refactoring updates
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@ -41,9 +41,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"Initial definition of emojis used later"
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]
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@ -491,7 +489,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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@ -41,9 +41,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"Initial definition of emojis used later"
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]
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@ -88,13 +86,13 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /Users/Carsten/nltk_data...\n",
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"[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
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"[nltk_data] Package punkt is already up-to-date!\n",
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"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
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"[nltk_data] /Users/Carsten/nltk_data...\n",
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"[nltk_data] /home/jonas/nltk_data...\n",
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"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
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"[nltk_data] date!\n",
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"[nltk_data] Downloading package wordnet to /Users/Carsten/nltk_data...\n",
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"[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
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"[nltk_data] Package wordnet is already up-to-date!\n"
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]
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}
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@ -105,9 +103,9 @@
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"sys.path.append(\"..\")\n",
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"sys.path.append(\"../naive_approach\")\n",
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"\n",
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"import simple_approach.simple_twitter_learning as stl\n",
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"import advanced_approach.twitter_learning as twl\n",
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"#clf_advanced = stl.pipeline_manager.load_pipeline_from_files( '\"/Users/Carsten/DataSets/NLP_LAB/tfidf_final/final_epoch01.pipeline', ['keras_model'], ['vectorizer', 'keras_model'])\n",
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"clf_advanced = stl.pipeline_manager.load_from_pipeline_file(\"/Users/Carsten/DataSets/NLP_LAB/tfidf_final/final_epoch01.pipeline\")\n",
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"clf_advanced = twl.pipeline_manager.load_from_pipeline_file(\"/home/jonas/Dokumente/NLP_DATA/python_dumps/pipelines/tfidf_final/final_epoch01.pipeline\")\n",
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"import Tools.Emoji_Distance as ed"
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]
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},
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@ -559,12 +557,12 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6c16f36840984c1e9749b70846926f66",
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"model_id": "de02918c53e745d7b1b8fd47226464db",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"A Jupyter Widget"
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"VBox(children=(VBox(children=(Output(layout=Layout(max_height='500px')),)), HBox(children=(Button(description=…"
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]
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},
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"metadata": {},
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@ -581,9 +579,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"improve naive approach\n",
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"word2vec\n",
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@ -594,9 +590,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"source": [
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"## I Am Groot"
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]
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@ -655,12 +649,12 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "503a0e49088a4374972a5f16abfa7e79",
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"model_id": "4e56d0b43b594e34b799365a468301ee",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"A Jupyter Widget"
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"VBox(children=(VBox(children=(Output(layout=Layout(max_height='500px')),)), HBox(children=(Text(value=''),))),…"
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]
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},
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"metadata": {},
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@ -698,7 +692,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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@ -198,7 +198,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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