glmnet_cf_sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import glmnet_python\n",
    "# from glmnet import glmnet\n",
    "\n",
    "# Import relevant modules and setup for calling glmnet\n",
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "import sys\n",
    "import os\n",
    "import re\n",
    "from itertools import compress\n",
    "from glob import glob\n",
    "import pickle\n",
    "\n",
    "import tensorflow as tf # data is in TFRecord format\n",
    "\n",
    "import scipy, importlib, pprint, matplotlib.pyplot as plt, warnings\n",
    "from scipy.io import loadmat\n",
    "\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "# from glmnet import glmnet; from glmnetPlot import glmnetPlot\n",
    "# from glmnetPrint import glmnetPrint; from glmnetCoef import glmnetCoef; from glmnetPredict import glmnetPredict\n",
    "# from cvglmnet import cvglmnet; from cvglmnetCoef import cvglmnetCoef\n",
    "# from cvglmnetPlot import cvglmnetPlot; from cvglmnetPredict import cvglmnetPredict\n",
    "\n",
    "# glmnet has many deprecation warnings from using scipy.* instad of numpy.*\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "\n",
    "def plot_distributions(y, y_hat):\n",
    "\n",
    "    fig, axs = plt.subplots(2, 4, sharey=False, sharex=False, tight_layout=True)\n",
    "    fig.set_size_inches(20,10)\n",
    "\n",
    "    for ic in range(0,8):\n",
    "        idx = np.unravel_index(ic, axs.shape)\n",
    "        if ic < 4:\n",
    "            axs[idx].set_title(\"Real %s\"%names[ic])\n",
    "            x = y[:,ic]        \n",
    "        else:\n",
    "            axs[idx].set_title(\"Predicted %s\"%names[ic-4])\n",
    "            x = y_hat[:,ic-4]\n",
    "        axs[idx].hist(x, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define constants\n",
    "ncores = 28 # 56\n",
    "names = ['DA', '5HT', 'pH', 'NE']\n",
    "results_prefix = '/mnt/nfs/proj/in-vitro/Leonardo/glmnet/sklearn/fits/'\n",
    "prefix = '/mnt/nfs/proj/in-vitro/Mark/four_analyte/slow/allin'\n",
    "# good_probes = ['CF025']\n",
    "good_probes = ['CF025', 'CF027', 'CF057', 'CF064', 'CF066', 'CF078', 'CF081', 'CF082']\n",
    "# nrecords_per_session = 1 # One is special, loads one sweep per concentration per TFRecord\n",
    "# nrecords_per_session = 2 # Any other number loads that amount of records in sequence from each TFRecord\n",
    "nrecords_per_session = -1 # Loads all records\n",
    "val_ratio = .1\n",
    "hold_probe = 0\n",
    "# hold_probe = -1 # split data randomly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55485, 999) (55485, 4)\n",
      "(6165, 999) (6165, 4)\n",
      "--- 2.4435653686523438 seconds ---\n"
     ]
    }
   ],
   "source": [
    "# Load data\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "def natural_keys(text):\n",
    "    '''\n",
    "    alist.sort(key=natural_keys) sorts in human order\n",
    "    http://nedbatchelder.com/blog/200712/human_sorting.html\n",
    "    (See Toothy's implementation in the comments)\n",
    "    '''\n",
    "    def atoi(text):\n",
    "        return int(text) if text.isdigit() else text\n",
    "    return [atoi(c) for c in re.split(r'(\\d+)', text)]\n",
    "\n",
    "def preprocess(serialized_example):\n",
    "    features = tf.io.parse_example(\n",
    "        serialized_example,\n",
    "        features={\n",
    "            'gram': tf.io.FixedLenFeature([], tf.string),\n",
    "            'label': tf.io.FixedLenFeature([], tf.string)\n",
    "        })\n",
    "    data = tf.io.decode_raw(features['gram'], tf.float32)\n",
    "    label = tf.io.decode_raw(features['label'], tf.float32)\n",
    "    data.set_shape((None, 999))\n",
    "    label.set_shape((None, 4))\n",
    "    return data, label\n",
    "\n",
    "def merge_datasets(vfiles, projy=lambda x: x, asnumpy=False):\n",
    "    yv = []\n",
    "    yl = []\n",
    "    for filename in vfiles:\n",
    "        ds = tf.data.TFRecordDataset(filename)\n",
    "        ds = ds.batch(batch_size=2**13)\n",
    "        ds = ds.map(map_func=preprocess)\n",
    "        for v,l in ds:\n",
    "            v = np.array(v).astype(np.float64)\n",
    "            l = np.array(l).astype(np.float64)\n",
    "            l = np.apply_along_axis(projy, axis=1, arr=l) \n",
    "            if nrecords_per_session == 1:\n",
    "                _, ulidx = np.unique(l, return_index=True, axis=0)\n",
    "                yv.append(v[ulidx,:])\n",
    "                yl.append(l[ulidx,:])\n",
    "            elif nrecords_per_session > 0:\n",
    "                yv.append(v[:nrecords_per_session,:])\n",
    "                yl.append(l[:nrecords_per_session,:])\n",
    "            else:\n",
    "                yv.append(v)\n",
    "                yl.append(l)\n",
    "#             print(yv[-1].shape)\n",
    "#             print(yl[-1].shape)       \n",
    "    x = np.vstack(yv)\n",
    "    y = np.vstack(yl)\n",
    "\n",
    "    if asnumpy:\n",
    "        return x,y\n",
    "    else:\n",
    "        d = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "        return d\n",
    "\n",
    "if hold_probe < 0:\n",
    "    all_files = sum([\n",
    "        sorted(tf.io.gfile.glob(os.path.join(prefix, probe, 'total_records', '*')),\n",
    "               key=natural_keys) for probe in good_probes\n",
    "    ], [])\n",
    "\n",
    "    x, y = merge_datasets(all_files, asnumpy=True)\n",
    "    \n",
    "    idxs = np.random.permutation(x.shape[0])\n",
    "    lim = int(x.shape[0]*(1-val_ratio))\n",
    "    d1idx = idxs[idxs[:lim]]\n",
    "    d2idx = idxs[idxs[lim:]]\n",
    "    x_train, y_train, x_val, y_val = x[d1idx,:], y[d1idx,:], x[d2idx,:], y[d2idx,:]\n",
    "else:\n",
    "    hold_probe = good_probes.pop(hold_probe)\n",
    "    train_files = sum([\n",
    "        sorted(tf.io.gfile.glob(os.path.join(prefix, probe, 'total_records', '*')),\n",
    "               key=natural_keys) for probe in good_probes\n",
    "    ], [])\n",
    "    x_train, y_train = merge_datasets(train_files, asnumpy=True)\n",
    "    \n",
    "    val_files = sum([\n",
    "        sorted(tf.io.gfile.glob(os.path.join(prefix, probe, 'total_records', '*')),\n",
    "               key=natural_keys) for probe in [hold_probe]\n",
    "    ], [])\n",
    "    x_val, y_val = merge_datasets(val_files, asnumpy=True)\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_val.shape, y_val.shape)  \n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    }
   ],
   "source": [
    "# alphas = np.linspace(0,.9,10)\n",
    "l1_ratios = [1.,]\n",
    "print(l1_ratios)\n",
    "models = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing 10 cross-validations for alpha 1.00... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46263968.53723526, tolerance: 27565379.505566817\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 28.6min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45323505.07303238, tolerance: 27561878.216361415\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40077653.084171295, tolerance: 27638406.130073536\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29597189.931324005, tolerance: 27611684.357435733\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42496830.08595276, tolerance: 27637242.334782887\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46356768.811820984, tolerance: 27609841.813211627\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leonardo.barbosa/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:463: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30175419.354106903, tolerance: 27602538.660085507\n",
      "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: 019 out of 020\n",
      "Path: 000 out of 020\n",
      "Path: 001 out of 020\n",
      "Path: 002 out of 020\n",
      "Path: 003 out of 020\n",
      "Path: 004 out of 020\n",
      "Path: 005 out of 020\n",
      "Path: 006 out of 020\n",
      "Path: 007 out of 020\n",
      "Path: 008 out of 020\n",
      "Path: 009 out of 020\n",
      "Path: 010 out of 020\n",
      "Path: 011 out of 020\n",
      "Path: 012 out of 020\n",
      "Path: 013 out of 020\n",
      "Path: 014 out of 020\n",
      "Path: 015 out of 020\n",
      "Path: 016 out of 020\n",
      "Path: 017 out of 020\n",
      "Path: 018 out of 020\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-e7190938a6e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml1_ratio\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiTaskElasticNetCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml1_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ml1_ratio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mncross\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_alphas\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mmodels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml1_ratio\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" took %s seconds\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1182\u001b[0m                 for train, test in folds)\n\u001b[1;32m   1183\u001b[0m         mse_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0;32m-> 1184\u001b[0;31m                              **_joblib_parallel_args(prefer=\"threads\"))(jobs)\n\u001b[0m\u001b[1;32m   1185\u001b[0m         \u001b[0mmse_paths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmse_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mn_l1_ratio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \u001b[0mmean_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmse_paths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    922\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py\u001b[0m in \u001b[0;36m_path_residuals\u001b[0;34m(X, y, train, test, path, path_params, alphas, l1_ratio, X_order, dtype)\u001b[0m\n\u001b[1;32m   1007\u001b[0m     \u001b[0;31m# X is copied and a reference is kept here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1008\u001b[0m     \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1009\u001b[0;31m     \u001b[0malphas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoefs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpath_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1010\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/inception/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py\u001b[0m in \u001b[0;36menet_path\u001b[0;34m(X, y, l1_ratio, eps, n_alphas, alphas, precompute, Xy, copy_X, coef_init, verbose, return_n_iter, positive, check_input, **params)\u001b[0m\n\u001b[1;32m    461\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m             model = cd_fast.enet_coordinate_descent_multi_task(\n\u001b[0;32m--> 463\u001b[0;31m                 coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random)\n\u001b[0m\u001b[1;32m    464\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecompute\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m             \u001b[0;31m# We expect precompute to be already Fortran ordered when bypassing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32msklearn/linear_model/_cd_fast.pyx\u001b[0m in \u001b[0;36msklearn.linear_model._cd_fast.enet_coordinate_descent_multi_task\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# fit GLMNET in parallel (ncores) with cross validation to find lambda\n",
    "ncross = 10\n",
    "for l1_ratio in l1_ratios:\n",
    "    print('Computing %d cross-validations for alpha %2.2f... '%(ncross, l1_ratio))\n",
    "    start_time = time.time()\n",
    "    models[l1_ratio] = linear_model.MultiTaskElasticNetCV(l1_ratio=l1_ratio, cv=ncross, verbose=2, n_alphas=20, max_iter=3000)\n",
    "    models[l1_ratio].fit(x_train, y_train)\n",
    "    print(\" took %s seconds\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(results_prefix, 'cf_alpha_select.pickle'), 'wb') as f:\n",
    "    pickle.dump(models, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for l1_ratio in models:\n",
    "    print('predicting validation data for l1_ratio %2.2f (best alpha %5.5f)... '%(l1_ratio, models[l1_ratio].alpha_), flush=True)\n",
    "    y_hat = models[l1_ratio].predict(x_val)\n",
    "    plot_distributions(y_val, y_hat)\n",
    "    for (error, name) in zip(np.mean(np.sqrt((y_hat-y_val)**2),axis=0),names):\n",
    "        print('%s: %4.5f'%(name,error), flush=True)\n",
    "# 1.0\n",
    "# alpha 1.00\n",
    "# DA: 240.01289\n",
    "# 5HT: 84.59803\n",
    "# pH: 0.01701\n",
    "# NE: 285.41925\n",
    "# 1.0\n",
    "# alpha 1.00\n",
    "# DA: 308.92286\n",
    "# 5HT: 150.38150\n",
    "# pH: 0.02517\n",
    "# NE: 370.36699\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1_ratio = 1.\n",
    "fig, axs = plt.subplots(2, 2, sharey=False, sharex=False, tight_layout=True)\n",
    "fig.set_size_inches(20,10)\n",
    "for ia in range(4):\n",
    "    idx = np.unravel_index(ia, axs.shape)\n",
    "    x = models[l1_ratio].coef_[ia,:]\n",
    "    axs[idx].plot(x)\n",
    "    axs[idx].set_title(\"Coeficients %s\"%names[ia])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2aad4baa9e50>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def smooth(y, w):\n",
    "    #     box = np.ones(w)/w\n",
    "    #     return np.convolve(y, box, mode='same')\n",
    "        return np.convolve(y, np.ones(w), 'valid') / w\n",
    "\n",
    "a = 1.\n",
    "l = np.where([x == fits[a]['lambda_1se'][0] for x in fits[a]['lambdau']])[0][0]\n",
    "fig, axs = plt.subplots()\n",
    "fig.set_size_inches(20,10)\n",
    "for ia in range(4):\n",
    "    x = fits[1.]['glmnet_fit']['beta'][ia][:, l].copy()\n",
    "    if ia == 2:\n",
    "        x *= 15000\n",
    "#     if ia == 3 or ia == 1:\n",
    "#         axs.plot(-x, label=names[ia])\n",
    "#     else:\n",
    "    axs.plot(smooth(x, 10), label=names[ia])\n",
    "axs.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}