{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3722712c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  \n",
    "\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import random\n",
    "\n",
    "from PIL import Image, ImageColor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from backend.disentangle_concepts import *\n",
    "import dnnlib \n",
    "import legacy\n",
    "from backend.color_annotations import *\n",
    "\n",
    "import random\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe7acfaf-dc61-4211-9c78-8e4433bc9deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations_file = './data/textile_annotated_files/seeds0000-100000.pkl'\n",
    "with open(annotations_file, 'rb') as f:\n",
    "    annotations = pickle.load(f)\n",
    "\n",
    "ann_df = pd.read_csv('./data/textile_annotated_files/top_three_colours.csv').fillna('#000000')\n",
    "\n",
    "with dnnlib.util.open_url('./data/textile_model_files/network-snapshot-005000.pkl') as f:\n",
    "    model = legacy.load_network_pkl(f)['G_ema'].to('cpu') # type: ignore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd114cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann_df = tohsv(ann_df)\n",
    "ann_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb64168",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(annotations['w_vectors']).reshape((len(annotations['w_vectors']), 512))\n",
    "print(X.shape)\n",
    "y_h = np.array(ann_df['H1'].values)\n",
    "y_s = np.array(ann_df['S1'].values)\n",
    "y_v = np.array(ann_df['S1'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e814959",
   "metadata": {},
   "source": [
    "### Unsupervised approaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9493f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93596853",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=20)\n",
    "\n",
    "dims_pca = pca.fit_transform(x_trainhc.T)\n",
    "dims_pca /= np.linalg.norm(dims_pca, axis=0)\n",
    "print(dims_pca.shape, np.linalg.norm(dims_pca, axis=0).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd0b1501",
   "metadata": {},
   "outputs": [],
   "source": [
    "method = 'PCA dimension'\n",
    "for sep, num in zip(dims_pca.T, range(10)):\n",
    "    images, lambdas = regenerate_images(model, original_image_vec, sep, min_epsilon=-(int(4)), max_epsilon=int(4), count=5, latent_space='W')\n",
    "    fig, axs = plt.subplots(1, len(images), figsize=(50,10))\n",
    "    fig.suptitle(method +': ' + str(num), fontsize=20)\n",
    "    for i,im in enumerate(images):\n",
    "        axs[i].imshow(im)\n",
    "        axs[i].set_title(np.round(lambdas[i], 2))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0c7808",
   "metadata": {},
   "source": [
    "## dimensionality reduction e vediamo dove finiscono i vari colori"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833ed31f",
   "metadata": {},
   "source": [
    "## clustering per vedere quali sono i centroid di questo spazio e se ci sono regioni determinate dai colori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c19e820",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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