Week7 exercise

week7/community_contributions/Week_7_exercise_final.py

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+# -*- coding: utf-8 -*-
+"""Week7_Exercise.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1wxcBNWbsDDC_SwXnQZP2dmo7ddOxkJmU
+"""
+
+#my pip installtions (some of them were not used in the project but i initally planned to use them (and we're left here so that I after the project i revisit the notebook and update when i have time and more sources.))
+!pip install -q --upgrade pip
+!pip install -q transformers accelerate peft bitsandbytes trl sentencepiece safetensors
+!pip install -q wandb
+!pip install -q git+https://github.com/huggingface/peft.git@main
+!pip install datasets==3.0.1
+!pip install evaluate -q
+!pip install --upgrade scikit-learn
+
+#All imports
+import os, random, json, re
+import pandas as pd
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from sklearn.model_selection import train_test_split
+from datasets import load_dataset
+from IPython.display import Markdown as md
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    TrainingArguments,
+    Trainer,
+    DataCollatorForLanguageModeling
+)
+
+#I tried a lot of models and every time my colab ran out of RAM, i did a lot of tweakings including replacing models with smaller ones
+#I also used try and error to come up with samples size, eval size etc that would fit in my limited T4 Ram (had started from sample size of 15k down to 200)
+MODEL_NAME = "facebook/opt-125m"
+SAMPLE_SIZE = 200
+EVAL_SIZE = 50
+MAX_LENGTH = 128
+RANDOM_SEED = 42
+
+#Seeting LoRa hyper parameters
+LORA_R = 4
+LORA_ALPHA = 8
+LORA_DROPOUT = 0.05
+#Target modules to apply LoRA. I kept these to just "q_proj" and "v_proj" to lower memory usage on a T4 GPU.
+TARGET_MODULES = ["q_proj", "v_proj"]
+
+#to make sure thes expriment is reproducible
+random.seed(RANDOM_SEED)
+np.random.seed(RANDOM_SEED)
+torch.manual_seed(RANDOM_SEED)
+
+#Hf data
+DATASET_NAME = "McAuley-Lab/Amazon-Reviews-2023"
+SUBSET = "raw_meta_Appliances"
+
+#loading the data
+dataset = load_dataset(DATASET_NAME, SUBSET, split="full")
+df = dataset.to_pandas()
+
+from datasets import Dataset, DatasetDict
+
+# #this took forever to run making me update it
+
+# Split into train/eval
+split = dataset.train_test_split(test_size=0.2, seed=42)
+train_dataset = split["train"]
+eval_dataset = split["test"]
+
+# Reduce dataset sizes for quick experimentation
+MAX_TRAIN_SAMPLES = 2000   # or 2000 if you want it even faster
+MAX_EVAL_SAMPLES = 500
+
+train_dataset = train_dataset.shuffle(seed=42).select(range(min(MAX_TRAIN_SAMPLES, len(train_dataset))))
+eval_dataset = eval_dataset.shuffle(seed=42).select(range(min(MAX_EVAL_SAMPLES, len(eval_dataset))))
+
+
+
+# Wrap into a DatasetDict for Trainer compatibility
+dataset = DatasetDict({"train": train_dataset, "eval": eval_dataset})
+
+# Prepare columns for your preprocessing
+# Rename relevant columns to match what preprocess_function expects
+dataset = dataset.rename_columns({
+    "title": "input",
+    "price": "output"
+})
+
+# Add a fixed instruction since your dataset doesn’t have one
+def add_instruction(example):
+    example["instruction"] = "Estimate the fair market price of this product in USD. Return only a single number."
+    return example
+
+dataset = dataset.map(add_instruction)
+
+print(dataset)
+print(dataset["train"][0])
+
+# somecleaning on prices
+df["price_clean"] = pd.to_numeric(df["price"], errors="coerce")
+
+# Bringing the text fields togeter
+def combine_text(row):
+    title = row["title"] or ""
+    features = " ".join(row["features"]) if isinstance(row["features"], list) else ""
+    description = " ".join(row["description"]) if isinstance(row["description"], list) else ""
+    return f"TITLE: {title}\n\nFEATURES: {features}\n\nDESCRIPTION: {description}"
+
+df["text"] = df.apply(combine_text, axis=1)
+
+df_clean = df.dropna(subset=["price_clean"]).reset_index(drop=True)
+
+# trying to downsamble for RAM purposes-hoping the punshment to results wasn't much
+if len(df_clean) > SAMPLE_SIZE + EVAL_SIZE:
+    df_sample = df_clean.sample(SAMPLE_SIZE + EVAL_SIZE, random_state=RANDOM_SEED).reset_index(drop=True)
+else:
+    df_sample = df_clean.copy()
+
+train_df, eval_df = train_test_split(df_sample, test_size=EVAL_SIZE, random_state=RANDOM_SEED)
+
+# FHow to format the examples
+def make_example(row):
+    instruction = "Estimate the fair market price of this product in USD. Return only a single number."
+    input_text = row["text"]
+    output = f"{float(row['price_clean']):.2f}"
+    return {"instruction": instruction, "input": input_text, "output": output}
+
+train_examples = [make_example(r) for _, r in train_df.iterrows()]
+eval_examples  = [make_example(r) for _, r in eval_df.iterrows()]
+
+# Saving into JSONL
+with open("pricing_train.jsonl", "w") as f:
+    for ex in train_examples:
+        f.write(json.dumps(ex) + "\n")
+
+with open("pricing_eval.jsonl", "w") as f:
+    for ex in eval_examples:
+        f.write(json.dumps(ex) + "\n")
+
+#A good formating for the llm
+def format_for_model(ex):
+    return f"### Instruction:\n{ex['instruction']}\n\n### Input:\n{ex['input']}\n\n### Response:\n{ex['output']}"
+
+#seeing the examples
+print("Example formatted prompts (3):")
+
+#iterating over the egs
+for ex in train_examples[:3]:
+    print(format_for_model(ex))
+    print("-"*80)
+
+#tokenization now
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto")
+
+#check ifthe model successful
+print(f"{MODEL_NAME} succceeded")
+
+# Sample random evaluation
+sample_eval = random.sample(eval_examples, 10)
+
+baseline_preds, baseline_truths = [], []
+
+#iteration over the evals
+for ex in sample_eval:
+    prompt = f"### Instruction:\n{ex['instruction']}\n\n### Input:\n{ex['input']}\n\n### Response:"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output = model.generate(**inputs, max_new_tokens=50, temperature=0.2, do_sample=False)
+    text_output = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    # Extract numeric prediction from model output
+    match = re.search(r"\$?(\d+(\.\d+)?)", text_output)
+    pred_price = float(match.group(1)) if match else None
+    true_price = float(ex["output"])
+
+    if pred_price is not None:
+        baseline_preds.append(pred_price)
+        baseline_truths.append(true_price)
+
+    print(f"Predicted: {pred_price}, True: {true_price}")
+
+# Manual computation of metrics
+if baseline_preds:
+    mae = mean_absolute_error(baseline_truths, baseline_preds)
+    mse = mean_squared_error(baseline_truths, baseline_preds)
+    rmse = mse ** 0.5  # take square root manually
+    print(f"\nBaseline MAE: ${mae:.2f}")
+    print(f"Baseline RMSE: ${rmse:.2f}")
+
+#inspectthe data a little
+print(dataset)
+print(dataset["train"].column_names)
+print(dataset["train"][0])  # show one sample
+
+def preprocess_function(examples):
+    prompts = [
+        f"### Instruction:\n{instr}\n\n### Input:\n{inp}\n\n### Response:\n{out}"
+        for instr, inp, out in zip(examples["instruction"], examples["input"], examples["output"])
+    ]
+    return tokenizer(prompts, truncation=True, padding="max_length", max_length=MAX_LENGTH)
+
+tokenized_datasets = dataset.map(preprocess_function, batched=True)
+
+data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
+
+#updated for faster exp
+training_args = TrainingArguments(
+    output_dir="./price-predictor-checkpoints",
+    num_train_epochs=1,  # ⬅️ change from 2 to 1
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=2,
+    learning_rate=2e-4,
+    fp16=True,
+    save_total_limit=1,
+    logging_steps=10,
+    report_to="none",
+)
+
+#our trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["eval"],
+    tokenizer=tokenizer,
+    data_collator=data_collator
+)
+
+#outcomes
+train_result = trainer.train()
+trainer.save_model("./price-predictor-finetuned")
+
+# Loading fine-tuned model
+model = AutoModelForCausalLM.from_pretrained("./price-predictor-finetuned", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("./price-predictor-finetuned")
+
+#small evaluation subset
+eval_dataset_small = dataset["eval"].shuffle(seed=42).select(range(min(50, len(dataset["eval"]))))
+pred_prices, true_prices = [], []
+
+# #iteration Over the tqdm
+# for ex in tqdm(eval_dataset_small, desc="Evaluating"):
+#     prompt = f"### Instruction:\nEstimate the fair market price of this product in USD. Return only a single number.\n\n### Input:\n{ex['input']}\n\n### Response:"
+#     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+#     with torch.no_grad():
+#         output = model.generate(**inputs, max_new_tokens=20)
+#     text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+#     # Extract numeric prediction
+#     numbers = re.findall(r"[-+]?\d*\.\d+|\d+", text)
+#     pred = float(numbers[-1]) if numbers else np.nan
+
+#     pred_prices.append(pred)
+#     true_prices.append(float(ex["output"]))
+
+
+# Safe evaluation loop
+for ex in tqdm(eval_dataset_small, desc="Evaluating"):
+    # Skip if output is missing or invalid
+    try:
+        true_val = float(ex["output"])
+    except (ValueError, TypeError):
+        continue  # skip this example
+
+    prompt = (
+        "### Instruction:\nEstimate the fair market price of this product in USD. "
+        "Return only a single number.\n\n"
+        f"### Input:\n{ex['input']}\n\n### Response:"
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        output = model.generate(**inputs, max_new_tokens=20)
+
+    text = tokenizer.decode(output[0], skip_special_tokens=True)
+    numbers = re.findall(r"[-+]?\d*\.\d+|\d+", text)
+    pred = float(numbers[-1]) if numbers else np.nan
+
+    pred_prices.append(pred)
+    true_prices.append(true_val)
+
+# Filter out invalid predictions
+mask = ~np.isnan(pred_prices)
+
+pred_prices = np.array(pred_prices)[mask]
+
+# Convert to numpy arrays and align lengths
+pred_prices = np.array(pred_prices, dtype=float)
+true_prices = np.array(true_prices, dtype=float)
+
+# Ensure equal lengths just in case (zip trims to shortest)
+min_len = min(len(pred_prices), len(true_prices))
+pred_prices = pred_prices[:min_len]
+true_prices = true_prices[:min_len]
+
+# Drop NaNs safely
+mask = ~np.isnan(pred_prices)
+pred_prices = pred_prices[mask]
+true_prices = true_prices[mask]
+
+# Compute metrics manually again
+mae = mean_absolute_error(true_prices, pred_prices)
+
+mse = mean_squared_error(true_prices, pred_prices)
+
+rmse = np.sqrt(mse)
+
+r2 = r2_score(true_prices, pred_prices)
+
+print(f"Fine-Tuned Evaluation:\nMAE: ${mae:.2f}, RMSE: ${rmse:.2f}, R²: {r2:.4f}")
+
+#see what was predicted
+plt.figure(figsize=(6,6))
+plt.scatter(true_prices, pred_prices, alpha=0.5)
+plt.plot([0, max(true_prices)], [0, max(true_prices)], 'r--', label="Perfect Prediction")
+plt.xlabel("Actual Price (USD)")
+plt.ylabel("Predicted Price (USD)")
+plt.title("Predicted vs Actual Prices")
+plt.legend()
+plt.grid(True)
+plt.show()
+
+#check the distribution
+errors = np.abs(pred_prices - true_prices)
+plt.figure(figsize=(8,4))
+plt.hist(errors, bins=30, edgecolor='k', alpha=0.7)
+plt.title("Distribution of Absolute Errors")
+plt.xlabel("Absolute Error ($)")
+plt.ylabel("Frequency")
+plt.show()
+
+print(f"Average Error: ${np.mean(errors):.2f}, Median Error: ${np.median(errors):.2f}")