grad_norm                = 1.0,
+    weight_decay                 = 0.01,
+    num_train_epochs             = 2,
+    label_names                  = ['category_label', 'misconception_label']
+)
+
+
+trainer = Trainer(
+    model,
+    args           = training_args,
+    train_dataset  = train_ds,
+    eval_dataset   = val_ds,
+    tokenizer      = tokenizer,
+    compute_metrics = compute_multi_map,
+    data_collator  = DataCollatorWithPadding(tokenizer)
+    
+)
+##########################################################<-END->###############################################################
+
+if __name__ == "__main__":
+    
+    trainer.train()
+    trainer.save_model("MAP_EXP_18")
+
+    source_file           = "MAP_EXP_18.py"
+    destination_directory = "MAP_EXP_18"
+    
+    shutil.copy(source_file, destination_directory)
+    print(f"File '{source_file}' copied to '{destination_directory}'")
+    
+    print("Training completed and model saved!")

README.md

@@ -0,0 +1,202 @@
+---
+base_model: MathGenie/MathCoder2-DeepSeekMath-7B
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.15.2

adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": null,
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