5.analyze-stability/2.stability-across-z.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining Across Z Dimension Stability with SVCCA\n",
    "\n",
    "**Gregory Way 2018**\n",
    "\n",
    "Here, we apply Singular Vector Canonical Correlation Analysis ([Raghu et al. 2017](https://arxiv.org/abs/1706.05806 \"SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability\")) ([github](https://github.com/google/svcca)) to the neuron matrices Z to quantify model stability both across algorithms and datasets.\n",
    "We test the ability of algorithms to identify stable compressed gene expression features across z dimensions.\n",
    "\n",
    "Briefly, SVCCA uses Singular Value Decomposition (SVC) to extract the components explaining 99% of the variation.\n",
    "This is done to remove potential dimensions described by noise.\n",
    "Next, SVCCA performs a Canonical Correlation Analysis (CCA) on the SVD matrices to identify maximum correlations of linear combinations of both input matrices. The algorithm will identify the canonical correlations of highest magnitude across and within algorithms of the same dimensionality. \n",
    "\n",
    "The output of the SVCCA analysis is the SVCCA mean similarity score. This single number can be interpreted as a measure of similarity, or stability, in the solutions identified from each compression algorithm.\n",
    "\n",
    "We perform SVCCA across dimensions, and save the mean estimated SVCCA mean similarity score for each comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scripts.util import read_in_matrices, get_svcca_across_z_stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = ['TARGET', 'TCGA', 'GTEX']\n",
    "algorithms = ['pca', 'ica', 'nmf', 'dae', 'vae']\n",
    "z_dims = [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30,\n",
    "          35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating... dataset TARGET, algorithm pca, and dimension 2 vs. 3\n",
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      "Calculating... dataset TARGET, algorithm pca, and dimension 6 vs. 80\n",
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     "output_type": "stream",
     "text": [
      "Calculating... dataset GTEX, algorithm vae, and dimension 70 vs. 200\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 80 vs. 90\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 80 vs. 100\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 80 vs. 125\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 80 vs. 150\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 80 vs. 200\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 90 vs. 100\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 90 vs. 125\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 90 vs. 150\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 90 vs. 200\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 100 vs. 125\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 100 vs. 150\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 100 vs. 200\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 125 vs. 150\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 125 vs. 200\n",
      "Calculating... dataset GTEX, algorithm vae, and dimension 150 vs. 200\n"
     ]
    }
   ],
   "source": [
    "for dataset in datasets:\n",
    "    \n",
    "    dataset_svcca_results_list = []\n",
    "    for algorithm in algorithms:\n",
    "\n",
    "        for z_a in z_dims:\n",
    "\n",
    "            # Read in the first dictionary\n",
    "            z_dict_a = read_in_matrices(\n",
    "                dataset=dataset,\n",
    "                z_dim=z_a,\n",
    "                algorithm=algorithm,\n",
    "                shuffled_data=False,\n",
    "                load_weights=True\n",
    "            )\n",
    "            \n",
    "            # Only compare to higher z dimensions\n",
    "            z_bs = [x for x in z_dims if x > z_a]\n",
    "\n",
    "            for z_b in z_bs:\n",
    "\n",
    "                # Read in the second dictionary\n",
    "                z_dict_b = read_in_matrices(\n",
    "                    dataset=dataset,\n",
    "                    z_dim=z_b,\n",
    "                    algorithm=algorithm,\n",
    "                    shuffled_data=False,\n",
    "                    load_weights=True\n",
    "                )\n",
    "\n",
    "                print(\"Calculating... dataset {}, algorithm {}, and dimension {} vs. {}\"\n",
    "                      .format(dataset, algorithm, z_a, z_b))\n",
    "\n",
    "                # Perform across z dimension SVCCA\n",
    "                svcca_out = get_svcca_across_z_stability(\n",
    "                    z_dict_a=z_dict_a,\n",
    "                    z_dict_b=z_dict_b,\n",
    "                    algorithm=algorithm,\n",
    "                )\n",
    "\n",
    "                # Append info to the output dataframe\n",
    "                svcca_out = svcca_out.assign(\n",
    "                    dataset=dataset,\n",
    "                    algorithm=algorithm,\n",
    "                    z_dim_a=z_a,\n",
    "                    z_dim_b=z_b\n",
    "                )\n",
    "\n",
    "                # Append to final list\n",
    "                dataset_svcca_results_list.append(svcca_out)\n",
    "                \n",
    "    # Save dataset specific results\n",
    "    svcca_results_df = pd.concat(dataset_svcca_results_list)\n",
    "    svcca_results_df.columns = ['svcca_mean_similarity', 'dataset', 'algorithm',\n",
    "                                'z_dim_a', 'z_dim_b']\n",
    "\n",
    "    out_file = os.path.join('results',\n",
    "                            'svcca_across_z_{}_mean_correlation.tsv.gz'.format(dataset))\n",
    "    svcca_results_df.to_csv(out_file, sep='\\t', index=False, compression='gzip')"
   ]
  }
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