From c9cfccb82375fa75a1f70f8dc37db9d09474e6b3 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Sun, 10 Dec 2017 16:19:51 +0530 Subject: [PATCH 1/5] Updates Poincare eval notebook with regularized model results --- docs/notebooks/Poincare Evaluation.ipynb | 678 ++++++++++++++++------- 1 file changed, 475 insertions(+), 203 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 839e54c760..a2c052050c 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -41,14 +41,14 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/jayant/projects/gensim\n" + "/home/jayant/Projects/gensim/gensim\n" ] } ], @@ -58,24 +58,7 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jayant/projects/gensim/docs/notebooks\n" - ] - } - ], - "source": [ - "% cd docs/notebooks/" - ] - }, - { - "cell_type": "code", - "execution_count": 63, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -92,7 +75,7 @@ "True" ] }, - "execution_count": 63, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -100,6 +83,7 @@ "source": [ "import csv\n", "from collections import OrderedDict\n", + "from IPython.display import display, HTML\n", "import logging\n", "import os\n", "import pickle\n", @@ -135,7 +119,24 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/Projects/gensim/gensim/docs/notebooks\n" + ] + } + ], + "source": [ + "% cd docs/notebooks/" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -158,14 +159,14 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/jayant/projects/gensim/docs/notebooks/poincare\n" + "/home/jayant/Projects/gensim/gensim/docs/notebooks/poincare\n" ] } ], @@ -192,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -349,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -395,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -407,6 +408,7 @@ " 'eps': 1e-6,\n", " 'burn_in': 0,\n", " 'batch_size': 10,\n", + " 'reg': 0.0\n", "}\n", "\n", "non_default_params = {\n", @@ -418,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 23, "metadata": { "scrolled": true }, @@ -470,7 +472,7 @@ " elif implementation == 'gensim':\n", " train_gensim_model(\n", " train_file, output_file, model_size, params['epochs'],\n", - " params['neg'], params['burn_in'], params['batch_size'], seed=0)\n", + " params['neg'], params['burn_in'], params['batch_size'], params['reg'], seed=0)\n", " else:\n", " raise ValueError('Given implementation %s not found' % implementation)\n", " files[model_size] = output_file\n", @@ -479,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -520,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -595,17 +597,17 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "def gensim_model_name_from_params(params, prefix):\n", - " param_keys = ['neg', 'epochs', 'burn_in', 'batch_size']\n", + " param_keys = ['neg', 'epochs', 'burn_in', 'batch_size', 'reg']\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", "def train_gensim_model(\n", - " data_file, output_file, dim, epochs, neg, burn_in, batch_size, seed=0):\n", + " data_file, output_file, dim, epochs, neg, burn_in, batch_size, reg, seed=0):\n", " \"\"\"Train a poincare embedding using gensim implementation\n", " \n", " Args:\n", @@ -616,6 +618,7 @@ " neg (int): Number of negative samples to use\n", " burn_in (int): Number of epochs to use for burn-in initialization\n", " batch_size (int): Size of batch to use for training\n", + " reg (float): Coefficient used for l2-regularization while training\n", " \n", " Notes: \n", " If `output_file` already exists, skips training\n", @@ -624,22 +627,23 @@ " print('File %s exists, skipping' % output_file)\n", " return\n", " train_data = PoincareRelations(data_file)\n", - " model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in)\n", + " model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in, regularization_coeff=reg)\n", " model.train(epochs=epochs, batch_size=batch_size)\n", " model.save(output_file)" ] }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "non_default_params_gensim = {\n", - " 'neg': [10],\n", - " 'burn_in': [10],\n", - " 'batch_size': [50]\n", - "}" + "non_default_params_gensim = [\n", + " {'neg': 10,},\n", + " {'burn_in': 10,},\n", + " {'batch_size': 50,},\n", + " {'neg': 10, 'reg': 1, 'burn_in': 10, 'epochs': 200},\n", + "]" ] }, { @@ -655,14 +659,13 @@ "for dim, filepath in files.items():\n", " model_files['gensim'][model_name][dim] = filepath\n", "# Train models with non-default params\n", - "for param, values in non_default_params_gensim.items():\n", + "for new_params in non_default_params_gensim:\n", " params = default_params.copy()\n", - " for value in values:\n", - " params[param] = value\n", - " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", - " model_files['gensim'][model_name] = {}\n", - " for dim, filepath in files.items():\n", - " model_files['gensim'][model_name][dim] = filepath" + " params.update(new_params)\n", + " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", + " model_files['gensim'][model_name] = {}\n", + " for dim, filepath in files.items():\n", + " model_files['gensim'][model_name][dim] = filepath" ] }, { @@ -674,7 +677,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -808,33 +811,41 @@ " results (dict): mapping between embeddings and corresponding results\n", " \n", " \"\"\"\n", - " data = PrettyTable()\n", - " data.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n", + " result_table = PrettyTable()\n", + " result_table.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n", " for model_name, model_results in results.items():\n", " metrics = [metric for metric in model_results.keys()]\n", " dims = sorted([dim for dim in model_results[metrics[0]].keys()])\n", - " row = [model_name, '\\n'.join(metrics) + '\\n']\n", + " description = model_description_from_name(model_name)\n", + " row = [description, '\\n'.join(metrics) + '\\n']\n", " for dim in dims:\n", " scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n", " row.append('\\n'.join(scores))\n", - " data.add_row(row)\n", - " data.align = 'r'\n", - " data_cols = data.get_string().split('\\n')[0].split('+')[1:-1]\n", - " col_lengths = [len(col) for col in data_cols]\n", - " header_col_1_length = col_lengths[0] + col_lengths[1] - 1\n", - " header_col_2_length = sum(col_lengths[2:]) + len(col_lengths[2:-1]) - 2\n", + " result_table.add_row(row)\n", + " result_table.align = 'r'\n", + " result_html = result_table.get_html_string()\n", + " search = \"\"\n", + " insert_at = result_html.index(search) + len(search)\n", + " new_row = \"\"\"\"\"\" % task_name\n", + " result_html = result_html[:insert_at] + new_row + result_html[insert_at:]\n", + " display(HTML(result_html))\n", " \n", - " header_col_2_content = \"Model Dimensions\"\n", - " header_col_2_left_margin = (header_col_2_length - len(header_col_2_content)) // 2\n", - " header_col_2_right_margin = header_col_2_length - len(header_col_2_content) - header_col_2_left_margin\n", - " header_col_2_string = \"%s%s%s\" % (\n", - " \" \" * header_col_2_left_margin, header_col_2_content, \" \" * header_col_2_right_margin)\n", - " header = PrettyTable()\n", - " header.field_names = [\" \" * header_col_1_length, header_col_2_string]\n", - " header_lines = header.get_string(start=0, end=0).split(\"\\n\")[:2]\n", - " print('Results for %s task' % task_name)\n", - " print(\"\\n\".join(header_lines))\n", - " print(data) " + "def model_description_from_name(model_name):\n", + " if model_name.startswith('gensim'):\n", + " implementation = 'Gensim'\n", + " elif model_name.startswith('cpp'):\n", + " implementation = 'C++'\n", + " elif model_name.startswith('np'):\n", + " implementation = 'Numpy'\n", + " else:\n", + " raise ValueError('Unsupported implementation for model: %s' % model_name)\n", + " description = []\n", + " for param_key in sorted(default_params.keys()):\n", + " pattern = '%s_([^_]*)_?' % param_key\n", + " match = re.search(pattern, model_name)\n", + " if match:\n", + " description.append(\"%s=%s\" % (param_key, match.groups()[0]))\n", + " return \"%s: %s\" % (implementation, \", \".join(description))" ] }, { @@ -846,7 +857,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -880,48 +891,133 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, + "execution_count": 148, + "metadata": { + "scrolled": false + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results for WordNet Reconstruction task\n", - "+-----------------------------------------------------------------------+----------------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", - "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", - "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", - "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank | 154.41 | 62.77 | 27.32 | 20.22 | 16.15 | 13.20 |\n", - "| | MAP | 0.40 | 0.63 | 0.72 | 0.77 | 0.78 | 0.79 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank | 148.51 | 63.67 | 28.36 | 20.23 | 15.75 | 13.59 |\n", - "| | MAP | 0.38 | 0.62 | 0.72 | 0.76 | 0.78 | 0.79 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank | 108.01 | 100.73 | 97.38 | 94.49 | 94.68 | 89.66 |\n", - "| | MAP | 0.37 | 0.47 | 0.48 | 0.49 | 0.48 | 0.49 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank | 211.71 | 54.42 | 24.90 | 21.42 | 15.80 | 15.13 |\n", - "| | MAP | 0.33 | 0.60 | 0.72 | 0.76 | 0.78 | 0.79 |\n", - "| | | | | | | | |\n", - "| np_model_epochs_50_neg_20 | mean_rank | 10169.24 | 5602.84 | 3631.57 | 1088.31 | 537.43 | 345.45 |\n", - "| | MAP | 0.14 | 0.16 | 0.20 | 0.25 | 0.30 | 0.36 |\n", - "| | | | | | | | |\n", - "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n" - ] + "data": { + "text/html": [ + "
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WordNet Reconstruction Dimensions
Model DescriptionMetric5102050100200
C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8mean_rank
MAP
191.69
0.34		97.65
0.43		72.07
0.51		55.48
0.57		46.76
0.59		49.62
0.59
C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8mean_rank
MAP
280.17
0.27		129.46
0.40		92.06
0.49		80.41
0.53		71.42
0.56		69.30
0.56
C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8mean_rank
MAP
265.72
0.28		116.94
0.41		90.81
0.49		59.47
0.56		55.14
0.58		54.31
0.59
C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8mean_rank
MAP
252.86
0.26		195.73
0.32		182.57
0.34		165.33
0.36		157.37
0.36		155.78
0.36
Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0mean_rank
MAP
108.01
0.37		100.73
0.47		97.38
0.48		94.49
0.49		94.68
0.48		89.66
0.49
Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0mean_rank
MAP
154.41
0.40		62.77
0.63		27.32
0.72		20.22
0.77		16.15
0.78		13.20
0.79
Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0mean_rank
MAP
211.71
0.33		54.42
0.60		24.90
0.72		21.42
0.76		15.80
0.78		15.13
0.79
Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0mean_rank
MAP
148.51
0.38		63.67
0.62		28.36
0.72		20.23
0.76		15.75
0.78		13.59
0.79
Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1mean_rank
MAP
61.48
0.38		54.70
0.41		53.02
0.41		50.80
0.42		49.58
0.42		48.56
0.43
Numpy: epochs=50, neg=20mean_rank
MAP
9617.57
0.14		5902.65
0.16		3868.78
0.19		1117.77
0.25		529.92
0.30		377.45
0.35
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -944,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1008,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -1042,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -1066,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1124,14 +1220,13 @@ "for dim, filepath in files.items():\n", " lp_model_files['gensim'][model_name][dim] = filepath\n", "# Train models with non-default params\n", - "for param, values in non_default_params_gensim.items():\n", + "for new_params in non_default_params_gensim:\n", " params = default_params.copy()\n", - " for value in values:\n", - " params[param] = value\n", - " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'gensim_lp_model', 'gensim')\n", - " lp_model_files['gensim'][model_name] = {}\n", - " for dim, filepath in files.items():\n", - " lp_model_files['gensim'][model_name][dim] = filepath" + " params.update(new_params)\n", + " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_lp_model', 'gensim')\n", + " lp_model_files['gensim'][model_name] = {}\n", + " for dim, filepath in files.items():\n", + " lp_model_files['gensim'][model_name][dim] = filepath" ] }, { @@ -1143,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1173,48 +1268,131 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 149, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results for WordNet Link Prediction task\n", - "+--------------------------------------------------------------------------+------------------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n", - "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", - "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", - "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", - "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", - "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", - "| | | | | | | | |\n", - "| gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank | 141.52 | 58.89 | 31.66 | 22.13 | 21.29 | 19.38 |\n", - "| | MAP | 0.18 | 0.34 | 0.46 | 0.51 | 0.52 | 0.53 |\n", - "| | | | | | | | |\n", - "| gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank | 121.42 | 52.51 | 24.61 | 19.96 | 20.44 | 19.55 |\n", - "| | MAP | 0.19 | 0.37 | 0.46 | 0.52 | 0.50 | 0.54 |\n", - "| | | | | | | | |\n", - "| gensim_lp_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank | 154.95 | 138.12 | 122.06 | 117.96 | 112.99 | 110.84 |\n", - "| | MAP | 0.16 | 0.21 | 0.24 | 0.26 | 0.25 | 0.26 |\n", - "| | | | | | | | |\n", - "| gensim_lp_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank | 144.19 | 53.65 | 25.21 | 20.68 | 21.32 | 18.97 |\n", - "| | MAP | 0.19 | 0.35 | 0.47 | 0.52 | 0.51 | 0.53 |\n", - "| | | | | | | | |\n", - "| np_lp_model_epochs_50_neg_20 | mean_rank | 14100.99 | 7538.02 | 5297.38 | 2365.27 | 1552.72 | 1741.87 |\n", - "| | MAP | 0.00 | 0.02 | 0.03 | 0.08 | 0.10 | 0.13 |\n", - "| | | | | | | | |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n" - ] + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WordNet Link Prediction Dimensions
Model DescriptionMetric5102050100200
C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8mean_rank
MAP
218.26
0.15		99.09
0.24		60.50
0.31		52.24
0.35		60.81
0.36		69.13
0.36
C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8mean_rank
MAP
687.48
0.12		281.88
0.15		72.95
0.31		57.37
0.35		52.56
0.36		61.42
0.36
C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8mean_rank
MAP
230.34
0.14		123.24
0.22		75.62
0.28		65.97
0.31		55.33
0.33		56.89
0.34
C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8mean_rank
MAP
236.31
0.10		214.85
0.13		193.30
0.14		180.27
0.15		169.00
0.16		163.22
0.16
Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0mean_rank
MAP
141.52
0.18		58.89
0.34		31.66
0.46		22.13
0.51		21.29
0.52		19.38
0.53
Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0mean_rank
MAP
121.42
0.19		52.51
0.37		24.61
0.46		19.96
0.52		20.44
0.50		19.55
0.54
Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0mean_rank
MAP
144.19
0.19		53.65
0.35		25.21
0.47		20.68
0.52		21.32
0.51		18.97
0.53
Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0mean_rank
MAP
154.95
0.16		138.12
0.21		122.06
0.24		117.96
0.26		112.99
0.25		110.84
0.26
Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1mean_rank
MAP
51.72
0.22		39.85
0.28		38.60
0.29		36.55
0.30		35.32
0.31		34.66
0.31
Numpy: epochs=50, neg=20mean_rank
MAP
14526.67
0.01		8411.10
0.02		5749.57
0.04		1873.12
0.07		1639.50
0.10		1350.13
0.13
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1230,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 152, "metadata": {}, "outputs": [], "source": [ @@ -1241,7 +1419,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "for implementation, models in sorted(model_files.items()):\n", @@ -1258,39 +1438,131 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 166, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results for Lexical Entailment (HyperLex) task\n", - "+----------------------------------------------------------------------+-----------------------------------------+\n", - "| | Model Dimensions |\n", - "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.44 | 0.47 | 0.44 | 0.46 | 0.45 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.42 | 0.42 | 0.44 | 0.44 | 0.44 | 0.45 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.42 | 0.43 | 0.44 | 0.43 | 0.44 | 0.44 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.47 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | spearman | 0.46 | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.47 | 0.47 | 0.47 | 0.48 | 0.48 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.46 |\n", - "| | | | | | | | |\n", - "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 | 0.48 |\n", - "| | | | | | | | |\n", - "| np_model_epochs_50_neg_20 | spearman | 0.15 | 0.20 | 0.21 | 0.21 | 0.25 | 0.27 |\n", - "| | | | | | | | |\n", - "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n" - ] + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Lexical Entailment (HyperLex) Dimensions
Model DescriptionMetric5102050100200
C++: burn_in=0, epochs=200, eps=1e-06, neg=20, threads=8spearman
0.450.460.450.450.450.46
C++: burn_in=0, epochs=50, eps=1e-06, neg=10, threads=8spearman
0.420.410.430.420.430.43
C++: burn_in=0, epochs=50, eps=1e-06, neg=20, threads=8spearman
0.440.430.470.440.450.44
C++: burn_in=10, epochs=50, eps=1e-06, neg=20, threads=8spearman
0.430.420.440.440.440.45
Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0spearman
0.460.460.450.470.470.48
Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0spearman
0.470.450.470.470.480.47
Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1spearman
0.520.510.510.510.520.51
Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0spearman
0.450.460.450.460.450.46
Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0spearman
0.460.460.470.470.480.47
Numpy: epochs=50, neg=20spearman
0.150.190.200.200.240.26
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1306,12 +1578,12 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ "# TODO - quite tricky, since the loss function used for training the model on this network is different\n", - "# Will require changes to how gradients are calculated in C++ code" + "# Will require changes to how gradients are calculated" ] } ], @@ -1331,7 +1603,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.4" } }, "nbformat": 4, From 5372dd21238ff2d0891c04f41047ddcb72dde861 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Sun, 10 Dec 2017 16:42:57 +0530 Subject: [PATCH 2/5] Moves all evaluation details to Poincare evaluation notebook, cleans up tutorial notebook --- docs/notebooks/Poincare Evaluation.ipynb | 153 +- docs/notebooks/Poincare Tutorial.ipynb | 106899 +------------------- 2 files changed, 133 insertions(+), 106919 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index a2c052050c..7904a3be34 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -855,6 +855,15 @@ "### 4.1 WordNet reconstruction" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n", + "\n", + "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability." + ] + }, { "cell_type": "code", "execution_count": 36, @@ -1024,6 +1033,24 @@ "display_results('WordNet Reconstruction', reconstruction_results)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results from the paper -\n", + "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figures above illustrate a few things - \n", + "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", + "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", + "3. Using burn-in and regularization leads to much better results with low model sizes, however the results do not improve significantly with increasing model size. This might have to do with tuning the regularization coefficient, which the paper does not mention." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1031,6 +1058,15 @@ "### 4.2 WordNet link prediction" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n", + "\n", + "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1399,6 +1435,26 @@ "display_results('WordNet Link Prediction', lp_results)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results from the paper -\n", + "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These results follow similar trends as the reconstruction results. Repeating here for ease of reading - \n", + "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", + "2. The results from the original paper have not been achieved by our implementation. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the link prediction task.\n", + "4. Using burn-in and regularization leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n", + "\n", + "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1406,9 +1462,22 @@ "### 4.3 HyperLex Lexical Entailment" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n", + " \n", + "`girl person 9.85`\n", + "\n", + "These scores are out of 10.\n", + "\n", + "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n" + ] + }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 168, "metadata": {}, "outputs": [], "source": [ @@ -1438,7 +1507,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -1496,14 +1565,14 @@ " 0.45\n", " \n", " \n", - " Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0\n", + " Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0\n", " spearman
\n", + " 0.45\n", " 0.46\n", + " 0.45\n", " 0.46\n", " 0.45\n", - " 0.47\n", - " 0.47\n", - " 0.48\n", + " 0.46\n", " \n", " \n", " Gensim: batch_size=10, burn_in=0, epochs=50, neg=20, reg=0.0\n", @@ -1516,24 +1585,14 @@ " 0.47\n", " \n", " \n", - " Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1\n", - " spearman
\n", - " 0.52\n", - " 0.51\n", - " 0.51\n", - " 0.51\n", - " 0.52\n", - " 0.51\n", - " \n", - " \n", - " Gensim: batch_size=10, burn_in=10, epochs=50, neg=20, reg=0.0\n", + " Gensim: batch_size=10, burn_in=0, epochs=50, neg=10, reg=0.0\n", " spearman
\n", - " 0.45\n", " 0.46\n", - " 0.45\n", " 0.46\n", " 0.45\n", - " 0.46\n", + " 0.47\n", + " 0.47\n", + " 0.48\n", " \n", " \n", " Gensim: batch_size=50, burn_in=0, epochs=50, neg=20, reg=0.0\n", @@ -1546,6 +1605,16 @@ " 0.47\n", " \n", " \n", + " Gensim: batch_size=10, burn_in=10, epochs=200, neg=10, reg=1\n", + " spearman
\n", + " 0.52\n", + " 0.51\n", + " 0.51\n", + " 0.51\n", + " 0.52\n", + " 0.51\n", + " \n", + " \n", " Numpy: epochs=50, neg=20\n", " spearman
\n", " 0.15\n", @@ -1573,17 +1642,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.4 Link Prediction for collaboration networks\n" + "Results from paper (for Poincaré Embeddings, as well as other embeddings from previous papers) - \n", + "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)" ] }, { - "cell_type": "code", - "execution_count": 167, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some observations - \n", + "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n", + "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n", + "\n", + "However, there are a few ambiguities and caveats - \n", + "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n", + "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n", + "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Link Prediction on Collaboration Networks\n", + "\n", + "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", + "\n", + "This variant has not been implemented yet, and is therefore not a part of our experiments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Next Steps" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# TODO - quite tricky, since the loss function used for training the model on this network is different\n", - "# Will require changes to how gradients are calculated" + "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguitities in communication with the authors.\n", + "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 45 seconds to complete an epoch (~15k relations per second), whereas the open source C++ implementation takes around 1/6th the time (~95k relations per second).\n", + "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report." ] } ], diff --git a/docs/notebooks/Poincare Tutorial.ipynb b/docs/notebooks/Poincare Tutorial.ipynb index 88eff23862..17d88dc7a8 100644 --- a/docs/notebooks/Poincare Tutorial.ipynb +++ b/docs/notebooks/Poincare Tutorial.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook discusses the motivation and idea behind Poincaré embeddings and demonstrates what kind of operations can be done with them. It also presents quantitative evaluation results on evaluation tasks mentioned in the [original paper](https://arxiv.org/pdf/1705.08039.pdf), and compares them to results obtained from other, non-gensim implementations. Lastly, it tries to provide some intuition behind the nature of the embeddings and hyperbolic space through visualizations." + "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations can be done with them. For more comprehensive technical details and results, this [blog post](link) may be a more appropriate resource." ] }, { @@ -35,7 +35,7 @@ "\n", "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", "\n", - "This variant has not been implemented yet, and is therefore not a part of our experiments.\n", + "This variant has not been implemented yet, and is therefore not a part of our tutorial and experiments.\n", "\n", "\n", "### 1.2 Motivation\n", @@ -44,7 +44,7 @@ " \n", " ![Example tree](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/example_tree.png)\n", "\n", - "Here, the positions of nodes represent their position in 2-D euclidean space. Ideally, the distances between the nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph).\n", + "Here, the positions of nodes represent the positions of their vectors in 2-D euclidean space. Ideally, the distances between the vectors for nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph), making this harder.\n", "\n", "There is no representation of this simple tree in 2-dimensional Euclidean space which can reflect these distances correctly. This can be solved by adding more dimensions, but this becomes computationally infeasible as the number of required dimensions grows exponentially. \n", "Hyperbolic space is a metric space in which distances aren't straight lines - they are curves, and this allows such tree-like hierarchical structures to have a representation that captures the distances more accurately even in low dimensions." @@ -71,7 +71,6 @@ } ], "source": [ - "# Import required modules and train an example embedding\n", "% cd ../.." ] }, @@ -663,106892 +662,6 @@ "source": [ "This is despite the fact that Poincaré distance is symmetric (like any distance in a metric space). The asymmetry stems from the fact that even if node `Y` is the closest node to node `X` amongst all nodes with a higher norm (lower in the hierarchy) than `X`, node `X` may not be the closest node to node `Y` amongst all the nodes with a lower norm (higher in the hierarchy) than `Y`." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following section presents the results of Poincaré models trained using some open-source implementations of the model, as well as the Gensim implementation. Original results as mentioned in the paper are also provided for reference." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following two external, open-source implementations are used - \n", - "1. [C++](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", - "2. [Numpy](https://github.com/nishnik/poincare_embeddings)\n", - "\n", - "This is the list of tasks from the paper that have been evaluated - \n", - "1. WordNet reconstruction\n", - "2. WordNet link prediction\n", - "4. Lexical entailment on HyperLex" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.1 WordNet Reconstruction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n", - "\n", - "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability.\n", - "\n", - "The prefix in the model names denote - \n", - "1. `cpp_`: Model trained using [open source C++ implementation](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", - "2. `numpy_`: Model trained using [open source Numpy implementation](https://github.com/nishnik/poincare_embeddings)\n", - "3. `gensim_`: Model trained using Gensim implementation\n", - "\n", - "The rest of the model name contains information about the model hyperparameters used for training. \n", - "\n", - "For more details about the exact evaluation method, and to reproduce the results described above, refer to the detailed [evaluation notebook](http://nbviewer.jupyter.org/github/RaRe-Technologies/gensim/blob/9e3190f0e4e38f1c93804feb0a62ce025a8489d3/docs/notebooks/Poincare%20Evaluation.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our results -" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_eval.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Results from the paper -\n", - "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The figures above illustrate a few things - \n", - "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", - "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", - "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n", - "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.2 Link Prediction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n", - "\n", - "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task\n", - "\n", - "Our results -" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Link Prediction Eval](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_eval.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These results follow similar trends to the reconstruction results. Specifically - \n", - "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", - "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", - "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n", - "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n", - "\n", - "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.3 Lexical Entailment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n", - " \n", - "`girl person 9.85`\n", - "\n", - "These scores are out of 10.\n", - "\n", - "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n", - "\n", - "Our results - \n", - "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_eval.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Results from paper (for Poincaré Embeddings, as well as other embeddings from previous papers) - \n", - "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some observations - \n", - "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n", - "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n", - "\n", - "However, there are a few ambiguities and caveats - \n", - "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n", - "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n", - "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4.4 Link Prediction on Collaboration Networks\n", - "\n", - "The paper also describes a variant of the Poincaré model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", - "\n", - "This variant has not been implemented yet, and is therefore not a part of our experiments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The paper presents a visual representation of a 2-D model trained on the mammals subtree of the WordNet noun hierarchy. This is a useful tool to get an intuitive sense of the model, and is also helpful for the purposes of debugging where the model could be learning incorrect representations. Visualizations for some models are presented below." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/vnd.plotly.v1+html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import pickle\n", - "from plotly.offline import init_notebook_mode, iplot\n", - "from gensim.viz.poincare import poincare_2d_visualization, poincare_distance_heatmap\n", - "\n", - "init_notebook_mode(connected=True)\n", - "\n", - "show_node_labels = [\n", - " 'mammal.n.01', 'placental.n.01', 'ungulate.n.01', 'carnivore.n.01', 'rodent.n.01',\n", - " 'canine.n.02', 'even-toed_ungulate.n.01', 'odd-toed_ungulate.n.01', 'elephant.n.01',\n", - " 'rhinoceros.n.01', 'german_shepherd.n.01', 'feline.n.01', 'tiger.n.02', 'homo_sapiens.n.01']" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "tree = pickle.load(open(os.path.join(poincare_directory, 'data', 'mammal_tree.pkl'), 'rb'))" - 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"sambar.n.01", - "potoroo.n.01", - "chow.n.03", - "lincoln.n.03", - "black_squirrel.n.01", - "common_raccoon.n.01", - "wild_sheep.n.01", - "goat_antelope.n.01", - "pygmy_chimpanzee.n.01", - "eared_seal.n.01", - "lesser_rorqual.n.01", - "woodland_caribou.n.01", - "common_shrew.n.01", - "gemsbok.n.01", - "tabby.n.02", - "marmoset.n.01", - "english_toy_spaniel.n.01", - "fawn.n.02", - "pacer.n.02", - "deer_mouse.n.01", - "beef.n.01", - "ox.n.02", - "shire.n.02", - "american_saddle_horse.n.01", - "bassarisk.n.01", - "coach_horse.n.01", - "greater_pichiciego.n.01", - "armadillo.n.01", - "water_rat.n.03", - "guereza.n.01", - "carnivorous_bat.n.01", - "thomson's_gazelle.n.01", - "stablemate.n.01", - "leveret.n.01", - "eurasian_hamster.n.01", - "steed.n.01", - "harpy.n.03", - "fruit_bat.n.01", - "irish_wolfhound.n.01", - "talapoin.n.01", - "stallion.n.01", - "ermine.n.02", - "musteline_mammal.n.01", - "leporid.n.01", - "african_elephant.n.01", - "wood_rat.n.01", - "megatherian.n.01", - "kangaroo_mouse.n.01", - "dairy_cattle.n.01", - "boston_bull.n.01", - "harbor_porpoise.n.01", - "pichiciago.n.01", - "domestic_ass.n.01", - "homo_soloensis.n.01", - "kuvasz.n.01", - "watchdog.n.02", - "black-footed_ferret.n.01", - "german_shepherd.n.01", - "cavalry_horse.n.01", - "mylodontid.n.01", - "wild_ass.n.01", - "big_cat.n.01", - "clumber.n.01", - "spaniel.n.01", - "ant_bear.n.01", - "tamarin.n.01", - "rat.n.01", - "sinanthropus.n.01", - "charolais.n.01", - "citation.n.06", - "liver-spotted_dalmatian.n.01", - "tapir.n.01", - "flying_phalanger.n.01", - "atlantic_walrus.n.01", - "gelding.n.01", - "alaskan_brown_bear.n.01", - "jackass_bat.n.01", - "bull.n.01", - "jaguar.n.01", - "affirmed.n.01", - "false_vampire.n.01", - "stud.n.04", - "male_horse.n.01", - "bird_dog.n.01", - "cattalo.n.01", - "two-year-old_horse.n.01", - "ewe.n.03", - "standard_schnauzer.n.01", - "lemur.n.01", - "zinjanthropus.n.01", - "genet.n.03", - "gordon_setter.n.01", - "groundhog.n.01", - "earless_seal.n.01", - "killer_whale.n.01", - "toothed_whale.n.01", - "jerboa_rat.n.01", - "plains_pocket_mouse.n.01", - "curly-coated_retriever.n.01", - "retriever.n.01", - "fanaloka.n.01", - "viverrine.n.01", - "muishond.n.01", - "corgi.n.01", - "grevy's_zebra.n.01", - "zebra.n.01", - "homo_sapiens_sapiens.n.01", - "homo_sapiens.n.01", - "english_setter.n.01", - "wombat.n.01", - "whirlaway.n.01", - "heifer.n.01", - "uakari.n.01", - "hognose_bat.n.01", - "northern_pocket_gopher.n.01", - "gopher.n.04", - "crabeater_seal.n.01", - "kiang.n.01", - "sennenhunde.n.01", - "pony.n.02", - "tatouay.n.01", - "white-footed_mouse.n.01", - "gnu.n.01", - "tiglon.n.01", - "golden_hamster.n.01", - "appaloosa.n.01", - "pricket.n.02", - "neandertal_man.n.01", - "angora.n.03", - "ass.n.03", - "basset.n.01", - "marten.n.01", - "golden_mole.n.01", - "common_dolphin.n.01", - "sivapithecus.n.01", - "friesian.n.01", - "bovine.n.01", - "water_buffalo.n.01", - "short-tailed_shrew.n.01", - "serval.n.01", - "war_admiral.n.01", - "dachshund.n.01", - "pomeranian.n.01", - "spitz.n.01", - "gaur.n.01", - "cynopterus_sphinx.n.01", - "cotton_mouse.n.01", - "mouse.n.01", - "steller_sea_lion.n.01", - "brocket.n.02", - "shrew.n.02", - "bucking_bronco.n.01", - "rock_wallaby.n.01", - "carthorse.n.01", - "tamarau.n.01", - "buckskin.n.01", - "brahman.n.04", - "bonnet_macaque.n.01", - "anthropoid.n.02", - "entlebucher.n.01", - "pinniped_mammal.n.01", - "newfoundland.n.01", - "durham.n.02", - "wood_mouse.n.01", - "assault.n.03", - "aardvark.n.01", - "giant_schnauzer.n.01", - "common_wallaby.n.01", - "mantled_ground_squirrel.n.01", - "bullterrier.n.01", - "homo.n.02", - "gorilla.n.01", - "great_ape.n.01", - "jackrabbit.n.01", - "roe_deer.n.01", - "imperial_mammoth.n.01", - "elephant.n.01", - "rabbit-eared_bandicoot.n.01", - "chickeree.n.01", - "flickertail.n.01", - "reynard.n.01", - "clydesdale_terrier.n.01", - "bear_cub.n.01", - "bear.n.01", - "stoat.n.01", - "rhodesian_man.n.01", - "ram.n.05", - "sheep.n.01", - "santa_gertrudis.n.01", - "mountain_sheep.n.01", - "eastern_chimpanzee.n.01", - "pouched_mouse.n.01", - "pacific_bottlenose_dolphin.n.01", - "attack_dog.n.01", - "ungulata.n.01", - "deer.n.01", - "domestic_sheep.n.01", - "welsh.n.03", - "solo_man.n.01", - "great_dane.n.01", - "pithecanthropus.n.01", - "lhasa.n.02", - "italian_greyhound.n.01", - "bull.n.11", - "jersey.n.05", - "polo_pony.n.01", - "hare.n.01", - "working_dog.n.01", - "trotting_horse.n.01", - "wirehair.n.01", - "lionet.n.01", - "zebu.n.01", - "glutton.n.02", - "irish_water_spaniel.n.01", - "sealyham_terrier.n.01", - "musk_kangaroo.n.01", - "water_chevrotain.n.01", - "baronduki.n.01", - "guinea_pig.n.02", - "english_springer.n.01", - "viscacha.n.01", - "harnessed_antelope.n.01", - "chesapeake_bay_retriever.n.01", - "numbat.n.01", - "dasyurid_marsupial.n.01", - "large_poodle.n.01", - "roan.n.02", - "entellus.n.01", - "old_world_monkey.n.01", - "squirrel_monkey.n.01", - "wild_goat.n.01", - "anoa.n.01", - "native_cat.n.01", - "bandicoot.n.01", - "mare.n.01", - "river_dolphin.n.01", - "wharf_rat.n.02", - "cardigan.n.02", - "saki.n.03", - "zoril.n.01", - "dark_horse.n.02", - "world.n.08", - "schnauzer.n.01", - "irish_terrier.n.01", - "keeshond.n.01", - "aardwolf.n.01", - "pembroke.n.01", - "foxhound.n.01", - "red_fox.n.03", - "hooded_skunk.n.01", - "woolly_indris.n.01", - "miniature_poodle.n.01", - "pocket_rat.n.01", - "japanese_deer.n.01", - "lesser_ape.n.01", - "slender_loris.n.01", - "cow.n.02", - "merino.n.01", - "bullock.n.01", - "cavy.n.01", - "fur_seal.n.02", - "jird.n.01", - "protohippus.n.01", - "greater_swiss_mountain_dog.n.01", - "capuchin.n.02", - "chigetai.n.01", - "southern_flying_squirrel.n.01", - "mangabey.n.01", - "sled_dog.n.01", - "spouter.n.03", - "tennessee_walker.n.01", - "pangolin.n.01", - "old_world_porcupine.n.01", - "sewer_rat.n.01", - "brown_rat.n.01", - "swamp_rabbit.n.02", - "cheviot.n.01", - "collared_peccary.n.01", - "mountain_goat.n.01", - "golden_retriever.n.01", - "yorkshire_terrier.n.01", - "pachyderm.n.01", - "fisher.n.02", - "tenrec.n.01", - "jumping_mouse.n.01", - "cinnamon_bear.n.01", - "american_black_bear.n.01", - "liver_chestnut.n.01", - "howler_monkey.n.01", - "sow.n.01", - "manchester_terrier.n.01", - "king_charles_spaniel.n.01", - "mouser.n.01", - "american_shrew_mole.n.01", - "peba.n.01", - "asiatic_shrew_mole.n.01", - "hudson_bay_collared_lemming.n.01", - "bunny.n.02", - "rat_terrier.n.01", - "langur.n.01", - "european_wood_mouse.n.01", - "nude_mouse.n.01", - "mole.n.06", - "mule.n.01", - "barren_ground_caribou.n.01", - "galago.n.01", - "paca.n.01", - "wolfhound.n.01", - "mountain_nyala.n.01", - "bezoar_goat.n.01", - "muntjac.n.01", - "packrat.n.02", - "skye_terrier.n.01", - "douroucouli.n.01", - "prairie_dog.n.01", - "lucy.n.01", - "hack.n.07", - "seeing_eye_dog.n.01", - "grivet.n.01", - "rhodesian_ridgeback.n.01", - "manatee.n.01", - "farm_horse.n.01", - "quagga.n.01", - "bouvier_des_flandres.n.01", - "poodle.n.01", - "stepper.n.03", - "rottweiler.n.01", - "tarsius_syrichta.n.01", - "affenpinscher.n.01", - "tailless_tenrec.n.01", - "eastern_dasyure.n.01", - "dasyure.n.01", - "elk.n.01", - "boarhound.n.01", - "woolly_rhinoceros.n.01", - "rambouillet.n.01", - "bowhead.n.01", - "grasshopper_mouse.n.01", - "dall_sheep.n.01", - "gerbil.n.01", - "prairie_vole.n.01", - "finback.n.01", - "polar_hare.n.01", - "hairy-legged_vampire_bat.n.01", - "white-lipped_peccary.n.01", - "peccary.n.01", - "dwarf_sperm_whale.n.01", - "mule_deer.n.01", - "sausage_dog.n.01", - "female_mammal.n.01", - "digitigrade_mammal.n.01", - "siamang.n.01", - "kanchil.n.01", - "chevrotain.n.01", - "giant_eland.n.01", - "timber_wolf.n.01", - "european_hare.n.01", - "guernsey.n.02", - "warrigal.n.01", - "european_rabbit.n.01", - "pug.n.01", - "white_wolf.n.01", - "new_world_mouse.n.01", - "beaked_whale.n.01", - "leonberg.n.01", - "pinche.n.01", - "big_brown_bat.n.01", - "mexican_hairless.n.01", - "american_marten.n.01", - "asian_wild_ox.n.01", - "steller's_sea_cow.n.01", - "old_world_buffalo.n.01", - "ord_kangaroo_rat.n.01", - "kangaroo_rat.n.01", - "lynx.n.02", - "alley_cat.n.01", - "serow.n.01", - "grison.n.01", - "common_eland.n.01", - "phyllostomus_hastatus.n.01", - "grizzly.n.01", - "eland.n.01", - "silky_pocket_mouse.n.01", - "elephant_seal.n.01", - "starnose_mole.n.01", - "gazella_subgutturosa.n.01", - "kerry_blue_terrier.n.01", - "vespertilian_bat.n.01", - "colobus.n.01", - "sea_otter.n.01", - "flying_fox.n.01", - "black-tailed_deer.n.01", - "mouse-eared_bat.n.01", - "chestnut.n.06", - "burro.n.01", - "kid.n.05", - "hereford.n.01", - "snow_leopard.n.01", - "indri.n.01", - "cocker_spaniel.n.01", - "kob.n.01", - "european_lemming.n.01", - "carabao.n.01", - "harbor_seal.n.01", - "shetland_pony.n.01", - "water_spaniel.n.01", - "grey_whale.n.01", - "papillon.n.01", - "toy_dog.n.01", - "draft_horse.n.01", - "eastern_grey_squirrel.n.01", - "arabian.n.02", - "northern_bog_lemming.n.01", - "lemming.n.01", - "weimaraner.n.01", - "civet.n.01", - "saiga.n.01", - "jennet.n.01", - "edentate.n.01", - "pilot_whale.n.01", - "dolphin.n.02", - "grey_fox.n.01", - "grampus.n.02", - "devon.n.02", - "walker_hound.n.01", - "shrew_mole.n.01", - "chihuahua.n.03", - "redbone.n.01", - "borzoi.n.01", - "mapinguari.n.01", - "binturong.n.01", - "scottish_deerhound.n.01", - "greyhound.n.01", - "field_spaniel.n.01", - "griffon.n.03", - "forest_goat.n.01", - "pocket_mouse.n.01", - "hart.n.03", - "pere_david's_deer.n.01", - "algeripithecus_minutus.n.01", - "red_bat.n.01", - "vixen.n.02", - "malamute.n.01", - "western_grey_squirrel.n.01", - "striped_muishond.n.01", - "gallant_fox.n.01", - "margay.n.01", - "saber-toothed_tiger.n.01", - "groenendael.n.01", - "silver_fox.n.01", - "jack.n.12", - "bengal_tiger.n.01", - "mandrill.n.01", - "asiatic_black_bear.n.01", - "pied_lemming.n.01", - "lioness.n.01", - "bloodhound.n.01", - "australopithecus_boisei.n.01", - "milking_shorthorn.n.01", - "jerboa.n.01", - "bongo.n.02", - "norfolk_terrier.n.01", - "serotine.n.01", - "rock_hyrax.n.01", - "welsh_terrier.n.01", - "west_highland_white_terrier.n.01", - "water_dog.n.02", - "red_deer.n.01", - "takin.n.01", - "blue_whale.n.01", - "hooded_seal.n.01", - "secretariat.n.02", - "cape_buffalo.n.01", - "stone_marten.n.01", - "whippet.n.01", - "aoudad.n.01", - "bottlenose_dolphin.n.01", - "lechwe.n.01", - "lion.n.01", - "hartebeest.n.01", - "red_poll.n.01", - "eastern_cottontail.n.01", - "murine.n.01", - "sussex_spaniel.n.01", - "leopard_cat.n.01", - "coydog.n.01", - "big-eared_bat.n.01", - "java_man.n.01", - "coyote.n.01", - "macaque.n.01", - "american_harvest_mouse.n.01", - "belgian_sheepdog.n.01", - "eohippus.n.01", - "ibex.n.01", - "longwool.n.01", - "nilgai.n.01", - "quarter_horse.n.01", - "africander.n.01", - "stalking-horse.n.04", - "apar.n.01", - "three-toed_sloth.n.01", - "indian_elephant.n.01", - "gayal.n.01", - "malayan_tapir.n.01", - "cairn.n.02", - "chiacoan_peccary.n.01", - "rhinoceros.n.01", - "naked_mole_rat.n.01", - "loir.n.01", - "hack.n.08", - "eskimo_dog.n.01", - "queen.n.09", - "south_american_sea_lion.n.01", - "lippizan.n.01", - "smiledon_californicus.n.01", - 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\n200 epochs, model is closer to convergence", - "width": 800 - } - }, - "text/html": [ - "
" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = PoincareModel.load(os.path.join(models_directory, 'gensim_mammals_epochs_200_dim_2'))\n", - "figure_title = \"\"\"\n", - "2-D Visualization of model trained on mammals subtree
\n", - "200 epochs, model is closer to convergence\"\"\"\n", - "iplot(poincare_2d_visualization(model, tree, figure_title, show_node_labels=show_node_labels))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is slightly different from the representation shown in the paper. Some key differences are - \n", - "1. Some of the nodes fairly high in the hierarchy (carnivore, canine, odd-toed ungulate) are much closer to the boundary than in the paper. As a result, it is likely that distances from them to certain positive nodes will be higher than they should be.\n", - "2. Some nodes very close to the boundary have an edge with a node which is also very close to the boundary, but on the other side of the disk. This is certainly an incorrect placement, as the distance between these two nodes is going be much higher.\n", - "\n", - "Note that the actual distance between two nodes is not the same as the \"naked-eye distance\" seen in the above representation. The actual distance is the Poincaré distance, whereas the \"naked-eye distance\" is the Euclidean distance.\n", - "\n", - "To get a better sense of Poincaré distance, a visualization is presented below." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "marker": { - "color": [ - 7.140956639559867, - 5.936527629641392, - 5.449964500555572, - 5.157886659013344, - 4.960443770623403, - 4.820682578168151, - 4.721070602881184, - 4.652180583089688, - 4.608667030835196, - 4.587573855895887, - 4.58757386106396, - 4.608667046656536, - 4.652180610584842, - 4.7210706440155175, - 4.8206826363984625, - 4.960443852127046, - 5.157886775775228, - 5.449964679934476, - 5.936527958167716, - 7.14095785502446, - 6.723979765587077, - 5.5515046016534635, - 5.0544233193020895, - 4.745089491653391, - 4.526739420273301, - 4.362948960205523, - 4.2360776763275485, - 4.136293726117703, - 4.057613613930194, - 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" - ], - "text/vnd.plotly.v1+html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "iplot(poincare_distance_heatmap([0.7, 0.7]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some interesting things to note here are - \n", - "1. Distances increase much more quickly when the origin point is closer to the boundary - this can be seen visually from the fact that the darker region around the origin point grows smaller and smaller (though the changing scales make this slightly error-prone, the fact still holds). \n", - "This is also borne out from the mathematical formula for Poincare distance.\n", - "\n", - "2. For points close to the boundary, distance to other points close to the boundary is relatively low to points with a low euclidean distance, but then increases significantly and saturates - this explains the positioning of children of parent node close to the boundary in a small arc around the parent node, and the presence of all negative nodes to the parent node (nodes without an edge to the parent node) outside of that small arc." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Next steps" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguitities in communication with the authors.\n", - "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 70 seconds to complete an epoch (~10k relations per second), whereas the open source C++ implementation takes around 1/10th the time (~95k relations per second).\n", - "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report." - ] } ], "metadata": { @@ -107560,14 +673,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.5.4" } }, "nbformat": 4, From 8abecd5f45a04a924bf4e826c19d03b52f8db846 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Sun, 10 Dec 2017 18:09:00 +0530 Subject: [PATCH 3/5] Adds relevant links to Poincare tutorial --- docs/notebooks/Poincare Evaluation.ipynb | 6 ++---- docs/notebooks/Poincare Tutorial.ipynb | 18 +++++++++++++++++- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 7904a3be34..0a75ff97a3 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -799,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ @@ -901,9 +901,7 @@ { "cell_type": "code", "execution_count": 148, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { diff --git a/docs/notebooks/Poincare Tutorial.ipynb b/docs/notebooks/Poincare Tutorial.ipynb index 17d88dc7a8..c7eeb23b3f 100644 --- a/docs/notebooks/Poincare Tutorial.ipynb +++ b/docs/notebooks/Poincare Tutorial.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations can be done with them. For more comprehensive technical details and results, this [blog post](link) may be a more appropriate resource." + "This notebook discusses the basic ideas and use-cases for Poincaré embeddings and demonstrates what kind of operations can be done with them. For more comprehensive technical details and results, this [blog post](https://rare-technologies.com/implementing-poincare-embeddings) may be a more appropriate resource." ] }, { @@ -662,6 +662,22 @@ "source": [ "This is despite the fact that Poincaré distance is symmetric (like any distance in a metric space). The asymmetry stems from the fact that even if node `Y` is the closest node to node `X` amongst all nodes with a higher norm (lower in the hierarchy) than `X`, node `X` may not be the closest node to node `Y` amongst all the nodes with a lower norm (higher in the hierarchy) than `Y`." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Useful Links" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. [Original paper by Facebook AI Research](https://arxiv.org/pdf/1705.08039)\n", + "2. [Blog post describing technical challenges in implementation](https://rare-technologies.com/implementing-poincare-embeddings)\n", + "3. [Detailed evaluation notebook to reproduce results](https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/Poincare%20Evaluation.ipynb)" + ] } ], "metadata": { From 7414eecce5bec252345bf730e4debf39a11f5a76 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 12 Dec 2017 03:04:19 +0530 Subject: [PATCH 4/5] Adds dependency installation to Poincare eval notebook --- docs/notebooks/Poincare Evaluation.ipynb | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 0a75ff97a3..8d1575a273 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -56,6 +56,16 @@ "% cd ../.." ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Some libraries need to be installed that are not part of Gensim\n", + "! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2" + ] + }, { "cell_type": "code", "execution_count": 71, @@ -103,13 +113,6 @@ "nltk.download('wordnet')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that not all the above libraries are part of the gensim dependencies, so they might need to be installed separately. These requirements are listed in the poincare [requirements.txt](poincare/requirements.txt)" - ] - }, { "cell_type": "markdown", "metadata": {}, From 1a078d4442010c976e5c8cdbe8622a95d16966f3 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 12 Dec 2017 16:54:51 +0530 Subject: [PATCH 5/5] Updates html structure of result table in poincare eval notebook --- docs/notebooks/Poincare Evaluation.ipynb | 28 ++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 8d1575a273..72ccf4e01d 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -829,7 +829,12 @@ " result_html = result_table.get_html_string()\n", " search = \"\"\n", " insert_at = result_html.index(search) + len(search)\n", - " new_row = \"\"\"\"\"\" % task_name\n", + " new_row = \"\"\"\n", + " \n", + " \n", + " \n", + " \n", + " \"\"\" % (task_name, len(model_sizes))\n", " result_html = result_html[:insert_at] + new_row + result_html[insert_at:]\n", " display(HTML(result_html))\n", " \n", @@ -909,7 +914,12 @@ { "data": { "text/html": [ - "
%s Dimensions
%s Dimensions
\n", + "
WordNet Reconstruction Dimensions
\n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1311,7 +1321,12 @@ { "data": { "text/html": [ - "
WordNet Reconstruction Dimensions
Model DescriptionMetric
\n", + "
WordNet Link Prediction Dimensions
\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1514,7 +1529,12 @@ { "data": { "text/html": [ - "
WordNet Link Prediction Dimensions
Model DescriptionMetric
\n", + "
Lexical Entailment (HyperLex) Dimensions
\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n",
Lexical Entailment (HyperLex) Dimensions
Model DescriptionMetric