--- a
+++ b/baselines/logistic_regression.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import f1_score\n",
+    "import multiprocessing as mp\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Test different feature vectors for each sequence\n",
+    "d = {'A':0,'G':1,'C':2,'T':3}\n",
+    "\n",
+    "# Construct k-mer dictionary\n",
+    "kmer_to_ind = {}\n",
+    "ind_to_kmer = {}\n",
+    "k_ind = 0\n",
+    "for k in range(1,6):\n",
+    "    for kmer in [''.join(i) for i in itertools.product('ACGT', repeat = k)]:\n",
+    "        kmer_to_ind[kmer] = k_ind\n",
+    "        ind_to_kmer[k_ind] = kmer\n",
+    "        k_ind += 1\n",
+    "\n",
+    "# Feature mapping 1: char to int\n",
+    "def seq_to_int(s):\n",
+    "    return map(lambda x: d[x], s)\n",
+    "\n",
+    "# Feature mapping 2: k-mer counting for k = 1, 2, 3, 4, 5\n",
+    "def kmer_count(s):\n",
+    "    v = np.zeros(len(kmer_to_ind))\n",
+    "    for k in range(1,6):\n",
+    "        for kmer in [s[i:i+k] for i in range(len(s)-k+1)]:\n",
+    "            v[kmer_to_ind[kmer]] += 1\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
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+   ],
+   "source": [
+    "def load_features(feature_extractor,filename,num_steps=200):\n",
+    "    # Read in sequence\n",
+    "    f = open(filename)\n",
+    "    X = []\n",
+    "    start_time = time.time()\n",
+    "    for i,line in enumerate(f):\n",
+    "        if i % 1000 == 0:\n",
+    "            print time.time()-start_time, ' s'\n",
+    "        s = line.split()[0]\n",
+    "        a = int(len(s)/2-num_steps/2)\n",
+    "        b = int(len(s)/2+num_steps/2)\n",
+    "        X.append(feature_extractor(s[a:b]))\n",
+    "    return np.array(X)\n",
+    "  \n",
+    "fileprefix = '../jz-rnn-tensorflow/data/deepsea_multitask/deepsea'\n",
+    "feature_extractor = kmer_count\n",
+    "\n",
+    "X_test = load_features(feature_extractor,fileprefix + '.data.test.txt')\n",
+    "X_train = load_features(feature_extractor,fileprefix + '.data.train.txt')\n",
+    "Y_test = np.loadtxt(fileprefix + '.labels.test.txt')\n",
+    "Y_train = np.loadtxt(fileprefix + '.labels.train.txt')\n",
+    "\n",
+    "pickle.dump((X_test,X_train,Y_test,Y_train),file('data_200.pickle','wb'))\n",
+    "# X_test,X_train,Y_test,Y_train = pickle.load(file('data.pickle','rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# logistic regression model\n",
+    "\n",
+    "def logistic_reg(inputs):\n",
+    "    X_test,X_train,Y_test,Y_train,i = inputs\n",
+    "    logreg = linear_model.LogisticRegression(C=1e6)\n",
+    "    logreg.fit(X_train, Y_train)\n",
+    "    Yhat_train = logreg.predict(X_train)\n",
+    "    Yhat_test = logreg.predict(X_test)\n",
+    "    f1_train = f1_score(Y_train,Yhat_train)\n",
+    "    f1_test = f1_score(Y_test,Yhat_test)\n",
+    "    f = open('logistic_regression_results/'+str(i))\n",
+    "    f.write('%.3f\\t%.3f\\n'%(f1_train,f1_test))\n",
+    "    f.close()\n",
+    "    return f1_train,f1_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "num_tasks = np.shape(Y_train)[1]\n",
+    "\n",
+    "all_f1_train = []\n",
+    "all_f1_test = []\n",
+    "start_time = time.time()\n",
+    "for i in range(num_tasks):\n",
+    "    inputs = (X_test,X_train,Y_test[:,i],Y_train[:,i])\n",
+    "    f1_train,f1_test = logistic_reg(inputs)\n",
+    "    all_f1_train.append(f1_train)\n",
+    "    all_f1_test.append(f1_test)\n",
+    "    f = open('logistic_regression_results','a')\n",
+    "    f.write('Task ' + str(i) + ':\\tf1 train = %.3f\\tf1 test = %.3f\\n'%(f1_train,f1_test))\n",
+    "    f.close()\n",
+    "    print 'Task ' + str(i) + ':\\tf1 train = %.3f\\tf1 test = %.3f'%(f1_train,f1_test) \n",
+    "    print time.time()-start_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "num_tasks = np.shape(Y_train)[1]\n",
+    "all_inputs = [(X_test,X_train,Y_test[:,i],Y_train[:,i],i) for i in range(num_tasks)]\n",
+    "pool=mp.Pool(processes=40)\n",
+    "pool.map(logistic_reg,all_inputs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistic Regression Output Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Read in logistic regression results as a K-by-5 matrix where K = num tasks\n",
+    "# 5 entries are:\n",
+    "#   1. task ID\n",
+    "#   2. number of non-zero training examples\n",
+    "#   3. number of non-zero testing examples\n",
+    "#   4. F1 score for training (-1 if no non-zero labels)\n",
+    "#   5. F1 score for testing (-1 if no non-zero labels)\n",
+    "\n",
+    "def logistic_regression_analysis_matrix(dirname):\n",
+    "    X = np.zeros([919,5])\n",
+    "    for i in range(919):\n",
+    "        f = open(dirname+str(i))\n",
+    "        for line in f:\n",
+    "            z = line.split()\n",
+    "        X[i,0] = i\n",
+    "        X[i,1] = float(z[0])\n",
+    "        X[i,2] = float(z[1])\n",
+    "        if len(z) > 3:\n",
+    "            X[i,3] = float(z[2])\n",
+    "            X[i,4] = float(z[3])\n",
+    "        else:\n",
+    "            X[i,3] = -1\n",
+    "            X[i,4] = -1\n",
+    "    return X\n",
+    "\n",
+    "def get_best_tasks(X):\n",
+    "    # Sort tasks by F1 score (descending)\n",
+    "    a = sorted([(val,i,X[i,1]/float(80000)) for i,val in enumerate(X[:,4])])\n",
+    "    a.reverse()\n",
+    "    # Grab tasks with best scores\n",
+    "    good_inds = np.array([i[1] for i in a])\n",
+    "    return a, good_inds\n",
+    "\n",
+    "X = logistic_regression_analysis_matrix('logistic_regression_100_results/')\n",
+    "a, good_inds = get_best_tasks(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7fae33ed3510>"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9mfVThraVd1KcU4ha/+NEraWnA+KUJEmSJEmSJEmSJEmSJEmSJEmSJE00/w/6\n8Ryox7cMNgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae34f32090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae334f3b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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iSfL4bbJ+AxEUltN+dfEW4AHgRWAjcBORUm7HuijZmr+JJcn2qRXbc10n7XhT\nWgdwHZEeKfd1+nN/57HlANGORCphAc25czxvjqN/zKBd6+J+YL9kuYeoh3asi4OJmXY7E+9pFvAP\ntFdddLHlAPLWvveHiBlpHQyBAWRov5vS3kbkxx8j0iOPEh/SeGIgtdrUsa8Q9TMXODnLwmboOPpn\nE7VrXRxM9AzmEFfDY2jfuvgS/VNLZxG96Xapix8RYyXriTHVD7Nt7700tXQ+8O3tXmpJkiRJkiRJ\nkiRJkiRJkiRJkiRJ0vDx/wGJNJ2lcBSNxAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae3445b710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "# Histograms of total # of 1's in labels\n",
+    "plt.hist(X[:,1],bins=20)\n",
+    "plt.title('histogram of # of non-zero training examples')\n",
+    "plt.figure()\n",
+    "plt.hist(X[:,2],bins=20)\n",
+    "plt.title('histogram of # of non-zero testing examples')\n",
+    "\n",
+    "# Plot of F1 scores in sorted order\n",
+    "plt.figure()\n",
+    "plt.plot(np.sort(X[:,4]))\n",
+    "plt.title('F1 scores on testing set (sorted)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Neural Network Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Copy over validation errors from training model on AWS\n",
+    "import os\n",
+    "key = '54.153.39.112'\n",
+    "os.system('scp -i ../cs224d.pem ubuntu@'+key\n",
+    "          +':deep_learning_genomics_nlp/jz-rnn-tensorflow/weights/*'\n",
+    "          +' ../jz-rnn-tensorflow/aws_weights/weights2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(61, 919)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the F1 matrix from the validation errors file\n",
+    "# f = open('../jz-rnn-tensorflow/aws_weights/weights1/validation_errors')\n",
+    "f = open('../jz-rnn-tensorflow/aws_weights/validation_errors')\n",
+    "\n",
+    "F1 = []\n",
+    "train_loss = []\n",
+    "valid_loss = []\n",
+    "for line in f:\n",
+    "    z = line.split('\\t')\n",
+    "    F1.append([float(i) for i in z[3].translate(None,'[').translate(None,']').translate(None,'\\n').split(',')])\n",
+    "    train_loss.append(float(z[0]))\n",
+    "    valid_loss.append(float(z[1]))\n",
+    "F1 = np.array(F1)\n",
+    "train_loss = np.array(train_loss)\n",
+    "valid_loss = np.array(valid_loss)\n",
+    "print np.shape(F1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch\tDeep RNN F1\tLog Reg F1\tnumber of positive training examples\n",
+      "================================================================================\n",
+      "The RNN beats the logistic regression model on 65.6% of the 421 tasks \n",
+      "    with at least 10% positive training examples\n",
+      "On average, the RNN F1 score is 0.086 times greater.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print out the max F1 for each of logistic regressions best-performing tasks\n",
+    "n = 919#179\n",
+    "test = []\n",
+    "save_tasks = []\n",
+    "print 'Epoch\\tDeep RNN F1\\tLog Reg F1\\tnumber of positive training examples'\n",
+    "for j,(i,k) in enumerate([(np.max(F1[:,good_inds[i]]),np.argmax(F1[:,good_inds[i]])) \n",
+    "                          for i in range(n)]): \n",
+    "    if a[j][2]*80000 <= 800:\n",
+    "#         print '%d\\t%3f\\t%3f\\t%d'%(k,i,a[j][0],a[j][2]*80000)\n",
+    "        test.append([i,a[j][0]])\n",
+    "        save_tasks.append(a[j][1])\n",
+    "test = np.array(test)\n",
+    "save_tasks = np.array(save_tasks)\n",
+    "print '='*80\n",
+    "print \"\"\"The RNN beats the logistic regression model on %.1f%% of the %d tasks \n",
+    "    with at least 10%% positive training examples\"\"\"%(100*np.sum(test[:,0]>test[:,1])/float(len(test)),len(test))\n",
+    "print 'On average, the RNN F1 score is %.3f times greater.'%(np.sum(test[:,0]-test[:,1])/len(test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\t0.026\t-0.008\n",
+      "1\t0.318\t0.018\n",
+      "2\t0.342\t0.024\n",
+      "3\t0.029\t-0.007\n",
+      "4\t0.026\t-0.008\n",
+      "5\t0.055\t-0.001\n",
+      "6\t0.040\t-0.005\n",
+      "7\t0.029\t-0.007\n",
+      "8\t0.033\t-0.006\n",
+      "9\t0.048\t-0.004\n",
+      "10\t0.036\t-0.006\n",
+      "11\t0.033\t-0.006\n",
+      "12\t0.036\t-0.006\n",
+      "13\t0.040\t-0.006\n",
+      "14\t0.045\t-0.005\n",
+      "15\t0.040\t-0.005\n",
+      "16\t0.050\t-0.002\n",
+      "17\t0.033\t-0.007\n",
+      "18\t0.062\t-0.002\n",
+      "19\t0.088\t0.005\n",
+      "20\t0.038\t-0.005\n",
+      "21\t0.048\t-0.004\n",
+      "22\t0.083\t0.005\n",
+      "23\t0.048\t-0.004\n",
+      "24\t0.064\t0.001\n",
+      "25\t0.083\t0.007\n",
+      "26\t0.052\t-0.003\n",
+      "27\t0.074\t0.002\n",
+      "28\t0.100\t0.008\n",
+      "29\t0.074\t0.001\n",
+      "30\t0.052\t-0.001\n",
+      "31\t0.062\t-0.000\n",
+      "32\t0.048\t-0.003\n",
+      "33\t0.071\t-0.001\n",
+      "34\t0.093\t0.002\n",
+      "35\t0.083\t0.002\n",
+      "36\t0.069\t-0.003\n",
+      "37\t0.126\t0.006\n",
+      "38\t0.081\t0.001\n",
+      "39\t0.071\t-0.002\n",
+      "40\t0.067\t-0.001\n",
+      "41\t0.107\t0.002\n",
+      "42\t0.093\t0.001\n",
+      "43\t0.432\t0.048\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare the performance of the RNN at the most recent epoch to that of the LR model \n",
+    "for ii in range(len(F1)):\n",
+    "    test2 = []\n",
+    "    for j,i in enumerate([F1[ii,good_inds[i]] for i in range(n)]):\n",
+    "        if a[j][2]*80000 <= 800:\n",
+    "            test2.append([i,a[j][0]])\n",
+    "    test2 = np.array(test2)\n",
+    "\n",
+    "    print '%d\\t%.3f\\t%.3f'%(ii,np.sum(test2[:,0]>test[:,1])/float(len(test2)),\n",
+    "                            (np.sum(test2[:,0]-test2[:,1])/len(test2)))\n",
+    "    \n",
+    "    if ii > 42: break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P8u230K2bBeOhQ63TZrt24U5x6DiOnfMFF8DMmXDJJYG9v6jIigKuuQbuvdez\nvqLCOsBu3WpDZ/ztb9C5M0ycaBfobt3q9zwC9c47drP34oueABcXZ0uTJpbTSEqypXnz6kHQcaCs\nzJYmTWx7CChQBOnAAZvx0H3N3bDBfutpadCnjwWO7t3t+5iWZkvnztCqVb18fHW7dlnHkKFDrddn\nQoL/73UcyM+3oLF1q12Ymze3O7nmzW3p3NmyUDWjoOPYndnMmfDRR3ZHN2aMDaCYlOT5gicmWm6h\nsNCWggJ7XL/e+pwMHAgTJsDVV0P79pZzueUWC1DPP1978ZrbsWNWJPfZZ7asWgUXXWRt1UeNsh+Q\nN5WVnn/crl3VL9SVlfb+8eNr//FVVlpHq/nz7WJzxhnWxj89vfqP+bvvLNDm5nruOjZtsqCdn2/l\nmz172hemWzfP0qWL/S0WL7Zl2TJbN3Ik/PSnljP0pazMhrU+etTmay4vt3Xl5RbUzznH/rfefPed\nfebHH9tIAm3a2P+mXTvPY6dOlp60tOrFJmVl9qNYutSzHDpkn9W5s72vc2c7Rn6+fe+2brW/UUqK\nXej/8Aff51abXbtgyBC76SkosCLYwkLLLXTtCldcAddeaz/QSFFebiM25OXZb8p9jXIHgZISz1Je\nbr+nykrbVlFh3++mTe07574GjBljN3T1lNtXoKhH5eX2fd+wATZutO/s3r1WSuNe4uPt+1pzSU+3\npXt3+x02uiGh9u2Dl1+2i0JJiV283Y+lpTYESmpq9SUz04JLWtrJx3McmDfPchbjx9vgXAkJdjFx\nX1hycy1ar14N/ftbMdzIkZY7ev99u0PbsQMmT7Yc1mmnWUB0B5RFi+wC2Ldv9Qt0erpdXP/yFxtm\n4ZZbbOyujh0tbfn5MGuWBbEWLSx9+/ZZ4Fu3zu5s+/WzIJmba/t3727B4LTT7Afcu7c9ZmTUHshq\nqqiwzpoffGC9elNT4T//04Ks+0JdWQmff25/uzfesL9xx472Ge4LSpMmlvNcscKC+uDBFnS+9z37\n4n78sf1t+vaFH/zAAvmRI3bhPXDAs+TnWzDfs8fOtUsXO9769TYEwvnn20V7yBD7Uufn2w9i7157\nvn+/BYzTTrOlR4+TpxANRl6eBeGOHS0otW/v/9840lVU2O8qPt7zv3RfLI4etU6J7grX5s0tYPTq\nZfslJNjSpIn9vvbts/9d1YtUQoLdFGZleR6zsohLTwcFiob6cPu97d5tOeyqy65d9v3eudNuvtPT\n7Rpy5plBAkJwAAAOxklEQVRWZH/WWXbtCVHOMnIVFFiw+Pvf7UeSkeG5Az/tNAsQQ4fWPgfAunXw\n0kvwyisWsNx35NnZVsTRtavvz1+7Fv74R/v8K6+0IPDPf8KPfmRDRg8efHJUP3jQPre01NKZnl7/\nF6qKCsvFzZhhuYbrr7cLx7x5NqbYtddaAMnMrP0YjmOBbPlyCxqrVtmF4Qc/sByZvw0fHMfu2vfs\nsS/4mWdazqAeFRdbLGx0N1Dh4jh2k/P++3bBqaiwO1n3Y1ycBVJ3kYf7sazMhhravNlyva7HuL17\nQYEishQXW+DYutX+12vW2JKba9edgQOtUn3QILsJDFcdXINyFx8EUqxW1fHj1sStQ4fg3n/ggOWY\nWrWyi3A9XwhPyfbtlrsBK7Y588ywJicURoyAcePg9tvDnZLYpKKnRqS01HL1q1dbpfqXX1pJSlqa\nBQ13I6b+/e3GW3dfEg2++84yScnJVqLUtm24UxR7FCgaufJy+/F8+aXlPtyLuwTgjDOsqLFXL3vs\n2dP/5tp5edawqajIgs+ZZ3qKO0UaytKlVhVzzjmWkZs2Ldwpij2RFCheAi4D9gH9a9nnj8Bo4Bhw\nA7DKtf5+4DqgEvgauBHwNhZx1AWK2hQWWvH6+vVWzLhliz1u22Z1ewMHeuoZzz3X0xqroMCK4+fO\ntff+6EdWWrN2rQWgPXusHvbMM611l7tO1lujKJH68NRTVlz+0EP2vVu+3KqnpOFEUqAYARQBs/Ee\nKMYAt7kezwOeBoYAmcC/gL5YcHgNeB/4q5djxEygqE1FheUUvvjC03Jx1SrLaXTqZD/C0aOtifml\nl57c+bSoyALI2rWWk/nmG/sRb91q9WFnnlk9AMVE/YmE1IQJ1nhn0iRr/PbVV1E/k2jEiaRAAXbR\nfw/vgeJZ4FMsEABsBEYCZcASLGgcBd7GgshCL8eI+UDhzfHj9uPbtQsuvji4vh7l5VanumaNNfdf\nutSKwzIy4LzzrHHQ4MFWhBXtfeikfmVmWovdrCxrbd27txWJDh0a7pTFjsYUKN4Dfge4J5pdCNwL\nfAncBEwHSoCPgOtrOb4CRQMqK7Ocx7JlllNZvtyKvgYM8ASOIUOsGEEV7+LNnj2WSz1wwPMdmT3b\nWgUvXqzvTUNpbHNme0toT+BOLMh0AZKBiQ2YJqlF06ZWF/Lzn1tXhrVrrW/PE09Yv7a33rKuDJ06\nWYfZ3/3OOjpH8tTI0rCWLTu5q8p111kuWMVPkSuc7V2+BdKrvO7mWpeN5TIKXOvfAoYCr3o7yNSp\nU088z87OJjuQ6QvllLVqZf3dRo60145jRV5Lllhx1QMPWKV569aeETHcj1lZjbTXugRt2TLLdVYV\nHw/Tp8ONN1ofSH/HDRT/5eTkkJOTE/T7w1n0VLUyewjwlOvxe8Ac4FygFJgFLAf+4uUYKnpqBCor\nrcLdPSLGunWe1luOYy2s3MuAATBsWO1DFknjduGFcN991rCipiuvhOHDbUptCa1IqqOYh1VOtwfy\ngYcBd7XnTNfjn4FRQDHWBPZL1/p7gclY89gvgZ9ildw1KVA0Yo5jzXfdIwxs3mwV5kuWWCesYcNs\nGTrU+nuouW7jVl5u/9e8PO+d7DZtskDx2GNw000Nn75YEkmBoiEoUEQh9yCw//63LcuWWQustm2t\nxUxGhj327m3FGL17h3EuEfHbmjXWNHbDhtr3+eYbG1dy+HCbhiXmxkJrIAoUEpUqK63FzI4dFjS2\nb7cirCVLrLL8vPM8g5uefrqNFaiLTGSZOdPqrV5+2fd+R4/CDTfY4Jpvvln3OI8SOAUKiTl799oF\nyF2Bvn27BZWUFLvIdOliI14PHWqD0WVkhDvFsenGGy2Q33xz3fs6jrWm+9OfrI/FiBGhT18sUaAQ\nwXIgBw7YXenu3Vb/8fnnNi1D8+ZWtDFihM1q2KdPZA0eG6369bOhZHxNY13TRx9ZD+7x4y2XMXCg\n71ZyR4/aoMJdu6o1nS8KFCI+OI4FjUWLbFmzxipR3fMd9eljS69e1nEwI0NFWPXh0CGbxuPgwcAH\nody1y6bNnj3bBsCcNMn6XnTpYv0vli2DhQttWpHVq21U2qIiq7ty/z9797a+Pu7ZKWO9Ca4ChUiA\nKittgin3TIYbN3om3du50zoQuieyc4+627+/9QER/3z8MTz+uM20GyzHsVzh7NlWd9GzpwX500+H\n73/fhqsZNsyCyaFDnrHLNm60R3fuMj/f9klLs5xHRsbJS5cuJ4+LVtXRo3bcDRssNxrsUDnhokAh\nUo/Kyy1Y5ObaxcY96u7atXZX2r+/FaUMHGhLVlbwczFFs0cftXGdnniifo5XUmI5if79baruQLib\nZe/ZY8Fjx46Tl/x8CwBVJ4xr1cpGbV6/3nJG7hzL/v1WN3beeZ7prXv3juyiLwUKkQbg7oH+9dee\niadWrbILzIABluvo0sWzpKXZY6dOsdmUd8wY6xtx1VXhTol/Kis9wcQ9BfWRI1Yk2bevTZle9f/o\nnt76/fdtSUiwOhn39OHuKcQ7dfIEkKqP7do17PdCgUIkjA4dssCxYYNnfvvduz3L0aN20XBPPOWu\nC+nc2S4i7dpFX47EcayYbu1aC5jRznE801O7izDdy/79nn3cKipstsusLE+dStW6stqmjz8VChQi\nEayoyIqxtmzxTD61davlRPbts0DTrp0FjZ49bUpc95zqjfUiu3kzXHKJNVsW744e9dSnuJcNG+y7\n0b69BRH30ru3LZmZwd9UKFCINGLl5XbXmZ/vmRLXvTRrZvUhmZnWgqfq0rWrtfaJRK+8AvPnW38I\nCUxFhdWRbdpki3tisY0b7caiZ0/LeZx+uo1ckJJSfWna1KYHqLqUl8PVVytQiEQdx7ELxurV9rhr\nV/Xl22/t7rJz5+oVsO7moFWXhi4Pv/VWK0L55S8b7jNjQXGx5dY2brRc6uHDVo9SdTl+3IJFzeUf\n/1CgEIk5jmPFF+6KV/djzWX3biv+6tTJU8nuXjp0sGKOdu08S/v2p97n4Oyz4c9/tiFWJDKo6ElE\nfCot9R5I9u+3lj7u5cABe0xMtMDiXjp3tqKuqq152rXz3hz02DELQO7jSGRQoBCReuM4VsGen2/L\n3r32uHOnTYO7bZtVuFZUWN1J69bWmc29lJZa0diyZeE+E6lKgUJEGtzBg9ZR7ehRy0UcO2Zl6MeO\nWZ+SoUPDnUKpSoFCRER8CjRQxGAfURERCYQChYiI+KRAISIiPilQiIiITwoUIiLikwKFiIj4pEAh\nIiI+KVCIiIhPChQiIuKTAoWIiPikQCEiIj4pUIiIiE+hDBQvAfnA1z72+SOwGVgDDKyyvg3wBrAB\nWA8MCVEaRUSkDqEMFC8Do3xsHwP0Ak4HbgJmVNn2NPA+0BcYgAWMmJOTkxPuJIRMNJ8b6Pwau2g/\nv0CFMlAsAg762H4F8FfX82VYLqIT0BoYgeVIAMqBwyFKY0SL5i9rNJ8b6Pwau2g/v0CFs46iK7Cz\nyutdQDegB7Afy5F8CTwPtGjw1ImICBD+yuyaE2c4QBNgEPCM67EYuK+B0yUiIg0kk9ors58FJlR5\nvREreuoMbKuyfjgwv5ZjbMGCixYtWrRo8X/ZQgCaBLJzPXsXuA34G9aq6RDWSgqsSCoL2AR8H1hX\nyzF6hTiNIiISQvOA3cBx7ML/E+Bm1+L2ZyyyrcGKmdzOAla41r+FVXCLiIiIiIjUj1FYncZm4Ndh\nTkt98NY5MRX4BCt++xhrPtxYpQOfYkWIa4FfuNZHyzkmYk28V2MdRH/nWh8t5weQAKwC3nO9jqZz\n2w58hZ3fcte6aDq/mh2YzyO6zs+rBKy4KhNoiv04+4YzQfVgBNYzvWqgeBK41/X818ATDZ2oetQZ\n+J7reTLwDfY/i6ZzdDfhbgIsxRphRNP53QW8itUtQnSd2zbswllVNJ3fX7Gif7DvZ2ui6/y8Oh/4\nsMrr+4iO5rOZVA8U7lZgYBfajQ2doBD6B9ZIIRrPsQVWv3YG0XN+3YCFwIV4chTRcm5ggaJdjXXR\ncn6tga1e1gd0fuHuRxEMbx31uoYpLaHUCU8rsHw8/9TGLhPLPS0jus4xHsvd5uMpZouW8/sDcA9Q\nWWVdtJwbWHPRhcBK4GeuddFyft46MLckwPNrjIHCCXcCwsDd9rmxSwbeBO4AjtbY1tjPsRIrXusG\nXIDdfVfVWM9vLLAPK7+v2UHWrbGem9sw7OZlNHArVhRcVWM+P386MNd5fo0xUHyLVY66pWO5imiT\nj2UJAdKwH2tj1hQLEq9gRU8QfecINi7ZAuBsouP8hmLjsm3DmrxfhP0Po+Hc3Pa4HvcDbwODiZ7z\n2+VaVrhev4EFjL0EcH6NMVCsxEaczQSaAePxVLBFk3eBya7nk/FcXBujOOBFrMXFU1XWR8s5tsfT\naiQJuAS7A4+G83sAuxnrgY2k8C/geqLj3MDqlFq5nrcEfoDVFUbL+e3F04EZPB2Y3yM6zs+n0VjL\nmS3A/WFOS32o2TnxRqwVxkKio/nacKxoZjV2AV2FNXGOlnPsj5X/rsaaWd7jWh8t5+c2Es9NWbSc\nWw/s/7Yaa7rtvp5Ey/mB9w7M0XR+IiIiIiIiIiIiIiIiIiIiIiIiIiIiIlJ/KvD0/ViFZ8TN+pBJ\n7VMFi0SMcE6FKtIYHMPGARKJWY1xCA+RSLAd+B+sJ/YyoKdrfSY2zMUarOere1yyTtg4Qu5ewENc\n6xOA57BewR9hkyCJiEgjUk71oqcfu9ZvwzPcw/V45ml4z/UabCiWt13PX8Mzs188kIIFlTJgQJV9\nJtb3CYiISGjVHA7dbRt2oQcbGfeA6/l+LJfgXr/f9Xyf63VVmdhYO273Ar8JPqkioaGiJ5H6UXU8\n/9rmbfC2/rsqzytQvaFEIAUKkeCNr/K42PV8MTYcN1gx0v+5nv8TuMX1PAErehJpFHT3IuJbElY3\n4fYBNkcDQFus0roUuMa17nZs2sl7sOKmG13r78AqradgOYefY5Pj1JxZrLHOpCYiIjVsw8b0F4l6\nKnoSCY7u/EVEREREREREREREREREREREREREREQan/8HKunegzVgEVMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae34528e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot training and validation losses for num_steps = 100\n",
+    "plt.figure(figsize=(6,4))\n",
+    "plt.plot(train_loss,label='Training')\n",
+    "plt.plot(valid_loss,'r',label='Validation')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Perplexity')\n",
+    "plt.legend()\n",
+    "plt.savefig('/data/deep_learning/project_deliverables/loss_100.png', format='png', dpi=900)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/BeDgwYOEhIQQGBjo1qZ27dpkZGSQmZnJ0aNHAahTp06x/brooovc9gMCAgA4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\nkiRJkiRJkiRJkiRJkiRJkiRJkjRV/H8B2F8dFSE1nAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae342bbe10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot everything using a bar plot\n",
+    "import colorsys\n",
+    "def bars_curves_set(curves_set,labels,method_names):\n",
+    "    N_sets = len(curves_set)\n",
+    "    HSV_tuples = [(x*1.0/N_sets, 0.6, 0.7) for x in range(N_sets)]\n",
+    "    RGB_tuples = map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples)\n",
+    "    N = len(curves_set[0])\n",
+    "    ind = np.arange(N)\n",
+    "    width = 0.35       \n",
+    "    fig, ax = plt.subplots(figsize=(20,8))\n",
+    "    plt.grid(which='minor',zorder=0)\n",
+    "    plt.grid(which='major',zorder=0)\n",
+    "    for i in range(N_sets):\n",
+    "        rects = ax.bar(ind-0.2+i*0.2,curves_set[i],width,color=RGB_tuples[i], \n",
+    "                    ecolor='k',align='center',label=labels[i],zorder=3)\n",
+    "    ax.set_ylabel('F1 score',size=15)\n",
+    "    xticks_pos = [0.65*patch.get_width() + patch.get_xy()[0] -0.2 for patch in rects]\n",
+    "    plt.xticks(xticks_pos, method_names, ha='right', rotation=90)\n",
+    "    plt.yticks(np.arange(0,1.1*max([max(i) for i in curves_set]),0.05))\n",
+    "    plt.xlim([np.min(xticks_pos)-0.5,np.max(xticks_pos)+0.5])\n",
+    "    plt.xlabel('Tasks sorted by logistic regression performance',size=15)\n",
+    "    plt.legend(fontsize=15)\n",
+    "    plt.savefig('/data/deep_learning/project_deliverables/F1_scores.png', format='png', dpi=200)\n",
+    "\n",
+    "k = 20\n",
+    "curve_set = [test[:k,0],test2[:k,0],test[:k,1]]\n",
+    "bars_curves_set(curve_set,\n",
+    "            labels = ['RNN (max over all epochs)','RNN (best epoch)','Logistic Regression'],\n",
+    "            method_names = np.array([str(i) for i in save_tasks[:k]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326,\n",
+       "        327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339,\n",
+       "        340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352,\n",
+       "        353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365,\n",
+       "        366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378,\n",
+       "        379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391,\n",
+       "        392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404,\n",
+       "        405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417,\n",
+       "        418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430,\n",
+       "        431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443,\n",
+       "        444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456,\n",
+       "        457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,\n",
+       "        470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,\n",
+       "        483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495,\n",
+       "        496, 497]),)"
+      ]
+     },
+     "execution_count": 340,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(test[:,1] == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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59a9/TX19PW63mw8++CCm88nKyuKKK67g3nvvxWg08qtf/Yprr702kMBWUVER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v47GcwMcoI3KO1nVWHPjdDBq1XRIkXc3oGLG/oCIoGDpVtt5m4W2DC0gZJVYqIxga9rRIocE2ec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\niBAhQoQIESJEGD78/7e3s0nKKLmBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae34696750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sort indices where logistic regression fails\n",
+    "iz = np.where(test[:,1] == 0)[0][0]\n",
+    "ii = np.array([j[1] for j in sorted([(a,i) for i,a in enumerate(test2[iz:,0])],reverse=True)])\n",
+    "\n",
+    "iz_ind = range(iz,iz+len(ii))\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "plt.plot(test[:iz,0],label='RNN (max over all epochs)',c='r')\n",
+    "temp1 = test[iz:,0]\n",
+    "plt.plot(iz_ind,temp1[ii],c='r')\n",
+    "plt.plot(test2[:iz,0],label='RNN (best epoch)',c='g')\n",
+    "temp2 = test2[iz:,0]\n",
+    "plt.plot(iz_ind,temp2[ii],c='g')\n",
+    "plt.plot(test[:iz,1],label='Logistic Regression',c='b')\n",
+    "temp3 = test2[iz:,1]\n",
+    "plt.plot(iz_ind,temp3[ii],c='b')\n",
+    "plt.xlabel('Tasks sorted by logistic regression performance')\n",
+    "plt.ylabel('F1 score')\n",
+    "plt.legend()\n",
+    "plt.savefig('/data/deep_learning/project_deliverables/F1_scores_498.png', format='png', dpi=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Repeating above analysis for 200 num_steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7fae34423c10>"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jZU9zc63fTeMwGEgalKeeihuxVq2KdM3zz8OyZdH4r18fDf+IEXFjVktL9OxnzYLx42HM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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae34594610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = logistic_regression_analysis_matrix('logistic_regression_200_results/')\n",
+    "a, good_inds = get_best_tasks(X)\n",
+    "\n",
+    "# Plot of F1 scores in sorted order\n",
+    "plt.figure()\n",
+    "plt.plot(np.sort(X[:,4]))\n",
+    "plt.title('F1 scores on testing set (sorted)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(26, 919)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ip = '54.67.103.213'\n",
+    "# os.system('scp -i ../cs224d_200.pem ubuntu@'+key\n",
+    "#           +':deep_learning_genomics_nlp/jz-rnn-tensorflow/weights/validation_errors'\n",
+    "#           +' ../jz-rnn-tensorflow/aws_weights_200/')\n",
+    "\n",
+    "f = open('../validation_errors')\n",
+    "\n",
+    "F1 = []\n",
+    "train_loss = []\n",
+    "valid_loss = []\n",
+    "for line in f:\n",
+    "    z = line.split('\\t')\n",
+    "    F1.append([float(i) for i in z[3].translate(None,'[').translate(None,']').translate(None,'\\n').split(',')])\n",
+    "    train_loss.append(float(z[0]))\n",
+    "    valid_loss.append(float(z[1]))\n",
+    "F1 = np.array(F1)\n",
+    "train_loss = np.array(train_loss)\n",
+    "valid_loss = np.array(valid_loss)\n",
+    "print np.shape(F1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\t0.000000\t1.000000 23.0\n",
+      "21\t0.616601\t0.587000 5410.0\n",
+      "18\t0.431138\t0.414000 4354.0\n",
+      "18\t0.426117\t0.402000 7545.0\n",
+      "18\t0.403433\t0.399000 6527.0\n",
+      "21\t0.454082\t0.399000 9322.0\n",
+      "8\t0.412698\t0.380000 1373.0\n",
+      "21\t0.447458\t0.371000 7140.0\n",
+      "23\t0.387324\t0.358000 7501.0\n",
+      "21\t0.412698\t0.353000 9684.0\n",
+      "23\t0.355828\t0.349000 3851.0\n",
+      "0\t0.000000\t0.333000 22.0\n",
+      "9\t0.356164\t0.326000 1658.0\n",
+      "19\t0.342857\t0.323000 3535.0\n",
+      "21\t0.330579\t0.320000 5876.0\n",
+      "21\t0.372093\t0.315000 8599.0\n",
+      "2\t0.095238\t0.315000 517.0\n",
+      "0\t0.000000\t0.308000 62.0\n",
+      "21\t0.334677\t0.302000 13762.0\n",
+      "21\t0.336770\t0.290000 7230.0\n",
+      "10\t0.388060\t0.287000 1569.0\n",
+      "0\t0.000000\t0.286000 53.0\n",
+      "20\t0.363636\t0.282000 439.0\n",
+      "13\t0.383838\t0.281000 2219.0\n",
+      "3\t0.334601\t0.280000 5529.0\n",
+      "13\t0.260870\t0.277000 1305.0\n",
+      "25\t0.181818\t0.267000 1020.0\n",
+      "20\t0.393305\t0.266000 5933.0\n",
+      "16\t0.216216\t0.266000 1124.0\n",
+      "22\t0.285714\t0.263000 743.0\n",
+      "0\t0.400000\t0.254000 253.0\n",
+      "5\t0.267658\t0.253000 7283.0\n",
+      "21\t0.133333\t0.253000 408.0\n",
+      "21\t0.285047\t0.252000 12573.0\n",
+      "5\t0.260870\t0.252000 939.0\n",
+      "1\t0.044444\t0.250000 41.0\n",
+      "21\t0.328638\t0.247000 5530.0\n",
+      "13\t0.269231\t0.244000 8530.0\n",
+      "3\t0.210526\t0.242000 1521.0\n",
+      "0\t0.250000\t0.241000 909.0\n",
+      "23\t0.291667\t0.240000 4125.0\n",
+      "0\t0.000000\t0.240000 127.0\n",
+      "0\t0.000000\t0.238000 118.0\n",
+      "21\t0.212766\t0.229000 1077.0\n",
+      "21\t0.280443\t0.227000 7275.0\n",
+      "21\t0.230769\t0.226000 1568.0\n",
+      "21\t0.285714\t0.224000 748.0\n",
+      "2\t0.228571\t0.224000 764.0\n",
+      "21\t0.294840\t0.213000 12118.0\n",
+      "2\t0.226804\t0.213000 5114.0\n",
+      "0\t0.000000\t0.211000 196.0\n",
+      "3\t0.292135\t0.207000 3792.0\n",
+      "2\t0.142857\t0.206000 1368.0\n",
+      "0\t0.235294\t0.201000 1141.0\n",
+      "0\t0.000000\t0.200000 120.0\n",
+      "0\t0.000000\t0.200000 67.0\n",
+      "16\t0.189781\t0.197000 4096.0\n",
+      "3\t0.222222\t0.197000 1870.0\n",
+      "10\t0.142857\t0.195000 907.0\n",
+      "7\t0.105263\t0.195000 656.0\n",
+      "0\t0.000000\t0.194000 88.0\n",
+      "0\t0.322581\t0.194000 895.0\n",
+      "21\t0.251121\t0.192000 5731.0\n",
+      "14\t0.275862\t0.192000 1084.0\n",
+      "23\t0.255892\t0.191000 8429.0\n",
+      "1\t0.148148\t0.191000 1703.0\n",
+      "0\t0.000000\t0.190000 78.0\n",
+      "4\t0.256881\t0.189000 8411.0\n",
+      "0\t0.333333\t0.189000 278.0\n",
+      "18\t0.250000\t0.186000 1204.0\n",
+      "2\t0.281690\t0.184000 1393.0\n",
+      "5\t0.268293\t0.183000 1719.0\n",
+      "25\t0.500000\t0.182000 504.0\n",
+      "14\t0.222222\t0.182000 368.0\n",
+      "3\t0.113208\t0.180000 717.0\n",
+      "0\t0.121212\t0.176000 863.0\n",
+      "25\t0.111111\t0.175000 793.0\n",
+      "3\t0.176471\t0.175000 1104.0\n",
+      "2\t0.080000\t0.175000 532.0\n",
+      "0\t0.222222\t0.174000 577.0\n",
+      "3\t0.190840\t0.173000 6859.0\n",
+      "1\t0.133333\t0.172000 579.0\n",
+      "11\t0.200000\t0.169000 228.0\n",
+      "21\t0.200000\t0.168000 703.0\n",
+      "21\t0.210526\t0.167000 643.0\n",
+      "0\t0.250000\t0.167000 870.0\n",
+      "6\t0.285714\t0.167000 556.0\n",
+      "0\t0.000000\t0.163000 286.0\n",
+      "3\t0.276190\t0.162000 5842.0\n",
+      "0\t0.260870\t0.162000 678.0\n",
+      "21\t0.370370\t0.162000 696.0\n",
+      "3\t0.289474\t0.162000 1156.0\n",
+      "0\t0.000000\t0.162000 539.0\n",
+      "0\t0.000000\t0.160000 304.0\n",
+      "0\t0.000000\t0.160000 188.0\n",
+      "3\t0.218750\t0.160000 1171.0\n",
+      "0\t0.000000\t0.160000 72.0\n",
+      "0\t0.166667\t0.160000 316.0\n",
+      "14\t0.160000\t0.159000 1104.0\n",
+      "0\t0.000000\t0.157000 226.0\n",
+      "0\t0.148148\t0.155000 684.0\n",
+      "0\t0.200000\t0.154000 872.0\n",
+      "0\t0.000000\t0.154000 327.0\n",
+      "1\t0.031746\t0.153000 868.0\n",
+      "13\t0.250000\t0.153000 976.0\n",
+      "0\t0.142857\t0.152000 830.0\n",
+      "17\t0.214286\t0.152000 1010.0\n",
+      "2\t0.142857\t0.151000 815.0\n",
+      "0\t0.235294\t0.150000 840.0\n",
+      "0\t0.181818\t0.150000 654.0\n",
+      "0\t0.000000\t0.148000 105.0\n",
+      "0\t0.190476\t0.148000 1082.0\n",
+      "0\t0.000000\t0.148000 241.0\n",
+      "3\t0.111111\t0.148000 1456.0\n",
+      "7\t0.166667\t0.147000 645.0\n",
+      "0\t0.666667\t0.145000 150.0\n",
+      "14\t0.285714\t0.145000 258.0\n",
+      "3\t0.186047\t0.145000 489.0\n",
+      "1\t0.129032\t0.144000 998.0\n",
+      "3\t0.259259\t0.144000 3240.0\n",
+      "10\t0.153846\t0.143000 517.0\n",
+      "0\t0.000000\t0.143000 153.0\n",
+      "0\t0.142857\t0.143000 748.0\n",
+      "4\t0.024691\t0.143000 384.0\n",
+      "3\t0.172185\t0.140000 3763.0\n",
+      "0\t0.142857\t0.140000 394.0\n",
+      "22\t0.190476\t0.139000 1780.0\n",
+      "2\t0.086957\t0.139000 447.0\n",
+      "0\t0.285714\t0.138000 580.0\n",
+      "0\t0.105263\t0.138000 853.0\n",
+      "3\t0.054054\t0.138000 197.0\n",
+      "0\t0.000000\t0.136000 258.0\n",
+      "0\t0.210526\t0.135000 738.0\n",
+      "3\t0.080000\t0.135000 784.0\n",
+      "18\t0.169492\t0.134000 1585.0\n",
+      "0\t0.181818\t0.133000 758.0\n",
+      "0\t0.000000\t0.133000 192.0\n",
+      "21\t0.162162\t0.133000 3487.0\n",
+      "0\t0.000000\t0.130000 256.0\n",
+      "22\t0.206897\t0.130000 1030.0\n",
+      "0\t0.333333\t0.129000 397.0\n",
+      "3\t0.140000\t0.127000 5396.0\n",
+      "2\t0.090909\t0.127000 325.0\n",
+      "0\t0.137931\t0.127000 940.0\n",
+      "0\t0.062500\t0.127000 1129.0\n",
+      "1\t0.196078\t0.126000 732.0\n",
+      "0\t0.125000\t0.126000 1383.0\n",
+      "1\t0.041667\t0.125000 172.0\n",
+      "4\t0.025000\t0.125000 412.0\n",
+      "0\t0.000000\t0.125000 197.0\n",
+      "3\t0.050000\t0.125000 428.0\n",
+      "18\t0.153846\t0.125000 760.0\n",
+      "0\t0.000000\t0.125000 70.0\n",
+      "1\t0.041667\t0.125000 275.0\n",
+      "23\t0.226415\t0.124000 2629.0\n",
+      "0\t0.117647\t0.124000 806.0\n",
+      "25\t0.135593\t0.123000 2046.0\n",
+      "3\t0.205128\t0.122000 7599.0\n",
+      "0\t0.400000\t0.120000 302.0\n",
+      "5\t0.285714\t0.120000 828.0\n",
+      "13\t0.142857\t0.120000 795.0\n",
+      "14\t0.272727\t0.120000 856.0\n",
+      "0\t0.000000\t0.118000 335.0\n",
+      "0\t0.000000\t0.118000 302.0\n",
+      "0\t0.000000\t0.118000 59.0\n",
+      "7\t0.181818\t0.115000 890.0\n",
+      "13\t0.133333\t0.115000 753.0\n",
+      "4\t0.224719\t0.115000 2962.0\n",
+      "3\t0.300000\t0.114000 798.0\n",
+      "0\t0.222222\t0.114000 495.0\n",
+      "2\t0.100000\t0.114000 428.0\n",
+      "3\t0.123077\t0.113000 1159.0\n",
+      "3\t0.228571\t0.113000 1926.0\n",
+      "1\t0.111111\t0.112000 1266.0\n",
+      "0\t0.000000\t0.111000 118.0\n",
+      "0\t0.000000\t0.111000 316.0\n",
+      "1\t0.033333\t0.111000 746.0\n",
+      "3\t0.177778\t0.110000 578.0\n",
+      "1\t0.083333\t0.110000 2335.0\n",
+      "0\t0.000000\t0.109000 199.0\n",
+      "6\t0.285714\t0.109000 690.0\n",
+      "7\t0.166667\t0.108000 445.0\n",
+      "3\t0.093023\t0.107000 283.0\n",
+      "1\t0.129032\t0.107000 954.0\n",
+      "3\t0.149254\t0.107000 1190.0\n",
+      "13\t0.117647\t0.107000 1070.0\n",
+      "0\t0.285714\t0.107000 505.0\n",
+      "0\t0.090909\t0.106000 738.0\n",
+      "1\t0.183908\t0.106000 1938.0\n",
+      "1\t0.034483\t0.106000 748.0\n",
+      "0\t0.000000\t0.105000 125.0\n",
+      "3\t0.107143\t0.104000 857.0\n",
+      "21\t0.233766\t0.104000 2445.0\n",
+      "0\t0.000000\t0.103000 639.0\n",
+      "0\t0.000000\t0.102000 866.0\n",
+      "0\t0.095238\t0.102000 820.0\n",
+      "4\t0.107143\t0.102000 5255.0\n",
+      "2\t0.150943\t0.101000 1428.0\n",
+      "0\t0.181818\t0.101000 717.0\n",
+      "8\t0.148148\t0.100000 944.0\n",
+      "================================================================================\n",
+      "The RNN beats the logistic regression model on 77.7% of the 103 tasks \n",
+      "    with at least 10% positive training examples\n",
+      "On average, the RNN F1 score is 1.207 times greater.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print out the max F1 for each of logistic regressions best-performing tasks\n",
+    "n = 200\n",
+    "test = []\n",
+    "for j,(i,k) in enumerate([(np.max(F1[:,good_inds[i]]),np.argmax(F1[:,good_inds[i]])) \n",
+    "                          for i in range(n)]): \n",
+    "    print '%d\\t%3f\\t%3f'%(k,i,a[j][0]), a[j][2]*80000\n",
+    "    if a[j][2]*80000 > 800:\n",
+    "        test.append([i,a[j][0]])\n",
+    "test = np.array(test)\n",
+    "print '='*80\n",
+    "print \"\"\"The RNN beats the logistic regression model on %.1f%% of the %d tasks \n",
+    "    with at least 10%% positive training examples\"\"\"%(100*np.sum(test[:,0]>test[:,1])/float(len(test)),len(test))\n",
+    "print 'On average, the RNN F1 score is %.3f times greater.'%(np.sum(test[:,0]/test[:,1])/len(test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Line 0\n",
+      "0.000000\t1.000000\n",
+      "0.411483\t0.587000\n",
+      "0.211268\t0.414000\n",
+      "0.289963\t0.402000\n",
+      "0.257143\t0.399000\n",
+      "0.221498\t0.399000\n",
+      "0.290909\t0.380000\n",
+      "0.252033\t0.371000\n",
+      "0.203390\t0.358000\n",
+      "0.258675\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 1\n",
+      "0.000000\t1.000000\n",
+      "0.442396\t0.587000\n",
+      "0.229885\t0.414000\n",
+      "0.262774\t0.402000\n",
+      "0.218341\t0.399000\n",
+      "0.278261\t0.399000\n",
+      "0.282609\t0.380000\n",
+      "0.262172\t0.371000\n",
+      "0.212121\t0.358000\n",
+      "0.259259\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 2\n",
+      "0.000000\t1.000000\n",
+      "0.472103\t0.587000\n",
+      "0.350282\t0.414000\n",
+      "0.330986\t0.402000\n",
+      "0.310345\t0.399000\n",
+      "0.329609\t0.399000\n",
+      "0.318841\t0.380000\n",
+      "0.312727\t0.371000\n",
+      "0.299625\t0.358000\n",
+      "0.331429\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 3\n",
+      "0.000000\t1.000000\n",
+      "0.458150\t0.587000\n",
+      "0.365591\t0.414000\n",
+      "0.348684\t0.402000\n",
+      "0.309623\t0.399000\n",
+      "0.318182\t0.399000\n",
+      "0.326531\t0.380000\n",
+      "0.329749\t0.371000\n",
+      "0.315018\t0.358000\n",
+      "0.300885\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 4\n",
+      "0.000000\t1.000000\n",
+      "0.417391\t0.587000\n",
+      "0.380435\t0.414000\n",
+      "0.280303\t0.402000\n",
+      "0.333333\t0.399000\n",
+      "0.333333\t0.399000\n",
+      "0.257426\t0.380000\n",
+      "0.292135\t0.371000\n",
+      "0.306513\t0.358000\n",
+      "0.313953\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 5\n",
+      "0.000000\t1.000000\n",
+      "0.511111\t0.587000\n",
+      "0.410526\t0.414000\n",
+      "0.344828\t0.402000\n",
+      "0.355731\t0.399000\n",
+      "0.336088\t0.399000\n",
+      "0.300000\t0.380000\n",
+      "0.356436\t0.371000\n",
+      "0.360424\t0.358000\n",
+      "0.345404\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 6\n",
+      "0.000000\t1.000000\n",
+      "0.373737\t0.587000\n",
+      "0.251656\t0.414000\n",
+      "0.178723\t0.402000\n",
+      "0.254717\t0.399000\n",
+      "0.197368\t0.399000\n",
+      "0.230769\t0.380000\n",
+      "0.206612\t0.371000\n",
+      "0.215768\t0.358000\n",
+      "0.234921\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 7\n",
+      "0.000000\t1.000000\n",
+      "0.421569\t0.587000\n",
+      "0.253333\t0.414000\n",
+      "0.180258\t0.402000\n",
+      "0.190955\t0.399000\n",
+      "0.225166\t0.399000\n",
+      "0.244898\t0.380000\n",
+      "0.170940\t0.371000\n",
+      "0.182609\t0.358000\n",
+      "0.242038\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 8\n",
+      "0.000000\t1.000000\n",
+      "0.533898\t0.587000\n",
+      "0.395480\t0.414000\n",
+      "0.356643\t0.402000\n",
+      "0.338983\t0.399000\n",
+      "0.362606\t0.399000\n",
+      "0.412698\t0.380000\n",
+      "0.329749\t0.371000\n",
+      "0.350365\t0.358000\n",
+      "0.309038\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 9\n",
+      "0.000000\t1.000000\n",
+      "0.520000\t0.587000\n",
+      "0.406593\t0.414000\n",
+      "0.317690\t0.402000\n",
+      "0.342857\t0.399000\n",
+      "0.321839\t0.399000\n",
+      "0.260870\t0.380000\n",
+      "0.341637\t0.371000\n",
+      "0.357143\t0.358000\n",
+      "0.338028\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 10\n",
+      "0.000000\t1.000000\n",
+      "0.550000\t0.587000\n",
+      "0.355828\t0.414000\n",
+      "0.376712\t0.402000\n",
+      "0.347826\t0.399000\n",
+      "0.354651\t0.399000\n",
+      "0.204082\t0.380000\n",
+      "0.362319\t0.371000\n",
+      "0.358974\t0.358000\n",
+      "0.342029\t0.353000\n",
+      "--------------------------------------------------------------------------------\n",
+      "Line 11\n",
+      "0.000000\t1.000000\n",
+      "0.561404\t0.587000\n",
+      "0.275862\t0.414000\n",
+      "0.242678\t0.402000\n",
+      "0.278846\t0.399000\n",
+      "0.340426\t0.399000\n",
+      "0.055556\t0.380000\n",
+      "0.243902\t0.371000\n",
+      "0.273092\t0.358000\n",
+      "0.330330\t0.353000\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare the performance of the RNN at each epoch to that of the LR model \n",
+    "for j in range(len(F1)):\n",
+    "    print 'Line '+str(j)\n",
+    "    for i in range(10):\n",
+    "        print '%3f\\t%3f'%(F1[j,good_inds[i]],a[i][0])\n",
+    "    print '-'*80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D2mGHhf5p111X/PeeOxfOOQf69Sv+e0fBkCZJUiyVMqTdBqwDXs5z/QhgPrAL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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fae346ad310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot training and validation losses for num_steps = 100\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.plot(train_loss[1:],label='Training')\n",
+    "plt.plot(valid_loss[1:],'r',label='Validation')\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Perplexity')\n",
+    "plt.legend()\n",
+    "plt.savefig('/data/deep_learning/project_deliverables/loss_200.eps', format='eps', dpi=900)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}