{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model stability analysis\n", "In this notebook, we perform stability analysis on the FDA experiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optional : Creation of a temp db" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import shutil\n", "# shutil.copy(\"snorkel_fda_beta.db\",\"_snorkel_fda_temp.db\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os, sys\n", "import re\n", "from six.moves.cPickle import load\n", "import argparse\n", "from time import time, strftime\n", "from inspect import getmembers, isfunction\n", "from imp import load_source\n", "import numpy as np\n", "from pandas import DataFrame, Series\n", "import json\n", "from subprocess import check_output\n", "import pickle\n", "import pandas as pd\n", "# Note: We import this *before* any import of TF to avoid weird issues\n", "# see- https://github.com/tensorflow/models/issues/523\n", "# Is a bit of a hack but seems harmless as long as SpaCy is installed\n", "import spacy\n", "\n", "### Note: No Snorkel imports until after the $SNORKELDB env var is set!\n", "### This is clearly annoying, but also annoying to fix... TODO later in snorkel\n", "\n", "expts = list(filter(lambda p : not re.match(r'.*\\.pyc?$', p),\n", " os.listdir('experiments/')))\n", "\n", "from utils_mderoche import ArgumentEmulator\n", "\n", "\n", "args = ArgumentEmulator(verbose=True, exp=\"fda\", disc_model_search_space=1, start_at=7,db_name = \"_snorkel_fda_temp\")\n", "DEV_SPLIT = 1\n", "TEST_SPLIT = 2 \n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "$SNORKELDB = sqlite:///_snorkel_fda_temp.db\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/.snorkelenv.gpu/local/lib/python2.7/site-packages/tensorflow/contrib/tensorboard/plugins/trace/trace.py:22: ImportWarning: Not importing directory '/home/matthieu/Documents/Snorkel/snorkel/parser': missing __init__.py\n", " import parser\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'gen-init-params': {'lf_propensity': True, 'seed': 123}, 'deps-thresh': 0.01, 'disc-params-range': {'dim': [50, 100], 'dropout': [0.1, 0.25, 0.5], 'lr': [0.0002, 1e-05, 0.0001, 0.0005, 5e-05], 'l2_penalty': [0.01, 0.001, 0.0001], 'rebalance': [0.1, 0.2, 0.05, 0.4], 'l1_penalty': [0.001, 0.0001, 1e-05]}, 'featurizer-init-params': {}, 'disc-eval-batch-size': 32, 'disc-params-default': {'dropout': 0.5, 'batch_size': 64, 'n_epochs': 750, 'beta': 0.9, 'max_sentence_length': 100, 'dim': 50, 'print_freq': 50, 'lr': 0.0002, 'l2_penalty': 0.0001, 'rebalance': 0.1, 'l1_penalty': 0.0002, 'allchecks': False}, 'gen-params-default': {'epochs': 50, 'reg_param': 0.1, 'decay': 0.95}, 'disc-model-class': , 'disc-init-params': {'seed': 123, 'n_threads': 4}, 'featurizer-class': , 'gen-params-range': {'step_size': [0.01, 0.001, 0.0001, 1e-05], 'reg_param': [0.0, 0.01, 0.1, 0.5], 'LF_acc_prior_weight_default': [0.5, 1.0, 1.5]}}\n" ] } ], "source": [ "\n", "if args.verbose:\n", " print(args)\n", "\n", "# Get the DB connection string and add to globals\n", "DB_NAME = \"snorkel_\" + args.exp if args.db_name is None else args.db_name\n", "if not args.postgres:\n", " DB_NAME += \".db\"\n", "DB_TYPE = \"postgres\" if args.postgres else \"sqlite\"\n", "DB_ADDR = \"localhost:{0}\".format(args.db_port) if args.db_port else \"\"\n", "os.environ['SNORKELDB'] = '{0}://{1}/{2}'.format(DB_TYPE, DB_ADDR, DB_NAME)\n", "print(\"$SNORKELDB = {0}\".format(os.environ['SNORKELDB']))\n", "\n", "# All Snorkel imports here, after SNORKELDB has been set\n", "from snorkel.annotations import (\n", " LabelAnnotator, load_label_matrix, load_gold_labels, load_marginals\n", ")\n", "from snorkel.learning.structure import DependencySelector\n", "from snorkel.learning import GenerativeModel, RandomSearch\n", "from snorkel.models.meta import SnorkelBase, snorkel_engine\n", "from snorkel.models import Document, Sentence\n", "from snorkel import SnorkelSession\n", "\n", "from utils import *\n", "\n", "############################################################################\n", "### [0] Start from clean slate: Clear the DB\n", "############################################################################\n", "if args.start_at == 0:\n", " if args.verbose > 0:\n", " print(\"Reseting DB...\")\n", " if args.postgres:\n", " raise NotImplementedError(\"TODO: DB clearing for Postgres.\")\n", " else:\n", " try:\n", " _ = check_output(['rm', DB_NAME])\n", " except:\n", " pass\n", " SnorkelBase.metadata.create_all(snorkel_engine)\n", "# Start Snorkel sess\n", "sess = SnorkelSession()\n", "\n", "# Only use parallelism > 1 with UDFs if using Postgres\n", "UDF_THREADS = args.n_threads if args.postgres else 1\n", "\n", "# Import from parsers.py\n", "preprocess = load_source('preprocess',\n", " os.path.join('experiments', args.exp, 'preprocess.py'))\n", "\n", "# Get candidate subclass from loaded module\n", "C = preprocess.C\n", "\n", "# Load the config dictionary\n", "# Load global first, then override any entries with local config file\n", "from config import config\n", "local_config_path = os.path.join('experiments', args.exp, 'config.py')\n", "if os.path.exists(local_config_path):\n", " local_config = load_source('local_config', local_config_path)\n", " config = recursive_merge_dicts(config, local_config.config)\n", "if args.verbose > 0:\n", " print(config)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "GEN_MODEL_NAME = 'G_final_{0}'.format(args.exp)\n", "L_train, L_dev, L_test = None, None, None\n", "Y_dev, Y_test = None, None\n", "gen_model = None\n", "if args.start_at <= 5:\n", "\n", " # Load L_train if starting here\n", " if L_train is None or L_dev is None or L_test is None:\n", " with PrintTimer(\"[5.0] Loading label matrices...\"):\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " L_train = load_label_matrix(sess, cids_query=cids_query)\n", " else:\n", " L_train = load_label_matrix(sess, split=0)\n", " L_train = load_label_matrix(sess, split=0)\n", " assert L_train.nnz > 0\n", " L_dev = load_label_matrix(sess, split=DEV_SPLIT)\n", " assert L_dev.nnz > 0\n", " L_test = load_label_matrix(sess, split=TEST_SPLIT)\n", " assert L_test.nnz > 0\n", " if args.verbose > 0:\n", " print(\"Using L_train: {0}\".format(L_train.__repr__()))\n", " print(\"Using L_dev: {0}\".format(L_dev.__repr__()))\n", " print(\"Using L_test: {0}\".format(L_test.__repr__()))\n", "\n", " # Select dependencies to model\n", " with PrintTimer(\"[5.1] Selecting dependencies...\"):\n", " if args.deps:\n", " ds = DependencySelector()\n", " np.random.seed(args.rand_seed)\n", " deps = ds.select(L_train, threshold=config['deps-thresh'])\n", " if args.verbose > 0:\n", " print(\"Selected {0} dependencies.\".format(len(deps)))\n", " else:\n", " deps = ()\n", " if args.verbose > 0:\n", " print(\"Skipping.\")\n", "\n", "# Run grid search to select best generative model\n", " with PrintTimer(\"[5.2] Searching over & training generative models\"):\n", " # Load dev and test labels\n", " if Y_dev is None:\n", " Y_dev = load_gold_labels(sess, annotator_name='gold', split=DEV_SPLIT)\n", " assert Y_dev.nonzero()[0].shape[0] > 0\n", " if Y_test is None:\n", " Y_test = load_gold_labels(sess, annotator_name='gold', split=TEST_SPLIT)\n", " assert Y_test.nonzero()[0].shape[0] > 0\n", "\n", " # Pass in the dependencies via default params\n", " gen_params_default = config['gen-params-default']\n", " gen_params_default['deps'] = deps\n", "\n", " # Train generative model with grid search if applicable\n", " gen_model = train_model(\n", " GenerativeModel,\n", " L_train,\n", " X_dev=L_dev,\n", " Y_dev=Y_dev,\n", " search_size=2,#args.gen_model_search_space,\n", " search_params=config['gen-params-range'],\n", " rand_seed=args.rand_seed,\n", " n_threads=args.n_threads,\n", " verbose=(args.verbose > 0),\n", " params_default=gen_params_default,\n", " model_init_params=config['gen-init-params'],\n", " model_name=GEN_MODEL_NAME,\n", " save_dir=args.save_dir,\n", " beta=args.gen_f_beta\n", " )\n", "\n", " # Save training marginals\n", " gen_model.save_marginals(sess, L_train, training=True)\n", "\n", " # Score generative model on test set\n", " print(\"\\n### Gen. model (DP) score on test set:\")\n", " _ = gen_model.error_analysis(sess, L_test, Y_test, display=True)\n", "\n", " if args.one_only:\n", " sys.exit(0)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We select a model to load: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DISC_MODEL_NAME = \"SparseLogisticRegression_fullchk_pr3__epoch_439\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "############################################################################\n", "### [6] Fit discriminative model\n", "############################################################################\n", "\n", "# DISC_MODEL_NAME = 'D_final_{0}'.format(args.exp)\n", "\n", "X_train, X_dev, Y_train, disc_model = None, None, None, None\n", "F_train,F_dev,F_test,featurizer = None,None,None,None\n", "if args.start_at <= 6:\n", " # Load data: candidates, dev labels, training marginals\n", " with PrintTimer(\"[6.0] Loading data\"):\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " X_train = sess.query(C)\\\n", " .filter(C.id == cids_query.subquery().c.id)\\\n", " .order_by(C.id)\\\n", " .all()\n", " Y_train = load_marginals(sess, cids_query=cids_query)\n", " else:\n", " X_train = sess.query(C).filter(C.split==0).order_by(C.id).all()\n", " Y_train = load_marginals(sess, split=0)\n", " assert len(X_train) > 0\n", " assert Y_train.nonzero()[0].shape[0] > 0\n", "\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT).order_by(C.id).all()\n", " assert len(X_dev) > 0\n", " if Y_dev is None:\n", " Y_dev = load_gold_labels(sess,annotator_name='gold',split=DEV_SPLIT)\n", " assert Y_dev.nonzero()[0].shape[0] > 0\n", " if args.verbose > 0:\n", " print(\"Loaded X_train: {0}\".format(len(X_train)))\n", " print(\"Loaded Y_train: {0}\".format(Y_train.shape))\n", " print(\"Loaded X_dev: {0}\".format(len(X_dev)))\n", " print(\"Loaded Y_dev: {0}\".format(Y_dev.shape))\n", "\n", " if not config['disc-model-class'].representation:\n", " featurizer = config['featurizer-class'](**config.get('featurizer-init-params',dict()))\n", " with PrintTimer(\"[6.0.1] Computing Features\"):\n", " if args.recompute_feats:\n", " F_train = featurizer.apply(split=0)\n", " F_dev = featurizer.apply_existing(split=DEV_SPLIT)\n", " else:\n", " F_train = featurizer.load_matrix(sess,split=0)\n", " F_dev = featurizer.load_matrix(sess,split=DEV_SPLIT)\n", "\n", " else:\n", " F_train = X_train\n", " F_dev = X_dev\n", " # Run grid search to select best generative model\n", " with PrintTimer(\"[6.1] Searching over & training end disc. models\"):\n", " disc_model = train_model(\n", " config['disc-model-class'],\n", " F_train,\n", " Y_train=Y_train,\n", " X_dev=F_dev,\n", " Y_dev=Y_dev,\n", " cardinality=C.cardinality,\n", " search_size=args.disc_model_search_space,\n", " search_params=config['disc-params-range'],\n", " rand_seed=args.rand_seed,\n", " n_threads=args.n_threads,\n", " verbose=(args.verbose > 0),\n", " params_default=config['disc-params-default'],\n", " model_init_params=config['disc-init-params'],\n", " model_name=DISC_MODEL_NAME,\n", " save_dir=args.save_dir,\n", " eval_batch_size=config['disc-eval-batch-size']\n", " )\n", "\n", " if args.one_only:\n", " sys.exit(0)\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### [7.0] Loading all data for final evaluation\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr3__epoch_439/SparseLogisticRegression_fullchk_pr3__epoch_439-439\n", "[SparseLogisticRegression] Loaded model \n", "[GenerativeModel] Model loaded.\n", "Loaded X_test: 1232\n", "Loaded Y_test: (1232, 1)\n", "Loaded F_test: representation is False\n", "### Done in 13.6s.\n", "\n", "### [7.2] Evaluating heuristic baselines\n", "### Done in 0.0s.\n", "\n", "### [7.3] Evaluating generative model\n", "### Done in 0.3s.\n", "\n", "### [7.4] Evaluate full DP pipeline (disc. model)\n", "### Done in 0.1s.\n", "\n", " Coverage F1 Score Precision Recall\n", "CS 1.000000 0.079501 0.041396 1.000000\n", "MV 0.727273 0.338462 0.785714 0.215686\n", "Gen 0.731331 0.338462 0.785714 0.215686\n", "DP 1.000000 0.552632 0.840000 0.411765\n", "fda & 4.1 & 100.0 & 8.0 & 78.6 & 21.6 & 33.8 & 78.6 & 21.6 & 33.8 & - & - & - & 84.0 & 41.2 & 55.3 & - & - & -\\\\\n" ] } ], "source": [ " if args.start_at <= 7:\n", " with PrintTimer(\"[7.0] Loading all data for final evaluation\"):\n", " # TODO: Reload models if needed\n", " if disc_model is None:\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=DISC_MODEL_NAME,\n", " save_dir=args.save_dir)\n", "\n", " if gen_model is None:\n", " gen_model = GenerativeModel(**config['gen-init-params'])\n", " gen_model.load(model_name=GEN_MODEL_NAME, save_dir=args.save_dir)\n", " if not disc_model.representation and (featurizer is None):\n", " featurizer = config['featurizer-class'](**config.get('featurizer-init-params', dict()))\n", "\n", "\n", " # TODO: Handle logistic regression as well!\n", " X_test = sess.query(C).filter(C.split == TEST_SPLIT).order_by(C.id).all()\n", " if Y_test is None:\n", " Y_test = load_gold_labels(sess, annotator_name='gold', split=TEST_SPLIT)\n", " assert Y_test.nonzero()[0].shape[0] > 0\n", " if L_test is None:\n", " L_test = load_label_matrix(sess, split=TEST_SPLIT)\n", " assert L_test.nnz > 0\n", "\n", " if F_test is None:\n", " if not disc_model.representation:\n", " if args.recompute_feats:\n", " F_test = featurizer.apply_existing(split=TEST_SPLIT)\n", " F_test = featurizer.load_matrix(sess,split=TEST_SPLIT)\n", " else:\n", " F_test = X_test\n", "\n", " if args.verbose > 0:\n", " print(\"Loaded X_test: {0}\".format(len(X_test)))\n", " print(\"Loaded Y_test: {0}\".format(Y_test.shape))\n", " print(\"Loaded F_test: representation is {}\".format(disc_model.representation))\n", "\n", " if args.custom_error_analysis:\n", " with PrintTimer(\"[7.1] Custom error analysis exportation\"):\n", " if X_dev is None:\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT).order_by(C.id).all()\n", " if L_dev is None:\n", " L_dev = load_label_matrix(sess, split=DEV_SPLIT)\n", " assert L_test.nnz > 0\n", " if Y_dev is None:\n", " Y_dev = load_gold_labels(sess, annotator_name='gold', split=DEV_SPLIT)\n", " if F_dev is None:\n", " if not disc_model.representation:\n", " F_dev = featurizer.load_matrix(sess,split=DEV_SPLIT)\n", " else:\n", " F_dev = X_dev\n", "\n", "\n", " custom_report_dir = os.path.join(args.reports_dir, strftime(\"%Y_%m_%d\"))\n", " custom_report_name = '{0}_{1}_custom.pkl'.format(args.exp, strftime(\"%H_%M_%S\"))\n", " if not os.path.exists(custom_report_dir):\n", " os.makedirs(custom_report_dir)\n", " print \"Generative Model\"\n", " custom_report = dict()\n", " tp, fp, tn, fn = gen_model.error_analysis(sess, L_dev, Y_dev)\n", " for key,cand_list in zip(['tp', 'fp', 'tn', 'fn'],[tp, fp, tn, fn]):\n", " custom_report[key] = [cand.id for cand in cand_list]\n", "\n", " print \"Discriminative Model\"\n", " tpd, fpd, tnd, fnd = disc_model.error_analysis(sess, F_dev, Y_dev)\n", " for key,cand_list in zip(['tpd', 'fpd', 'tnd', 'fnd'],[tpd, fpd, tnd, fnd]):\n", " custom_report[key] = [cand.id for cand in cand_list]\n", "\n", " with open(os.path.join(custom_report_dir, custom_report_name), 'wb') as f:\n", " pickle.dump(custom_report, f)\n", "\n", "\n", " if args.export_pred:\n", " if X_train is None:\n", " X_train = sess.query(C).filter(C.split == 0).order_by(C.id).all()\n", "\n", " if F_train is None:\n", " if not disc_model.representation:\n", " if args.recompute_feats:\n", " F_train = featurizer.apply_existing(split=0)\n", " F_train = featurizer.load_matrix(sess,split=0)\n", " else:\n", " F_train = X_train\n", "\n", " if X_dev is None:\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT).order_by(C.id).all()\n", "\n", " if F_dev is None:\n", " if not disc_model.representation:\n", " if args.recompute_feats:\n", " F_dev = featurizer.apply_existing(split=DEV_SPLIT)\n", " F_dev = featurizer.load_matrix(sess, split=DEV_SPLIT)\n", " else:\n", " F_dev = X_dev\n", "\n", " pred_train = disc_model.predictions(F_train,batch_size = int(len(X_train)/400)+1)\n", " pred_dev = disc_model.predictions(F_dev, batch_size=int(len(X_dev) / 400) + 1)\n", " pred_test = disc_model.predictions(F_test)\n", "\n", " list_dic_res= list()\n", " for cand,pr in zip(X_train+X_dev+X_test,list(pred_train)+list(pred_dev)+list(pred_test)):\n", " dico_info = dict()\n", " for i,c in enumerate(cand.get_contexts()):\n", " dico_info[\"cand_\"+str(i)+\"_start\"] = c.char_start\n", " dico_info[\"cand_\" + str(i) + \"_end\"] = c.char_end\n", "\n", " dico_info[\"sentence_pos\"] = cand.get_parent().position\n", " dico_info[\"doc_id\"] = cand.get_parent().document.name\n", " dico_info[\"prediction\"] = pr\n", " list_dic_res.append(dico_info)\n", " custom_report_dir = os.path.join(args.reports_dir, strftime(\"%Y_%m_%d\"))\n", " custom_report_name = '{0}_{1}_preds.csv'.format(args.exp, strftime(\"%H_%M_%S\"))\n", " pd.DataFrame(list_dic_res).to_csv(os.path.join(custom_report_dir, custom_report_name),sep = \";\")\n", "\n", "\n", " scores = {}\n", " with PrintTimer(\"[7.2] Evaluating heuristic baselines\"):\n", " # Test candidate set score - applicable for binary case only\n", " if C.cardinality == 2:\n", " cs_test = np.ones(Y_test.shape[0])\n", " scores['CS'] = score_marginals(cs_test, Y_test)\n", " else:\n", " if args.verbose > 0:\n", " print(\"Candidate-set not applicable for categorical tasks.\")\n", "\n", " # Test majority vote of LFs on test set\n", " mv_test = majority_vote_marginals(L_test, cardinality=C.cardinality)\n", " scores['MV'] = score_marginals(mv_test, Y_test)\n", "\n", " with PrintTimer(\"[7.3] Evaluating generative model\"):\n", " # Score generative model on test set\n", " # TODO: Make sure this is the same as scuba.utils score function!!!\n", " np.random.seed(args.rand_seed)\n", " scores['Gen'] = score_marginals(gen_model.marginals(L_test), Y_test)\n", "\n", " with PrintTimer(\"[7.4] Evaluate full DP pipeline (disc. model)\"):\n", " # Score discriminative model trained on generative model predictions\n", " # TODO: Make sure this is the same as scuba.utils score function!!!\n", " np.random.seed(args.rand_seed)\n", " scores['DP'] = score_marginals(disc_model.marginals(F_test,\n", " batch_size=config['disc-eval-batch-size']), Y_test)\n", "\n", " if args.ds_tests:\n", " with PrintTimer(\"[7.4] Evaluating distant supervision baseline\"):\n", " # Score discriminative model trained on LF majority vote (hard)\n", " # Load data\n", " if L_train is None:\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " L_train = load_label_matrix(sess, cids_query=cids_query)\n", " else:\n", " L_train = load_label_matrix(sess, split=0)\n", " assert L_train.nnz > 0\n", " if X_train is None:\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " X_train = sess.query(C)\\\n", " .filter(C.id == cids_query.subquery().c.id)\\\n", " .order_by(C.id)\\\n", " .all()\n", " else:\n", " X_train = sess.query(C).filter(C.split == 0)\\\n", " .order_by(C.id).all()\n", " assert len(X_train) > 0\n", " if X_dev is None:\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT)\\\n", " .order_by(C.id).all()\n", " assert len(X_dev) > 0\n", " if Y_dev is None:\n", " Y_dev = load_gold_labels(sess,annotator_name='gold',split=DEV_SPLIT)\n", " assert Y_dev.nonzero()[0].shape[0] > 0\n", "\n", " # Compute soft ([0,1]) majority vote training marginals\n", " Y_train_mv = majority_vote_marginals(L_train,\n", " cardinality=C.cardinality)\n", "\n", " # Train discriminative model with MV training labels\n", " disc_model = train_model(\n", " config['disc-model-class'],\n", " X_train,\n", " Y_train=Y_train_mv,\n", " X_dev=X_dev,\n", " Y_dev=Y_dev,\n", " cardinality=C.cardinality,\n", " search_size=args.disc_model_search_space,\n", " search_params=config['disc-params-range'],\n", " rand_seed=args.rand_seed,\n", " n_threads=args.n_threads,\n", " verbose=(args.verbose > 0),\n", " params_default=config['disc-params-default'],\n", " model_init_params=config['disc-init-params'],\n", " model_name=DISC_MODEL_NAME + \"_ds\",\n", " save_dir=args.save_dir,\n", " eval_batch_size=config['disc-eval-batch-size']\n", " )\n", " np.random.seed(args.rand_seed)\n", " scores['DS-MV'] = score_marginals(disc_model.marginals(X_test,\n", " batch_size=config['disc-eval-batch-size']), Y_test)\n", "\n", " if args.supervised_tests:\n", " with PrintTimer(\"[7.5] Evaluating fully-supervised baseline\"):\n", " # Load data\n", " if X_train is None:\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " X_train = sess.query(C)\\\n", " .filter(C.id == cids_query.subquery().c.id)\\\n", " .order_by(C.id)\\\n", " .all()\n", " else:\n", " X_train = sess.query(C).filter(C.split == 0)\\\n", " .order_by(C.id).all()\n", " assert len(X_train) > 0\n", " if X_dev is None:\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT)\\\n", " .order_by(C.id).all()\n", " assert len(X_dev) > 0\n", " if Y_dev is None:\n", " Y_dev = load_gold_labels(sess,annotator_name='gold',split=DEV_SPLIT)\n", " assert Y_dev.nonzero()[0].shape[0] > 0\n", "\n", " # Load ground-truth training set labels\n", " # Note we load in {0,1} not {-1,1} format for binary\n", " # Optionally subsample the training set here\n", " if args.training_docs > 0:\n", " cids_query = get_training_cids_query(\n", " sess,\n", " preprocess.CONTEXT_HIERARCHY,\n", " C,\n", " preprocess.CANDIDATE_CONTEXT,\n", " args.training_docs,\n", " training_docs_shuffle=args.training_docs_shuffle,\n", " verbose=args.verbose,\n", " training_splits=args.training_splits\n", " )\n", " Y_train_gt = load_gold_labels(sess, cids_query=cids_query,\n", " zero_one=(C.cardinality == 2), load_as_array=True,\n", " annotator_name='gold')\n", " else:\n", " Y_train_gt = load_gold_labels(sess, split=0,\n", " zero_one=(C.cardinality == 2), load_as_array=True,\n", " annotator_name='gold')\n", " assert Y_train_gt.nonzero()[0].shape[0] > 0\n", "\n", " # If categorical, convert to one-hot\n", " if C.cardinality > 2:\n", " Y_train_gt = labels_to_one_hot(Y_train_gt, C.cardinality)\n", "\n", " # Train discriminative model and score\n", " disc_model = train_model(\n", " config['disc-model-class'],\n", " X_train,\n", " Y_train=Y_train_gt,\n", " X_dev=X_dev,\n", " Y_dev=Y_dev,\n", " cardinality=C.cardinality,\n", " search_size=args.disc_model_search_space,\n", " search_params=config['disc-params-range'],\n", " rand_seed=args.rand_seed,\n", " n_threads=args.n_threads,\n", " verbose=(args.verbose > 0),\n", " params_default=config['disc-params-default'],\n", " model_init_params=config['disc-init-params'],\n", " model_name=DISC_MODEL_NAME + \"_supervised\",\n", " save_dir=args.save_dir,\n", " eval_batch_size=config['disc-eval-batch-size']\n", " )\n", " np.random.seed(args.rand_seed)\n", " scores['Sup'] = score_marginals(disc_model.marginals(X_test,\n", " batch_size=config['disc-eval-batch-size']), Y_test)\n", "\n", " # Print and save final score report\n", " ks = list(scores.keys())\n", " if C.cardinality > 2:\n", " cols = ['Accuracy', 'Coverage']\n", " d = {\n", " 'Accuracy': Series(data=[scores[k][0] for k in ks], index=ks),\n", " 'Coverage': Series(data=[scores[k][1] for k in ks], index=ks),\n", " }\n", " else:\n", " cols = ['Precision', 'Recall', 'F1 Score']\n", " d = {\n", " 'Precision' : Series(data=[scores[k][0] for k in ks], index=ks),\n", " 'Recall' : Series(data=[scores[k][1] for k in ks], index=ks),\n", " 'F1 Score' : Series(data=[scores[k][2] for k in ks], index=ks),\n", " 'Coverage' : Series(data=[scores[k][3] for k in ks], index=ks)\n", " }\n", " df = DataFrame(data=d, index=ks)\n", " print(df)\n", "\n", " # Assemble the report, to be saved as a json file\n", " df_scores = df.to_dict()\n", " row_str = print_latex_table_row(args.exp, scores,\n", " cardinality=C.cardinality)\n", " if args.verbose > 0:\n", " print(row_str)\n", " report = {\n", " 'scores': df_scores,\n", " 'row-string': row_str,\n", " 'scuba-commit': git_commit_hash(),\n", " 'snorkel-commit': git_commit_hash(path=os.environ['SNORKELHOME']),\n", " 'args': vars(args)\n", " }\n", "\n", " # Save to file\n", " report_dir = os.path.join(args.reports_dir, strftime(\"%Y_%m_%d\"))\n", " report_name = '{0}_{1}.json'.format(args.exp, strftime(\"%H_%M_%S\"))\n", " if not os.path.exists(report_dir):\n", " os.makedirs(report_dir)\n", " with open(os.path.join(report_dir, report_name), 'wb') as f:\n", " json.dump(report, f, indent=2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the marginals and labels for external analysis\n", "\n", "The following cells export the marginals and labels for external analysis" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "dico_golabs = dict()\n", "dico_labels = dict()\n", "dico_genmarg = dict()\n", "\n", "for split in range(3):\n", " if split>0:\n", " dico_golabs[split] = load_gold_labels(annotator_name=\"gold\",session=sess,split=split).toarray().ravel()\n", " dico_labels[split] = load_label_matrix(session=sess,split=split).toarray()\n", " dico_genmarg[split] = gen_model.marginals(load_label_matrix(session=sess,split=split))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "os.mkdir(\"../ExtraData/Unipolar LFs/fda_regular\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "OUTPUT_FOLDER = \"../ExtraData/Unipolar LFs/fda_regular/\"\n", "for split in range(3):\n", " if split>0:\n", " np.save(allow_pickle=False,arr=dico_golabs[split],file=OUTPUT_FOLDER+\"golabs_{}.npy\".format(split))\n", " np.save(allow_pickle=False,arr=dico_labels[split],file=OUTPUT_FOLDER+\"labels_{}.npy\".format(split))\n", " np.save(allow_pickle=False,arr=dico_genmarg[split],file=OUTPUT_FOLDER+\"marg_{}.npy\".format(split))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load feats (for SparseLogisticRegression)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "featurizer = config['featurizer-class'](**config.get('featurizer-init-params',dict()))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "F_train = featurizer.load_matrix(sess,split=0)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "F_dev = featurizer.load_matrix(sess,split=1)\n", "F_test = featurizer.load_matrix(sess,split=2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(<65398x373571 sparse matrix of type ''\n", " \twith 2739674 stored elements in Compressed Sparse Row format>,\n", " <1292x373571 sparse matrix of type ''\n", " \twith 43241 stored elements in Compressed Sparse Row format>,\n", " <1232x373571 sparse matrix of type ''\n", " \twith 30593 stored elements in Compressed Sparse Row format>)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "F_train,F_dev,F_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recompute feats (If needed)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clearing existing...\n", "Running UDF...\n", "[========================================] 100%\n", "\n" ] } ], "source": [ "F_train = featurizer.apply(split=0)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Clearing existing...\n", "Running UDF...\n", "[========================================] 100%\n", "\n", "Clearing existing...\n", "Running UDF...\n", "[========================================] 100%\n", "\n" ] } ], "source": [ "F_dev = featurizer.apply_existing(split=1)\n", "F_test = featurizer.apply_existing(split=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (Optional) : defining a validation set" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from tqdm import tqdm_notebook" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "from snorkel.models import GoldLabel,GoldLabelKey,Document" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "all_training_cands = sess.query(C).filter(C.split==0).order_by(C.id).all()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "training_docs = set()\n", "for cand in all_training_cands:\n", " training_docs.add(cand.get_parent().document.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell below should be uncommented to generate a validation set and export it" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# chosen_val_docs = np.random.choice(list(training_docs),30,replace = False)\n", "# with open(\"experiments/fda/data/valid/val_docs.txt\",\"w\") as f:\n", "# for doc in chosen_val_docs:\n", "# f.write(doc+'\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cell below loads an existing val set" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "chosen_val_docs =list()\n", "with open(\"experiments/fda/data/valid/val_docs.txt\",\"r\") as f:\n", " for line in f:\n", " chosen_val_docs.append(line.strip())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(chosen_val_docs)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3350283722574191847db1acc94d75e1", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type HBox.

\n", "

\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "

\n", "

\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "

\n" ], "text/plain": [ "HBox(children=(IntProgress(value=0, max=65398), HTML(value=u'')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "## Load the associated candidates\n", "val_training_cands = list()\n", "for cand in tqdm_notebook(all_training_cands):\n", " if cand.get_parent().document.name in chosen_val_docs:\n", " val_training_cands.append(cand)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1254" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(val_training_cands)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding some labels" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from snorkel.viewer import SentenceNgramViewer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "141" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(set (x.get_parent().id for x in val_training_cands))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "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\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msv\u001b[0m \u001b[0;34m=\u001b[0m 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annotator_name)\u001b[0m\n\u001b[1;32m 123\u001b[0m value=existing_annotation.value)\n\u001b[1;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexisting_annotation_stable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\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 126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotations_stable\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexisting_annotation_stable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/matthieu/Documents/Snorkel/snorkel/.snorkelenv.gpu/local/lib/python2.7/site-packages/sqlalchemy/orm/session.pyc\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0msa_exc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No transaction is begun.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 942\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 943\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransaction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\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 944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[0;32mdef\u001b[0m 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"metadata": {}, "outputs": [], "source": [ "glk = sess.query(GoldLabelKey).filter(GoldLabelKey.name==\"validation_set_custom\").first()\n", "gls = sess.query(GoldLabel).filter(GoldLabel.key==glk).all()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "Y_valid = [a.value for a in sess.query(GoldLabel).filter(GoldLabel.key==glk).order_by(GoldLabel.candidate_id).all()]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "gls_sort = sess.query(GoldLabel).filter(GoldLabel.key==glk).order_by(GoldLabel.candidate_id).all()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "sorted_dev_cands = sorted(val_training_cands,key = lambda a : a.id)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1254, 1254)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(Y_valid),len(sorted_dev_cands)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exportation of the gold labels if needed" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# with open(\"experiments/fda/data/valid/labels_dev.txt\",\"w\") as f:\n", "# for golab in gls:\n", "# f.write(str(golab.candidate_id)+'\\t'+str(golab.value)+'\\t'+golab.candidate.chem1.get_stable_id()+'\\t'+golab.candidate.chem2.get_stable_id()+'\\n')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 78 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 79 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 80 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 81 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 82 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 83 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 84 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 85 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 86 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 87 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 88 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 89 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 92 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 93 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 94 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 97 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 98 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 99 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 102 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 103 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 104 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 105 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 106 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 107 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "========================================\n", "Scores (Un-adjusted)\n", "========================================\n", "Pos. class accuracy: 0.271\n", "Neg. class accuracy: 0.989\n", "Precision 0.594\n", "Recall 0.271\n", "F1 0.373\n", "----------------------------------------\n", "TP: 19 | FP: 13 | TN: 1171 | FN: 51\n", "========================================\n", "\n" ] } ], "source": [ "_ = disc_model.error_analysis(X_test=sorted_dev_cands,Y_test=Y_valid,display=True,session=sess)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Load existing gold labels " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "with open(\"experiments/fda/data/valid/labels_dev.txt\",\"r\") as f:\n", " for line in f:\n", " list_items = line.split('\\t')\n", " gl = GoldLabel(candidate = sess.query(C).filter(C.id==list_items[0]).first(),key = glk,value = int(list_items[1]))\n", " sess.add(gl)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "sess.commit()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5806" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(sess.query(GoldLabel).all())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare data for evaluation and load appropriate sets" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "if X_dev is None:\n", " X_dev = sess.query(C).filter(C.split == DEV_SPLIT)\\\n", " .order_by(C.id).all()\n", " assert len(X_dev) > 0\n", "if Y_dev is None:\n", " Y_dev = load_gold_labels(sess,annotator_name='gold',split=DEV_SPLIT)\n", " assert Y_dev.nonzero()[0].shape[0] > 0" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "_=disc_model.error_analysis(sess, X_test, Y_test, display=True)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========================================\n", "Scores (Un-adjusted)\n", "========================================\n", "Pos. class accuracy: 0.433\n", "Neg. class accuracy: 0.984\n", "Precision 0.565\n", "Recall 0.433\n", "F1 0.491\n", "----------------------------------------\n", "TP: 26 | FP: 20 | TN: 1212 | FN: 34\n", "========================================\n", "\n" ] } ], "source": [ "_=disc_model.error_analysis(sess, X_dev, Y_dev, display=True)" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "========================================\n", "Scores (Un-adjusted)\n", "========================================\n", "Pos. class accuracy: 0.312\n", "Neg. class accuracy: 0.982\n", "Precision 0.537\n", "Recall 0.312\n", "F1 0.395\n", "----------------------------------------\n", "TP: 29 | FP: 25 | TN: 1372 | FN: 64\n", "========================================\n", "\n" ] } ], "source": [ "tp, fp, tn, fn =disc_model.error_analysis(X_test=sorted_dev_cands,Y_test=Y_valid,display=True,session=sess)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "require.undef('viewer');\n", "\n", "// NOTE: all elements should be selected using this.$el.find to avoid collisions with other Viewers\n", "\n", "define('viewer', [\"@jupyter-widgets/base\"], function(widgets) {\n", " var ViewerView = widgets.DOMWidgetView.extend({\n", " render: function() {\n", " this.cids = this.model.get('cids');\n", " this.nPages = this.cids.length;\n", " this.pid = 0;\n", " this.cxid = 0;\n", " this.cid = 0;\n", "\n", " // Insert the html payload\n", " this.$el.append(this.model.get('html'));\n", "\n", " // Initialize all labels from previous sessions\n", " this.labels = this.deserializeDict(this.model.get('_labels_serialized'));\n", " for (var i=0; i < this.nPages; i++) {\n", " this.pid = i;\n", " for (var j=0; j < this.cids[i].length; j++) {\n", " this.cxid = j;\n", " for (var k=0; k < this.cids[i][j].length; k++) {\n", " this.cid = k;\n", " if (this.cids[i][j][k] in this.labels) {\n", " this.markCurrentCandidate(false);\n", " }\n", " }\n", " }\n", " }\n", " this.pid = 0;\n", " this.cxid = 0;\n", " this.cid = 0;\n", "\n", " // Enable button functionality for navigation\n", " var that = this;\n", " this.$el.find(\"#next-cand\").click(function() {\n", " that.switchCandidate(1);\n", " });\n", " this.$el.find(\"#prev-cand\").click(function() {\n", " that.switchCandidate(-1);\n", " });\n", " this.$el.find(\"#next-context\").click(function() {\n", " that.switchContext(1);\n", " });\n", " this.$el.find(\"#prev-context\").click(function() {\n", " that.switchContext(-1);\n", " });\n", " this.$el.find(\"#next-page\").click(function() {\n", " that.switchPage(1);\n", " });\n", " this.$el.find(\"#prev-page\").click(function() {\n", " that.switchPage(-1);\n", " });\n", " this.$el.find(\"#label-true\").click(function() {\n", " that.labelCandidate(true, true);\n", " });\n", " this.$el.find(\"#label-false\").click(function() {\n", " that.labelCandidate(false, true);\n", " });\n", "\n", " // Arrow key functionality\n", " this.$el.keydown(function(e) {\n", " switch(e.which) {\n", " case 74: // j\n", " that.switchCandidate(-1);\n", " break;\n", "\n", " case 73: // i\n", " that.switchPage(-1);\n", " break;\n", "\n", " case 76: // l\n", " that.switchCandidate(1);\n", " break;\n", "\n", " case 75: // k\n", " that.switchPage(1);\n", " break;\n", "\n", " case 84: // t\n", " that.labelCandidate(true, true);\n", " break;\n", "\n", " case 70: // f\n", " that.labelCandidate(false, true);\n", " break;\n", " }\n", " });\n", "\n", " // Show the first page and highlight the first candidate\n", " this.$el.find(\"#viewer-page-0\").show();\n", " this.switchCandidate(0);\n", " },\n", "\n", " // Get candidate selector for currently selected candidate, escaping id properly\n", " getCandidate: function() {\n", " return this.$el.find(\".\"+this.cids[this.pid][this.cxid][this.cid]);\n", " }, \n", "\n", " // Color the candidate correctly according to registered label, as well as set highlighting\n", " markCurrentCandidate: function(highlight) {\n", " var cid = this.cids[this.pid][this.cxid][this.cid];\n", " var tags = this.$el.find(\".\"+cid);\n", "\n", " // Clear color classes\n", " tags.removeClass(\"candidate-h\");\n", " tags.removeClass(\"true-candidate\");\n", " tags.removeClass(\"true-candidate-h\");\n", " tags.removeClass(\"false-candidate\");\n", " tags.removeClass(\"false-candidate-h\");\n", " tags.removeClass(\"highlighted\");\n", "\n", " if (highlight) {\n", " if (cid in this.labels) {\n", " tags.addClass(String(this.labels[cid]) + \"-candidate-h\");\n", " } else {\n", " tags.addClass(\"candidate-h\");\n", " }\n", " \n", " // If un-highlighting, leave with first non-null coloring\n", " } else {\n", " var that = this;\n", " tags.each(function() {\n", " var cids = $(this).attr('class').split(/\\s+/).map(function(item) {\n", " return parseInt(item);\n", " });\n", " cids.sort();\n", " for (var i in cids) {\n", " if (cids[i] in that.labels) {\n", " var label = that.labels[cids[i]];\n", " $(this).addClass(String(label) + \"-candidate\");\n", " $(this).removeClass(String(!label) + \"-candidate\");\n", " break;\n", " }\n", " }\n", " });\n", " }\n", "\n", " // Extra highlighting css\n", " if (highlight) {\n", " tags.addClass(\"highlighted\");\n", " }\n", "\n", " // Classes for showing direction of relation\n", " if (highlight) {\n", " this.$el.find(\".\"+cid+\"-0\").addClass(\"left-candidate\");\n", " this.$el.find(\".\"+cid+\"-1\").addClass(\"right-candidate\");\n", " } else {\n", " this.$el.find(\".\"+cid+\"-0\").removeClass(\"left-candidate\");\n", " this.$el.find(\".\"+cid+\"-1\").removeClass(\"right-candidate\");\n", " }\n", " },\n", "\n", " // Cycle through candidates and highlight, by increment inc\n", " switchCandidate: function(inc) {\n", " var N = this.cids[this.pid].length\n", " var M = this.cids[this.pid][this.cxid].length;\n", " if (N == 0 || M == 0) { return false; }\n", "\n", " // Clear highlighting from previous candidate\n", " if (inc != 0) {\n", " this.markCurrentCandidate(false);\n", "\n", " // Increment the cid counter\n", "\n", " // Move to next context\n", " if (this.cid + inc >= M) {\n", " while (this.cid + inc >= M) {\n", " \n", " // At last context on page, halt\n", " if (this.cxid == N - 1) {\n", " this.cid = M - 1;\n", " inc = 0;\n", " break;\n", " \n", " // Increment to next context\n", " } else {\n", " inc -= M - this.cid;\n", " this.cxid += 1;\n", " M = this.cids[this.pid][this.cxid].length;\n", " this.cid = 0;\n", " }\n", " }\n", "\n", " // Move to previous context\n", " } else if (this.cid + inc < 0) {\n", " while (this.cid + inc < 0) {\n", " \n", " // At first context on page, halt\n", " if (this.cxid == 0) {\n", " this.cid = 0;\n", " inc = 0;\n", " break;\n", " \n", " // Increment to previous context\n", " } else {\n", " inc += this.cid + 1;\n", " this.cxid -= 1;\n", " M = this.cids[this.pid][this.cxid].length;\n", " this.cid = M - 1;\n", " }\n", " }\n", " }\n", "\n", " // Move within current context\n", " this.cid += inc;\n", " }\n", " this.markCurrentCandidate(true);\n", "\n", " // Push this new cid to the model\n", " this.model.set('_selected_cid', this.cids[this.pid][this.cxid][this.cid]);\n", " this.touch();\n", " },\n", "\n", " // Switch through contexts\n", " switchContext: function(inc) {\n", " this.markCurrentCandidate(false);\n", "\n", " // Iterate context on this page\n", " var M = this.cids[this.pid].length;\n", " if (this.cxid + inc < 0) {\n", " this.cxid = 0;\n", " } else if (this.cxid + inc >= M) {\n", " this.cxid = M - 1;\n", " } else {\n", " this.cxid += inc;\n", " }\n", "\n", " // Reset cid and set to first candidate\n", " this.cid = 0;\n", " this.switchCandidate(0);\n", " },\n", "\n", " // Switch through pages\n", " switchPage: function(inc) {\n", " this.markCurrentCandidate(false);\n", " this.$el.find(\".viewer-page\").hide();\n", " if (this.pid + inc < 0) {\n", " this.pid = 0;\n", " } else if (this.pid + inc > this.nPages - 1) {\n", " this.pid = this.nPages - 1;\n", " } else {\n", " this.pid += inc;\n", " }\n", " this.$el.find(\"#viewer-page-\"+this.pid).show();\n", "\n", " // Show pagination\n", " this.$el.find(\"#page\").html(this.pid);\n", "\n", " // Reset cid and set to first candidate\n", " this.cid = 0;\n", " this.cxid = 0;\n", " this.switchCandidate(0);\n", " },\n", "\n", " // Label currently-selected candidate\n", " labelCandidate: function(label, highlighted) {\n", " var c = this.getCandidate();\n", " var cid = this.cids[this.pid][this.cxid][this.cid];\n", " var cl = String(label) + \"-candidate\";\n", " var clh = String(label) + \"-candidate-h\";\n", " var cln = String(!label) + \"-candidate\";\n", " var clnh = String(!label) + \"-candidate-h\";\n", "\n", " // Toggle label highlighting\n", " if (c.hasClass(cl) || c.hasClass(clh)) {\n", " c.removeClass(cl);\n", " c.removeClass(clh);\n", " if (highlighted) {\n", " c.addClass(\"candidate-h\");\n", " }\n", " this.labels[cid] = null;\n", " this.send({event: 'delete_label', cid: cid});\n", " } else {\n", " c.removeClass(cln);\n", " c.removeClass(clnh);\n", " if (highlighted) {\n", " c.addClass(clh);\n", " } else {\n", " c.addClass(cl);\n", " }\n", " this.labels[cid] = label;\n", " this.send({event: 'set_label', cid: cid, value: label});\n", " }\n", "\n", " // Set the label and pass back to the model\n", " this.model.set('_labels_serialized', this.serializeDict(this.labels));\n", " this.touch();\n", " },\n", "\n", " // Serialization of hash maps, because traitlets Dict doesn't seem to work...\n", " serializeDict: function(d) {\n", " var s = [];\n", " for (var key in d) {\n", " s.push(key+\"~~\"+d[key]);\n", " }\n", " return s.join();\n", " },\n", "\n", " // Deserialization of hash maps\n", " deserializeDict: function(s) {\n", " var d = {};\n", " var entries = s.split(/,/);\n", " var kv;\n", " for (var i in entries) {\n", " kv = entries[i].split(/~~/);\n", " if (kv[1] == \"true\") {\n", " d[kv[0]] = true;\n", " } else if (kv[1] == \"false\") {\n", " d[kv[0]] = false;\n", " }\n", " }\n", " return d;\n", " },\n", " });\n", "\n", " return {\n", " ViewerView: ViewerView\n", " };\n", "});\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sv_val = SentenceNgramViewer(annotator_name=\"validation_set_custom\",session=sess,candidates=fn,height=500)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fp" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cfb094a71d9640ed9f551da356bb02a3", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type SentenceNgramViewer.

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\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or NBViewer),\n", " it may mean that your frontend doesn't currently support widgets.\n", "

\n" ], "text/plain": [ "SentenceNgramViewer(cids=[[[0], [23], [58]], [[59], [41, 42, 44, 45, 46], [21, 28, 38]], [[51, 52], [25, 29], [26, 27, 31, 32]], [[8, 20], [56, 57, 60], [14]], [[61], [39, 40], [30]], [[24], [2], [9, 13, 47, 48, 49]], [[53, 54], [63], [4, 7]], [[15, 16], [1], [34, 43, 50]], [[55, 62], [3, 6], [17, 18, 19]], [[5], [22], [33]], [[35, 36, 37], [10, 11, 12]]], html=u'\\n\\n\\n\\n
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  • Coniferyl aldehyde 5-hydroxylation and methylation direct syringyl lignin biosynthesis in angiosperms.
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  • The results indicate that the catabolic pathway involves an initial dioxygenase attack on nitrobenzene with the release of nitrite and formation of catechol, which is subsequently degraded by a meta cleavage pathway.
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  • These defects included: (i) the inability to synthesize UDP-ManNAcA due to Tn10 insertions in the structural genes for UDP-GlcNAc-2-epimerase (rffE) and UDP-ManNAcA (N-acetyl-D-mannosaminuronic acid) dehydrogenase (rffD), (ii) defects in the synthesis of C55-GlcNAc-ManNAcA (lipid II) due to insertion of transposon Tn10 in the structural gene for the UDP-ManNAcA transferase (rffM), (iii) the inability to synthesize TDP-Fuc4NAc (4-acetamido-4,6-dideoxy-D-galactose) due to Tn10 insertions in the structural gene for the transaminase that catalyzes the conversion of TDP-4-keto-6-deoxy-D-glucose to TDP-4-amino-4,6-dideoxy-D-galactose (rffA), and (iv) defects in steps subsequent to the synthesis of C55-GlcNAc-ManNAcA-Fuc4NAc (lipid III).
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\\n')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sv_val" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the proprtion of positives in the val set (should be around 5 %)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.062416107382550337" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(np.array(Y_valid)>0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stability study for RNN" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "disc_model = None" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/D_final_fda/D_final_fda-0\n", "[reRNN] Loaded model \n" ] } ], "source": [ "if disc_model is None:\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"D_final_fda\",\n", " save_dir=args.save_dir)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/D_FINAL_FDA_NEW_SAVE/D_FINAL_FDA_NEW_SAVE-0\n", "[reRNN] Loaded model \n" ] } ], "source": [ "if disc_model is None:\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"D_FINAL_FDA_NEW_SAVE\",\n", " save_dir=args.save_dir)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disc_model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define placeholders" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "p_test_list = list()\n", "r_test_list = list()\n", "p_dev_list = list()\n", "r_dev_list = list()\n", "p_val_list = list()\n", "r_val_list = list()\n", "fscore_dev_list = list()\n", "fscore_test_list = list()\n", "fscore_val_list = list()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the models and issue predictions. \n", "The model must have been trained with the allchecks = True parameter. Change the model name pattern below and select the number of epochs to load." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk3_epoch_0/reRNN_fullchk3_epoch_0-0\n", "[reRNN] Loaded model \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 14 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 15 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 16 has argument past max length for model:\t[arg ends at index 107; 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max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 29 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 30 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 1 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 2 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 3 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 6 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 7 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 8 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 9 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 10 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 11 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "P dev:0.62\tP test:0.54\tP val:0.42\tR dev:0.43\tRtest:0.41\tRval:0.19\n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk3_epoch_89/reRNN_fullchk3_epoch_89-89\n", "[reRNN] Loaded model \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 14 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 15 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 16 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 17 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 18 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 19 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 20 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 21 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 22 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 23 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 24 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 25 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 28 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 29 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 30 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 1 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 2 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 3 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 6 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 7 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 8 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 9 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 10 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 11 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "P dev:0.54\tP test:0.44\tP val:0.39\tR dev:0.47\tRtest:0.43\tRval:0.21\n" ] } ], "source": [ "epochs_to_load = 90\n", "\n", "for n_epoch in range(epochs_to_load):\n", " disc_model = None\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"reRNN_fullchk3_epoch_{}\".format(n_epoch),\n", " save_dir=args.save_dir)\n", " pt,rt,ft = disc_model.score(X_test, Y_test, batch_size=32,beta=0.8)\n", " pd,rd,fd = disc_model.score(X_dev, Y_dev, batch_size=32,beta=0.8)\n", " pv,rv,fv = disc_model.score(sorted_dev_cands, Y_valid, batch_size=32,beta=0.8)\n", " p_test_list.append(pt) \n", " r_test_list.append(rt) \n", " p_dev_list.append(pd) \n", " r_dev_list.append(rd) \n", " fscore_dev_list.append(fd) \n", " fscore_test_list.append(ft)\n", " p_val_list.append(pv)\n", " r_val_list.append(rv)\n", " fscore_val_list.append(fv)\n", " print(\"P dev:{0:.2f}\\tP test:{1:.2f}\\tP val:{2:.2f}\\tR dev:{3:.2f}\\tRtest:{4:.2f}\\tRval:{5:.2f}\".format(pd,pt,pv,rd,rt,rv) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the precision" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5,1,u'recall')" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(r_dev_list,label = \"RDev\")\n", "plt.plot(r_test_list,label = \"RTest\")\n", "plt.plot(r_val_list,label = \"RVal\")\n", "plt.legend()\n", "plt.title(\"recall\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Compute and plot the F1 score" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "f1_list_dev = list()\n", "f1_list_test = list()\n", "f1_list_val = list()\n", "for p,r in zip(p_dev_list,r_dev_list):\n", " f1_list_dev.append(2*p*r/(p+r))\n", "for p,r in zip(p_test_list,r_test_list):\n", " f1_list_test.append(2*p*r/(p+r))\n", "for p,r in zip(p_val_list,r_val_list):\n", " f1_list_val.append(2*p*r/(p+r))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5,1,u'loss')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(lst_loss)\n", "plt.title(\"loss\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stability plots\n", "\n", "This section defines **stability plots**.\n", "\n", "The purpose of these plots is to visualize the evolutions of the errors made by the network along the training and see if the errors can be clustered by document, and more generally if we have the same kind of errors at the beginning and end of the training or if they are different" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Load models and issue predictions" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "dict_doc_ids = dict()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk_epoch_0/reRNN_fullchk_epoch_0-0\n", "[reRNN] Loaded model \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 78 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 79 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 80 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 81 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 82 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 83 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 84 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 85 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 86 has argument past max length for model:\t[arg ends at index 123; 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max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 99 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 102 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 103 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 104 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 105 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 106 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 107 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk_epoch_44/reRNN_fullchk_epoch_44-44\n", "[reRNN] Loaded model \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 78 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 79 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 80 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 81 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 82 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 83 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 84 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 85 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 86 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 87 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 88 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 89 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 92 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 93 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 94 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 97 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 98 has argument past max length for model:\t[arg ends at index 107; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 99 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 102 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 103 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 104 has argument past max length for model:\t[arg ends at index 123; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 105 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 106 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n", "/home/matthieu/Documents/Snorkel/snorkel/snorkel/learning/disc_models/rnn/rnn_base.py:40: UserWarning: Candidate 107 has argument past max length for model:\t[arg ends at index 110; max len 100]\n", " warnings.warn('\\t'.join([w.format(i), info]))\n" ] } ], "source": [ "dic_info_lst = list()\n", "for n_epoch in range(45):\n", " disc_model = None\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"reRNN_fullchk_epoch_{}\".format(n_epoch),\n", " save_dir=args.save_dir)\n", " _, fpv, _, fnv =disc_model.error_analysis(X_test=sorted_dev_cands,Y_test=Y_valid,display=False,session=sess)\n", " _, fpd, _, fnd =disc_model.error_analysis(X_test=X_dev,Y_test=Y_dev,display=False,session=sess)\n", " _, fpt, _, fnt =disc_model.error_analysis(X_test=X_test,Y_test=Y_test,display=False,session=sess)\n", " for x in list(fpv)+list(fnv)+list(fpd)+list(fnd)+list(fpt)+list(fnt):\n", " dict_doc_ids[x.id] = x[0].get_parent().document.name\n", " dic_info_lst.append(dict(epoch = n_epoch, fpv_id = [c.id for c in fpv], fnv_id = [c.id for c in fnv], fpt_id = [c.id for c in fpt], fnt_id = [c.id for c in fnt], fpd_id = [c.id for c in fpd], fnd_id = [c.id for c in fnd]))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# Compute a document mapping \n", "dict_doc_ids_rev = dict()\n", "\n", "for k in dict_doc_ids.keys():\n", " if dict_doc_ids_rev.has_key(dict_doc_ids[k]):\n", " dict_doc_ids_rev[dict_doc_ids[k]].append(dict_doc_ids[k])\n", " else:\n", " dict_doc_ids_rev[dict_doc_ids[k]] = [dict_doc_ids[k]]\n", "dico_colmap_doc = dict([(k,1.00*i/len(dict_doc_ids_rev)) for i,k in enumerate(dict_doc_ids_rev.keys())])" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# prepare lists of documents for plotting\n", "set_all_fp_ids_dev = set()\n", "set_all_fn_ids_dev = set()\n", "\n", "\n", "for d in dic_info_lst:\n", " set_all_fp_ids_dev = set_all_fp_ids_dev.union(set(d[\"fpd_id\"]))\n", " set_all_fn_ids_dev = set_all_fn_ids_dev.union(set(d[\"fnd_id\"]))\n", " \n", "dic_all_fp_ids_dev = dict([(x,y) for y,x in enumerate(set_all_fp_ids_dev)])\n", "dic_all_fn_ids_dev = dict([(x,y) for y,x in enumerate(set_all_fn_ids_dev)])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(70, 46)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# chekcnumber of documents\n", "len(set_all_fp_ids_dev), len(set_all_fn_ids_dev)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "matrix_fp_dev = np.zeros((len(set_all_fp_ids_dev),len(dic_info_lst)))\n", "matrix_fn_dev = np.zeros((len(set_all_fn_ids_dev),len(dic_info_lst)))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "for d in dic_info_lst:\n", " for fp_id in d[\"fpd_id\"]:\n", " matrix_fp_dev[dic_all_fp_ids_dev[fp_id],d[\"epoch\"]] = 0.2+ 0.8* dico_colmap_doc[dict_doc_ids[fp_id]]\n", " for fn_id in d[\"fnd_id\"]:\n", " matrix_fn_dev[dic_all_fn_ids_dev[fn_id],d[\"epoch\"]] = 0.2+ 0.8* dico_colmap_doc[dict_doc_ids[fn_id]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the **stability map**.\n", "\n", "The **stability map** can be read as follows: \n", "\n", "* The x axis is the epoch\n", "* The y axis is the candidate\n", "* The color is the document\n", "\n", "So the visualization allows to see the variety of documents that accounts for the error and the stability of the error.\n", "\n", "**NB: ** A clustering of the lines would help to visualize" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
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')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.imshow(matrix_fn_dev,interpolation='none',cmap=\"cubehelix_r\")\n", "plt.title(\"False negatives\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logit model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "p_test_list = list()\n", "r_test_list = list()\n", "p_dev_list = list()\n", "r_dev_list = list()\n", "p_val_list = list()\n", "r_val_list = list()\n", "fscore_dev_list = list()\n", "fscore_test_list = list()\n", "fscore_val_list = list()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_0/SparseLogisticRegression_fullchk4_epoch_0-0\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.23\tP test:0.27\tR dev:0.23\tRtest:0.16\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_1/SparseLogisticRegression_fullchk4_epoch_1-1\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.64\tR dev:0.25\tRtest:0.18\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_2/SparseLogisticRegression_fullchk4_epoch_2-2\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.86\tR dev:0.27\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_3/SparseLogisticRegression_fullchk4_epoch_3-3\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.86\tR dev:0.27\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_4/SparseLogisticRegression_fullchk4_epoch_4-4\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.88\tR dev:0.27\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_133/SparseLogisticRegression_fullchk4_epoch_133-133\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_134/SparseLogisticRegression_fullchk4_epoch_134-134\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_135/SparseLogisticRegression_fullchk4_epoch_135-135\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_144/SparseLogisticRegression_fullchk4_epoch_144-144\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.43\tP test:0.47\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_145/SparseLogisticRegression_fullchk4_epoch_145-145\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.50\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_146/SparseLogisticRegression_fullchk4_epoch_146-146\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.43\tP test:0.47\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_147/SparseLogisticRegression_fullchk4_epoch_147-147\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.43\tP test:0.50\tR 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Loaded model \n", "P dev:0.41\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_164/SparseLogisticRegression_fullchk4_epoch_164-164\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_165/SparseLogisticRegression_fullchk4_epoch_165-165\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.50\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_166/SparseLogisticRegression_fullchk4_epoch_166-166\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.49\tR dev:0.28\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_167/SparseLogisticRegression_fullchk4_epoch_167-167\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_168/SparseLogisticRegression_fullchk4_epoch_168-168\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_169/SparseLogisticRegression_fullchk4_epoch_169-169\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.49\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_170/SparseLogisticRegression_fullchk4_epoch_170-170\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.47\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_179/SparseLogisticRegression_fullchk4_epoch_179-179\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.47\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_180/SparseLogisticRegression_fullchk4_epoch_180-180\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.46\tR dev:0.30\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_181/SparseLogisticRegression_fullchk4_epoch_181-181\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.47\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_182/SparseLogisticRegression_fullchk4_epoch_182-182\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.46\tR 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Loaded model \n", "P dev:0.38\tP test:0.46\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_199/SparseLogisticRegression_fullchk4_epoch_199-199\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.46\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_200/SparseLogisticRegression_fullchk4_epoch_200-200\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.45\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_201/SparseLogisticRegression_fullchk4_epoch_201-201\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.46\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_202/SparseLogisticRegression_fullchk4_epoch_202-202\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.45\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_203/SparseLogisticRegression_fullchk4_epoch_203-203\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_204/SparseLogisticRegression_fullchk4_epoch_204-204\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_205/SparseLogisticRegression_fullchk4_epoch_205-205\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_214/SparseLogisticRegression_fullchk4_epoch_214-214\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.44\tR dev:0.28\tRtest:0.31\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_215/SparseLogisticRegression_fullchk4_epoch_215-215\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.45\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_216/SparseLogisticRegression_fullchk4_epoch_216-216\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.46\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_217/SparseLogisticRegression_fullchk4_epoch_217-217\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.46\tR 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Loaded model \n", "P dev:0.37\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_234/SparseLogisticRegression_fullchk4_epoch_234-234\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_235/SparseLogisticRegression_fullchk4_epoch_235-235\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_236/SparseLogisticRegression_fullchk4_epoch_236-236\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_237/SparseLogisticRegression_fullchk4_epoch_237-237\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_238/SparseLogisticRegression_fullchk4_epoch_238-238\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.30\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_239/SparseLogisticRegression_fullchk4_epoch_239-239\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.47\tR dev:0.28\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_240/SparseLogisticRegression_fullchk4_epoch_240-240\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_249/SparseLogisticRegression_fullchk4_epoch_249-249\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_250/SparseLogisticRegression_fullchk4_epoch_250-250\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_251/SparseLogisticRegression_fullchk4_epoch_251-251\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.47\tR dev:0.30\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_252/SparseLogisticRegression_fullchk4_epoch_252-252\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR 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Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_269/SparseLogisticRegression_fullchk4_epoch_269-269\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_270/SparseLogisticRegression_fullchk4_epoch_270-270\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.33\tP test:0.47\tR dev:0.28\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_271/SparseLogisticRegression_fullchk4_epoch_271-271\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_272/SparseLogisticRegression_fullchk4_epoch_272-272\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_273/SparseLogisticRegression_fullchk4_epoch_273-273\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_274/SparseLogisticRegression_fullchk4_epoch_274-274\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_275/SparseLogisticRegression_fullchk4_epoch_275-275\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_284/SparseLogisticRegression_fullchk4_epoch_284-284\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.46\tR dev:0.30\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_285/SparseLogisticRegression_fullchk4_epoch_285-285\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.45\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_286/SparseLogisticRegression_fullchk4_epoch_286-286\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_287/SparseLogisticRegression_fullchk4_epoch_287-287\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.45\tR 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Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_304/SparseLogisticRegression_fullchk4_epoch_304-304\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_305/SparseLogisticRegression_fullchk4_epoch_305-305\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.42\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_306/SparseLogisticRegression_fullchk4_epoch_306-306\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_307/SparseLogisticRegression_fullchk4_epoch_307-307\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_308/SparseLogisticRegression_fullchk4_epoch_308-308\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_309/SparseLogisticRegression_fullchk4_epoch_309-309\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.42\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_310/SparseLogisticRegression_fullchk4_epoch_310-310\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.41\tR dev:0.32\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_319/SparseLogisticRegression_fullchk4_epoch_319-319\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.33\tRtest:0.33\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_320/SparseLogisticRegression_fullchk4_epoch_320-320\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.46\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_321/SparseLogisticRegression_fullchk4_epoch_321-321\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_322/SparseLogisticRegression_fullchk4_epoch_322-322\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.45\tR 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Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_339/SparseLogisticRegression_fullchk4_epoch_339-339\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.45\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_340/SparseLogisticRegression_fullchk4_epoch_340-340\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_341/SparseLogisticRegression_fullchk4_epoch_341-341\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.33\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_342/SparseLogisticRegression_fullchk4_epoch_342-342\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_343/SparseLogisticRegression_fullchk4_epoch_343-343\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_344/SparseLogisticRegression_fullchk4_epoch_344-344\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_345/SparseLogisticRegression_fullchk4_epoch_345-345\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.33\tP test:0.45\tR dev:0.30\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_346/SparseLogisticRegression_fullchk4_epoch_346-346\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.41\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_347/SparseLogisticRegression_fullchk4_epoch_347-347\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.33\tP test:0.45\tR dev:0.30\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_348/SparseLogisticRegression_fullchk4_epoch_348-348\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_349/SparseLogisticRegression_fullchk4_epoch_349-349\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.41\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_350/SparseLogisticRegression_fullchk4_epoch_350-350\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_351/SparseLogisticRegression_fullchk4_epoch_351-351\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_352/SparseLogisticRegression_fullchk4_epoch_352-352\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_353/SparseLogisticRegression_fullchk4_epoch_353-353\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_354/SparseLogisticRegression_fullchk4_epoch_354-354\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_355/SparseLogisticRegression_fullchk4_epoch_355-355\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_356/SparseLogisticRegression_fullchk4_epoch_356-356\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_357/SparseLogisticRegression_fullchk4_epoch_357-357\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_358/SparseLogisticRegression_fullchk4_epoch_358-358\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.41\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_359/SparseLogisticRegression_fullchk4_epoch_359-359\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_360/SparseLogisticRegression_fullchk4_epoch_360-360\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_361/SparseLogisticRegression_fullchk4_epoch_361-361\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.45\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_362/SparseLogisticRegression_fullchk4_epoch_362-362\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.40\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_363/SparseLogisticRegression_fullchk4_epoch_363-363\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.43\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_364/SparseLogisticRegression_fullchk4_epoch_364-364\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_365/SparseLogisticRegression_fullchk4_epoch_365-365\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_366/SparseLogisticRegression_fullchk4_epoch_366-366\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_367/SparseLogisticRegression_fullchk4_epoch_367-367\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.44\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_368/SparseLogisticRegression_fullchk4_epoch_368-368\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_369/SparseLogisticRegression_fullchk4_epoch_369-369\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_370/SparseLogisticRegression_fullchk4_epoch_370-370\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.42\tR dev:0.37\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_371/SparseLogisticRegression_fullchk4_epoch_371-371\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_372/SparseLogisticRegression_fullchk4_epoch_372-372\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_373/SparseLogisticRegression_fullchk4_epoch_373-373\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.35\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_374/SparseLogisticRegression_fullchk4_epoch_374-374\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.42\tR dev:0.35\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_375/SparseLogisticRegression_fullchk4_epoch_375-375\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.42\tR dev:0.35\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_376/SparseLogisticRegression_fullchk4_epoch_376-376\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_377/SparseLogisticRegression_fullchk4_epoch_377-377\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_378/SparseLogisticRegression_fullchk4_epoch_378-378\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_379/SparseLogisticRegression_fullchk4_epoch_379-379\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_380/SparseLogisticRegression_fullchk4_epoch_380-380\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.41\tR dev:0.32\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_381/SparseLogisticRegression_fullchk4_epoch_381-381\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_382/SparseLogisticRegression_fullchk4_epoch_382-382\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_383/SparseLogisticRegression_fullchk4_epoch_383-383\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.42\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_384/SparseLogisticRegression_fullchk4_epoch_384-384\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.41\tR dev:0.37\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_385/SparseLogisticRegression_fullchk4_epoch_385-385\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_386/SparseLogisticRegression_fullchk4_epoch_386-386\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.44\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_387/SparseLogisticRegression_fullchk4_epoch_387-387\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_388/SparseLogisticRegression_fullchk4_epoch_388-388\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.43\tR dev:0.33\tRtest:0.35\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_389/SparseLogisticRegression_fullchk4_epoch_389-389\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.42\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_390/SparseLogisticRegression_fullchk4_epoch_390-390\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_391/SparseLogisticRegression_fullchk4_epoch_391-391\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.43\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_392/SparseLogisticRegression_fullchk4_epoch_392-392\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.43\tR dev:0.37\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_393/SparseLogisticRegression_fullchk4_epoch_393-393\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_394/SparseLogisticRegression_fullchk4_epoch_394-394\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.44\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_395/SparseLogisticRegression_fullchk4_epoch_395-395\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.43\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_396/SparseLogisticRegression_fullchk4_epoch_396-396\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.42\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_397/SparseLogisticRegression_fullchk4_epoch_397-397\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.34\tP test:0.42\tR dev:0.33\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_398/SparseLogisticRegression_fullchk4_epoch_398-398\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.43\tR dev:0.35\tRtest:0.37\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk4_epoch_399/SparseLogisticRegression_fullchk4_epoch_399-399\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.43\tR dev:0.35\tRtest:0.37\t\n" ] } ], "source": [ "for n_epoch in range(400):\n", " disc_model = None\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"SparseLogisticRegression_fullchk4_epoch_{}\".format(n_epoch),\n", " save_dir=args.save_dir)\n", " pt,rt,ft = disc_model.score(F_test, Y_test, batch_size=32,beta=0.8)\n", " pd,rd,fd = disc_model.score(F_dev, Y_dev, batch_size=32,beta=0.8)\n", "# pv,rv,fv = disc_model.score(sorted_dev_cands, Y_valid, batch_size=32,beta=0.8)\n", " p_test_list.append(pt) \n", " r_test_list.append(rt) \n", " p_dev_list.append(pd) \n", " r_dev_list.append(rd) \n", " fscore_dev_list.append(fd) \n", " fscore_test_list.append(ft)\n", "# p_val_list.append(pv)\n", "# r_val_list.append(rv)\n", "# fscore_val_list.append(fv)\n", " print(\"P dev:{0:.2f}\\tP test:{1:.2f}\\tR dev:{2:.2f}\\tRtest:{3:.2f}\\t\".format(pd,pt,rd,rt) )" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5,1,u'recall')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(r_dev_list,label = \"RDev\")\n", "plt.plot(r_test_list,label = \"RTest\")\n", "plt.plot(r_val_list,label = \"RVal\")\n", "plt.legend()\n", "plt.title(\"recall\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "p_test_list = list()\n", "r_test_list = list()\n", "p_dev_list = list()\n", "r_dev_list = list()\n", "p_val_list = list()\n", "r_val_list = list()\n", "fscore_dev_list = list()\n", "fscore_test_list = list()\n", "fscore_val_list = list()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_0/SparseLogisticRegression_fullchk_pr1__epoch_0-0\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.94\tR dev:0.27\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_1/SparseLogisticRegression_fullchk_pr1__epoch_1-1\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.88\tR dev:0.35\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_2/SparseLogisticRegression_fullchk_pr1__epoch_2-2\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.94\tR dev:0.33\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_3/SparseLogisticRegression_fullchk_pr1__epoch_3-3\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:1.00\tR dev:0.32\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_4/SparseLogisticRegression_fullchk_pr1__epoch_4-4\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:1.00\tR 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP test:0.70\tR dev:0.30\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_13/SparseLogisticRegression_fullchk_pr1__epoch_13-13\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.70\tR dev:0.30\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_14/SparseLogisticRegression_fullchk_pr1__epoch_14-14\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.68\tR dev:0.33\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_15/SparseLogisticRegression_fullchk_pr1__epoch_15-15\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.70\tR dev:0.30\tRtest:0.31\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_20/SparseLogisticRegression_fullchk_pr1__epoch_20-20\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.67\tR dev:0.25\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_21/SparseLogisticRegression_fullchk_pr1__epoch_21-21\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.68\tR dev:0.35\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_22/SparseLogisticRegression_fullchk_pr1__epoch_22-22\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.60\tR dev:0.30\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_23/SparseLogisticRegression_fullchk_pr1__epoch_23-23\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.65\tR dev:0.37\tRtest:0.29\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_24/SparseLogisticRegression_fullchk_pr1__epoch_24-24\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.64\tR dev:0.30\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_25/SparseLogisticRegression_fullchk_pr1__epoch_25-25\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.61\tR dev:0.32\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_26/SparseLogisticRegression_fullchk_pr1__epoch_26-26\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.62\tR dev:0.27\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_27/SparseLogisticRegression_fullchk_pr1__epoch_27-27\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.61\tR dev:0.30\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_28/SparseLogisticRegression_fullchk_pr1__epoch_28-28\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.64\tR dev:0.28\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_29/SparseLogisticRegression_fullchk_pr1__epoch_29-29\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.67\tR dev:0.28\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_30/SparseLogisticRegression_fullchk_pr1__epoch_30-30\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.61\tR dev:0.32\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_31/SparseLogisticRegression_fullchk_pr1__epoch_31-31\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.67\tR dev:0.32\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_32/SparseLogisticRegression_fullchk_pr1__epoch_32-32\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.65\tR dev:0.35\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_33/SparseLogisticRegression_fullchk_pr1__epoch_33-33\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.57\tR dev:0.30\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_34/SparseLogisticRegression_fullchk_pr1__epoch_34-34\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.67\tR dev:0.32\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_35/SparseLogisticRegression_fullchk_pr1__epoch_35-35\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.54\tR dev:0.33\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_36/SparseLogisticRegression_fullchk_pr1__epoch_36-36\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.69\tP test:0.61\tR dev:0.37\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_37/SparseLogisticRegression_fullchk_pr1__epoch_37-37\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.57\tR dev:0.30\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_38/SparseLogisticRegression_fullchk_pr1__epoch_38-38\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.59\tR dev:0.30\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_39/SparseLogisticRegression_fullchk_pr1__epoch_39-39\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.70\tP test:0.59\tR dev:0.35\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_40/SparseLogisticRegression_fullchk_pr1__epoch_40-40\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.59\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_41/SparseLogisticRegression_fullchk_pr1__epoch_41-41\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.56\tR dev:0.33\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_42/SparseLogisticRegression_fullchk_pr1__epoch_42-42\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.61\tR 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.58\tR dev:0.35\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_51/SparseLogisticRegression_fullchk_pr1__epoch_51-51\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.56\tR dev:0.32\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_52/SparseLogisticRegression_fullchk_pr1__epoch_52-52\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.71\tP test:0.67\tR dev:0.37\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_53/SparseLogisticRegression_fullchk_pr1__epoch_53-53\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.55\tR dev:0.35\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_58/SparseLogisticRegression_fullchk_pr1__epoch_58-58\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.57\tR dev:0.35\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_59/SparseLogisticRegression_fullchk_pr1__epoch_59-59\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.58\tR dev:0.30\tRtest:0.27\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_60/SparseLogisticRegression_fullchk_pr1__epoch_60-60\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.69\tP test:0.55\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_61/SparseLogisticRegression_fullchk_pr1__epoch_61-61\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.52\tR 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.48\tR dev:0.35\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_70/SparseLogisticRegression_fullchk_pr1__epoch_70-70\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.52\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_71/SparseLogisticRegression_fullchk_pr1__epoch_71-71\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.52\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_72/SparseLogisticRegression_fullchk_pr1__epoch_72-72\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.46\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_77/SparseLogisticRegression_fullchk_pr1__epoch_77-77\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.43\tR dev:0.30\tRtest:0.20\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_78/SparseLogisticRegression_fullchk_pr1__epoch_78-78\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.48\tR dev:0.28\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_79/SparseLogisticRegression_fullchk_pr1__epoch_79-79\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.48\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_80/SparseLogisticRegression_fullchk_pr1__epoch_80-80\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.48\tR 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.48\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_89/SparseLogisticRegression_fullchk_pr1__epoch_89-89\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.50\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_90/SparseLogisticRegression_fullchk_pr1__epoch_90-90\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.46\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_91/SparseLogisticRegression_fullchk_pr1__epoch_91-91\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.44\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_96/SparseLogisticRegression_fullchk_pr1__epoch_96-96\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.42\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_97/SparseLogisticRegression_fullchk_pr1__epoch_97-97\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP test:0.48\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_98/SparseLogisticRegression_fullchk_pr1__epoch_98-98\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.50\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_99/SparseLogisticRegression_fullchk_pr1__epoch_99-99\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.69\tP test:0.44\tR 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_111/SparseLogisticRegression_fullchk_pr1__epoch_111-111\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.42\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_112/SparseLogisticRegression_fullchk_pr1__epoch_112-112\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.42\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_113/SparseLogisticRegression_fullchk_pr1__epoch_113-113\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.41\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_114/SparseLogisticRegression_fullchk_pr1__epoch_114-114\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.69\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.44\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_119/SparseLogisticRegression_fullchk_pr1__epoch_119-119\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.41\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_120/SparseLogisticRegression_fullchk_pr1__epoch_120-120\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.44\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_121/SparseLogisticRegression_fullchk_pr1__epoch_121-121\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.43\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_126/SparseLogisticRegression_fullchk_pr1__epoch_126-126\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.42\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_127/SparseLogisticRegression_fullchk_pr1__epoch_127-127\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.39\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_128/SparseLogisticRegression_fullchk_pr1__epoch_128-128\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP test:0.39\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_129/SparseLogisticRegression_fullchk_pr1__epoch_129-129\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.44\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_134/SparseLogisticRegression_fullchk_pr1__epoch_134-134\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.41\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_135/SparseLogisticRegression_fullchk_pr1__epoch_135-135\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.37\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_136/SparseLogisticRegression_fullchk_pr1__epoch_136-136\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.41\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_141/SparseLogisticRegression_fullchk_pr1__epoch_141-141\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.37\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_142/SparseLogisticRegression_fullchk_pr1__epoch_142-142\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.37\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_143/SparseLogisticRegression_fullchk_pr1__epoch_143-143\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.37\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_144/SparseLogisticRegression_fullchk_pr1__epoch_144-144\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.38\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_149/SparseLogisticRegression_fullchk_pr1__epoch_149-149\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.35\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_150/SparseLogisticRegression_fullchk_pr1__epoch_150-150\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.38\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_151/SparseLogisticRegression_fullchk_pr1__epoch_151-151\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.38\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_156/SparseLogisticRegression_fullchk_pr1__epoch_156-156\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.32\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_157/SparseLogisticRegression_fullchk_pr1__epoch_157-157\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.39\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_158/SparseLogisticRegression_fullchk_pr1__epoch_158-158\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.38\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_159/SparseLogisticRegression_fullchk_pr1__epoch_159-159\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.35\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_164/SparseLogisticRegression_fullchk_pr1__epoch_164-164\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.37\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_165/SparseLogisticRegression_fullchk_pr1__epoch_165-165\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.66\tP test:0.35\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_166/SparseLogisticRegression_fullchk_pr1__epoch_166-166\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.73\tP test:0.39\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_171/SparseLogisticRegression_fullchk_pr1__epoch_171-171\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.32\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_172/SparseLogisticRegression_fullchk_pr1__epoch_172-172\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.63\tP test:0.38\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_173/SparseLogisticRegression_fullchk_pr1__epoch_173-173\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.37\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_174/SparseLogisticRegression_fullchk_pr1__epoch_174-174\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.69\tP test:0.35\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_179/SparseLogisticRegression_fullchk_pr1__epoch_179-179\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.64\tP test:0.33\tR dev:0.35\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_180/SparseLogisticRegression_fullchk_pr1__epoch_180-180\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.37\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_181/SparseLogisticRegression_fullchk_pr1__epoch_181-181\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.68\tP test:0.33\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_182/SparseLogisticRegression_fullchk_pr1__epoch_182-182\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.34\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_183/SparseLogisticRegression_fullchk_pr1__epoch_183-183\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.32\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_184/SparseLogisticRegression_fullchk_pr1__epoch_184-184\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP test:0.34\tR dev:0.35\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_185/SparseLogisticRegression_fullchk_pr1__epoch_185-185\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.31\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_186/SparseLogisticRegression_fullchk_pr1__epoch_186-186\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.31\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_187/SparseLogisticRegression_fullchk_pr1__epoch_187-187\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.31\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_188/SparseLogisticRegression_fullchk_pr1__epoch_188-188\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.33\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_189/SparseLogisticRegression_fullchk_pr1__epoch_189-189\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.29\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_194/SparseLogisticRegression_fullchk_pr1__epoch_194-194\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_195/SparseLogisticRegression_fullchk_pr1__epoch_195-195\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.67\tP test:0.33\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_196/SparseLogisticRegression_fullchk_pr1__epoch_196-196\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.34\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_201/SparseLogisticRegression_fullchk_pr1__epoch_201-201\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.26\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_202/SparseLogisticRegression_fullchk_pr1__epoch_202-202\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.28\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_203/SparseLogisticRegression_fullchk_pr1__epoch_203-203\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.30\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_204/SparseLogisticRegression_fullchk_pr1__epoch_204-204\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.29\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_209/SparseLogisticRegression_fullchk_pr1__epoch_209-209\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_210/SparseLogisticRegression_fullchk_pr1__epoch_210-210\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.60\tP test:0.27\tR dev:0.35\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_211/SparseLogisticRegression_fullchk_pr1__epoch_211-211\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_216/SparseLogisticRegression_fullchk_pr1__epoch_216-216\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_217/SparseLogisticRegression_fullchk_pr1__epoch_217-217\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.26\tR dev:0.33\tRtest:0.20\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_218/SparseLogisticRegression_fullchk_pr1__epoch_218-218\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.27\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_219/SparseLogisticRegression_fullchk_pr1__epoch_219-219\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.65\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_224/SparseLogisticRegression_fullchk_pr1__epoch_224-224\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.24\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_225/SparseLogisticRegression_fullchk_pr1__epoch_225-225\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_226/SparseLogisticRegression_fullchk_pr1__epoch_226-226\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.28\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_231/SparseLogisticRegression_fullchk_pr1__epoch_231-231\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.27\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_232/SparseLogisticRegression_fullchk_pr1__epoch_232-232\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.25\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_233/SparseLogisticRegression_fullchk_pr1__epoch_233-233\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.27\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_234/SparseLogisticRegression_fullchk_pr1__epoch_234-234\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.27\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_239/SparseLogisticRegression_fullchk_pr1__epoch_239-239\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.25\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_240/SparseLogisticRegression_fullchk_pr1__epoch_240-240\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.26\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_241/SparseLogisticRegression_fullchk_pr1__epoch_241-241\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.24\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_246/SparseLogisticRegression_fullchk_pr1__epoch_246-246\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.27\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_247/SparseLogisticRegression_fullchk_pr1__epoch_247-247\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.25\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_248/SparseLogisticRegression_fullchk_pr1__epoch_248-248\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.24\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_249/SparseLogisticRegression_fullchk_pr1__epoch_249-249\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.62\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.23\tR dev:0.33\tRtest:0.20\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_254/SparseLogisticRegression_fullchk_pr1__epoch_254-254\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.26\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_255/SparseLogisticRegression_fullchk_pr1__epoch_255-255\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.20\tR dev:0.33\tRtest:0.20\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_256/SparseLogisticRegression_fullchk_pr1__epoch_256-256\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.26\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_261/SparseLogisticRegression_fullchk_pr1__epoch_261-261\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.61\tP test:0.24\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_262/SparseLogisticRegression_fullchk_pr1__epoch_262-262\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.27\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_263/SparseLogisticRegression_fullchk_pr1__epoch_263-263\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.24\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_264/SparseLogisticRegression_fullchk_pr1__epoch_264-264\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.24\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_269/SparseLogisticRegression_fullchk_pr1__epoch_269-269\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.26\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_270/SparseLogisticRegression_fullchk_pr1__epoch_270-270\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.25\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_271/SparseLogisticRegression_fullchk_pr1__epoch_271-271\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.20\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_276/SparseLogisticRegression_fullchk_pr1__epoch_276-276\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.20\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_277/SparseLogisticRegression_fullchk_pr1__epoch_277-277\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.20\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_278/SparseLogisticRegression_fullchk_pr1__epoch_278-278\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.25\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_279/SparseLogisticRegression_fullchk_pr1__epoch_279-279\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.20\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_284/SparseLogisticRegression_fullchk_pr1__epoch_284-284\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.26\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_285/SparseLogisticRegression_fullchk_pr1__epoch_285-285\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.22\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_286/SparseLogisticRegression_fullchk_pr1__epoch_286-286\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.23\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_291/SparseLogisticRegression_fullchk_pr1__epoch_291-291\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.19\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_292/SparseLogisticRegression_fullchk_pr1__epoch_292-292\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP test:0.20\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_293/SparseLogisticRegression_fullchk_pr1__epoch_293-293\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.21\tR dev:0.33\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_294/SparseLogisticRegression_fullchk_pr1__epoch_294-294\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.59\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.23\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_299/SparseLogisticRegression_fullchk_pr1__epoch_299-299\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.20\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_300/SparseLogisticRegression_fullchk_pr1__epoch_300-300\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.21\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_301/SparseLogisticRegression_fullchk_pr1__epoch_301-301\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.19\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_306/SparseLogisticRegression_fullchk_pr1__epoch_306-306\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP test:0.18\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_307/SparseLogisticRegression_fullchk_pr1__epoch_307-307\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.57\tP test:0.19\tR dev:0.33\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_308/SparseLogisticRegression_fullchk_pr1__epoch_308-308\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.20\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_309/SparseLogisticRegression_fullchk_pr1__epoch_309-309\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.58\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.18\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_314/SparseLogisticRegression_fullchk_pr1__epoch_314-314\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.21\tR dev:0.33\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_315/SparseLogisticRegression_fullchk_pr1__epoch_315-315\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.20\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_316/SparseLogisticRegression_fullchk_pr1__epoch_316-316\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.21\tR dev:0.30\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_321/SparseLogisticRegression_fullchk_pr1__epoch_321-321\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.19\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_322/SparseLogisticRegression_fullchk_pr1__epoch_322-322\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.21\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_323/SparseLogisticRegression_fullchk_pr1__epoch_323-323\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.21\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_324/SparseLogisticRegression_fullchk_pr1__epoch_324-324\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.20\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_329/SparseLogisticRegression_fullchk_pr1__epoch_329-329\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.22\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_330/SparseLogisticRegression_fullchk_pr1__epoch_330-330\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.44\tP test:0.22\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_331/SparseLogisticRegression_fullchk_pr1__epoch_331-331\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.19\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_336/SparseLogisticRegression_fullchk_pr1__epoch_336-336\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.51\tP test:0.19\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_337/SparseLogisticRegression_fullchk_pr1__epoch_337-337\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.54\tP test:0.20\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_338/SparseLogisticRegression_fullchk_pr1__epoch_338-338\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.20\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_339/SparseLogisticRegression_fullchk_pr1__epoch_339-339\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.43\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.43\tP test:0.20\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_344/SparseLogisticRegression_fullchk_pr1__epoch_344-344\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_345/SparseLogisticRegression_fullchk_pr1__epoch_345-345\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.53\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_346/SparseLogisticRegression_fullchk_pr1__epoch_346-346\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.20\tR dev:0.32\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_351/SparseLogisticRegression_fullchk_pr1__epoch_351-351\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.49\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_352/SparseLogisticRegression_fullchk_pr1__epoch_352-352\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.50\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_353/SparseLogisticRegression_fullchk_pr1__epoch_353-353\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.56\tP test:0.18\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_354/SparseLogisticRegression_fullchk_pr1__epoch_354-354\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.51\tP 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"INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_366/SparseLogisticRegression_fullchk_pr1__epoch_366-366\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_367/SparseLogisticRegression_fullchk_pr1__epoch_367-367\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.39\tP test:0.18\tR dev:0.28\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_368/SparseLogisticRegression_fullchk_pr1__epoch_368-368\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.49\tP test:0.17\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_369/SparseLogisticRegression_fullchk_pr1__epoch_369-369\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP 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"[SparseLogisticRegression] Loaded model \n", "P dev:0.47\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_374/SparseLogisticRegression_fullchk_pr1__epoch_374-374\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.39\tP test:0.16\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_375/SparseLogisticRegression_fullchk_pr1__epoch_375-375\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.18\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_376/SparseLogisticRegression_fullchk_pr1__epoch_376-376\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_377/SparseLogisticRegression_fullchk_pr1__epoch_377-377\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.42\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_378/SparseLogisticRegression_fullchk_pr1__epoch_378-378\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.18\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_379/SparseLogisticRegression_fullchk_pr1__epoch_379-379\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_380/SparseLogisticRegression_fullchk_pr1__epoch_380-380\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.16\tR dev:0.30\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_381/SparseLogisticRegression_fullchk_pr1__epoch_381-381\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_382/SparseLogisticRegression_fullchk_pr1__epoch_382-382\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.38\tP test:0.17\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_383/SparseLogisticRegression_fullchk_pr1__epoch_383-383\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.42\tP test:0.17\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_384/SparseLogisticRegression_fullchk_pr1__epoch_384-384\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_385/SparseLogisticRegression_fullchk_pr1__epoch_385-385\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.18\tR dev:0.30\tRtest:0.25\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_386/SparseLogisticRegression_fullchk_pr1__epoch_386-386\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.39\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_387/SparseLogisticRegression_fullchk_pr1__epoch_387-387\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.15\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_388/SparseLogisticRegression_fullchk_pr1__epoch_388-388\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.41\tP test:0.18\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_389/SparseLogisticRegression_fullchk_pr1__epoch_389-389\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.16\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_390/SparseLogisticRegression_fullchk_pr1__epoch_390-390\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.37\tP test:0.17\tR dev:0.30\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_391/SparseLogisticRegression_fullchk_pr1__epoch_391-391\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.16\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_392/SparseLogisticRegression_fullchk_pr1__epoch_392-392\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_393/SparseLogisticRegression_fullchk_pr1__epoch_393-393\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.16\tR dev:0.33\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_394/SparseLogisticRegression_fullchk_pr1__epoch_394-394\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_395/SparseLogisticRegression_fullchk_pr1__epoch_395-395\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.36\tP test:0.17\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_396/SparseLogisticRegression_fullchk_pr1__epoch_396-396\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.39\tP test:0.15\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_397/SparseLogisticRegression_fullchk_pr1__epoch_397-397\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.16\tR dev:0.32\tRtest:0.24\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_398/SparseLogisticRegression_fullchk_pr1__epoch_398-398\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.40\tP test:0.16\tR dev:0.32\tRtest:0.22\t\n", "INFO:tensorflow:Restoring parameters from checkpoints/SparseLogisticRegression_fullchk_pr1__epoch_399/SparseLogisticRegression_fullchk_pr1__epoch_399-399\n", "[SparseLogisticRegression] Loaded model \n", "P dev:0.35\tP test:0.16\tR dev:0.28\tRtest:0.24\t\n" ] } ], "source": [ "for n_epoch in range(400):\n", " disc_model = None\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"SparseLogisticRegression_fullchk_pr1__epoch_{}\".format(n_epoch),\n", " save_dir=args.save_dir)\n", " pt,rt,ft = disc_model.score(F_test, Y_test, batch_size=32,beta=0.8)\n", " pd,rd,fd = disc_model.score(F_dev, Y_dev, batch_size=32,beta=0.8)\n", "# pv,rv,fv = disc_model.score(sorted_dev_cands, Y_valid, batch_size=32,beta=0.8)\n", " p_test_list.append(pt) \n", " r_test_list.append(rt) \n", " p_dev_list.append(pd) \n", " r_dev_list.append(rd) \n", " fscore_dev_list.append(fd) \n", " fscore_test_list.append(ft)\n", "# p_val_list.append(pv)\n", "# r_val_list.append(rv)\n", "# fscore_val_list.append(fv)\n", " print(\"P dev:{0:.2f}\\tP test:{1:.2f}\\tR dev:{2:.2f}\\tRtest:{3:.2f}\\t\".format(pd,pt,rd,rt) )" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5,1,u'recall')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(r_dev_list,label = \"RDev\")\n", "plt.plot(r_test_list,label = \"RTest\")\n", "plt.plot(r_val_list,label = \"RVal\")\n", "plt.legend()\n", "plt.title(\"recall\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "f1_list_dev = list()\n", "f1_list_test = list()\n", "\n", "for p,r in zip(p_dev_list,r_dev_list):\n", " f1_list_dev.append(2*p*r/(p+r))\n", "for p,r in zip(p_test_list,r_test_list):\n", " f1_list_test.append(2*p*r/(p+r))\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '
');\n", " var titletext = $(\n", " '
');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('
')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var button = $('');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Text(0.5,1,u'loss')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.plot(lst_loss)\n", "plt.yscale(\"log\")\n", "plt.xscale(\"log\")\n", "plt.title(\"loss (log scales)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appendix: plot with clustered rows" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_0/reRNN_fullchk6_epoch_0-0\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_1/reRNN_fullchk6_epoch_1-1\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_2/reRNN_fullchk6_epoch_2-2\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_3/reRNN_fullchk6_epoch_3-3\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_4/reRNN_fullchk6_epoch_4-4\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_5/reRNN_fullchk6_epoch_5-5\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_6/reRNN_fullchk6_epoch_6-6\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_7/reRNN_fullchk6_epoch_7-7\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_8/reRNN_fullchk6_epoch_8-8\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_9/reRNN_fullchk6_epoch_9-9\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_10/reRNN_fullchk6_epoch_10-10\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_11/reRNN_fullchk6_epoch_11-11\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_12/reRNN_fullchk6_epoch_12-12\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_13/reRNN_fullchk6_epoch_13-13\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_14/reRNN_fullchk6_epoch_14-14\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_15/reRNN_fullchk6_epoch_15-15\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_16/reRNN_fullchk6_epoch_16-16\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_17/reRNN_fullchk6_epoch_17-17\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_18/reRNN_fullchk6_epoch_18-18\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_19/reRNN_fullchk6_epoch_19-19\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_20/reRNN_fullchk6_epoch_20-20\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_21/reRNN_fullchk6_epoch_21-21\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_22/reRNN_fullchk6_epoch_22-22\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_23/reRNN_fullchk6_epoch_23-23\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_24/reRNN_fullchk6_epoch_24-24\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_25/reRNN_fullchk6_epoch_25-25\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_26/reRNN_fullchk6_epoch_26-26\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_27/reRNN_fullchk6_epoch_27-27\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_28/reRNN_fullchk6_epoch_28-28\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_29/reRNN_fullchk6_epoch_29-29\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_30/reRNN_fullchk6_epoch_30-30\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_31/reRNN_fullchk6_epoch_31-31\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_32/reRNN_fullchk6_epoch_32-32\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_33/reRNN_fullchk6_epoch_33-33\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_34/reRNN_fullchk6_epoch_34-34\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_35/reRNN_fullchk6_epoch_35-35\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_36/reRNN_fullchk6_epoch_36-36\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_37/reRNN_fullchk6_epoch_37-37\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_38/reRNN_fullchk6_epoch_38-38\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_39/reRNN_fullchk6_epoch_39-39\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_40/reRNN_fullchk6_epoch_40-40\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_41/reRNN_fullchk6_epoch_41-41\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_42/reRNN_fullchk6_epoch_42-42\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_43/reRNN_fullchk6_epoch_43-43\n", "[reRNN] Loaded model \n", "INFO:tensorflow:Restoring parameters from checkpoints/reRNN_fullchk6_epoch_44/reRNN_fullchk6_epoch_44-44\n", "[reRNN] Loaded model \n" ] } ], "source": [ "list_dico_misclassif = list()\n", "dic_docs = dict()\n", "list_dico_misclassif_dev = list()\n", "dic_docs_dev = dict()\n", "for n_epoch in range(45):\n", " disc_model = None\n", " disc_model = config['disc-model-class'](\n", " cardinality=C.cardinality,\n", " **config['disc-init-params'])\n", " disc_model.load(model_name=\"reRNN_fullchk6_epoch_{}\".format(n_epoch),\n", " save_dir=args.save_dir)\n", " _, fp, _, fn =disc_model.error_analysis(X_test=X_test,Y_test=Y_test,display=False,session=sess)\n", " list_dico_misclassif.append(dict(epoch = n_epoch,false_positives = [x.id for x in fp],false_negatives = [x.id for x in fn]))\n", " for x in fp.union(fn):\n", " dic_docs[x.id] = x.chem1.sentence.document_id\n", " _, fpd, _, fnd =disc_model.error_analysis(X_test=X_dev,Y_test=Y_dev,display=False,session=sess)\n", " list_dico_misclassif_dev.append(dict(epoch = n_epoch,false_positives = [x.id for x in fp],false_negatives = [x.id for x in fn]))\n", " for x in fpd.union(fnd):\n", " dic_docs_dev[x.id] = x.chem1.sentence.document_id" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [], "source": [ "\n", "total_docs = set(dic_docs.values())\n", "colormap = dict([(x,0.25+0.75*y/len(total_docs)) for y,x in enumerate(total_docs)])\n", "\n", "total_docs_dev = set(dic_docs_dev.values())\n", "colormap_dev = dict([(x,0.25+0.75*y/len(total_docs_dev)) for y,x in enumerate(total_docs_dev)])" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(17, 17)" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(colormap),len(colormap_dev)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [], "source": [ "all_false_negs= list(set([x for d in list_dico_misclassif for x in d[\"false_negatives\"] ]))\n", "all_false_pos= list(set([x for d in list_dico_misclassif for x in d[\"false_positives\"] ]))\n", "dic_false_pos = dict([(x,y) for y,x in enumerate(all_false_pos)])\n", "dic_false_negs = dict([(x,y) for y,x in enumerate(all_false_negs)])" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(41, 48)" ] }, "execution_count": 191, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(all_false_negs),len(all_false_pos)" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "pers_matrix_false_negs = np.zeros((len(all_false_negs),len(list_dico_misclassif)))\n", "pers_matrix_false_pos = np.zeros((len(all_false_pos),len(list_dico_misclassif)))" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "for d in list_dico_misclassif:\n", " for i_fp in d[\"false_positives\"]:\n", " pers_matrix_false_pos[dic_false_pos[i_fp],d[\"epoch\"]] = colormap[dic_docs[i_fp]]\n", " for i_fn in d[\"false_negatives\"]:\n", " pers_matrix_false_negs[dic_false_negs[i_fn],d[\"epoch\"]] = colormap[dic_docs[i_fn]]" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.77941176, 0.77941176, 0.77941176, ..., 0.77941176,\n", " 0.77941176, 0.77941176],\n", " [ 0.77941176, 0.77941176, 0.77941176, ..., 0.77941176,\n", " 0.77941176, 0.77941176],\n", " [ 0. , 0.38235294, 0.38235294, ..., 0.38235294,\n", " 0.38235294, 0.38235294],\n", " ..., \n", " [ 0. , 0. , 0.82352941, ..., 0.82352941,\n", " 0.82352941, 0.82352941],\n", " [ 0. , 0. , 0.64705882, ..., 0.64705882,\n", " 0.64705882, 0.64705882],\n", " [ 0. , 0. , 0.64705882, ..., 0.64705882,\n", " 0.64705882, 0.64705882]])" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pers_matrix_false_negs" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0.38235294, 0. , ..., 0.38235294,\n", " 0.38235294, 0.38235294],\n", " [ 0. , 0. , 0. , ..., 0. ,\n", " 0. , 0. ],\n", " [ 0. , 0.33823529, 0. , ..., 0. ,\n", " 0. , 0. ],\n", " ..., \n", " [ 0. , 0. , 0. , ..., 0. ,\n", " 0. , 0. ],\n", " [ 0. , 0.29411765, 0. , ..., 0. ,\n", " 0. , 0. ],\n", " [ 0. , 0.42647059, 0.42647059, ..., 0.42647059,\n", " 0.42647059, 0.42647059]])" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pers_matrix_false_pos" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.38235294, 0.38235294, 0.38235294, 0. ,\n", " 0.38235294, 0. , 0.38235294, 0.38235294, 0. ,\n", " 0. , 0.38235294, 0.38235294, 0.38235294, 0.38235294,\n", " 0.38235294, 0.38235294, 0.38235294, 0.38235294, 0.38235294,\n", " 0.38235294, 0.38235294, 0.38235294, 0.38235294, 0.38235294,\n", " 0. , 0. , 0.38235294, 0.38235294, 0.38235294,\n", " 0.38235294, 0.38235294, 0.38235294, 0.38235294, 0.38235294,\n", " 0.38235294, 0.38235294, 0.38235294, 0.38235294, 0.38235294,\n", " 0.38235294, 0.38235294, 0.38235294, 0.38235294, 0.38235294])" ] }, "execution_count": 196, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pers_matrix_false_negs[2,:]" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "index_permut_fn = np.array(sorted(list(range(pers_matrix_false_negs.shape[0])),key = lambda i: (np.sum(pers_matrix_false_negs[i,:]),[j for j in range(pers_matrix_false_negs.shape[1]) if pers_matrix_false_negs[i,j]==1][-1])))\n", "index_permut_fp = np.array(sorted(list(range(pers_matrix_false_pos.shape[0])),key = lambda i: (np.sum(pers_matrix_false_pos[i,:]),[j for j in range(pers_matrix_false_pos.shape[1]) if pers_matrix_false_pos[i,j]==1][-1])))" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(41, 45)" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pers_matrix_false_negs.shape" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [], "source": [ "metri = 'correlation'\n", "#metri = 'seuclidean'\n", "from scipy.spatial.distance import pdist\n", "from scipy.cluster.hierarchy import linkage, dendrogram\n", "Y=pdist(pers_matrix_false_negs, metri)\n", "Z=linkage(Y, 'single')\n", "order_fn = np.array(dendrogram(Z, color_threshold=0,no_plot=True)[\"leaves\"])\n", "Y=pdist(pers_matrix_false_pos, metri)\n", "Z=linkage(Y, 'single')\n", "order_fp = np.array(dendrogram(Z, color_threshold=0,no_plot=True)[\"leaves\"])" ] } ], "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": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }