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 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries and read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.getLogger(\"lightning.pytorch.utilities.rank_zero\").setLevel(logging.WARNING)\n",
    "logging.getLogger(\"lightning.pytorch.accelerators.cuda\").setLevel(logging.WARNING)\n",
    "from time import time\n",
    "from pandas import DataFrame\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch import cuda\n",
    "from lightning import Trainer\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "from ideal_init.lightning_model import NNClassifier, merge_logs\n",
    "\n",
    "# Define training parameters\n",
    "BATCH_SIZE = 256\n",
    "LEARNING_RATE = 1e-1\n",
    "HIDDEN_SIZES = (10,)\n",
    "DIRECTORY = \"./\"\n",
    "EPOCHS = 10\n",
    "DATASET_NAME = \"Handwritten digits\"\n",
    "\n",
    "def experiment_data():\n",
    "    X, y = load_digits(return_X_y=True)\n",
    "    # Split data: 70% train, 10% validation, 20% test\n",
    "    X, X_test, y, y_test = train_test_split(X, y, test_size=0.2)\n",
    "    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.125)\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run experiments and measure performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep record of the results of all the iterations\n",
    "values=[\"IDEAL_acc_before_train\", \"IDEAL_acc_after_train\", \"IDEAL_init_time\", \"IDEAL_train_time\", \"He_acc_before_train\", \"He_acc_after_train\", \"He_init_time\", \"He_train_time\"]\n",
    "results = {value: [] for value in values}\n",
    "\n",
    "# repeat experiment multiple times\n",
    "NUM_EXPERIMENTS = 10\n",
    "for experiment in range(NUM_EXPERIMENTS):\n",
    "\n",
    "    print(f\"Running experiment {experiment+1}\")\n",
    "    X_train, y_train, X_val, y_val, X_test, y_test = experiment_data()\n",
    "\n",
    "    # Create models\n",
    "    init_model = NNClassifier(X_train, y_train, X_val, y_val, X_test, y_test, initialize=True, hidden_sizes=HIDDEN_SIZES, learning_rate=LEARNING_RATE, batch_size=BATCH_SIZE)\n",
    "    no_init_model = NNClassifier(X_train, y_train, X_val, y_val, X_test, y_test, initialize=False, hidden_sizes=HIDDEN_SIZES, learning_rate=LEARNING_RATE, batch_size=BATCH_SIZE)\n",
    "\n",
    "    # Create trainers\n",
    "    init_trainer = Trainer(default_root_dir=DIRECTORY, accelerator=\"auto\", devices=\"auto\", max_epochs=EPOCHS, logger=CSVLogger(save_dir=DIRECTORY), enable_checkpointing=False, enable_progress_bar=False, enable_model_summary=False, val_check_interval=1, log_every_n_steps=1, limit_val_batches=1, precision=64)\n",
    "    no_init_trainer = Trainer(default_root_dir=DIRECTORY, accelerator=\"auto\", devices=\"auto\", max_epochs=EPOCHS, logger=CSVLogger(save_dir=DIRECTORY), enable_checkpointing=False, enable_progress_bar=False, enable_model_summary=False, val_check_interval=1, log_every_n_steps=1, limit_val_batches=1, precision=64)\n",
    "\n",
    "    # Test models before training\n",
    "    results[\"IDEAL_acc_before_train\"].append(init_trainer.test(init_model, verbose=False)[0][\"test_metric\"])\n",
    "    results[\"He_acc_before_train\"].append(no_init_trainer.test(no_init_model, verbose=False)[0][\"test_metric\"])\n",
    "\n",
    "    # Train models and plot training comparison\n",
    "    init_time = time()\n",
    "    init_trainer.validate(init_model)\n",
    "    init_trainer.fit(init_model)\n",
    "    init_time = time() - init_time\n",
    "    init_model_logs = f\"{init_trainer.logger.log_dir}/metrics.csv\"\n",
    "\n",
    "    no_init_time = time()\n",
    "    no_init_trainer.validate(no_init_model)\n",
    "    no_init_trainer.fit(no_init_model)\n",
    "    no_init_time = time() - no_init_time\n",
    "    no_init_model_logs = f\"{no_init_trainer.logger.log_dir}/metrics.csv\"\n",
    "\n",
    "    logs = merge_logs(init_model_logs, no_init_model_logs)\n",
    "    logs[\"dataset\"] = DATASET_NAME\n",
    "\n",
    "    # Test models after training\n",
    "    results[\"IDEAL_acc_after_train\"].append(init_trainer.test(init_model, verbose=False)[0][\"test_metric\"])\n",
    "    results[\"He_acc_after_train\"].append(no_init_trainer.test(no_init_model, verbose=False)[0][\"test_metric\"])\n",
    "\n",
    "    # Init and train times\n",
    "    results[\"IDEAL_init_time\"].append(init_model.init_time)\n",
    "    results[\"IDEAL_train_time\"].append(init_time)\n",
    "    results[\"He_init_time\"].append(no_init_model.init_time)\n",
    "    results[\"He_train_time\"].append(no_init_time)\n",
    "\n",
    "    # store logs\n",
    "    with open(\"results.csv\", \"a\", encoding=\"utf8\") as results_file:\n",
    "        logs.to_csv(results_file, header=False, index=False, quoting=1)\n",
    "\n",
    "    #clear cache\n",
    "    del init_model, no_init_model\n",
    "    del init_trainer, no_init_trainer\n",
    "    del X_train, y_train, X_val, y_val, X_test, y_test\n",
    "    del logs\n",
    "    cuda.empty_cache()\n",
    "\n",
    "results = DataFrame(results)\n",
    "mean = results.mean()\n",
    "error = results.sem()*1.96\n",
    "print(\"Mean values:\")\n",
    "print(mean.astype(str).apply(lambda x: x[:6]))\n",
    "print(\"Confidence error:\")\n",
    "print(error.astype(str).apply(lambda x: x[:6]))"
   ]
  }
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