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mtambos/experiment2.ipynb

Created July 7, 2017 17:09
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experiment2.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%pylab inline\n",
"\n",
"import glob\n",
"from pathlib import Path\n",
"import pickle\n",
"import re\n",
"\n",
"import h5py\n",
"from IPython.display import SVG\n",
"from keras import applications\n",
"from keras import backend as K\n",
"from keras import datasets\n",
"from keras.layers.core import Flatten, Dense, Dropout\n",
"from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from keras.optimizers import Adam, SGD\n",
"from keras.models import Sequential\n",
"from keras.preprocessing.image import (ImageDataGenerator, array_to_img,\n",
" img_to_array, load_img)\n",
"from keras.utils.vis_utils import model_to_dot\n",
"import pandas as pd\n",
"from scipy.spatial.distance import cdist, pdist\n",
"from skimage.transform import rescale, resize\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"import seaborn as sns\n",
"import somoclu\n",
"import spacy\n",
"import tensorflow as tf\n",
"\n",
"from multimodal_som import *\n",
"\n",
"sns.set(style='ticks', context='poster')\n",
"pd.set_option('display.max_rows', 12)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nlp = spacy.load('en')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cnn_model = applications.VGG16(input_shape=(224, 224, 3))\n",
"inpt = cnn_model.input\n",
"outpt = cnn_model.layers[-2].output\n",
"# 0 after inpt means learning is disabled\n",
"functor = K.function([inpt, tf.constant(0)], [outpt])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tokens = pd.read_table('/data/master_thesis/Flickr8k.lemma.token.txt',\n",
" names=['fname', 'doc'], index_col='fname', )\n",
"tokens['fname'] = tokens.index.str.replace(r'#\\d', '')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>vector_0</th>\n",
" <th>vector_1</th>\n",
" <th>vector_2</th>\n",
" <th>vector_3</th>\n",
" <th>vector_4</th>\n",
" <th>vector_5</th>\n",
" <th>vector_6</th>\n",
" <th>vector_7</th>\n",
" <th>vector_8</th>\n",
" <th>vector_9</th>\n",
" <th>...</th>\n",
" <th>vector_291</th>\n",
" <th>vector_292</th>\n",
" <th>vector_293</th>\n",
" <th>vector_294</th>\n",
" <th>vector_295</th>\n",
" <th>vector_296</th>\n",
" <th>vector_297</th>\n",
" <th>vector_298</th>\n",
" <th>vector_299</th>\n",
" <th>word</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.225050</td>\n",
" <td>-0.060581</td>\n",
" <td>-0.50887</td>\n",
" <td>0.174330</td>\n",
" <td>0.544660</td>\n",
" <td>-0.293550</td>\n",
" <td>0.075048</td>\n",
" <td>-0.093825</td>\n",
" <td>-0.606130</td>\n",
" <td>1.70520</td>\n",
" <td>...</td>\n",
" <td>0.188000</td>\n",
" <td>0.219540</td>\n",
" <td>-0.879330</td>\n",
" <td>-0.211820</td>\n",
" <td>-0.305710</td>\n",
" <td>0.493100</td>\n",
" <td>0.053174</td>\n",
" <td>0.449270</td>\n",
" <td>-0.290590</td>\n",
" <td>rider</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.038548</td>\n",
" <td>0.542520</td>\n",
" <td>-0.21843</td>\n",
" <td>-0.188550</td>\n",
" <td>0.073000</td>\n",
" <td>0.131800</td>\n",
" <td>-0.104020</td>\n",
" <td>0.172310</td>\n",
" <td>-0.051587</td>\n",
" <td>2.86460</td>\n",
" <td>...</td>\n",
" <td>0.009412</td>\n",
" <td>-0.048502</td>\n",
" <td>-0.047309</td>\n",
" <td>0.348850</td>\n",
" <td>-0.120130</td>\n",
" <td>-0.405710</td>\n",
" <td>0.117980</td>\n",
" <td>0.245900</td>\n",
" <td>0.228730</td>\n",
" <td>what</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.177200</td>\n",
" <td>-0.067304</td>\n",
" <td>0.14145</td>\n",
" <td>-0.246770</td>\n",
" <td>0.167430</td>\n",
" <td>-0.183540</td>\n",
" <td>0.402410</td>\n",
" <td>0.357280</td>\n",
" <td>0.080200</td>\n",
" <td>2.30180</td>\n",
" <td>...</td>\n",
" <td>-0.045420</td>\n",
" <td>0.455240</td>\n",
" <td>-0.447370</td>\n",
" <td>-0.010748</td>\n",
" <td>0.063653</td>\n",
" <td>-0.403060</td>\n",
" <td>0.143340</td>\n",
" <td>0.451150</td>\n",
" <td>0.344800</td>\n",
" <td>stadium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.065913</td>\n",
" <td>0.086414</td>\n",
" <td>0.47390</td>\n",
" <td>-0.376280</td>\n",
" <td>0.276300</td>\n",
" <td>-0.149290</td>\n",
" <td>0.063827</td>\n",
" <td>-0.621840</td>\n",
" <td>-0.093700</td>\n",
" <td>3.08060</td>\n",
" <td>...</td>\n",
" <td>0.192650</td>\n",
" <td>0.836110</td>\n",
" <td>0.464060</td>\n",
" <td>-0.070811</td>\n",
" <td>-0.572880</td>\n",
" <td>0.297380</td>\n",
" <td>-0.045593</td>\n",
" <td>-0.002004</td>\n",
" <td>-0.016488</td>\n",
" <td>department</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.242450</td>\n",
" <td>-0.251440</td>\n",
" <td>0.50003</td>\n",
" <td>-0.282710</td>\n",
" <td>-0.190140</td>\n",
" <td>0.015834</td>\n",
" <td>0.548010</td>\n",
" <td>0.480110</td>\n",
" <td>-0.664380</td>\n",
" <td>1.02830</td>\n",
" <td>...</td>\n",
" <td>-0.080117</td>\n",
" <td>0.254020</td>\n",
" <td>0.048342</td>\n",
" <td>-0.200360</td>\n",
" <td>0.338520</td>\n",
" <td>-0.588150</td>\n",
" <td>-0.226090</td>\n",
" <td>-0.169700</td>\n",
" <td>0.029347</td>\n",
" <td>axe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.397270</td>\n",
" <td>-0.222020</td>\n",
" <td>0.11690</td>\n",
" <td>0.000451</td>\n",
" <td>0.709000</td>\n",
" <td>0.259200</td>\n",
" <td>-0.223600</td>\n",
" <td>0.323680</td>\n",
" <td>-0.864440</td>\n",
" <td>2.51520</td>\n",
" <td>...</td>\n",
" <td>-0.144790</td>\n",
" <td>-0.669500</td>\n",
" <td>-0.135580</td>\n",
" <td>-0.124030</td>\n",
" <td>0.202830</td>\n",
" <td>-0.077738</td>\n",
" <td>0.359790</td>\n",
" <td>-0.189390</td>\n",
" <td>0.073853</td>\n",
" <td>heritage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1687</th>\n",
" <td>-0.012506</td>\n",
" <td>-0.159870</td>\n",
" <td>-0.12129</td>\n",
" <td>-0.200360</td>\n",
" <td>-0.034712</td>\n",
" <td>-0.986850</td>\n",
" <td>0.174120</td>\n",
" <td>-0.030302</td>\n",
" <td>-0.453520</td>\n",
" <td>0.91377</td>\n",
" <td>...</td>\n",
" <td>-0.470660</td>\n",
" <td>-0.279030</td>\n",
" <td>0.220160</td>\n",
" <td>0.572350</td>\n",
" <td>0.193770</td>\n",
" <td>-0.575650</td>\n",
" <td>-0.237190</td>\n",
" <td>-0.053004</td>\n",
" <td>0.939000</td>\n",
" <td>pistol</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1688</th>\n",
" <td>0.113430</td>\n",
" <td>-0.002988</td>\n",
" <td>-0.22301</td>\n",
" <td>0.011532</td>\n",
" <td>0.247520</td>\n",
" <td>0.089260</td>\n",
" <td>-0.330400</td>\n",
" <td>-0.066286</td>\n",
" <td>-0.290140</td>\n",
" <td>1.79090</td>\n",
" <td>...</td>\n",
" <td>0.208120</td>\n",
" <td>0.340840</td>\n",
" <td>-0.298820</td>\n",
" <td>-0.011762</td>\n",
" <td>0.066409</td>\n",
" <td>0.200100</td>\n",
" <td>0.193020</td>\n",
" <td>-0.095904</td>\n",
" <td>0.614390</td>\n",
" <td>background</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1689</th>\n",
" <td>-0.063738</td>\n",
" <td>-0.149220</td>\n",
" <td>-0.21889</td>\n",
" <td>-0.080809</td>\n",
" <td>0.431430</td>\n",
" <td>0.045342</td>\n",
" <td>0.174730</td>\n",
" <td>-0.122940</td>\n",
" <td>0.149650</td>\n",
" <td>0.83784</td>\n",
" <td>...</td>\n",
" <td>0.141720</td>\n",
" <td>-0.280320</td>\n",
" <td>0.527440</td>\n",
" <td>-0.283700</td>\n",
" <td>0.317530</td>\n",
" <td>0.181770</td>\n",
" <td>-0.331110</td>\n",
" <td>0.282510</td>\n",
" <td>0.056025</td>\n",
" <td>scratch</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1690</th>\n",
" <td>0.448670</td>\n",
" <td>0.016682</td>\n",
" <td>0.19710</td>\n",
" <td>-0.024449</td>\n",
" <td>-0.191050</td>\n",
" <td>-0.033394</td>\n",
" <td>0.078947</td>\n",
" <td>-0.273550</td>\n",
" <td>0.040849</td>\n",
" <td>1.58970</td>\n",
" <td>...</td>\n",
" <td>0.099695</td>\n",
" <td>0.166790</td>\n",
" <td>-0.465120</td>\n",
" <td>-0.705620</td>\n",
" <td>0.028763</td>\n",
" <td>0.097989</td>\n",
" <td>1.037900</td>\n",
" <td>0.147430</td>\n",
" <td>-0.160180</td>\n",
" <td>tent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1691</th>\n",
" <td>0.252430</td>\n",
" <td>0.044044</td>\n",
" <td>0.28378</td>\n",
" <td>0.602740</td>\n",
" <td>0.308290</td>\n",
" <td>-0.119690</td>\n",
" <td>0.107680</td>\n",
" <td>-0.452350</td>\n",
" <td>0.287250</td>\n",
" <td>2.12310</td>\n",
" <td>...</td>\n",
" <td>0.315050</td>\n",
" <td>0.297060</td>\n",
" <td>-0.161670</td>\n",
" <td>-0.050952</td>\n",
" <td>-0.244470</td>\n",
" <td>0.240560</td>\n",
" <td>-0.015893</td>\n",
" <td>-0.487270</td>\n",
" <td>-0.221420</td>\n",
" <td>session</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1692</th>\n",
" <td>0.223200</td>\n",
" <td>-0.453440</td>\n",
" <td>-0.20542</td>\n",
" <td>-0.052944</td>\n",
" <td>-0.383520</td>\n",
" <td>-0.243110</td>\n",
" <td>-0.047375</td>\n",
" <td>-0.066950</td>\n",
" <td>-0.207840</td>\n",
" <td>0.83993</td>\n",
" <td>...</td>\n",
" <td>-0.060356</td>\n",
" <td>-0.111290</td>\n",
" <td>-0.260460</td>\n",
" <td>-0.614290</td>\n",
" <td>0.314880</td>\n",
" <td>0.505590</td>\n",
" <td>0.250240</td>\n",
" <td>-0.079418</td>\n",
" <td>0.010726</td>\n",
" <td>coat</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1693 rows × 301 columns</p>\n",
"</div>"
],
"text/plain": [
" vector_0 vector_1 vector_2 vector_3 vector_4 vector_5 vector_6 \\\n",
"0 0.225050 -0.060581 -0.50887 0.174330 0.544660 -0.293550 0.075048 \n",
"1 -0.038548 0.542520 -0.21843 -0.188550 0.073000 0.131800 -0.104020 \n",
"2 1.177200 -0.067304 0.14145 -0.246770 0.167430 -0.183540 0.402410 \n",
"3 -0.065913 0.086414 0.47390 -0.376280 0.276300 -0.149290 0.063827 \n",
"4 -0.242450 -0.251440 0.50003 -0.282710 -0.190140 0.015834 0.548010 \n",
"5 0.397270 -0.222020 0.11690 0.000451 0.709000 0.259200 -0.223600 \n",
"... ... ... ... ... ... ... ... \n",
"1687 -0.012506 -0.159870 -0.12129 -0.200360 -0.034712 -0.986850 0.174120 \n",
"1688 0.113430 -0.002988 -0.22301 0.011532 0.247520 0.089260 -0.330400 \n",
"1689 -0.063738 -0.149220 -0.21889 -0.080809 0.431430 0.045342 0.174730 \n",
"1690 0.448670 0.016682 0.19710 -0.024449 -0.191050 -0.033394 0.078947 \n",
"1691 0.252430 0.044044 0.28378 0.602740 0.308290 -0.119690 0.107680 \n",
"1692 0.223200 -0.453440 -0.20542 -0.052944 -0.383520 -0.243110 -0.047375 \n",
"\n",
" vector_7 vector_8 vector_9 ... vector_291 vector_292 \\\n",
"0 -0.093825 -0.606130 1.70520 ... 0.188000 0.219540 \n",
"1 0.172310 -0.051587 2.86460 ... 0.009412 -0.048502 \n",
"2 0.357280 0.080200 2.30180 ... -0.045420 0.455240 \n",
"3 -0.621840 -0.093700 3.08060 ... 0.192650 0.836110 \n",
"4 0.480110 -0.664380 1.02830 ... -0.080117 0.254020 \n",
"5 0.323680 -0.864440 2.51520 ... -0.144790 -0.669500 \n",
"... ... ... ... ... ... ... \n",
"1687 -0.030302 -0.453520 0.91377 ... -0.470660 -0.279030 \n",
"1688 -0.066286 -0.290140 1.79090 ... 0.208120 0.340840 \n",
"1689 -0.122940 0.149650 0.83784 ... 0.141720 -0.280320 \n",
"1690 -0.273550 0.040849 1.58970 ... 0.099695 0.166790 \n",
"1691 -0.452350 0.287250 2.12310 ... 0.315050 0.297060 \n",
"1692 -0.066950 -0.207840 0.83993 ... -0.060356 -0.111290 \n",
"\n",
" vector_293 vector_294 vector_295 vector_296 vector_297 vector_298 \\\n",
"0 -0.879330 -0.211820 -0.305710 0.493100 0.053174 0.449270 \n",
"1 -0.047309 0.348850 -0.120130 -0.405710 0.117980 0.245900 \n",
"2 -0.447370 -0.010748 0.063653 -0.403060 0.143340 0.451150 \n",
"3 0.464060 -0.070811 -0.572880 0.297380 -0.045593 -0.002004 \n",
"4 0.048342 -0.200360 0.338520 -0.588150 -0.226090 -0.169700 \n",
"5 -0.135580 -0.124030 0.202830 -0.077738 0.359790 -0.189390 \n",
"... ... ... ... ... ... ... \n",
"1687 0.220160 0.572350 0.193770 -0.575650 -0.237190 -0.053004 \n",
"1688 -0.298820 -0.011762 0.066409 0.200100 0.193020 -0.095904 \n",
"1689 0.527440 -0.283700 0.317530 0.181770 -0.331110 0.282510 \n",
"1690 -0.465120 -0.705620 0.028763 0.097989 1.037900 0.147430 \n",
"1691 -0.161670 -0.050952 -0.244470 0.240560 -0.015893 -0.487270 \n",
"1692 -0.260460 -0.614290 0.314880 0.505590 0.250240 -0.079418 \n",
"\n",
" vector_299 word \n",
"0 -0.290590 rider \n",
"1 0.228730 what \n",
"2 0.344800 stadium \n",
"3 -0.016488 department \n",
"4 0.029347 axe \n",
"5 0.073853 heritage \n",
"... ... ... \n",
"1687 0.939000 pistol \n",
"1688 0.614390 background \n",
"1689 0.056025 scratch \n",
"1690 -0.160180 tent \n",
"1691 -0.221420 session \n",
"1692 0.010726 coat \n",
"\n",
"[1693 rows x 301 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vec2word_fname = Path('vec2word.csv')\n",
"if vec2word_fname.exists():\n",
" vec2word = pd.read_csv(vec2word_fname)\n",
"else:\n",
" words = set()\n",
" for doc in tokens.doc:\n",
" words |= set(map(str.strip, map(str.lower, doc.split())))\n",
" vec2word = [token for word in words for token in nlp(word)]\n",
" vec2word = [list(token.vector) + [token.orth_] for token in vec2word\n",
" if token.pos_ in ('NOUN', 'PROPN')]\n",
" vec2word = pd.DataFrame(vec2word, columns=[f'vector_{i}' for i in range(300)] + ['word'])\n",
" vec2word = vec2word[~np.isclose(vec2word.iloc[:, :-1], 0).all(axis=1)]\n",
" vec2word.to_csv(vec2word_fname, index=False)\n",
"vec2word"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"half 7\n",
"swing 7\n",
"rock 6\n",
"style 5\n",
"port 5\n",
"hand 5\n",
" ..\n",
"anime 1\n",
"outfit 1\n",
"bush 1\n",
"sucker 1\n",
"breaker 1\n",
"trial 1\n",
"Name: word, Length: 1450, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vec2word.word.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"inputHidden": false,
"outputHidden": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting nouns\n",
"Building nouns_df\n",
"Building nouns_df\n",
"getting imgs features\n",
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"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 2.669598 2.611406 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 2.384654 0.958648 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 4.600369 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 5.467183 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 0.000000 8.352444 \n",
"\n",
" vector8 vector9 \\\n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 0.000000 1.831622 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 1.238015 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 0.000000 4.273909 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.000000 1.652865 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 1.273453 0.000000 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 2.376340 1.523699 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 0.547680 5.759399 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 0.980417 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 1.678141 4.496348 \n",
"\n",
" ... vector4086 \\\n",
"fname ... \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... ... 0.036246 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... ... 0.691206 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... ... 0.000000 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... ... 0.166158 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... ... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... ... 0.000000 \n",
"\n",
" vector4087 vector4088 \\\n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 2.603582 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 1.786312 3.234845 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.000000 6.393643 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 0.270415 0.937897 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.000000 0.546451 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 2.321201 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 1.000075 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 0.000000 0.000000 \n",
"\n",
" vector4089 vector4090 \\\n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 2.735082 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.000000 0.167838 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 0.000000 0.000000 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.000000 3.097225 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 0.000000 0.000000 \n",
"\n",
" vector4091 vector4092 \\\n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 0.000000 2.116478 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 0.000000 5.685127 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 0.000000 2.566352 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 2.859266 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.000000 0.705887 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 0.000000 0.000000 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 1.196244 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 2.700328 0.000000 \n",
"\n",
" vector4093 vector4094 \\\n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 0.0 1.339029 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 0.0 0.536650 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.0 0.587141 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 0.0 0.000000 \n",
"... ... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 0.0 1.822726 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.0 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 0.0 0.000000 \n",
"\n",
" vector4095 \n",
"fname \n",
"/data/master_thesis/Flicker8k_Dataset/130556499... 1.809592 \n",
"/data/master_thesis/Flicker8k_Dataset/135176458... 5.729015 \n",
"/data/master_thesis/Flicker8k_Dataset/135808913... 0.788472 \n",
"/data/master_thesis/Flicker8k_Dataset/136212802... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/138369800... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/146810328... 3.806203 \n",
"... ... \n",
"/data/master_thesis/Flicker8k_Dataset/975131015... 0.554753 \n",
"/data/master_thesis/Flicker8k_Dataset/976392326... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/978580450... 0.988387 \n",
"/data/master_thesis/Flicker8k_Dataset/979383193... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/985067019... 0.000000 \n",
"/data/master_thesis/Flicker8k_Dataset/989754491... 3.131091 \n",
"\n",
"[8091 rows x 4096 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vec2img_fname = Path('vec2img.csv')\n",
"if vec2img_fname.exists():\n",
" vec2img = pd.read_csv(vec2img_fname, index_col='fname')\n",
"else:\n",
" img_dir = Path('/data/master_thesis/Flicker8k_Dataset')\n",
" print(\"Getting nouns\")\n",
" nouns = tokens.doc.apply(nlp).apply(\n",
" lambda d: np.array([t for t in d if t.pos_ in ('NOUN', 'PROPN')])\n",
" )\n",
" nouns_df = []\n",
" print(\"Building nouns_df\")\n",
" for idx, row in tokens.iloc[:10].iterrows():\n",
" fname = img_dir / row.fname\n",
" if fname.exists():\n",
" for noun in nouns[idx]:\n",
" nouns_df.append((fname, noun))\n",
" print(\"Building nouns_df\")\n",
" nouns_df = pd.DataFrame(nouns_df, columns=['fname', 'noun'])\n",
" print(\"getting imgs features\")\n",
" vec2img = get_images_features(nouns_df)\n",
" vec2img.to_csv(vec2img_fname)\n",
"vec2img"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"vector391 0.0\n",
"vector1269 0.0\n",
"vector1345 0.0\n",
"vector2184 0.0\n",
"vector2287 0.0\n",
"vector2310 0.0\n",
" ... \n",
"vector2801 0.0\n",
"vector2823 0.0\n",
"vector3030 0.0\n",
"vector3332 0.0\n",
"vector3883 0.0\n",
"vector4059 0.0\n",
"Length: 13, dtype: float64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vec2img.std()[vec2img.std()==0]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true,
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"model = MultimodalSOM()\n",
"model.load('som_planar_rectangular')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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