{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import logging\n",
    "import os\n",
    "from pathlib import Path\n",
    "import click\n",
    "from dotenv import find_dotenv, load_dotenv\n",
    "\n",
    "from datasets import load_dataset, DatasetDict, Image, Dataset, concatenate_datasets\n",
    "import wandb\n",
    "import yaml\n",
    "from transformers.trainer_callback import EarlyStoppingCallback\n",
    "from artifact_classification.utils import ConfigLoader\n",
    "from torchvision.transforms import (\n",
    "    Compose,\n",
    "    Normalize,\n",
    "    ToTensor,\n",
    "    CenterCrop,\n",
    "    Resize,\n",
    ")\n",
    "from transformers import (\n",
    "    AutoImageProcessor,\n",
    "    AutoModelForImageClassification,\n",
    "    TrainingArguments,\n",
    "    Trainer,\n",
    "    DefaultDataCollator,\n",
    "    AutoModelForSequenceClassification,\n",
    "    DataCollatorWithPadding,\n",
    "    AutoTokenizer,\n",
    ")\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import gc\n",
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating with:\n",
      "{'config': 'bm5-white_date', 'dataset': 'james-burton/BritishMuseum-white', 'wandb_proj_name': 'British Museum', 'model_base': 'google/efficientnet-b3', 'problem_type': 'image', 'lower_lim': 5, 'label_col': 'production_date_middle'}\n",
      "\n",
      "\n",
      "{'config': 'bm5-white_date', 'fast_dev_run': False, 'do_train': True, 'do_predict': True, 'batch_size': 128, 'output_root': 'models/', 'num_epochs': 100, 'early_stopping_patience': 5, 'grad_accumulation_steps': 1, 'seed': 42, 'logging_steps': 10, 'lr_scheduler': 'linear', 'warmup_ratio': 0, 'weight_decay': 0, 'device': 'cuda', 'num_workers': 1, 'resume_from_checkpoint': False, 'predict_batch_size': 16, 'save_total_limit': 1, 'lr': 5e-05, 'pytorch2_0': True, 'max_length': 512, 'text_column': 'Description', 'fp16': True, 'testset_size': 0.13, 'dataset': 'james-burton/BritishMuseum-white', 'wandb_proj_name': 'British Museum', 'model_base': 'google/efficientnet-b3', 'problem_type': 'image', 'lower_lim': 5, 'label_col': 'production_date_middle'}\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d6829164a6f4fb9a4dc06f32ef81a23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading dataset shards:   0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "config = \"bm5-white_date\"\n",
    "\n",
    "args = ConfigLoader(config, \"../configs/train_bm_configs.yaml\", \"../configs/train_bm_default.yaml\")\n",
    "if args.problem_type == \"image\":\n",
    "    fts = [\"image\", args.label_col]\n",
    "elif args.problem_type == \"text\":\n",
    "    fts = [args.text_column, args.label_col]\n",
    "ds_lim = (\n",
    "    load_dataset(args.dataset)[\"train\"]\n",
    "    .select_columns(fts)\n",
    "    .to_pandas()\n",
    "    .dropna(subset=[args.label_col], inplace=False)\n",
    ")\n",
    "\n",
    "num_counts = ds_lim[args.label_col].value_counts()\n",
    "ds_lim = ds_lim[ds_lim[args.label_col].isin(num_counts[num_counts > args.lower_lim].index)]\n",
    "\n",
    "train, val_test = train_test_split(\n",
    "    ds_lim,\n",
    "    stratify=ds_lim[args.label_col],\n",
    "    test_size=2 * args.testset_size,\n",
    "    random_state=42,\n",
    ")\n",
    "val, test = train_test_split(\n",
    "    val_test, stratify=val_test[args.label_col], test_size=0.5, random_state=42\n",
    ")\n",
    "del ds_lim, val_test, num_counts\n",
    "gc.collect()\n",
    "\n",
    "# Chunking is necessary as the dataset is too large to fit into memory\n",
    "chunk_size = 10000\n",
    "train_chunk_data = []\n",
    "for i in range(0, len(train), chunk_size):\n",
    "    train_chunk = train[i : i + chunk_size]\n",
    "    train_chunk_dataset = Dataset.from_pandas(train_chunk)\n",
    "    train_chunk_data.append(train_chunk_dataset)\n",
    "\n",
    "# Concatenate the chunks into a single Dataset\n",
    "train = concatenate_datasets(train_chunk_data)\n",
    "val = Dataset.from_pandas(val)\n",
    "test = Dataset.from_pandas(test)\n",
    "dataset = DatasetDict(\n",
    "    {\n",
    "        \"train\": train,\n",
    "        \"validation\": val,\n",
    "        \"test\": test,\n",
    "    }\n",
    ")\n",
    "dataset = dataset.rename_column(args.label_col, \"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5.4293,    nan,    nan,  ...,    nan, 5.6664,    nan])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.log(torch.tensor(dataset[\"train\"][\"label\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_data = np.array(torch.tensor(dataset[\"train\"][\"label\"])).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "scaled_data = scaler.fit_transform(normal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(scaled_data, bins=100)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142208, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(normal_data, bins=100)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import gaussian_filter1d\n",
    "from scipy.signal.windows import triang\n",
    "from scipy.ndimage import convolve1d\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def get_lds_kernel_window(lds_kernel=\"gaussian\", lds_ks=9, lds_sigma=1):\n",
    "    r\"\"\"Function to determine the label distribution smoothing kernel window\n",
    "\n",
    "    lds_kernel (str): LDS kernel type\n",
    "    lds_ks (int): LDS kernel size (should be an odd number).\n",
    "    lds_sigma (float): LDS gaussian/laplace kernel sigma\n",
    "    \"\"\"\n",
    "\n",
    "    assert lds_kernel in [\"gaussian\", \"triang\", \"laplace\"]\n",
    "    half_ks = (lds_ks - 1) // 2\n",
    "\n",
    "    if lds_kernel == \"gaussian\":\n",
    "        base_kernel = [0.0] * half_ks + [1.0] + [0.0] * half_ks\n",
    "        kernel_window = gaussian_filter1d(base_kernel, sigma=lds_sigma) / max(\n",
    "            gaussian_filter1d(base_kernel, sigma=lds_sigma)\n",
    "        )\n",
    "    elif lds_kernel == \"triang\":\n",
    "        kernel_window = triang(lds_ks)\n",
    "    else:\n",
    "\n",
    "        def laplace(x):\n",
    "            return np.exp(-abs(x) / lds_sigma) / (2.0 * lds_sigma)\n",
    "\n",
    "        kernel_window = list(map(laplace, np.arange(-half_ks, half_ks + 1))) / max(\n",
    "            map(laplace, np.arange(-half_ks, half_ks + 1))\n",
    "        )\n",
    "\n",
    "    return kernel_window\n",
    "\n",
    "\n",
    "def prepare_LDS_weights(\n",
    "    labels,\n",
    "    n_bins=None,\n",
    "    label_range=None,\n",
    "    reweight=\"inv\",\n",
    "    lds_kernel=\"gaussian\",\n",
    "    lds_ks=9,\n",
    "    lds_sigma=1,\n",
    "    max_rel_weight=None,\n",
    "    show_plot=True,\n",
    "):\n",
    "\n",
    "    assert reweight in {\"inv\", \"sqrt_inv\"}\n",
    "    labels_shape = labels.shape\n",
    "    if n_bins is None:\n",
    "        labels = labels.astype(int)\n",
    "        n_bins = np.max(labels) - np.min(labels)\n",
    "    num_per_label, bin_edges = np.histogram(labels, bins=n_bins, range=label_range)\n",
    "    new_labels = np.searchsorted(bin_edges, labels, side=\"left\")\n",
    "    new_labels[new_labels == 0] = 1\n",
    "    if reweight == \"sqrt_inv\":\n",
    "        num_per_label = np.sqrt(num_per_label)\n",
    "    lds_kernel_window = get_lds_kernel_window(\n",
    "        lds_kernel=lds_kernel, lds_ks=lds_ks, lds_sigma=lds_sigma\n",
    "    )\n",
    "    smoothed_value = convolve1d(num_per_label, weights=lds_kernel_window, mode=\"constant\")\n",
    "    if show_plot:\n",
    "        plt.figure(figsize=(20, 6))  # Set the figure size to 10 inches by 6 inches\n",
    "        plt.bar(\n",
    "            bin_edges[:-1],\n",
    "            num_per_label / num_per_label.sum(),\n",
    "            width=(bin_edges[1] - bin_edges[0]),\n",
    "            color=\"lime\",\n",
    "            edgecolor=\"black\",\n",
    "            label=\"original\",\n",
    "        )\n",
    "        plt.plot(\n",
    "            bin_edges[:-1], smoothed_value / smoothed_value.sum(), color=\"red\", label=\"smoothed\"\n",
    "        )\n",
    "        plt.title(f\"Label distribution by bin (reweight={reweight})\")\n",
    "        plt.legend(loc=\"best\")\n",
    "        plt.show()\n",
    "    num_per_label = smoothed_value[new_labels.flatten() - 1].reshape(*labels_shape)\n",
    "    weights = 1 / num_per_label\n",
    "    weights[num_per_label == 0] = 0\n",
    "    if max_rel_weight is not None:\n",
    "        weights = np.clip(weights, None, np.min(weights) * max_rel_weight)\n",
    "    weights = weights / weights.sum() * len(labels)\n",
    "    return torch.Tensor(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.6786],\n",
       "        [1.0187],\n",
       "        [4.2340],\n",
       "        ...,\n",
       "        [1.3606],\n",
       "        [0.5498],\n",
       "        [0.7079]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_LDS_weights(\n",
    "    # scaled_data,\n",
    "    normal_data,\n",
    "    n_bins=5021,\n",
    "    label_range=(-3000, 2021),\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.7511],\n",
       "        [0.9125],\n",
       "        [4.6445],\n",
       "        ...,\n",
       "        [0.8185],\n",
       "        [0.5370],\n",
       "        [0.7946]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_LDS_weights(\n",
    "    scaled_data,\n",
    "    # normal_data,\n",
    "    n_bins=5021,\n",
    "    # label_range=(-3000, 2021),\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_less_than_3000 = np.sum(normal_data < -3000)\n",
    "sum_greater_than_500 = np.sum(normal_data > 500)\n",
    "\n",
    "normal_data_3000_500 = normal_data[(normal_data > -3000) & (normal_data < 500)].reshape(-1, 1)\n",
    "\n",
    "\n",
    "def transform_labels(data, scaler=None):\n",
    "    r\"\"\"Function to transform the labels\"\"\"\n",
    "    lower_bound = -3000\n",
    "    upper_bound = 500\n",
    "    data = np.clip(data, lower_bound, upper_bound)\n",
    "    data = -data\n",
    "    lower_bound = -upper_bound\n",
    "    upper_bound = -lower_bound\n",
    "    data = np.log(data - lower_bound + 1)\n",
    "\n",
    "    if scaler == None:\n",
    "        scaler = RobustScaler()\n",
    "        scaled_data = scaler.fit_transform(data)\n",
    "        return scaled_data, scaler\n",
    "    else:\n",
    "        scaled_data = scaler.transform(data)\n",
    "        return scaled_data\n",
    "\n",
    "\n",
    "scaled_data, scaler = transform_labels(normal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data_3000_500 = scaler.fit_transform(normal_data_3000_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.9194],\n",
       "        [3.0496],\n",
       "        [4.3712],\n",
       "        ...,\n",
       "        [0.9326],\n",
       "        [0.5665],\n",
       "        [0.7626]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_LDS_weights(\n",
    "    scaled_data_3000_500,\n",
    "    # normal_data,\n",
    "    n_bins=3500,\n",
    "    # label_range=(-3000, 2021),\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.079927 ],\n",
       "       [7.790696 ],\n",
       "       [7.716906 ],\n",
       "       ...,\n",
       "       [7.862882 ],\n",
       "       [8.098643 ],\n",
       "       [7.9606724]], dtype=float32)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def move_and_log_scale(data, lower_bound, upper_bound):\n",
    "    r\"\"\"Function to move and log scale the data\n",
    "\n",
    "    data (np.array): input data\n",
    "    lower_bound (float): lower bound of the data\n",
    "    upper_bound (float): upper bound of the data\n",
    "    \"\"\"\n",
    "\n",
    "    data = np.clip(data, lower_bound, upper_bound)\n",
    "    data = np.log(data - lower_bound + 1)\n",
    "    return data\n",
    "\n",
    "\n",
    "move_and_log_scale(normal_data, -3000, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.9963],\n",
       "        [0.5624],\n",
       "        [1.2755],\n",
       "        ...,\n",
       "        [0.7193],\n",
       "        [0.8355],\n",
       "        [0.7722]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_LDS_weights(\n",
    "    # move_and_log_scale(normal_data, -3000, 500),\n",
    "    scaler.fit_transform(move_and_log_scale(-normal_data_3000_500, -500, 3000)),\n",
    "    # normal_data,\n",
    "    n_bins=1750,\n",
    "    # label_range=(-3000, 2021),\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))  # Set the figure size to 10 inches by 6 inches\n",
    "plt.hist(normal_data, bins=1750)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))  # Set the figure size to 10 inches by 6 inches\n",
    "plt.hist(move_and_log_scale(-normal_data_3000_500, -500, 3000), bins=1750)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.3726935], dtype=float32)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(scaled_data_3000_500_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data_3000_500 = scaler.fit_transform(move_and_log_scale(-normal_data_3000_500, -500, 3000))\n",
    "scaled_data_3000_500_2 = scaler.fit_transform(normal_data_3000_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7366078 -0.06513282\n",
      "1.016828 -0.23512606\n"
     ]
    }
   ],
   "source": [
    "print(np.std(scaled_data_3000_500), np.mean(scaled_data_3000_500))\n",
    "print(np.std(scaled_data_3000_500_2), np.mean(scaled_data_3000_500_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.31282866] [1.1173744]\n",
      "[-2.1683104] [5.4529696]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew, kurtosis\n",
    "\n",
    "print(skew(scaled_data_3000_500), kurtosis(scaled_data_3000_500))\n",
    "print(skew(scaled_data_3000_500_2), kurtosis(scaled_data_3000_500_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))  # Set the figure size to 10 inches by 6 inches\n",
    "plt.hist(scaled_data_3000_500, bins=1750)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))  # Set the figure size to 10 inches by 6 inches\n",
    "plt.hist(scaled_data_3000_500_2, bins=1750)\n",
    "plt.xlabel(\"Scaled Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Histogram of Scaled Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = prepare_LDS_weights(\n",
    "    scaled_data_3000_500,\n",
    "    # normal_data,\n",
    "    n_bins=1750,\n",
    "    # label_range=(-3000, 2021),\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([135265, 1])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(135265, 1)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data_3000_500.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['image', 'label', '__index_level_0__'],\n",
       "    num_rows: 142208\n",
       "})"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating with:\n",
      "{'config': 'bm5-white_date', 'dataset': 'james-burton/BritishMuseum-white', 'wandb_proj_name': 'British Museum', 'model_base': 'google/efficientnet-b3', 'problem_type': 'image', 'lower_lim': 5, 'label_col': 'production_date_middle'}\n",
      "\n",
      "\n",
      "{'config': 'bm5-white_date', 'fast_dev_run': False, 'do_train': True, 'do_predict': True, 'batch_size': 128, 'output_root': 'models/', 'num_epochs': 100, 'early_stopping_patience': 5, 'grad_accumulation_steps': 1, 'seed': 42, 'logging_steps': 10, 'lr_scheduler': 'linear', 'warmup_ratio': 0, 'weight_decay': 0, 'device': 'cuda', 'num_workers': 1, 'resume_from_checkpoint': False, 'predict_batch_size': 16, 'save_total_limit': 1, 'lr': 5e-05, 'pytorch2_0': True, 'max_length': 512, 'text_column': 'Description', 'fp16': True, 'testset_size': 0.13, 'dataset': 'james-burton/BritishMuseum-white', 'wandb_proj_name': 'British Museum', 'model_base': 'google/efficientnet-b3', 'problem_type': 'image', 'lower_lim': 5, 'label_col': 'production_date_middle'}\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e187f6a472740f48b81372e9d417050",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading dataset shards:   0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "107788"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = \"bm5-white_date\"\n",
    "\n",
    "args = ConfigLoader(config, \"../configs/train_bm_configs.yaml\", \"../configs/train_bm_default.yaml\")\n",
    "\n",
    "\n",
    "# Load dataset, filter out na inputs and labels and encode labels (as label column can change)\n",
    "if args.problem_type == \"image\":\n",
    "    fts = [\"image\", args.label_col]\n",
    "elif args.problem_type == \"text\":\n",
    "    fts = [args.text_column, args.label_col]\n",
    "ds_lim = (\n",
    "    load_dataset(args.dataset)[\"train\"]\n",
    "    .select_columns(fts)\n",
    "    .to_pandas()\n",
    "    .dropna(subset=[args.label_col], inplace=False)\n",
    ")\n",
    "\n",
    "# num_counts = ds_lim[args.label_col].value_counts()\n",
    "# ds_lim = ds_lim[ds_lim[args.label_col].isin(num_counts[num_counts > args.lower_lim].index)]\n",
    "\n",
    "ds_lim = ds_lim[(ds_lim[args.label_col] > -3000) & (ds_lim[args.label_col] < 500)]\n",
    "\n",
    "train, val_test = train_test_split(\n",
    "    ds_lim,\n",
    "    # stratify=ds_lim[args.label_col],\n",
    "    test_size=2 * args.testset_size,\n",
    "    random_state=42,\n",
    ")\n",
    "val, test = train_test_split(\n",
    "    val_test,\n",
    "    # stratify=val_test[args.label_col],\n",
    "    test_size=0.5,\n",
    "    random_state=42,\n",
    ")\n",
    "del ds_lim, val_test\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   68.,   283., -2050., ...,  -360., -1338.,  -900.])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[args.label_col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################## Label distribution smoothing ##############################\n",
    "\n",
    "\n",
    "def transform_labels(data, scaler=None):\n",
    "    r\"\"\"Function to transform the labels\"\"\"\n",
    "    lower_bound = -3000\n",
    "    upper_bound = 500\n",
    "    data = np.clip(data, lower_bound, upper_bound)\n",
    "    data = -data\n",
    "    lower_bound = -upper_bound\n",
    "    upper_bound = -lower_bound\n",
    "    data = np.log(data - lower_bound + 1)\n",
    "\n",
    "    if scaler is None:\n",
    "        scaler = RobustScaler()\n",
    "        scaled_data = scaler.fit_transform(data)\n",
    "        return scaled_data, scaler\n",
    "    else:\n",
    "        scaled_data = scaler.transform(data)\n",
    "        return scaled_data\n",
    "\n",
    "\n",
    "def inverse_transform_labels(data, scaler):\n",
    "    r\"\"\"Function to inverse transform the labels\"\"\"\n",
    "    inv_lower_bound = -500\n",
    "    inv_upper_bound = 3000\n",
    "\n",
    "    data = scaler.inverse_transform(data)\n",
    "\n",
    "    data = np.exp(data) - 1 + inv_lower_bound\n",
    "    data = -data\n",
    "    return data\n",
    "\n",
    "\n",
    "# scaled_data, scaler = transform_labels(normal_data)\n",
    "train_labels, scaler = transform_labels(train[args.label_col].values.reshape(-1, 1))\n",
    "train[\"label\"] = train_labels\n",
    "val[\"label\"] = transform_labels(val[args.label_col].values.reshape(-1, 1), scaler)\n",
    "test[\"label\"] = transform_labels(test[args.label_col].values.reshape(-1, 1), scaler)\n",
    "\n",
    "train[\"lds_weights\"] = prepare_LDS_weights(\n",
    "    train[args.label_col],\n",
    "    n_bins=1750,\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    "    show_plot=False,\n",
    ").reshape(-1, 1)\n",
    "val[\"lds_weights\"] = 1\n",
    "test[\"lds_weights\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = prepare_LDS_weights(\n",
    "    train[args.label_col],\n",
    "    # normal_data,\n",
    "    n_bins=1750,\n",
    "    reweight=\"sqrt_inv\",\n",
    "    lds_ks=5,\n",
    "    lds_sigma=2,\n",
    "    show_plot=False,\n",
    ").reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5124],\n",
       "        [0.7686],\n",
       "        [1.9809],\n",
       "        ...,\n",
       "        [1.2182],\n",
       "        [3.6112],\n",
       "        [3.6545]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "w.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chunking is necessary as the dataset is too large to fit into memory\n",
    "chunk_size = 10000\n",
    "train_chunk_data = []\n",
    "for i in range(0, len(train), chunk_size):\n",
    "    train_chunk = train[i : i + chunk_size]\n",
    "    train_chunk_dataset = Dataset.from_pandas(train_chunk)\n",
    "    train_chunk_data.append(train_chunk_dataset)\n",
    "\n",
    "# Concatenate the chunks into a single Dataset\n",
    "train = concatenate_datasets(train_chunk_data)\n",
    "val = Dataset.from_pandas(val)\n",
    "test = Dataset.from_pandas(test)\n",
    "dataset = DatasetDict(\n",
    "    {\n",
    "        \"train\": train,\n",
    "        \"validation\": val,\n",
    "        \"test\": test,\n",
    "    }\n",
    ")\n",
    "dataset = dataset.rename_column(args.label_col, \"label\")"
   ]
  }
 ],
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