diff --git a/.gitignore b/.gitignore index cea410c7..1be68af1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# VSCode config +.vscode + # JupyterLab temp files .virtual_documents diff --git a/config/dev_reqs.txt b/config/dev_reqs.txt index b1ff8eaa..500793df 100644 --- a/config/dev_reqs.txt +++ b/config/dev_reqs.txt @@ -7,10 +7,11 @@ pytest pyyaml transformers>=3.0.0 # SpaCy models aren't stable across point releases -spacy==3.0.5 +spacy==3.6.0 ipywidgets ibm-watson twine +hypothesis # Documentation-related requirements have moved to non_36_reqs.txt. #sphinx diff --git a/config/non_36_reqs.txt b/config/non_36_reqs.txt index f6877e06..98e53e16 100644 --- a/config/non_36_reqs.txt +++ b/config/non_36_reqs.txt @@ -4,8 +4,14 @@ # This list will probably grow as libraries drop support. nltk -ray[default] -feather +ray[default] >= 2.0 + +# *** HACK ALERT *** +# Feather depends on Numpy being exactly 1.20.2, which breaks Pandas. +# So we don't include a dependency on it and hope for the best. +#feather +# *** END HACK *** + sphinx sphinxcontrib-apidoc diff --git a/notebooks/.gitignore b/notebooks/.gitignore new file mode 100644 index 00000000..217a2208 --- /dev/null +++ b/notebooks/.gitignore @@ -0,0 +1,2 @@ +CoNLL_u_test_inputs + diff --git a/notebooks/Analyze_Model_Outputs.ipynb b/notebooks/Analyze_Model_Outputs.ipynb index aaa1513f..102efeac 100644 --- a/notebooks/Analyze_Model_Outputs.ipynb +++ b/notebooks/Analyze_Model_Outputs.ipynb @@ -30,7 +30,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import os\n", @@ -72,7 +74,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -323,7 +327,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -504,7 +510,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -666,7 +674,9 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -4584,12 +4594,12 @@ } ], "source": [ - "from IPython.core.display import display, HTML\n", - "display(HTML(\"

PER entities in corpus for document 75:

\"))\n", - "display(corpus_person[75])\n", + "from IPython import display\n", + "display.display(display.HTML(\"

PER entities in corpus for document 75:

\"))\n", + "display.display(corpus_person[75])\n", "\n", - "display(HTML(\"

PER entities in model outputs for document 75:

\"))\n", - "display(bender_person[75])" + "display.display(display.HTML(\"

PER entities in model outputs for document 75:

\"))\n", + "display.display(bender_person[75])" ] }, { @@ -5382,7 +5392,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/notebooks/Analyze_Text.ipynb b/notebooks/Analyze_Text.ipynb index b0509c55..837e9816 100644 --- a/notebooks/Analyze_Text.ipynb +++ b/notebooks/Analyze_Text.ipynb @@ -143,7 +143,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -194,11 +194,11 @@ } ], "source": [ - "from IPython.core.display import display, HTML\n", + "from IPython.display import display, HTML\n", "doc_file = \"../resources/holy_grail_short.txt\"\n", "with open(doc_file, \"r\") as f:\n", " doc_text = f.read()\n", - " \n", + "\n", "display(HTML(f\"Document Text:
{doc_text}
\"))" ] }, @@ -376,9 +376,9 @@ " 'lemma': 'throughout'},\n", " {'text': 'Britain', 'part_of_speech': 'PROPN', 'location': [64, 71]},\n", " {'text': 'searching',\n", - " 'part_of_speech': 'VERB',\n", + " 'part_of_speech': 'NOUN',\n", " 'location': [72, 81],\n", - " 'lemma': 'search'},\n", + " 'lemma': 'searching'},\n", " {'text': 'for',\n", " 'part_of_speech': 'ADP',\n", " 'location': [82, 85],\n", @@ -412,13 +412,13 @@ " 'location': [113, 116],\n", " 'lemma': 'the'},\n", " {'text': 'Round',\n", - " 'part_of_speech': 'PROPN',\n", + " 'part_of_speech': 'ADJ',\n", " 'location': [117, 122],\n", - " 'lemma': 'Round'},\n", + " 'lemma': 'round'},\n", " {'text': 'Table',\n", - " 'part_of_speech': 'PROPN',\n", + " 'part_of_speech': 'NOUN',\n", " 'location': [123, 128],\n", - " 'lemma': 'Table'},\n", + " 'lemma': 'table'},\n", " {'text': '.', 'part_of_speech': 'PUNCT', 'location': [128, 129]},\n", " {'text': 'Along',\n", " 'part_of_speech': 'ADP',\n", @@ -492,10 +492,7 @@ " 'part_of_speech': 'DET',\n", " 'location': [236, 239],\n", " 'lemma': 'the'},\n", - " {'text': 'Not',\n", - " 'part_of_speech': 'ADV',\n", - " 'location': [240, 243],\n", - " 'lemma': 'not'},\n", + " {'text': 'Not', 'part_of_speech': 'PROPN', 'location': [240, 243]},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [243, 244]},\n", " {'text': 'Quite', 'part_of_speech': 'PROPN', 'location': [244, 249]},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [249, 250]},\n", @@ -505,9 +502,9 @@ " 'lemma': 'so'},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [252, 253]},\n", " {'text': 'Brave',\n", - " 'part_of_speech': 'ADJ',\n", + " 'part_of_speech': 'PROPN',\n", " 'location': [253, 258],\n", - " 'lemma': 'brave'},\n", + " 'lemma': 'Brave'},\n", " {'text': '-', 'part_of_speech': 'PUNCT', 'location': [258, 259]},\n", " {'text': 'as',\n", " 'part_of_speech': 'ADP',\n", @@ -698,10 +695,7 @@ " 'part_of_speech': 'VERB',\n", " 'location': [521, 525],\n", " 'lemma': 'turn'},\n", - " {'text': 'away',\n", - " 'part_of_speech': 'ADV',\n", - " 'location': [526, 530],\n", - " 'lemma': 'away'},\n", + " {'text': 'away', 'part_of_speech': 'ADP', 'location': [526, 530]},\n", " {'text': ',', 'part_of_speech': 'PUNCT', 'location': [530, 531]},\n", " {'text': 'God',\n", " 'part_of_speech': 'PROPN',\n", @@ -4726,7 +4720,7 @@ " [362, 368): 'Arthur'\n", " 0.996876\n", " positive\n", - " 0.721918\n", + " 0.721919\n", " 0.311653\n", " 2\n", " 0.999918\n", @@ -4741,7 +4735,7 @@ " [587, 593): 'Arthur'\n", " 0.973795\n", " positive\n", - " 0.721918\n", + " 0.721919\n", " 0.311653\n", " 2\n", " 0.999918\n", @@ -4759,8 +4753,8 @@ "1 Person Arthur [587, 593): 'Arthur' 0.973795 positive \n", "\n", " sentiment.score relevance count confidence_entity \\\n", - "0 0.721918 0.311653 2 0.999918 \n", - "1 0.721918 0.311653 2 0.999918 \n", + "0 0.721919 0.311653 2 0.999918 \n", + "1 0.721919 0.311653 2 0.999918 \n", "\n", " disambiguation.subtype disambiguation.name disambiguation.dbpedia_resource \n", "0 None None None \n", @@ -4827,12 +4821,12 @@ " Sir Bedevere\n", " positive\n", " 0.835873\n", - " 0.897263\n", - " 0.046902\n", - " 0.810654\n", - " 0.016340\n", - " 0.095661\n", - " 0.021033\n", + " 0.884359\n", + " 0.031301\n", + " 0.496318\n", + " 0.135650\n", + " 0.015545\n", + " 0.022961\n", " 1\n", " \n", " \n", @@ -4840,12 +4834,12 @@ " King Arthur\n", " neutral\n", " 0.000000\n", - " 0.852288\n", - " 0.062558\n", - " 0.620066\n", - " 0.054894\n", - " 0.088147\n", - " 0.182329\n", + " 0.850874\n", + " 0.441230\n", + " 0.330559\n", + " 0.043714\n", + " 0.020016\n", + " 0.025905\n", " 1\n", " \n", " \n", @@ -4853,38 +4847,38 @@ " Sir Lancelot\n", " positive\n", " 0.835873\n", - " 0.830106\n", - " 0.046902\n", - " 0.810654\n", - " 0.016340\n", - " 0.095661\n", - " 0.021033\n", + " 0.823645\n", + " 0.031301\n", + " 0.496318\n", + " 0.135650\n", + " 0.015545\n", + " 0.022961\n", " 1\n", " \n", " \n", " 3\n", " image of W. G. Grace\n", " positive\n", - " 0.721918\n", - " 0.736080\n", - " 0.047242\n", - " 0.614332\n", - " 0.159497\n", - " 0.040378\n", - " 0.155298\n", + " 0.721919\n", + " 0.722026\n", + " 0.044130\n", + " 0.901205\n", + " 0.039773\n", + " 0.012838\n", + " 0.027599\n", " 1\n", " \n", " \n", " 4\n", - " Sir Galahad\n", - " positive\n", - " 0.835873\n", - " 0.638135\n", - " 0.046902\n", - " 0.810654\n", - " 0.016340\n", - " 0.095661\n", - " 0.021033\n", + " musical number\n", + " neutral\n", + " 0.000000\n", + " 0.621432\n", + " 0.312246\n", + " 0.174343\n", + " 0.032726\n", + " 0.077707\n", + " 0.045592\n", " 1\n", " \n", " \n", @@ -4893,18 +4887,18 @@ ], "text/plain": [ " text sentiment.label sentiment.score relevance \\\n", - "0 Sir Bedevere positive 0.835873 0.897263 \n", - "1 King Arthur neutral 0.000000 0.852288 \n", - "2 Sir Lancelot positive 0.835873 0.830106 \n", - "3 image of W. G. Grace positive 0.721918 0.736080 \n", - "4 Sir Galahad positive 0.835873 0.638135 \n", + "0 Sir Bedevere positive 0.835873 0.884359 \n", + "1 King Arthur neutral 0.000000 0.850874 \n", + "2 Sir Lancelot positive 0.835873 0.823645 \n", + "3 image of W. G. Grace positive 0.721919 0.722026 \n", + "4 musical number neutral 0.000000 0.621432 \n", "\n", " emotion.sadness emotion.joy emotion.fear emotion.disgust emotion.anger \\\n", - "0 0.046902 0.810654 0.016340 0.095661 0.021033 \n", - "1 0.062558 0.620066 0.054894 0.088147 0.182329 \n", - "2 0.046902 0.810654 0.016340 0.095661 0.021033 \n", - "3 0.047242 0.614332 0.159497 0.040378 0.155298 \n", - "4 0.046902 0.810654 0.016340 0.095661 0.021033 \n", + "0 0.031301 0.496318 0.135650 0.015545 0.022961 \n", + "1 0.441230 0.330559 0.043714 0.020016 0.025905 \n", + "2 0.031301 0.496318 0.135650 0.015545 0.022961 \n", + "3 0.044130 0.901205 0.039773 0.012838 0.027599 \n", + "4 0.312246 0.174343 0.032726 0.077707 0.045592 \n", "\n", " count \n", "0 1 \n", @@ -5118,19 +5112,19 @@ " \n", " \n", " 0\n", - " men\n", + " for men\n", " In AD 932, King Arthur and his squire, Patsy, ...\n", " the Knights of the Round Table\n", " join\n", - " future\n", - " to join\n", - " to join\n", + " infinitive\n", + " join\n", + " join\n", " \n", " \n", " 1\n", " he\n", - " Along the way, he recruits Sir Bedevere the W...\n", - " Sir Bedevere the Wise\n", + " Along the way, he recruits Sir Bedevere the Wi...\n", + " Sir Bedevere the Wise, Sir Lancelot the Brave,...\n", " recruit\n", " present\n", " recruits\n", @@ -5139,8 +5133,8 @@ " \n", " 2\n", " Arthur\n", - " Arthur leads the men to Camelot, but upon fur...\n", - " the men\n", + " Arthur leads the men to Camelot, but upon furt...\n", + " the men to Camelot\n", " lead\n", " present\n", " leads\n", @@ -5149,8 +5143,8 @@ " \n", " 3\n", " he\n", - " Arthur leads the men to Camelot, but upon fur...\n", - " not to go there\n", + " Arthur leads the men to Camelot, but upon furt...\n", + " not to go there because it is \"a silly place\"\n", " decide\n", " present\n", " decides\n", @@ -5159,12 +5153,12 @@ " \n", " 4\n", " he\n", - " Arthur leads the men to Camelot, but upon fur...\n", - " a musical number)\n", + " Arthur leads the men to Camelot, but upon furt...\n", + " None\n", + " go\n", + " infinitive\n", + " go\n", " go\n", - " future\n", - " to go\n", - " to go\n", " \n", " \n", "\n", @@ -5172,25 +5166,25 @@ ], "text/plain": [ " subject.text sentence \\\n", - "0 men In AD 932, King Arthur and his squire, Patsy, ... \n", - "1 he Along the way, he recruits Sir Bedevere the W... \n", - "2 Arthur Arthur leads the men to Camelot, but upon fur... \n", - "3 he Arthur leads the men to Camelot, but upon fur... \n", - "4 he Arthur leads the men to Camelot, but upon fur... \n", - "\n", - " object.text action.verb.text action.verb.tense \\\n", - "0 the Knights of the Round Table join future \n", - "1 Sir Bedevere the Wise recruit present \n", - "2 the men lead present \n", - "3 not to go there decide present \n", - "4 a musical number) go future \n", - "\n", - " action.text action.normalized \n", - "0 to join to join \n", - "1 recruits recruit \n", - "2 leads lead \n", - "3 decides decide \n", - "4 to go to go " + "0 for men In AD 932, King Arthur and his squire, Patsy, ... \n", + "1 he Along the way, he recruits Sir Bedevere the Wi... \n", + "2 Arthur Arthur leads the men to Camelot, but upon furt... \n", + "3 he Arthur leads the men to Camelot, but upon furt... \n", + "4 he Arthur leads the men to Camelot, but upon furt... \n", + "\n", + " object.text action.verb.text \\\n", + "0 the Knights of the Round Table join \n", + "1 Sir Bedevere the Wise, Sir Lancelot the Brave,... recruit \n", + "2 the men to Camelot lead \n", + "3 not to go there because it is \"a silly place\" decide \n", + "4 None go \n", + "\n", + " action.verb.tense action.text action.normalized \n", + "0 infinitive join join \n", + "1 present recruits recruit \n", + "2 present leads lead \n", + "3 present decides decide \n", + "4 infinitive go go " ] }, "execution_count": 35, @@ -5226,7 +5220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/notebooks/Integrate_NLP_Libraries.ipynb b/notebooks/Integrate_NLP_Libraries.ipynb index 272b0eb6..62dfb9ad 100644 --- a/notebooks/Integrate_NLP_Libraries.ipynb +++ b/notebooks/Integrate_NLP_Libraries.ipynb @@ -174,7 +174,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -225,7 +225,7 @@ } ], "source": [ - "from IPython.core.display import display, HTML\n", + "from IPython.display import display, HTML\n", "doc_file = \"../resources/holy_grail_short.txt\"\n", "with open(doc_file, \"r\") as f:\n", " doc_text = f.read()\n", @@ -1949,8 +1949,8 @@ " compound\n", " 5\n", " Xxxx\n", - " O\n", - " \n", + " B\n", + " PERSON\n", " True\n", " False\n", " [0, 129): 'In AD 932, King Arthur and his squi...\n", @@ -1997,8 +1997,8 @@ " det\n", " 145\n", " xxx\n", - " O\n", - " \n", + " B\n", + " FAC\n", " True\n", " True\n", " [513, 629): 'As they turn away, God (an image ...\n", @@ -2013,8 +2013,8 @@ " compound\n", " 145\n", " Xxxx\n", - " O\n", - " \n", + " I\n", + " FAC\n", " True\n", " False\n", " [513, 629): 'As they turn away, God (an image ...\n", @@ -2029,8 +2029,8 @@ " dobj\n", " 142\n", " Xxxxx\n", - " O\n", - " \n", + " I\n", + " FAC\n", " True\n", " False\n", " [513, 629): 'As they turn away, God (an image ...\n", @@ -2075,12 +2075,12 @@ "1 B DATE True False \n", "2 I DATE False False \n", "3 O False False \n", - "4 O True False \n", + "4 B PERSON True False \n", ".. ... ... ... ... \n", "142 O True False \n", - "143 O True True \n", - "144 O True False \n", - "145 O True False \n", + "143 B FAC True True \n", + "144 I FAC True False \n", + "145 I FAC True False \n", "146 O False False \n", "\n", " sentence \n", @@ -2198,14 +2198,14 @@ " Galahad\n", " PROPN\n", " NNP\n", - " npadvmod\n", - " 32\n", + " appos\n", + " 39\n", " Xxxxx\n", " B\n", " PERSON\n", " True\n", " False\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 45\n", @@ -2217,27 +2217,27 @@ " det\n", " 46\n", " xxx\n", - " I\n", - " PERSON\n", + " O\n", + " \n", " True\n", " True\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 46\n", " 46\n", " [220, 224): 'Pure'\n", - " Pure\n", - " PROPN\n", - " NNP\n", + " pure\n", + " ADJ\n", + " JJ\n", " appos\n", " 44\n", " Xxxx\n", - " I\n", - " PERSON\n", + " O\n", + " \n", " True\n", " False\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 47\n", @@ -2247,13 +2247,13 @@ " PUNCT\n", " ,\n", " punct\n", - " 46\n", + " 39\n", " ,\n", " O\n", " \n", " False\n", " False\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 48\n", @@ -2269,7 +2269,7 @@ " \n", " True\n", " False\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 49\n", @@ -2279,13 +2279,13 @@ " PROPN\n", " NNP\n", " appos\n", - " 46\n", + " 39\n", " Xxxxx\n", " B\n", " PERSON\n", " True\n", " False\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 50\n", @@ -2295,13 +2295,13 @@ " DET\n", " DT\n", " det\n", - " 57\n", + " 63\n", " xxx\n", " O\n", " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 51\n", @@ -2317,7 +2317,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 52\n", @@ -2333,23 +2333,23 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 53\n", " 53\n", " [244, 249): 'Quite'\n", - " Quite\n", - " PROPN\n", - " NNP\n", - " compound\n", - " 55\n", + " quite\n", + " VERB\n", + " VB\n", + " nmod\n", + " 63\n", " Xxxxx\n", " O\n", " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 54\n", @@ -2359,21 +2359,21 @@ " PUNCT\n", " HYPH\n", " punct\n", - " 55\n", + " 53\n", " -\n", " O\n", " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 55\n", " 55\n", " [250, 252): 'So'\n", " so\n", - " ADV\n", - " RB\n", + " SCONJ\n", + " IN\n", " advmod\n", " 57\n", " Xx\n", @@ -2381,7 +2381,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 56\n", @@ -2397,23 +2397,23 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 57\n", " 57\n", " [253, 258): 'Brave'\n", " brave\n", - " NOUN\n", - " NN\n", - " ROOT\n", - " 57\n", + " VERB\n", + " VB\n", + " pobj\n", + " 53\n", " Xxxxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 58\n", @@ -2429,7 +2429,7 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 59\n", @@ -2445,7 +2445,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 60\n", @@ -2461,23 +2461,23 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 61\n", " 61\n", " [262, 265): 'Sir'\n", - " Sir\n", - " PROPN\n", - " NNP\n", - " compound\n", - " 63\n", + " sir\n", + " NOUN\n", + " NN\n", + " pobj\n", + " 59\n", " Xxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 62\n", @@ -2493,7 +2493,7 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 63\n", @@ -2502,14 +2502,14 @@ " Lancelot\n", " PROPN\n", " NNP\n", - " pobj\n", - " 59\n", + " appos\n", + " 49\n", " Xxxxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 64\n", @@ -2519,13 +2519,13 @@ " PUNCT\n", " ,\n", " punct\n", - " 57\n", + " 39\n", " ,\n", " O\n", " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 65\n", @@ -2535,13 +2535,13 @@ " CCONJ\n", " CC\n", " cc\n", - " 57\n", + " 39\n", " xxx\n", " O\n", " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 66\n", @@ -2550,14 +2550,14 @@ " Sir\n", " PROPN\n", " NNP\n", - " compound\n", + " npadvmod\n", " 69\n", " Xxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 67\n", @@ -2573,7 +2573,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 68\n", @@ -2589,23 +2589,23 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 69\n", " 69\n", " [288, 297): 'Appearing'\n", - " appearing\n", - " NOUN\n", - " NN\n", + " appear\n", + " VERB\n", + " VBG\n", " conj\n", - " 57\n", + " 39\n", " Xxxxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 70\n", @@ -2621,7 +2621,7 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 71\n", @@ -2637,7 +2637,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 72\n", @@ -2653,23 +2653,23 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 73\n", " 73\n", " [301, 305): 'this'\n", " this\n", - " DET\n", + " PRON\n", " DT\n", - " det\n", - " 75\n", + " pobj\n", + " 71\n", " xxxx\n", " O\n", " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 74\n", @@ -2685,7 +2685,7 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 75\n", @@ -2694,14 +2694,14 @@ " Film\n", " PROPN\n", " NNP\n", - " pobj\n", - " 71\n", + " appos\n", + " 69\n", " Xxxx\n", " O\n", " \n", " True\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 76\n", @@ -2717,7 +2717,7 @@ " \n", " False\n", " False\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 77\n", @@ -2727,13 +2727,13 @@ " ADP\n", " IN\n", " prep\n", - " 57\n", + " 69\n", " xxxx\n", " O\n", " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 78\n", @@ -2749,7 +2749,7 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", " 79\n", @@ -2765,126 +2765,126 @@ " \n", " True\n", " True\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id span lemma pos tag dep head \\\n", - "44 44 [208, 215): 'Galahad' Galahad PROPN NNP npadvmod 32 \n", - "45 45 [216, 219): 'the' the DET DT det 46 \n", - "46 46 [220, 224): 'Pure' Pure PROPN NNP appos 44 \n", - "47 47 [224, 225): ',' , PUNCT , punct 46 \n", - "48 48 [226, 229): 'Sir' Sir PROPN NNP compound 49 \n", - "49 49 [230, 235): 'Robin' Robin PROPN NNP appos 46 \n", - "50 50 [236, 239): 'the' the DET DT det 57 \n", - "51 51 [240, 243): 'Not' not PART RB neg 53 \n", - "52 52 [243, 244): '-' - PUNCT HYPH punct 53 \n", - "53 53 [244, 249): 'Quite' Quite PROPN NNP compound 55 \n", - "54 54 [249, 250): '-' - PUNCT HYPH punct 55 \n", - "55 55 [250, 252): 'So' so ADV RB advmod 57 \n", - "56 56 [252, 253): '-' - PUNCT HYPH punct 57 \n", - "57 57 [253, 258): 'Brave' brave NOUN NN ROOT 57 \n", - "58 58 [258, 259): '-' - PUNCT HYPH punct 57 \n", - "59 59 [259, 261): 'as' as ADP IN prep 57 \n", - "60 60 [261, 262): '-' - PUNCT HYPH punct 59 \n", - "61 61 [262, 265): 'Sir' Sir PROPN NNP compound 63 \n", - 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"44 Xxxxx B PERSON True False \n", - "45 xxx I PERSON True True \n", - "46 Xxxx I PERSON True False \n", - "47 , O False False \n", - "48 Xxx O True False \n", - "49 Xxxxx B PERSON True False \n", - "50 xxx O True True \n", - "51 Xxx O True True \n", - "52 - O False False \n", - "53 Xxxxx O True True \n", - "54 - O False False \n", - "55 Xx O True True \n", - "56 - O False False \n", - "57 Xxxxx O True False \n", - "58 - O False False \n", - "59 xx O True True \n", - "60 - O False False \n", - "61 Xxx O True False \n", - "62 - O False False \n", - "63 Xxxxx O True False \n", - "64 , O False False \n", - "65 xxx O True True \n", - "66 Xxx O True False \n", - "67 Xxx O True True \n", - "68 - O False False \n", - "69 Xxxxx O True False \n", - "70 - O False False \n", - "71 xx O True True \n", - "72 - O False False \n", - "73 xxxx O True True \n", - "74 - O False False \n", - "75 Xxxx O True False \n", - "76 , O False False \n", - "77 xxxx O True True \n", - "78 xxxx O True True \n", - "79 xxxx O True True \n", + " id span lemma pos tag dep head shape \\\n", + "44 44 [208, 215): 'Galahad' Galahad PROPN NNP appos 39 Xxxxx \n", + "45 45 [216, 219): 'the' the DET DT det 46 xxx \n", + "46 46 [220, 224): 'Pure' pure ADJ JJ appos 44 Xxxx \n", + "47 47 [224, 225): ',' , PUNCT , punct 39 , \n", + "48 48 [226, 229): 'Sir' Sir PROPN NNP compound 49 Xxx \n", + "49 49 [230, 235): 'Robin' Robin PROPN NNP appos 39 Xxxxx \n", + "50 50 [236, 239): 'the' the DET DT det 63 xxx \n", + "51 51 [240, 243): 'Not' not PART RB neg 53 Xxx \n", + "52 52 [243, 244): '-' - 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"44 [130, 235): 'Along the way, he recruits Sir Be... \n", - "45 [130, 235): 'Along the way, he recruits Sir Be... \n", - "46 [130, 235): 'Along the way, he recruits Sir Be... \n", - "47 [130, 235): 'Along the way, he recruits Sir Be... \n", - "48 [130, 235): 'Along the way, he recruits Sir Be... \n", - "49 [130, 235): 'Along the way, he recruits Sir Be... \n", - "50 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "51 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "52 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "53 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "54 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "55 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "56 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "57 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "58 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "59 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "60 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "61 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "62 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "63 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "64 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "65 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "66 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "67 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "68 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "69 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "70 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "71 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "72 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "73 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "74 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "75 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "76 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "77 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "78 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... \n", - "79 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan... 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" ] }, "execution_count": 21, @@ -2938,11 +2938,7 @@ " \n", " \n", " 44\n", - " [130, 235): 'Along the way, he recruits Sir Be...\n", - " \n", - " \n", - " 50\n", - " [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan...\n", + " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", "\n", @@ -2950,8 +2946,7 @@ ], "text/plain": [ " sentence\n", - "44 [130, 235): 'Along the way, he recruits Sir Be...\n", - "50 [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lan..." + "44 [130, 361): 'Along the way, he recruits Sir Be..." ] }, "execution_count": 22, @@ -3622,23 +3617,12 @@ " \n", " 0\n", " 130\n", - " 235\n", - "\n", - " 27\n", - " 50\n", - "\n", - " Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Galahad the Pure, Sir Robin\n", - " \n", - "\n", - " \n", - " 1\n", - " 236\n", " 361\n", "\n", - " 50\n", + " 27\n", " 86\n", "\n", - " the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this-Film, along with their squires and Robin's troubadours.\n", + " Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Galahad the Pure, Sir Robin the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this-Film, along with their squires and Robin's troubadours.\n", " \n", "\n", " \n", @@ -3650,11 +3634,7 @@ "\n", " In AD 932, King Arthur and his squire, Patsy, travel throughout Britain searching for men to join the Knights of the Round Table. \n", "\n", - " Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Galahad the Pure, Sir Robin\n", - "\n", - "\n", - "\n", - " the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this-Film, along with their squires and Robin's troubadours.\n", + " Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Galahad the Pure, Sir Robin the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this-Film, along with their squires and Robin's troubadours.\n", " Arthur leads the men to Camelot, but upon further consideration (thanks to a musical number) he decides not to go there because it is "a silly place". As they turn away, God (an image of W. G. Grace) speaks to them and gives Arthur the task of finding the Holy Grail.\n", "

\n", " \n", @@ -3669,10 +3649,10 @@ "\n", " {\n", "\n", - " const doc_spans = [[130,235],[236,361]]\n", + " const doc_spans = [[130,361]]\n", " const doc_text = 'In AD 932, King Arthur and his squire, Patsy, travel throughout Britain searching for men to join the Knights of the Round Table. Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, Sir Galahad the Pure, Sir Robin the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this-Film, along with their squires and Robin\\'s troubadours. Arthur leads the men to Camelot, but upon further consideration (thanks to a musical number) he decides not to go there because it is \"a silly place\". As they turn away, God (an image of W. G. Grace) speaks to them and gives Arthur the task of finding the Holy Grail.'\n", "\n", - " const doc_token_spans = [[27,50],[50,86]]\n", + " const doc_token_spans = [[27,86]]\n", " documents.push({doc_text: doc_text, doc_spans: doc_spans, doc_token_spans: doc_token_spans})\n", "\n", " }\n", @@ -3685,8 +3665,8 @@ ], "text/plain": [ "\n", - "[[130, 235): 'Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, [...]', [236, 361): 'the Not-Quite-So-Brave-as-Sir-Lancelot, and Sir Not-Appearing-in-this- [...]']\n", - "Length: 2, dtype: TokenSpanDtype" + "[[130, 361): 'Along the way, he recruits Sir Bedevere the Wise, Sir Lancelot the Brave, [...]']\n", + "Length: 1, dtype: TokenSpanDtype" ] }, "execution_count": 23, @@ -3792,8 +3772,8 @@ " \n", " 51\n", " [240, 243): 'Not'\n", - " ADV\n", - " not\n", + " PROPN\n", + " None\n", " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", @@ -3834,8 +3814,8 @@ " \n", " 57\n", " [253, 258): 'Brave'\n", - " ADJ\n", - " brave\n", + " PROPN\n", + " Brave\n", " [130, 361): 'Along the way, he recruits Sir Be...\n", " \n", " \n", @@ -4005,13 +3985,13 @@ "48 [226, 229): 'Sir' PROPN Sir \n", "49 [230, 235): 'Robin' PROPN Robin \n", "50 [236, 239): 'the' DET the \n", - "51 [240, 243): 'Not' ADV not \n", + "51 [240, 243): 'Not' PROPN None \n", "52 [243, 244): '-' PUNCT None \n", "53 [244, 249): 'Quite' PROPN None \n", "54 [249, 250): '-' PUNCT None \n", "55 [250, 252): 'So' ADV so \n", "56 [252, 253): '-' PUNCT None \n", - "57 [253, 258): 'Brave' ADJ brave \n", + "57 [253, 258): 'Brave' PROPN Brave \n", "58 [258, 259): '-' PUNCT None \n", "59 [259, 261): 'as' ADP as \n", "60 [261, 262): '-' PUNCT None \n", @@ -4861,8 +4841,8 @@ " Galahad\n", " PROPN\n", " NNP\n", - " npadvmod\n", - " 32\n", + " appos\n", + " 39\n", " Xxxxx\n", " B\n", " PERSON\n", @@ -4880,8 +4860,8 @@ " det\n", " 46\n", " xxx\n", - " I\n", - " PERSON\n", + " O\n", + " \n", " True\n", " True\n", " [130, 361): 'Along the way, he recruits Sir Be...\n", @@ -4890,14 +4870,14 @@ " 46\n", " 46\n", " [220, 224): 'Pure'\n", - " Pure\n", - " PROPN\n", - " NNP\n", + " pure\n", + " ADJ\n", + " JJ\n", " appos\n", " 44\n", " Xxxx\n", - " I\n", - " PERSON\n", + " O\n", + " \n", " True\n", " False\n", " [130, 361): 'Along the way, he recruits Sir Be...\n", @@ -4910,7 +4890,7 @@ " PUNCT\n", " ,\n", " punct\n", - " 46\n", + " 39\n", " ,\n", " O\n", " \n", @@ -4942,7 +4922,7 @@ " PROPN\n", " NNP\n", " appos\n", - " 46\n", + " 39\n", " Xxxxx\n", " B\n", " PERSON\n", @@ -4958,7 +4938,7 @@ " DET\n", " DT\n", " det\n", - " 57\n", + " 63\n", " xxx\n", " O\n", " \n", @@ -5002,11 +4982,11 @@ " 53\n", " 53\n", " [244, 249): 'Quite'\n", - " Quite\n", - " PROPN\n", - " NNP\n", - " compound\n", - " 55\n", + " quite\n", + " VERB\n", + " VB\n", + " nmod\n", + " 63\n", " Xxxxx\n", " O\n", " \n", @@ -5020,21 +5000,21 @@ ], "text/plain": [ " id span lemma pos tag dep head shape \\\n", - "44 44 [208, 215): 'Galahad' Galahad PROPN NNP npadvmod 32 Xxxxx \n", + "44 44 [208, 215): 'Galahad' Galahad PROPN NNP appos 39 Xxxxx \n", "45 45 [216, 219): 'the' the DET DT det 46 xxx \n", - "46 46 [220, 224): 'Pure' Pure PROPN NNP appos 44 Xxxx \n", - "47 47 [224, 225): ',' , PUNCT , punct 46 , \n", + "46 46 [220, 224): 'Pure' pure ADJ JJ appos 44 Xxxx \n", + "47 47 [224, 225): ',' , PUNCT , punct 39 , \n", "48 48 [226, 229): 'Sir' Sir PROPN NNP compound 49 Xxx \n", - "49 49 [230, 235): 'Robin' Robin PROPN NNP appos 46 Xxxxx \n", - "50 50 [236, 239): 'the' the DET DT det 57 xxx \n", + "49 49 [230, 235): 'Robin' Robin PROPN NNP appos 39 Xxxxx \n", + "50 50 [236, 239): 'the' the DET DT det 63 xxx \n", "51 51 [240, 243): 'Not' not PART RB neg 53 Xxx \n", "52 52 [243, 244): '-' - PUNCT HYPH punct 53 - \n", - "53 53 [244, 249): 'Quite' Quite PROPN NNP compound 55 Xxxxx \n", + "53 53 [244, 249): 'Quite' quite VERB VB nmod 63 Xxxxx \n", "\n", " ent_iob ent_type is_alpha is_stop \\\n", "44 B PERSON True False \n", - "45 I PERSON True True \n", - "46 I PERSON True False \n", + "45 O True True \n", + "46 O True False \n", "47 O False False \n", "48 O True False \n", "49 B PERSON True False \n", @@ -5090,449 +5070,417 @@ { "data": { "text/html": [ - "\n", - "\n", + "\n", + "\n", " Galahad\n", " NNP\n", "\n", "\n", - "\n", + "\n", " the\n", " DT\n", "\n", "\n", - "\n", + "\n", " Pure\n", - " NNP\n", + " JJ\n", "\n", "\n", - "\n", + "\n", " ,\n", " ,\n", "\n", "\n", - 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" \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " det\n", + " pobj\n", " \n", - " \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " punct\n", + " punct\n", " \n", - " \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " pobj\n", + " appos\n", " \n", - " \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " punct\n", + " punct\n", " \n", - " \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " prep\n", + " prep\n", " \n", - " \n", + " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " prep\n", + " prep\n", " \n", - " \n", + " \n", "\n", "" ], @@ -5583,7 +5531,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" }, "toc-autonumbering": false }, diff --git a/notebooks/Model_Training_with_BERT.ipynb b/notebooks/Model_Training_with_BERT.ipynb index bdc8520e..0ef137cd 100644 --- a/notebooks/Model_Training_with_BERT.ipynb +++ b/notebooks/Model_Training_with_BERT.ipynb @@ -1414,7 +1414,7 @@ " O\n", " <NA>\n", " O\n", - " [ -0.19854125, -0.46898478, 0.7755599...\n", + " [ -0.19854169, -0.46898514, 0.7755601...\n", " \n", " \n", " 11\n", @@ -1424,7 +1424,7 @@ " O\n", " <NA>\n", " O\n", - " [ -0.24190304, -0.42399377, 0.955406...\n", + " [ -0.24190396, -0.42399377, 0.9554063...\n", " \n", " \n", " 12\n", @@ -1434,7 +1434,7 @@ " O\n", " <NA>\n", " O\n", - " [ -0.20076738, -0.7481939, 1.302213...\n", + " [ -0.20076752, -0.7481933, 1.302213...\n", " \n", " \n", " 13\n", @@ -1444,7 +1444,7 @@ " B\n", " LOC\n", " B-LOC\n", - " [ 0.2020257, -0.26199907, 0.3297634...\n", + " [ 0.20202553, -0.26199815, 0.3297633...\n", " \n", " \n", " 14\n", @@ -1454,7 +1454,7 @@ " I\n", " LOC\n", " I-LOC\n", - " [ -0.5462166, -0.90924495, -0.05836733...\n", + " [ -0.5462168, -0.90924424, -0.0583674...\n", " \n", " \n", " 15\n", @@ -1464,7 +1464,7 @@ " I\n", " LOC\n", " I-LOC\n", - " [ -0.37400314, -0.6890743, -0.1446248...\n", + " [ -0.37400252, -0.6890734, -0.1446257...\n", " \n", " \n", " 16\n", @@ -1474,7 +1474,7 @@ " I\n", " LOC\n", " I-LOC\n", - " [ -0.46548596, -0.8717423, 0.3557480...\n", + " [ -0.46548516, -0.8717417, 0.3557479...\n", " \n", " \n", " 17\n", @@ -1484,7 +1484,7 @@ " I\n", " LOC\n", " I-LOC\n", - " [ -0.18682732, -0.9008188, 0.3601504...\n", + " [ -0.18682763, -0.90081865, 0.3601499...\n", " \n", " \n", " 18\n", @@ -1494,7 +1494,7 @@ " O\n", " <NA>\n", " O\n", - " [ -0.16640136, -0.8363809, 0.874061...\n", + " [ -0.16640103, -0.8363804, 0.8740610...\n", " \n", " \n", " 19\n", @@ -1504,7 +1504,7 @@ " B\n", " LOC\n", " B-LOC\n", - " [ -0.3024105, -0.8382667, 1.105809...\n", + " [ -0.30241105, -0.83826715, 1.105809...\n", " \n", " \n", "\n", @@ -1524,16 +1524,16 @@ "19 19 [31, 33): 'NE' 26546 B LOC B-LOC \n", "\n", " embedding \n", - "10 [ -0.19854125, -0.46898478, 0.7755599... \n", - "11 [ -0.24190304, -0.42399377, 0.955406... \n", - "12 [ -0.20076738, -0.7481939, 1.302213... \n", - "13 [ 0.2020257, -0.26199907, 0.3297634... \n", - "14 [ -0.5462166, -0.90924495, -0.05836733... \n", - "15 [ -0.37400314, -0.6890743, -0.1446248... \n", - "16 [ -0.46548596, -0.8717423, 0.3557480... \n", - "17 [ -0.18682732, -0.9008188, 0.3601504... \n", - "18 [ -0.16640136, -0.8363809, 0.874061... \n", - "19 [ -0.3024105, -0.8382667, 1.105809... " + "10 [ -0.19854169, -0.46898514, 0.7755601... \n", + "11 [ -0.24190396, -0.42399377, 0.9554063... \n", + "12 [ -0.20076752, -0.7481933, 1.302213... \n", + "13 [ 0.20202553, -0.26199815, 0.3297633... \n", + "14 [ -0.5462168, -0.90924424, -0.0583674... \n", + "15 [ -0.37400252, -0.6890734, -0.1446257... \n", + "16 [ -0.46548516, -0.8717417, 0.3557479... \n", + "17 [ -0.18682763, -0.90081865, 0.3601499... \n", + "18 [ -0.16640103, -0.8363804, 0.8740610... \n", + "19 [ -0.30241105, -0.83826715, 1.105809... " ] }, "execution_count": 12, @@ -1591,35 +1591,35 @@ " [155, 168): 'international'\n", " O\n", " <NA>\n", - " [ 0.23405041, -0.5534875, 0.9083985, ...\n", + " [ 0.23404993, -0.5534872, 0.9083986, ...\n", " \n", " \n", " 71\n", " [169, 176): 'between'\n", " O\n", " <NA>\n", - " [ 0.27792975, -0.6853796, 1.1050363, ...\n", + " [ 0.27793035, -0.68538034, 1.1050361, ...\n", " \n", " \n", " 72\n", " [177, 185): 'Pakistan'\n", " B\n", " LOC\n", - " [ 0.19718906, -0.46341094, 0.5182328, ...\n", + " [ 0.1971882, -0.4634109, 0.5182331, ...\n", " \n", " \n", " 73\n", " [186, 189): 'and'\n", " O\n", " <NA>\n", - " [ 0.20423545, -0.63758826, 0.82874423, ...\n", + " [ 0.20423535, -0.63758826, 0.82874435, ...\n", " \n", " \n", " 74\n", " [190, 193): 'New'\n", " B\n", " LOC\n", - " [ 0.28740737, -0.47174248, 0.77719426, ...\n", + " [ 0.2874066, -0.47174183, 0.7771955, ...\n", " \n", " \n", "\n", @@ -1634,11 +1634,11 @@ "74 [190, 193): 'New' B LOC \n", "\n", " embedding \n", - "70 [ 0.23405041, -0.5534875, 0.9083985, ... \n", - "71 [ 0.27792975, -0.6853796, 1.1050363, ... \n", - "72 [ 0.19718906, -0.46341094, 0.5182328, ... \n", - "73 [ 0.20423545, -0.63758826, 0.82874423, ... \n", - "74 [ 0.28740737, -0.47174248, 0.77719426, ... " + "70 [ 0.23404993, -0.5534872, 0.9083986, ... \n", + "71 [ 0.27793035, -0.68538034, 1.1050361, ... \n", + "72 [ 0.1971882, -0.4634109, 0.5182331, ... \n", + "73 [ 0.20423535, -0.63758826, 0.82874435, ... \n", + "74 [ 0.2874066, -0.47174183, 0.7771955, ... " ] }, "execution_count": 13, @@ -1658,7 +1658,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1763,7 +1763,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.08307116, -0.35959044, 1.015067...\n", + " [ -0.08307081, -0.35959032, 1.015068...\n", " \n", " \n", " 1\n", @@ -1777,7 +1777,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.22862588, -0.49313605, 1.284232...\n", + " [ -0.22862603, -0.49313632, 1.28423...\n", " \n", " \n", " 2\n", @@ -1791,7 +1791,7 @@ " <NA>\n", " O\n", " 0\n", - " [ 0.028480446, -0.17874268, 1.54320...\n", + " [ 0.028480662, -0.17874284, 1.54320...\n", " \n", " \n", " 3\n", @@ -1805,7 +1805,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.46517605, -0.29836014, 1.073768...\n", + " [ -0.4651753, -0.29836023, 1.073767...\n", " \n", " \n", " 4\n", @@ -1819,7 +1819,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.10730826, -0.3372096, 1.226979...\n", + " [ -0.10730811, -0.33720982, 1.226979...\n", " \n", " \n", " ...\n", @@ -1847,7 +1847,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.12806588, -0.002324244, 0.6781316...\n", + " [ -0.1280663, -0.0023243837, 0.678132...\n", " \n", " \n", " 685\n", @@ -1861,7 +1861,7 @@ " <NA>\n", " O\n", " 0\n", - " [ 0.30534068, -0.52625746, 0.8281702...\n", + " [ 0.3053407, -0.52625775, 0.8281702...\n", " \n", " \n", " 686\n", @@ -1875,7 +1875,7 @@ " LOC\n", " B-LOC\n", " 1\n", - " [ -0.04873929, -0.3379735, -0.0583514...\n", + " [ -0.048738778, -0.33797324, -0.0583509...\n", " \n", " \n", " 687\n", @@ -1889,7 +1889,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.0052893925, -0.29743084, 0.716173...\n", + " [ -0.005289644, -0.29743072, 0.716173...\n", " \n", " \n", " 688\n", @@ -1903,7 +1903,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.5030238, 0.36253875, 0.731493...\n", + " [ -0.50302404, 0.36253828, 0.7314933...\n", " \n", " \n", "\n", @@ -1938,17 +1938,17 @@ "688 1 True O O \n", "\n", " token_class_id embedding \n", - "0 0 [ -0.08307116, -0.35959044, 1.015067... \n", - "1 0 [ -0.22862588, -0.49313605, 1.284232... \n", - "2 0 [ 0.028480446, -0.17874268, 1.54320... \n", - "3 0 [ -0.46517605, -0.29836014, 1.073768... \n", - "4 0 [ -0.10730826, -0.3372096, 1.226979... \n", + "0 0 [ -0.08307081, -0.35959032, 1.015068... \n", + "1 0 [ -0.22862603, -0.49313632, 1.28423... \n", + "2 0 [ 0.028480662, -0.17874284, 1.54320... \n", + "3 0 [ -0.4651753, -0.29836023, 1.073767... \n", + "4 0 [ -0.10730811, -0.33720982, 1.226979... \n", ".. ... ... \n", - "684 0 [ -0.12806588, -0.002324244, 0.6781316... \n", - "685 0 [ 0.30534068, -0.52625746, 0.8281702... \n", - "686 1 [ -0.04873929, -0.3379735, -0.0583514... \n", - "687 0 [ -0.0052893925, -0.29743084, 0.716173... \n", - "688 0 [ -0.5030238, 0.36253875, 0.731493... \n", + "684 0 [ -0.1280663, -0.0023243837, 0.678132... \n", + "685 0 [ 0.3053407, -0.52625775, 0.8281702... \n", + "686 1 [ -0.048738778, -0.33797324, -0.0583509... \n", + "687 0 [ -0.005289644, -0.29743072, 0.716173... \n", + "688 0 [ -0.50302404, 0.36253828, 0.7314933... \n", "\n", "[689 rows x 11 columns]" ] @@ -1982,7 +1982,7 @@ "metadata": {}, "outputs": [], "source": [ - "SHRINK_EMBEDDINGS = True\n", + "SHRINK_EMBEDDINGS = False\n", "PROJECTION_DIMS = 256\n", "RANDOM_SEED=42\n", "\n", @@ -2011,7 +2011,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f61f03eedf5b43cab24475ac398fa9a8", + "model_id": "733bd98d8a8f4959b5668020f1984a3c", "version_major": 2, "version_minor": 0 }, @@ -2032,7 +2032,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ecf920613fe421ba4b444c386bf3254", + "model_id": "2e47f857a39a44d7ac5a3f34f60494cb", "version_major": 2, "version_minor": 0 }, @@ -2053,7 +2053,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ba49f8191854746a0e61dfc552da322", + "model_id": "31f95cf00b394566a13997459a76db17", "version_major": 2, "version_minor": 0 }, @@ -2111,7 +2111,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.06799730722665887, 2.664292496984028...\n", + " [ -0.17669655, -0.3989963, 0.908887...\n", " \n", " \n", " 1\n", @@ -2125,7 +2125,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.7262477871614377, 2.600414199244437...\n", + " [ -0.3855382, -0.50232756, 1.173232...\n", " \n", " \n", " 2\n", @@ -2139,7 +2139,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.09688767345391286, 2.951251600481012...\n", + " [ -0.11718995, -0.12701154, 1.38969...\n", " \n", " \n", " 3\n", @@ -2153,7 +2153,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.15686700764492822, 2.585945891391126...\n", + " [ -0.39025685, -0.25043246, 1.074507...\n", " \n", " \n", " 4\n", @@ -2167,7 +2167,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.13613133440041497, 2.820193808843421...\n", + " [ -0.27732754, -0.26160136, 1.078761...\n", " \n", " \n", " ...\n", @@ -2195,7 +2195,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.643701220752026, 1.257602895023083...\n", + " [ 0.015393024, -0.040650737, 1.001185...\n", " \n", " \n", " 2155\n", @@ -2209,7 +2209,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.6270134925747186, 1.351350566308111...\n", + " [ 0.075038865, 0.014400693, 1.043231...\n", " \n", " \n", " 2156\n", @@ -2223,7 +2223,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.4468312387950375, 1.38293831378890...\n", + " [ -0.085796565, 0.05905571, 1.114640...\n", " \n", " \n", " 2157\n", @@ -2237,7 +2237,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.6746394773845812, 1.611593948841774...\n", + " [ 0.0113782445, -0.26387203, 0.881803...\n", " \n", " \n", " 2158\n", @@ -2251,7 +2251,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.7103215591248637, 1.323178591000971...\n", + " [ 0.48513305, 1.5709875, 0.592935...\n", " \n", " \n", "\n", @@ -2286,17 +2286,17 @@ "2158 True O O 0 \n", "\n", " embedding \n", - "0 [ -0.06799730722665887, 2.664292496984028... \n", - "1 [ -0.7262477871614377, 2.600414199244437... \n", - "2 [ -0.09688767345391286, 2.951251600481012... \n", - "3 [ -0.15686700764492822, 2.585945891391126... \n", - "4 [ -0.13613133440041497, 2.820193808843421... \n", + "0 [ -0.17669655, -0.3989963, 0.908887... \n", + "1 [ -0.3855382, -0.50232756, 1.173232... \n", + "2 [ -0.11718995, -0.12701154, 1.38969... \n", + "3 [ -0.39025685, -0.25043246, 1.074507... \n", + "4 [ -0.27732754, -0.26160136, 1.078761... \n", "... ... \n", - "2154 [ -1.643701220752026, 1.257602895023083... \n", - "2155 [ -1.6270134925747186, 1.351350566308111... \n", - "2156 [ -1.4468312387950375, 1.38293831378890... \n", - "2157 [ -1.6746394773845812, 1.611593948841774... \n", - "2158 [ -1.7103215591248637, 1.323178591000971... \n", + "2154 [ 0.015393024, -0.040650737, 1.001185... \n", + "2155 [ 0.075038865, 0.014400693, 1.043231... \n", + "2156 [ -0.085796565, 0.05905571, 1.114640... \n", + "2157 [ 0.0113782445, -0.26387203, 0.881803... \n", + "2158 [ 0.48513305, 1.5709875, 0.592935... \n", "\n", "[2159 rows x 11 columns]" ] @@ -2383,7 +2383,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.1311553691542877, 2.76648593421354...\n", + " [ -0.098505504, -0.4050192, 0.742888...\n", " \n", " \n", " 1\n", @@ -2399,7 +2399,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.2222068473266146, 2.527425640627292...\n", + " [ -0.057021566, -0.48112106, 0.989868...\n", " \n", " \n", " 2\n", @@ -2415,7 +2415,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.7579851055799667, 2.73181597486195...\n", + " [ -0.04824192, -0.2532998, 1.16719...\n", " \n", " \n", " 3\n", @@ -2431,7 +2431,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.6730784947110267, 2.38562714803554...\n", + " [ -0.26682985, -0.31008705, 1.00747...\n", " \n", " \n", " 4\n", @@ -2447,7 +2447,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.5528018738380444, 2.76605626434104...\n", + " [ -0.22296886, -0.21308525, 0.933102...\n", " \n", " \n", " ...\n", @@ -2479,7 +2479,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.7699805568359452, 1.740577378824614...\n", + " [ -0.02817309, -0.08062352, 0.9804888...\n", " \n", " \n", " 416537\n", @@ -2495,7 +2495,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.217042553207956, 1.188014284432918...\n", + " [ 0.118173525, -0.07008511, 0.865484...\n", " \n", " \n", " 416538\n", @@ -2511,7 +2511,7 @@ " PER\n", " B-PER\n", " 4\n", - " [ 0.17265078748216925, 2.21287031816488...\n", + " [ -0.35689434, 0.31400475, 1.573854...\n", " \n", " \n", " 416539\n", @@ -2527,7 +2527,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.022874581969901, 1.548629892512103...\n", + " [ -0.18957116, -0.2458116, 0.66257...\n", " \n", " \n", " 416540\n", @@ -2543,7 +2543,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.196537811154486, 2.14273333538158...\n", + " [ -0.4468915, -0.31665248, 0.779688...\n", " \n", " \n", "\n", @@ -2591,17 +2591,17 @@ "416540 O 0 \n", "\n", " embedding \n", - "0 [ -1.1311553691542877, 2.76648593421354... \n", - "1 [ -1.2222068473266146, 2.527425640627292... \n", - "2 [ -0.7579851055799667, 2.73181597486195... \n", - "3 [ -0.6730784947110267, 2.38562714803554... \n", - "4 [ -0.5528018738380444, 2.76605626434104... \n", + "0 [ -0.098505504, -0.4050192, 0.742888... \n", + "1 [ -0.057021566, -0.48112106, 0.989868... \n", + "2 [ -0.04824192, -0.2532998, 1.16719... \n", + "3 [ -0.26682985, -0.31008705, 1.00747... \n", + "4 [ -0.22296886, -0.21308525, 0.933102... \n", "... ... \n", - "416536 [ -1.7699805568359452, 1.740577378824614... \n", - "416537 [ -2.217042553207956, 1.188014284432918... \n", - "416538 [ 0.17265078748216925, 2.21287031816488... \n", - "416539 [ -2.022874581969901, 1.548629892512103... \n", - "416540 [ -2.196537811154486, 2.14273333538158... \n", + "416536 [ -0.02817309, -0.08062352, 0.9804888... \n", + "416537 [ 0.118173525, -0.07008511, 0.865484... \n", + "416538 [ -0.35689434, 0.31400475, 1.573854... \n", + "416539 [ -0.18957116, -0.2458116, 0.66257... \n", + "416540 [ -0.4468915, -0.31665248, 0.779688... \n", "\n", "[416541 rows x 13 columns]" ] @@ -2702,7 +2702,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.1311553691542877, 2.76648593421354...\n", + " [ -0.098505504, -0.4050192, 0.742888...\n", " \n", " \n", " 1\n", @@ -2717,7 +2717,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.2222068473266146, 2.527425640627292...\n", + " [ -0.057021566, -0.48112106, 0.989868...\n", " \n", " \n", " 2\n", @@ -2732,7 +2732,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.7579851055799667, 2.73181597486195...\n", + " [ -0.04824192, -0.2532998, 1.16719...\n", " \n", " \n", " 3\n", @@ -2747,7 +2747,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.6730784947110267, 2.38562714803554...\n", + " [ -0.26682985, -0.31008705, 1.00747...\n", " \n", " \n", " 4\n", @@ -2762,7 +2762,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.5528018738380444, 2.76605626434104...\n", + " [ -0.22296886, -0.21308525, 0.933102...\n", " \n", " \n", " ...\n", @@ -2792,7 +2792,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.7699805568359452, 1.740577378824614...\n", + " [ -0.02817309, -0.08062352, 0.9804888...\n", " \n", " \n", " 416537\n", @@ -2807,7 +2807,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.217042553207956, 1.188014284432918...\n", + " [ 0.118173525, -0.07008511, 0.865484...\n", " \n", " \n", " 416538\n", @@ -2822,7 +2822,7 @@ " PER\n", " B-PER\n", " 4\n", - " [ 0.17265078748216925, 2.21287031816488...\n", + " [ -0.35689434, 0.31400475, 1.573854...\n", " \n", " \n", " 416539\n", @@ -2837,7 +2837,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.022874581969901, 1.548629892512103...\n", + " [ -0.18957116, -0.2458116, 0.66257...\n", " \n", " \n", " 416540\n", @@ -2852,7 +2852,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -2.196537811154486, 2.14273333538158...\n", + " [ -0.4468915, -0.31665248, 0.779688...\n", " \n", " \n", "\n", @@ -2887,17 +2887,17 @@ "416540 True O O 0 \n", "\n", " embedding \n", - "0 [ -1.1311553691542877, 2.76648593421354... \n", - "1 [ -1.2222068473266146, 2.527425640627292... \n", - "2 [ -0.7579851055799667, 2.73181597486195... \n", - "3 [ -0.6730784947110267, 2.38562714803554... \n", - "4 [ -0.5528018738380444, 2.76605626434104... \n", + "0 [ -0.098505504, -0.4050192, 0.742888... \n", + "1 [ -0.057021566, -0.48112106, 0.989868... \n", + "2 [ -0.04824192, -0.2532998, 1.16719... \n", + "3 [ -0.26682985, -0.31008705, 1.00747... \n", + "4 [ -0.22296886, -0.21308525, 0.933102... \n", "... ... \n", - "416536 [ -1.7699805568359452, 1.740577378824614... \n", - "416537 [ -2.217042553207956, 1.188014284432918... \n", - "416538 [ 0.17265078748216925, 2.21287031816488... \n", - "416539 [ -2.022874581969901, 1.548629892512103... \n", - "416540 [ -2.196537811154486, 2.14273333538158... \n", + "416536 [ -0.02817309, -0.08062352, 0.9804888... \n", + "416537 [ 0.118173525, -0.07008511, 0.865484... \n", + "416538 [ -0.35689434, 0.31400475, 1.573854... \n", + "416539 [ -0.18957116, -0.2458116, 0.66257... \n", + "416540 [ -0.4468915, -0.31665248, 0.779688... \n", "\n", "[416541 rows x 12 columns]" ] @@ -2978,7 +2978,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.1311553691542877, 2.76648593421354...\n", + " [ -0.098505504, -0.4050192, 0.742888...\n", " \n", " \n", " 1\n", @@ -2993,7 +2993,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.2222068473266146, 2.527425640627292...\n", + " [ -0.057021566, -0.48112106, 0.989868...\n", " \n", " \n", " 2\n", @@ -3008,7 +3008,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.7579851055799667, 2.73181597486195...\n", + " [ -0.04824192, -0.2532998, 1.16719...\n", " \n", " \n", " 3\n", @@ -3023,7 +3023,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.6730784947110267, 2.38562714803554...\n", + " [ -0.26682985, -0.31008705, 1.00747...\n", " \n", " \n", " 4\n", @@ -3038,7 +3038,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.5528018738380444, 2.76605626434104...\n", + " [ -0.22296886, -0.21308525, 0.933102...\n", " \n", " \n", " ...\n", @@ -3068,7 +3068,7 @@ " ORG\n", " B-ORG\n", " 3\n", - " [ -1.3644899324386204, 0.1387769900935160...\n", + " [ 0.7556371, -0.91891253, -0.1403036...\n", " \n", " \n", " 281105\n", @@ -3083,7 +3083,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.4544672314078606, 1.4293731057006...\n", + " [ -0.11528473, -0.44492027, 0.4715562...\n", " \n", " \n", " 281106\n", @@ -3098,7 +3098,7 @@ " ORG\n", " B-ORG\n", " 3\n", - " [ -1.0318755443110903, 0.4064806114217064...\n", + " [ 0.45602208, -0.8970848, 0.0678616...\n", " \n", " \n", " 281107\n", @@ -3113,7 +3113,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.2597896004962865, 1.395942742925384...\n", + " [ -0.19713743, -0.5427194, 0.294020...\n", " \n", " \n", " 281108\n", @@ -3128,7 +3128,7 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.6741569815858808, 1.901864765138888...\n", + " [ -0.57650733, -0.42160645, 0.994703...\n", " \n", " \n", "\n", @@ -3163,17 +3163,17 @@ "281108 True O O 0 \n", "\n", " embedding \n", - "0 [ -1.1311553691542877, 2.76648593421354... \n", - "1 [ -1.2222068473266146, 2.527425640627292... \n", - "2 [ -0.7579851055799667, 2.73181597486195... \n", - "3 [ -0.6730784947110267, 2.38562714803554... \n", - "4 [ -0.5528018738380444, 2.76605626434104... \n", + "0 [ -0.098505504, -0.4050192, 0.742888... \n", + "1 [ -0.057021566, -0.48112106, 0.989868... \n", + "2 [ -0.04824192, -0.2532998, 1.16719... \n", + "3 [ -0.26682985, -0.31008705, 1.00747... \n", + "4 [ -0.22296886, -0.21308525, 0.933102... \n", "... ... \n", - "281104 [ -1.3644899324386204, 0.1387769900935160... \n", - "281105 [ -1.4544672314078606, 1.4293731057006... \n", - "281106 [ -1.0318755443110903, 0.4064806114217064... \n", - "281107 [ -1.2597896004962865, 1.395942742925384... \n", - "281108 [ -1.6741569815858808, 1.901864765138888... \n", + "281104 [ 0.7556371, -0.91891253, -0.1403036... \n", + "281105 [ -0.11528473, -0.44492027, 0.4715562... \n", + "281106 [ 0.45602208, -0.8970848, 0.0678616... \n", + "281107 [ -0.19713743, -0.5427194, 0.294020... \n", + "281108 [ -0.57650733, -0.42160645, 0.994703... \n", "\n", "[281109 rows x 12 columns]" ] @@ -3195,29 +3195,127 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n" + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 6921 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 6.17660D+05 |proj g|= 4.23293D+05\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "CPU times: user 46min 3s, sys: 4min 23s, total: 50min 27s\n", - "Wall time: 6min 22s\n" + " This problem is unconstrained.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 6.4min finished\n" + "\n", + "At iterate 50 f= 1.22005D+04 |proj g|= 2.48275D+02\n", + "\n", + "At iterate 100 f= 8.87639D+03 |proj g|= 1.72205D+02\n", + "\n", + "At iterate 150 f= 8.07946D+03 |proj g|= 1.28633D+02\n", + "\n", + "At iterate 200 f= 7.87840D+03 |proj g|= 6.20068D+01\n", + "\n", + "At iterate 250 f= 7.81730D+03 |proj g|= 9.11741D+00\n", + "\n", + "At iterate 300 f= 7.80144D+03 |proj g|= 6.86435D+00\n", + "\n", + "At iterate 350 f= 7.79623D+03 |proj g|= 7.21843D+00\n", + "\n", + "At iterate 400 f= 7.79451D+03 |proj g|= 5.64213D+00\n", + "\n", + "At iterate 450 f= 7.79356D+03 |proj g|= 2.47884D+00\n", + "\n", + "At iterate 500 f= 7.79273D+03 |proj g|= 2.32130D+00\n", + "\n", + "At iterate 550 f= 7.79141D+03 |proj g|= 1.03513D+01\n", + "\n", + "At iterate 600 f= 7.78944D+03 |proj g|= 4.39763D+00\n", + "\n", + "At iterate 650 f= 7.78798D+03 |proj g|= 2.72198D+00\n", + "\n", + "At iterate 700 f= 7.78721D+03 |proj g|= 2.49312D+00\n", + "\n", + "At iterate 750 f= 7.78691D+03 |proj g|= 2.09049D+00\n", + "\n", + "At iterate 800 f= 7.78678D+03 |proj g|= 1.56225D+00\n", + "\n", + "At iterate 850 f= 7.78669D+03 |proj g|= 9.61272D-01\n", + "\n", + "At iterate 900 f= 7.78660D+03 |proj g|= 1.88970D+00\n", + "\n", + "At iterate 950 f= 7.78644D+03 |proj g|= 1.39468D+00\n", + "\n", + "At iterate 1000 f= 7.78615D+03 |proj g|= 1.56165D+00\n", + "\n", + "At iterate 1050 f= 7.78593D+03 |proj g|= 1.81700D+00\n", + "\n", + "At iterate 1100 f= 7.78581D+03 |proj g|= 1.11273D+00\n", + "\n", + "At iterate 1150 f= 7.78577D+03 |proj g|= 4.10524D-01\n", + "\n", + "At iterate 1200 f= 7.78575D+03 |proj g|= 3.49336D-01\n", + "\n", + "At iterate 1250 f= 7.78574D+03 |proj g|= 8.20185D-01\n", + "\n", + "At iterate 1300 f= 7.78571D+03 |proj g|= 9.94495D-01\n", + "\n", + "At iterate 1350 f= 7.78567D+03 |proj g|= 7.14421D-01\n", + "\n", + "At iterate 1400 f= 7.78563D+03 |proj g|= 3.46513D-01\n", + "\n", + "At iterate 1450 f= 7.78561D+03 |proj g|= 1.15784D+00\n", + "\n", + "At iterate 1500 f= 7.78559D+03 |proj g|= 5.66811D-01\n", + "\n", + "At iterate 1550 f= 7.78559D+03 |proj g|= 1.43156D-01\n", + "\n", + "At iterate 1600 f= 7.78558D+03 |proj g|= 1.60595D-01\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + " 6921 1604 1694 1 0 0 4.829D-01 7.786D+03\n", + " F = 7785.5829997825367 \n", + "\n", + "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH \n", + "CPU times: user 1h 34min 15s, sys: 6min 41s, total: 1h 40min 56s\n", + "Wall time: 12min 44s\n" ] }, { "data": { + "text/html": [ + "
Pipeline(steps=[('mlogreg',\n",
+       "                 LogisticRegression(C=0.1, max_iter=10000,\n",
+       "                                    multi_class='multinomial', verbose=1))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "Pipeline(steps=[('mlogreg',\n", " LogisticRegression(C=0.1, max_iter=10000,\n", @@ -3289,7 +3387,8 @@ " and `predicted_class_pr`.\n", " \"\"\"\n", " result_df = df.copy()\n", - " class_pr = tp.TensorArray(predictor.predict_proba(result_df[\"embedding\"]))\n", + " embeddings = result_df[\"embedding\"].to_numpy()\n", + " class_pr = tp.TensorArray(predictor.predict_proba(embeddings))\n", " result_df[\"predicted_id\"] = np.argmax(class_pr, axis=1)\n", " result_df[\"predicted_class\"] = [id_to_class[i]\n", " for i in result_df[\"predicted_id\"].values]\n", @@ -3359,12 +3458,12 @@ " <NA>\n", " O\n", " 0\n", - " [ 0.07419002371155237, 2.81491930509171...\n", + " [ -0.19626583, -0.450937, 0.6775361...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9997307514975134, 5.294607015948672e-0...\n", + " [ 0.9994774788863705, 1.9985127298723906e-0...\n", " \n", " \n", " 351002\n", @@ -3379,12 +3478,12 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.7553124891222318, 2.712434591871051...\n", + " [ -0.3187211, -0.5074784, 1.046454...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9980035154999108, 1.533050022629027e-0...\n", + " [ 0.9992964240340214, 3.7581023374440964e-0...\n", " \n", " \n", " 351003\n", @@ -3399,12 +3498,12 @@ " <NA>\n", " O\n", " 0\n", - " [ 0.11465290957193339, 3.11397875179331...\n", + " [ -0.080538824, -0.2477481, 1.356255...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9969301297651303, 0.000670705720761996...\n", + " [ 0.998973288221842, 0.0004299715907382311...\n", " \n", " \n", " 351004\n", @@ -3419,12 +3518,12 @@ " <NA>\n", " O\n", " 0\n", - " [ -0.14387838512527962, 2.9257680850885...\n", + " [ -0.6878579, -0.30290246, 0.8842714...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9990384089044105, 8.475109949412816e-0...\n", + " [ 0.9983217119367633, 4.888114850946988e-0...\n", " \n", " \n", " 351005\n", @@ -3439,12 +3538,12 @@ " <NA>\n", " O\n", " 0\n", - " [ 0.08375985078305932, 3.067161861783276...\n", + " [ -0.2963228, -0.23313177, 0.93988...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9996995206821001, 6.044135027078061e-0...\n", + " [ 0.9999185106741023, 8.938753477308423e-0...\n", " \n", " \n", "\n", @@ -3466,11 +3565,11 @@ "351005 False O O 0 \n", "\n", " embedding predicted_id \\\n", - "351001 [ 0.07419002371155237, 2.81491930509171... 0 \n", - "351002 [ -0.7553124891222318, 2.712434591871051... 0 \n", - "351003 [ 0.11465290957193339, 3.11397875179331... 0 \n", - "351004 [ -0.14387838512527962, 2.9257680850885... 0 \n", - "351005 [ 0.08375985078305932, 3.067161861783276... 0 \n", + "351001 [ -0.19626583, -0.450937, 0.6775361... 0 \n", + "351002 [ -0.3187211, -0.5074784, 1.046454... 0 \n", + "351003 [ -0.080538824, -0.2477481, 1.356255... 0 \n", + "351004 [ -0.6878579, -0.30290246, 0.8842714... 0 \n", + "351005 [ -0.2963228, -0.23313177, 0.93988... 0 \n", "\n", " predicted_class predicted_iob predicted_type \\\n", "351001 O O None \n", @@ -3480,11 +3579,11 @@ "351005 O O None \n", "\n", " predicted_class_pr \n", - "351001 [ 0.9997307514975134, 5.294607015948672e-0... \n", - "351002 [ 0.9980035154999108, 1.533050022629027e-0... \n", - "351003 [ 0.9969301297651303, 0.000670705720761996... \n", - "351004 [ 0.9990384089044105, 8.475109949412816e-0... \n", - "351005 [ 0.9996995206821001, 6.044135027078061e-0... " + "351001 [ 0.9994774788863705, 1.9985127298723906e-0... \n", + "351002 [ 0.9992964240340214, 3.7581023374440964e-0... \n", + "351003 [ 0.998973288221842, 0.0004299715907382311... \n", + "351004 [ 0.9983217119367633, 4.888114850946988e-0... \n", + "351005 [ 0.9999185106741023, 8.938753477308423e-0... " ] }, "execution_count": 25, @@ -3557,12 +3656,12 @@ " PER\n", " I-PER\n", " 8\n", - " [ 0.06028430363940268, 2.833449942439...\n", - " 5\n", - " I-LOC\n", + " [ -0.21029201, -0.8535674, 0.0002756594...\n", + " 6\n", + " I-MISC\n", " I\n", - " LOC\n", - " [ 0.05335241986368567, 0.01558548709678581...\n", + " MISC\n", + " [ 0.0010111308810159478, 1.6209660863726316e-0...\n", " \n", " \n", " 351042\n", @@ -3577,12 +3676,12 @@ " PER\n", " I-PER\n", " 8\n", - " [ 0.011815326065059528, 2.4804891126405...\n", - " 5\n", - " I-LOC\n", + " [ -0.23205486, -0.9290767, 0.3889118...\n", + " 6\n", + " I-MISC\n", " I\n", - " LOC\n", - " [ 0.26071159739023836, 0.0810894424222212...\n", + " MISC\n", + " [ 0.012755027203264928, 0.00554094580945546...\n", " \n", " \n", " 351043\n", @@ -3597,12 +3696,12 @@ " PER\n", " I-PER\n", " 8\n", - " [ 0.1896747233694964, 2.0841390182245...\n", + " [ 0.36844134, -0.68091154, -0.1059106...\n", " 5\n", " I-LOC\n", " I\n", " LOC\n", - " [ 0.0008087046569282995, 0.01409121178547858...\n", + " [ 0.008349822538261149, 0.180904633782168...\n", " \n", " \n", " 351044\n", @@ -3617,12 +3716,12 @@ " PER\n", " I-PER\n", " 8\n", - " [ -0.08919079934068028, 2.673042893674...\n", - " 5\n", - " I-LOC\n", + " [ -0.30131084, -0.6546019, -0.1726912...\n", + " 8\n", + " I-PER\n", " I\n", - " LOC\n", - " [ 0.01641422864584388, 0.01922057245520043...\n", + " PER\n", + " [ 0.013398092974719904, 0.000889872066127380...\n", " \n", " \n", " 351045\n", @@ -3637,12 +3736,12 @@ " PER\n", " I-PER\n", " 8\n", - " [ -0.5675588015558329, 2.1915603140880...\n", + " [ -0.1611614, -0.69891113, 0.2342468...\n", " 5\n", " I-LOC\n", " I\n", " LOC\n", - " [ 0.06287713949432004, 0.05853431405140322...\n", + " [ 0.014927046511081343, 0.0209250472885050...\n", " \n", " \n", " 351046\n", @@ -3657,12 +3756,12 @@ " LOC\n", " B-LOC\n", " 1\n", - " [ -0.025756110031628202, 2.4176568055402...\n", + " [ -0.058567554, -0.79558676, 0.3360603...\n", " 1\n", " B-LOC\n", " B\n", " LOC\n", - " [ 0.002164163302129627, 0.533655982403914...\n", + " [ 0.027281135850703336, 0.532249166723370...\n", " \n", " \n", " 351047\n", @@ -3677,12 +3776,12 @@ " LOC\n", " I-LOC\n", " 5\n", - " [ -0.8143908150954474, 2.2432229840625...\n", + " [ 0.2037595, -0.73730904, -0.0888521...\n", " 5\n", " I-LOC\n", " I\n", " LOC\n", - " [ 0.40170332018342714, 0.01464842540432879...\n", + " [ 0.22512840995098554, 0.00379439656874946...\n", " \n", " \n", " 351048\n", @@ -3697,12 +3796,12 @@ " LOC\n", " I-LOC\n", " 5\n", - " [ -0.7613811626814251, 2.1040792203968...\n", + " [ -0.10341229, -0.33681834, 0.1738456...\n", " 5\n", " I-LOC\n", " I\n", " LOC\n", - " [ 0.04547783920785417, 0.370807027150105...\n", + " [ 0.04472568023866835, 0.436126151622446...\n", " \n", " \n", " 351049\n", @@ -3717,12 +3816,12 @@ " LOC\n", " I-LOC\n", " 5\n", - " [ -0.5023455357742641, 2.467216928215...\n", + " [ -0.4054268, -0.6516522, 0.2469...\n", " 5\n", " I-LOC\n", " I\n", " LOC\n", - " [ 0.0014782178334539389, 0.01311886606422394...\n", + " [ 0.0009405393288526446, 0.00244544190700176...\n", " \n", " \n", " 351050\n", @@ -3737,12 +3836,12 @@ " <NA>\n", " O\n", " 0\n", - " [ -1.0898376005782766, 2.4839734026886...\n", + " [ -0.16829254, -0.6475861, 0.8149025...\n", " 0\n", " O\n", " O\n", " None\n", - " [ 0.9997009893806189, 3.928979951597114e-0...\n", + " [ 0.9999736550716568, 5.7005018158771435e-0...\n", " \n", " \n", "\n", @@ -3774,22 +3873,22 @@ "351050 False O O 0 \n", "\n", " embedding predicted_id \\\n", - "351041 [ 0.06028430363940268, 2.833449942439... 5 \n", - "351042 [ 0.011815326065059528, 2.4804891126405... 5 \n", - "351043 [ 0.1896747233694964, 2.0841390182245... 5 \n", - "351044 [ -0.08919079934068028, 2.673042893674... 5 \n", - "351045 [ -0.5675588015558329, 2.1915603140880... 5 \n", - "351046 [ -0.025756110031628202, 2.4176568055402... 1 \n", - "351047 [ -0.8143908150954474, 2.2432229840625... 5 \n", - "351048 [ -0.7613811626814251, 2.1040792203968... 5 \n", - "351049 [ -0.5023455357742641, 2.467216928215... 5 \n", - "351050 [ -1.0898376005782766, 2.4839734026886... 0 \n", + "351041 [ -0.21029201, -0.8535674, 0.0002756594... 6 \n", + "351042 [ -0.23205486, -0.9290767, 0.3889118... 6 \n", + "351043 [ 0.36844134, -0.68091154, -0.1059106... 5 \n", + "351044 [ -0.30131084, -0.6546019, -0.1726912... 8 \n", + "351045 [ -0.1611614, -0.69891113, 0.2342468... 5 \n", + "351046 [ -0.058567554, -0.79558676, 0.3360603... 1 \n", + "351047 [ 0.2037595, -0.73730904, -0.0888521... 5 \n", + "351048 [ -0.10341229, -0.33681834, 0.1738456... 5 \n", + "351049 [ -0.4054268, -0.6516522, 0.2469... 5 \n", + "351050 [ -0.16829254, -0.6475861, 0.8149025... 0 \n", "\n", " predicted_class predicted_iob predicted_type \\\n", - "351041 I-LOC I LOC \n", - "351042 I-LOC I LOC \n", + "351041 I-MISC I MISC \n", + "351042 I-MISC I MISC \n", "351043 I-LOC I LOC \n", - "351044 I-LOC I LOC \n", + "351044 I-PER I PER \n", "351045 I-LOC I LOC \n", "351046 B-LOC B LOC \n", "351047 I-LOC I LOC \n", @@ -3798,16 +3897,16 @@ "351050 O O None \n", "\n", " predicted_class_pr \n", - "351041 [ 0.05335241986368567, 0.01558548709678581... \n", - "351042 [ 0.26071159739023836, 0.0810894424222212... \n", - "351043 [ 0.0008087046569282995, 0.01409121178547858... \n", - "351044 [ 0.01641422864584388, 0.01922057245520043... \n", - "351045 [ 0.06287713949432004, 0.05853431405140322... \n", - "351046 [ 0.002164163302129627, 0.533655982403914... \n", - "351047 [ 0.40170332018342714, 0.01464842540432879... \n", - "351048 [ 0.04547783920785417, 0.370807027150105... \n", - "351049 [ 0.0014782178334539389, 0.01311886606422394... \n", - "351050 [ 0.9997009893806189, 3.928979951597114e-0... " + "351041 [ 0.0010111308810159478, 1.6209660863726316e-0... \n", + "351042 [ 0.012755027203264928, 0.00554094580945546... \n", + "351043 [ 0.008349822538261149, 0.180904633782168... \n", + "351044 [ 0.013398092974719904, 0.000889872066127380... \n", + "351045 [ 0.014927046511081343, 0.0209250472885050... \n", + "351046 [ 0.027281135850703336, 0.532249166723370... \n", + "351047 [ 0.22512840995098554, 0.00379439656874946... \n", + "351048 [ 0.04472568023866835, 0.436126151622446... \n", + "351049 [ 0.0009405393288526446, 0.00244544190700176... \n", + "351050 [ 0.9999736550716568, 5.7005018158771435e-0... " ] }, "execution_count": 26, @@ -3877,7 +3976,7 @@ " I\n", " PER\n", " I\n", - " LOC\n", + " MISC\n", " \n", " \n", " 41\n", @@ -3886,7 +3985,7 @@ " I\n", " PER\n", " I\n", - " LOC\n", + " MISC\n", " \n", " \n", " 42\n", @@ -3904,7 +4003,7 @@ " I\n", " PER\n", " I\n", - " LOC\n", + " PER\n", " \n", " \n", " 44\n", @@ -4078,10 +4177,10 @@ "59 59 [124, 129): 'began' O O \n", "\n", " predicted_type \n", - "40 LOC \n", - "41 LOC \n", + "40 MISC \n", + "41 MISC \n", "42 LOC \n", - "43 LOC \n", + "43 PER \n", "44 LOC \n", "45 LOC \n", "46 LOC \n", @@ -4178,12 +4277,12 @@ " \n", " 2\n", " [40, 45): 'CHINA'\n", - " LOC\n", + " ORG\n", " \n", " \n", " 3\n", " [66, 77): 'Nadim Ladki'\n", - " PER\n", + " LOC\n", " \n", " \n", " 4\n", @@ -4198,8 +4297,8 @@ " span ent_type\n", "0 [19, 24): 'JAPAN' PER\n", "1 [29, 34): 'LUCKY' LOC\n", - "2 [40, 45): 'CHINA' LOC\n", - "3 [66, 77): 'Nadim Ladki' PER\n", + "2 [40, 45): 'CHINA' ORG\n", + "3 [66, 77): 'Nadim Ladki' LOC\n", "4 [78, 84): 'AL-AIN' LOC" ] }, @@ -4261,12 +4360,12 @@ " 0\n", " test\n", " 0\n", - " 42\n", - " 46\n", + " 41\n", + " 47\n", " 45\n", - " 0.913043\n", - " 0.933333\n", - " 0.923077\n", + " 0.872340\n", + " 0.911111\n", + " 0.891304\n", " \n", " \n", " 1\n", @@ -4294,23 +4393,23 @@ " 3\n", " test\n", " 3\n", - " 41\n", - " 45\n", + " 42\n", " 44\n", - " 0.911111\n", - " 0.931818\n", - " 0.921348\n", + " 44\n", + " 0.954545\n", + " 0.954545\n", + " 0.954545\n", " \n", " \n", " 4\n", " test\n", " 4\n", - " 17\n", + " 18\n", " 19\n", " 19\n", - " 0.894737\n", - " 0.894737\n", - " 0.894737\n", + " 0.947368\n", + " 0.947368\n", + " 0.947368\n", " \n", " \n", " ...\n", @@ -4327,12 +4426,12 @@ " 226\n", " test\n", " 226\n", + " 6\n", " 7\n", " 7\n", - " 7\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", + " 0.857143\n", + " 0.857143\n", + " 0.857143\n", " \n", " \n", " 227\n", @@ -4350,33 +4449,33 @@ " test\n", " 228\n", " 24\n", - " 25\n", + " 28\n", " 27\n", - " 0.960000\n", + " 0.857143\n", " 0.888889\n", - " 0.923077\n", + " 0.872727\n", " \n", " \n", " 229\n", " test\n", " 229\n", - " 26\n", + " 25\n", " 27\n", " 27\n", - " 0.962963\n", - " 0.962963\n", - " 0.962963\n", + " 0.925926\n", + " 0.925926\n", + " 0.925926\n", " \n", " \n", " 230\n", " test\n", " 230\n", - " 24\n", + " 25\n", " 27\n", " 28\n", - " 0.888889\n", - " 0.857143\n", - " 0.872727\n", + " 0.925926\n", + " 0.892857\n", + " 0.909091\n", " \n", " \n", "\n", @@ -4385,30 +4484,30 @@ ], "text/plain": [ " fold doc_num num_true_positives num_extracted num_entities \\\n", - "0 test 0 42 46 45 \n", + "0 test 0 41 47 45 \n", "1 test 1 41 42 44 \n", "2 test 2 52 54 54 \n", - "3 test 3 41 45 44 \n", - "4 test 4 17 19 19 \n", + "3 test 3 42 44 44 \n", + "4 test 4 18 19 19 \n", ".. ... ... ... ... ... \n", - "226 test 226 7 7 7 \n", + "226 test 226 6 7 7 \n", "227 test 227 18 19 21 \n", - "228 test 228 24 25 27 \n", - "229 test 229 26 27 27 \n", - "230 test 230 24 27 28 \n", + "228 test 228 24 28 27 \n", + "229 test 229 25 27 27 \n", + "230 test 230 25 27 28 \n", "\n", " precision recall F1 \n", - "0 0.913043 0.933333 0.923077 \n", + "0 0.872340 0.911111 0.891304 \n", "1 0.976190 0.931818 0.953488 \n", "2 0.962963 0.962963 0.962963 \n", - "3 0.911111 0.931818 0.921348 \n", - "4 0.894737 0.894737 0.894737 \n", + "3 0.954545 0.954545 0.954545 \n", + "4 0.947368 0.947368 0.947368 \n", ".. ... ... ... \n", - "226 1.000000 1.000000 1.000000 \n", + "226 0.857143 0.857143 0.857143 \n", "227 0.947368 0.857143 0.900000 \n", - "228 0.960000 0.888889 0.923077 \n", - "229 0.962963 0.962963 0.962963 \n", - "230 0.888889 0.857143 0.872727 \n", + "228 0.857143 0.888889 0.872727 \n", + "229 0.925926 0.925926 0.925926 \n", + "230 0.925926 0.892857 0.909091 \n", "\n", "[231 rows x 8 columns]" ] @@ -4432,12 +4531,12 @@ { "data": { "text/plain": [ - "{'num_true_positives': 4749,\n", + "{'num_true_positives': 4881,\n", " 'num_entities': 5648,\n", - " 'num_extracted': 5591,\n", - " 'precision': 0.8494008227508496,\n", - " 'recall': 0.8408286118980169,\n", - " 'F1': 0.8450929798024736}" + " 'num_extracted': 5620,\n", + " 'precision': 0.8685053380782918,\n", + " 'recall': 0.8641997167138811,\n", + " 'F1': 0.8663471778487754}" ] }, "execution_count": 30, @@ -5074,7 +5173,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e808850bda33440b80da729bcd7dbbb9", + "model_id": "68bcf2a129584acf9b7ce27ccd66302a", "version_major": 2, "version_minor": 0 }, @@ -5235,12 +5334,12 @@ " 0\n", " test\n", " 0\n", - " 43\n", - " 46\n", + " 42\n", + " 47\n", " 45\n", - " 0.934783\n", - " 0.955556\n", - " 0.945055\n", + " 0.893617\n", + " 0.933333\n", + " 0.913043\n", " \n", " \n", " 1\n", @@ -5279,12 +5378,12 @@ " 4\n", " test\n", " 4\n", - " 17\n", + " 18\n", " 19\n", " 19\n", - " 0.894737\n", - " 0.894737\n", - " 0.894737\n", + " 0.947368\n", + " 0.947368\n", + " 0.947368\n", " \n", " \n", " ...\n", @@ -5324,11 +5423,11 @@ " test\n", " 228\n", " 24\n", - " 25\n", " 27\n", - " 0.960000\n", + " 27\n", + " 0.888889\n", + " 0.888889\n", " 0.888889\n", - " 0.923077\n", " \n", " \n", " 229\n", @@ -5345,12 +5444,12 @@ " 230\n", " test\n", " 230\n", - " 25\n", + " 26\n", " 27\n", " 28\n", - " 0.925926\n", - " 0.892857\n", - " 0.909091\n", + " 0.962963\n", + " 0.928571\n", + " 0.945455\n", " \n", " \n", "\n", @@ -5359,30 +5458,30 @@ ], "text/plain": [ " fold doc_num num_true_positives num_extracted num_entities \\\n", - "0 test 0 43 46 45 \n", + "0 test 0 42 47 45 \n", "1 test 1 41 42 44 \n", "2 test 2 52 54 54 \n", "3 test 3 42 44 44 \n", - "4 test 4 17 19 19 \n", + "4 test 4 18 19 19 \n", ".. ... ... ... ... ... \n", "226 test 226 7 7 7 \n", "227 test 227 18 19 21 \n", - "228 test 228 24 25 27 \n", + "228 test 228 24 27 27 \n", "229 test 229 26 27 27 \n", - "230 test 230 25 27 28 \n", + "230 test 230 26 27 28 \n", "\n", " precision recall F1 \n", - "0 0.934783 0.955556 0.945055 \n", + "0 0.893617 0.933333 0.913043 \n", "1 0.976190 0.931818 0.953488 \n", "2 0.962963 0.962963 0.962963 \n", "3 0.954545 0.954545 0.954545 \n", - "4 0.894737 0.894737 0.894737 \n", + "4 0.947368 0.947368 0.947368 \n", ".. ... ... ... \n", "226 1.000000 1.000000 1.000000 \n", "227 0.947368 0.857143 0.900000 \n", - "228 0.960000 0.888889 0.923077 \n", + "228 0.888889 0.888889 0.888889 \n", "229 0.962963 0.962963 0.962963 \n", - "230 0.925926 0.892857 0.909091 \n", + "230 0.962963 0.928571 0.945455 \n", "\n", "[231 rows x 8 columns]" ] @@ -5406,12 +5505,12 @@ { "data": { "text/plain": [ - "{'num_true_positives': 4893,\n", + "{'num_true_positives': 4971,\n", " 'num_entities': 5648,\n", - " 'num_extracted': 5520,\n", - " 'precision': 0.8864130434782609,\n", - " 'recall': 0.8663243626062322,\n", - " 'F1': 0.8762535816618912}" + " 'num_extracted': 5587,\n", + " 'precision': 0.889744048684446,\n", + " 'recall': 0.8801345609065155,\n", + " 'F1': 0.8849132176234981}" ] }, "execution_count": 38, @@ -5449,7 +5548,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/notebooks/Read_conllu_Files.ipynb b/notebooks/Read_conllu_Files.ipynb index b977df8d..248e745a 100644 --- a/notebooks/Read_conllu_Files.ipynb +++ b/notebooks/Read_conllu_Files.ipynb @@ -30,8 +30,6 @@ "import sys\n", "import numpy as np\n", "import pandas as pd\n", - "import json\n", - "import feather\n", "import sklearn.pipeline\n", "import sklearn.linear_model\n", "import transformers\n", @@ -47,8 +45,7 @@ " raise e\n", " if \"..\" not in sys.path:\n", " sys.path.insert(0, \"..\")\n", - " import text_extensions_for_pandas as tp\n", - " " + " import text_extensions_for_pandas as tp\n" ] }, { @@ -666,7 +663,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "size is 25151\n" + "size is 25152\n" ] }, { @@ -747,48 +744,48 @@ " ...\n", " \n", " \n", - " 25146\n", + " 25147\n", " [251, 254): 'and'\n", " and\n", " CCONJ\n", - " 25150\n", + " 25151\n", " cc\n", " \n", " \n", - " 25147\n", + " 25148\n", " [255, 256): 'a'\n", " a\n", " DET\n", - " 25150\n", + " 25151\n", " det\n", " \n", " \n", - " 25148\n", + " 25149\n", " [257, 261): 'very'\n", " very\n", " ADV\n", - " 25149\n", + " 25150\n", " advmod\n", " \n", " \n", - " 25149\n", + " 25150\n", " [262, 275): 'knowledgeable'\n", " knowledgeable\n", " ADJ\n", - " 25150\n", + " 25151\n", " amod\n", " \n", " \n", - " 25150\n", + " 25151\n", " [276, 281): 'staff'\n", " staff\n", " NOUN\n", - " 25145\n", + " 25146\n", " conj\n", " \n", " \n", "\n", - "

25151 rows × 5 columns

\n", + "

25152 rows × 5 columns

\n", "" ], "text/plain": [ @@ -799,13 +796,13 @@ "3 [12, 17): 'comes' come VERB root\n", "4 [18, 22): 'this' this DET 5 det\n", "... ... ... ... ... ...\n", - "25146 [251, 254): 'and' and CCONJ 25150 cc\n", - "25147 [255, 256): 'a' a DET 25150 det\n", - "25148 [257, 261): 'very' very ADV 25149 advmod\n", - "25149 [262, 275): 'knowledgeable' knowledgeable ADJ 25150 amod\n", - "25150 [276, 281): 'staff' staff NOUN 25145 conj\n", + "25147 [251, 254): 'and' and CCONJ 25151 cc\n", + "25148 [255, 256): 'a' a DET 25151 det\n", + "25149 [257, 261): 'very' very ADV 25150 advmod\n", + "25150 [262, 275): 'knowledgeable' knowledgeable ADJ 25151 amod\n", + "25151 [276, 281): 'staff' staff NOUN 25146 conj\n", "\n", - "[25151 rows x 5 columns]" + "[25152 rows x 5 columns]" ] }, "execution_count": 5, @@ -814,7 +811,8 @@ } ], "source": [ - "# because we are concatenating our dataframes, we need to modify the \"head\" feilds to still point at their desired targets \n", + "# Because we are concatenating our dataframes, we need to modify the \"head\" \n", + "# fields to still point at their desired targets \n", "df_starts_at =0\n", "temp = conll_u_docs.copy()\n", "for df in temp:\n", @@ -822,8 +820,7 @@ " df_starts_at += df.shape[0]\n", "\n", "# Now concatenate all our documents into one big dataframe\n", - "complete_df = temp[0]\n", - "complete_df = complete_df.append(temp[1:], ignore_index=True)\n", + "complete_df = pd.concat(temp, ignore_index=True)\n", "\n", "#show the last few rows of the dataframe, select just a few columns for compactness\n", "print(f\"size is {complete_df.shape[0]}\")\n", @@ -842,7 +839,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1e+03 ns, sys: 0 ns, total: 1e+03 ns\n", + "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n", "Wall time: 4.05 µs\n", "File written to CoNLL_u_test_inputs/conllu_database.feather\n" ] @@ -877,9 +874,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1 µs, sys: 1e+03 ns, total: 2 µs\n", - "Wall time: 4.77 µs\n", - "size is 25151\n" + "CPU times: user 2 µs, sys: 1e+03 ns, total: 3 µs\n", + "Wall time: 6.91 µs\n", + "size is 25152\n" ] }, { @@ -912,43 +909,43 @@ " \n", " \n", " \n", - " 25146\n", + " 25147\n", " [251, 254): 'and'\n", " and\n", " CCONJ\n", - " 25150.0\n", + " 25151.0\n", " cc\n", " \n", " \n", - " 25147\n", + " 25148\n", " [255, 256): 'a'\n", " a\n", " DET\n", - " 25150.0\n", + " 25151.0\n", " det\n", " \n", " \n", - " 25148\n", + " 25149\n", " [257, 261): 'very'\n", " very\n", " ADV\n", - " 25149.0\n", + " 25150.0\n", " advmod\n", " \n", " \n", - " 25149\n", + " 25150\n", " [262, 275): 'knowledgeable'\n", " knowledgeable\n", " ADJ\n", - " 25150.0\n", + " 25151.0\n", " amod\n", " \n", " \n", - " 25150\n", + " 25151\n", " [276, 281): 'staff'\n", " staff\n", " NOUN\n", - " 25145.0\n", + " 25146.0\n", " conj\n", " \n", " \n", @@ -957,11 +954,11 @@ ], "text/plain": [ " span lemma upostag head deprel\n", - "25146 [251, 254): 'and' and CCONJ 25150.0 cc\n", - "25147 [255, 256): 'a' a DET 25150.0 det\n", - "25148 [257, 261): 'very' very ADV 25149.0 advmod\n", - "25149 [262, 275): 'knowledgeable' knowledgeable ADJ 25150.0 amod\n", - "25150 [276, 281): 'staff' staff NOUN 25145.0 conj" + "25147 [251, 254): 'and' and CCONJ 25151.0 cc\n", + "25148 [255, 256): 'a' a DET 25151.0 det\n", + "25149 [257, 261): 'very' very ADV 25150.0 advmod\n", + "25150 [262, 275): 'knowledgeable' knowledgeable ADJ 25151.0 amod\n", + "25151 [276, 281): 'staff' staff NOUN 25146.0 conj" ] }, "execution_count": 7, @@ -1156,7 +1153,7 @@ { "data": { "text/html": [ - "\n", + "\n", "\n", " And\n", " CCONJ\n", @@ -1203,65 +1200,65 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", - " cc\n", + " cc\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " obj\n", + " obj\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " aux\n", + " aux\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " nsubj\n", + " nsubj\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " case\n", + " case\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " det\n", + " det\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " obl\n", + " obl\n", " \n", " \n", "\n", "\n", "\n", - " \n", + " \n", " \n", - " punct\n", + " punct\n", " \n", " \n", "\n", @@ -1400,7 +1397,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eef3add20ee4a2daf2fddebf1117ea5", + "model_id": "b5bfda1ee61d424f8a08c4456084d9c4", "version_major": 2, "version_minor": 0 }, @@ -1428,7 +1425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e4dcd12fdb6412095389c26ce08aa7d", + "model_id": "3e67e5add4354708b726f4d5bb1de9e8", "version_major": 2, "version_minor": 0 }, @@ -1449,7 +1446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c3b6b3ada3d24c04aced9f684c878328", + "model_id": "ebdb788b384443348ee614a1e0fdcf72", "version_major": 2, "version_minor": 0 }, @@ -1468,14 +1465,15 @@ " temp = tp.io.bert.make_bert_tokens(document.loc[0,'span'].target_text, tokenizer)\n", " # re-correlate our original spans with their bert-compatible equivalents\n", " spans = tp.TokenSpanArray.align_to_tokens(temp[\"span\"],document[\"span\"])\n", - " \n", - " # now carry over some features from the old spans to the new onesspans_df = spans.as_frame().drop(columns = [\"begin\",\"end\"])\n", - " spans_df = spans.as_frame().drop(columns = ['begin','end','covered_text'])\n", + "\n", + " # now carry over some features from the old spans to the new ones\n", + " #spans_df = spans.as_frame().drop(columns = [\"begin\",\"end\"])\n", + " spans_df = spans.as_frame().drop(columns=['begin','end','covered_text'])\n", " spans_df['postag'] = document['upostag']\n", - " printed = 20\n", - " for i,b_tok,e_tok,pos in spans_df.itertuples():\n", - " temp.loc[b_tok:e_tok-1 , [\"postag\",\"raw_span\",'raw_span_id']] = pos,spans[i],i\n", - " \n", + " # printed = 20\n", + " for i, b_tok, e_tok, pos in spans_df.itertuples():\n", + " temp.loc[b_tok:e_tok-1, [\"postag\",\"raw_span\",'raw_span_id']] = pos,spans[i],i\n", + "\n", " # now translate from text tags to postag \n", " temp['postag'].fillna('X',inplace=True) # in our Labels, 'X' is a standin for \"N/A\" so convert N/A's to 'X'\n", " temp[\"postag_id\"] = temp['postag'].apply(lambda t: int(upostag_dict[str(t)]))\n", @@ -1559,7 +1557,7 @@ " NaN\n", " NaN\n", " 14\n", - " [ -0.37686592, -0.14841378, 0.73980016, ...\n", + " [ -0.37686658, -0.14841351, 0.7398003, ...\n", " \n", " \n", "
\n", @@ -1576,7 +1574,7 @@ " [0, 4): 'What'\n", " 0.0\n", " 11\n", - " [ -0.23266968, -0.40546328, 0.6171929, ...\n", + " [ -0.23266977, -0.40546313, 0.61719275, ...\n", " What\n", "
\n", "
\n", @@ -1593,7 +1591,7 @@ " [5, 7): 'if'\n", " 1.0\n", " 13\n", - " [ -0.8156859, -0.04782569, 0.081484295, ...\n", + " [ -0.81568515, -0.047825783, 0.08148496, ...\n", " if\n", "
\n", "
\n", @@ -1610,7 +1608,7 @@ " [8, 14): 'Google'\n", " 2.0\n", " 2\n", - " [ 0.78967804, -0.8511879, -0.48812625, ...\n", + " [ 0.7896778, -0.85118735, -0.48812556, ...\n", " Google\n", "
\n", "
\n", @@ -1627,7 +1625,7 @@ " [15, 22): 'Morphed'\n", " 3.0\n", " 3\n", - " [ -0.25935018, 0.5710723, -0.09106647, ...\n", + " [ -0.25935066, 0.57107216, -0.09106692, ...\n", " Mo\n", "
\n", " \n", @@ -1650,11 +1648,11 @@ "4 1 False VERB [15, 22): 'Morphed' \n", "\n", " raw_span_id postag_id embedding \\\n", - "0 NaN 14 [ -0.37686592, -0.14841378, 0.73980016, ... \n", - "1 0.0 11 [ -0.23266968, -0.40546328, 0.6171929, ... \n", - "2 1.0 13 [ -0.8156859, -0.04782569, 0.081484295, ... \n", - "3 2.0 2 [ 0.78967804, -0.8511879, -0.48812625, ... \n", - "4 3.0 3 [ -0.25935018, 0.5710723, -0.09106647, ... \n", + "0 NaN 14 [ -0.37686658, -0.14841351, 0.7398003, ... \n", + "1 0.0 11 [ -0.23266977, -0.40546313, 0.61719275, ... \n", + "2 1.0 13 [ -0.81568515, -0.047825783, 0.08148496, ... \n", + "3 2.0 2 [ 0.7896778, -0.85118735, -0.48812556, ... \n", + "4 3.0 3 [ -0.25935066, 0.57107216, -0.09106692, ... \n", "\n", " text \n", "0 \n", @@ -1683,12 +1681,303 @@ "execution_count": 13, "id": "63efdd32-5458-4dcb-91f3-dca97a7d1772", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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folddoc_numtoken_idspaninput_idtoken_type_idattention_maskspecial_tokens_maskpostagraw_spanraw_span_idpostag_idembeddingtext
0test00[0, 0): ''10101TrueXNaNNaN14[ -0.37686658, -0.14841351, 0.739800...
1test01[0, 4): 'What'132701FalsePRON[0, 4): 'What'0.011[ -0.23266977, -0.40546313, 0.6171927...What
2test02[5, 7): 'if'119101FalseSCONJ[5, 7): 'if'1.013[ -0.81568515, -0.047825783, 0.0814849...if
3test03[8, 14): 'Google'798601FalsePROPN[8, 14): 'Google'2.02[ 0.7896778, -0.85118735, -0.4881255...Google
4test04[15, 17): 'Mo'1255601FalseVERB[15, 22): 'Morphed'3.03[ -0.25935066, 0.57107216, -0.0910669...Mo
.............................................
307907train539756[3152, 3154): 'my'113901FalsePRON[3152, 3154): 'my'690.011[ -0.06984619, -0.4646066, 0.854770...my
307908train539757[3155, 3158): 'car'161001FalseNOUN[3155, 3158): 'car'691.04[ 0.14624149, -0.46386155, 0.596684...car
307909train539758[3158, 3159): ')'11401FalsePUNCT[3158, 3159): ')'692.05[ -0.09065091, -0.29592815, 0.5970235...)
307910train539759[3159, 3160): '.'11901FalsePUNCT[3159, 3160): '.'693.05[ 0.03102289, -0.27608734, 0.782190....
307911train539760[0, 0): ''10201TrueXNaNNaN14[ -0.50887, -0.22885998, 0.54494...
\n", + "

307912 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " fold doc_num token_id span input_id \\\n", + "0 test 0 0 [0, 0): '' 101 \n", + "1 test 0 1 [0, 4): 'What' 1327 \n", + "2 test 0 2 [5, 7): 'if' 1191 \n", + "3 test 0 3 [8, 14): 'Google' 7986 \n", + "4 test 0 4 [15, 17): 'Mo' 12556 \n", + "... ... ... ... ... ... \n", + "307907 train 539 756 [3152, 3154): 'my' 1139 \n", + "307908 train 539 757 [3155, 3158): 'car' 1610 \n", + "307909 train 539 758 [3158, 3159): ')' 114 \n", + "307910 train 539 759 [3159, 3160): '.' 119 \n", + "307911 train 539 760 [0, 0): '' 102 \n", + "\n", + " token_type_id attention_mask special_tokens_mask postag \\\n", + "0 0 1 True X \n", + "1 0 1 False PRON \n", + "2 0 1 False SCONJ \n", + "3 0 1 False PROPN \n", + "4 0 1 False VERB \n", + "... ... ... ... ... \n", + "307907 0 1 False PRON \n", + "307908 0 1 False NOUN \n", + "307909 0 1 False PUNCT \n", + "307910 0 1 False PUNCT \n", + "307911 0 1 True X \n", + "\n", + " raw_span raw_span_id postag_id \\\n", + "0 NaN NaN 14 \n", + "1 [0, 4): 'What' 0.0 11 \n", + "2 [5, 7): 'if' 1.0 13 \n", + "3 [8, 14): 'Google' 2.0 2 \n", + "4 [15, 22): 'Morphed' 3.0 3 \n", + "... ... ... ... \n", + "307907 [3152, 3154): 'my' 690.0 11 \n", + "307908 [3155, 3158): 'car' 691.0 4 \n", + "307909 [3158, 3159): ')' 692.0 5 \n", + "307910 [3159, 3160): '.' 693.0 5 \n", + "307911 NaN NaN 14 \n", + "\n", + " embedding text \n", + "0 [ -0.37686658, -0.14841351, 0.739800... \n", + "1 [ -0.23266977, -0.40546313, 0.6171927... What \n", + "2 [ -0.81568515, -0.047825783, 0.0814849... if \n", + "3 [ 0.7896778, -0.85118735, -0.4881255... Google \n", + "4 [ -0.25935066, 0.57107216, -0.0910669... Mo \n", + "... ... ... \n", + "307907 [ -0.06984619, -0.4646066, 0.854770... my \n", + "307908 [ 0.14624149, -0.46386155, 0.596684... car \n", + "307909 [ -0.09065091, -0.29592815, 0.5970235... ) \n", + "307910 [ 0.03102289, -0.27608734, 0.782190... . \n", + "307911 [ -0.50887, -0.22885998, 0.54494... \n", + "\n", + "[307912 rows x 14 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# re-read feather document if need be: \n", "if corpus_df is None or corpus_df.size == 0:\n", " corpus_df = pd.read_feather(\"outputs/conll_u_corpus.feather\")\n", - " corpus_df" + "corpus_df" ] }, { @@ -1736,7 +2025,7 @@ " \n", " \n", " \n", - " 64729\n", + " 64731\n", " train\n", " 0\n", " 0\n", @@ -1749,11 +2038,11 @@ " NaN\n", " NaN\n", " 14\n", - " [ -0.41927838, -0.22575253, 0.6648760...\n", + " [ -0.41927913, -0.22575217, 0.6648752...\n", " \n", " \n", " \n", - " 64730\n", + " 64732\n", " train\n", " 0\n", " 1\n", @@ -1766,11 +2055,11 @@ " [0, 2): 'Al'\n", " 0.0\n", " 2\n", - " [ -0.36961424, -1.0804733, -0.283367...\n", + " [ -0.36961484, -1.0804743, -0.2833683...\n", " Al\n", " \n", " \n", - " 64731\n", + " 64733\n", " train\n", " 0\n", " 2\n", @@ -1783,11 +2072,11 @@ " [2, 3): '-'\n", " 1.0\n", " 5\n", - " [ -0.9178737, -0.94624436, -0.808995...\n", + " [ -0.9178743, -0.9462442, -0.808995...\n", " -\n", " \n", " \n", - " 64732\n", + " 64734\n", " train\n", " 0\n", " 3\n", @@ -1800,11 +2089,11 @@ " [4, 9): 'Zaman'\n", " 2.0\n", " 2\n", - " [ -0.90530086, -0.97086835, -1.440879...\n", + " [ -0.90530103, -0.97086823, -1.440878...\n", " Z\n", " \n", " \n", - " 64733\n", + " 64735\n", " train\n", " 0\n", " 4\n", @@ -1817,7 +2106,7 @@ " [4, 9): 'Zaman'\n", " 2.0\n", " 2\n", - " [ -1.1586123, -1.149766, -1.194975...\n", + " [ -1.158612, -1.1497651, -1.194976...\n", " aman\n", " \n", " \n", @@ -1838,7 +2127,7 @@ " ...\n", " \n", " \n", - " 307892\n", + " 307907\n", " train\n", " 539\n", " 756\n", @@ -1851,11 +2140,11 @@ " [3152, 3154): 'my'\n", " 690.0\n", " 11\n", - " [ -0.06984596, -0.4646067, 0.8547705...\n", + " [ -0.06984619, -0.4646066, 0.854770...\n", " my\n", " \n", " \n", - " 307893\n", + " 307908\n", " train\n", " 539\n", " 757\n", @@ -1868,11 +2157,11 @@ " [3155, 3158): 'car'\n", " 691.0\n", " 4\n", - " [ 0.14624132, -0.46386197, 0.596684...\n", + " [ 0.14624149, -0.46386155, 0.596684...\n", " car\n", " \n", " \n", - " 307894\n", + " 307909\n", " train\n", " 539\n", " 758\n", @@ -1885,11 +2174,11 @@ " [3158, 3159): ')'\n", " 692.0\n", " 5\n", - " [ -0.090651065, -0.29592788, 0.597023...\n", + " [ -0.09065091, -0.29592815, 0.5970235...\n", " )\n", " \n", " \n", - " 307895\n", + " 307910\n", " train\n", " 539\n", " 759\n", @@ -1902,11 +2191,11 @@ " [3159, 3160): '.'\n", " 693.0\n", " 5\n", - " [ 0.031023545, -0.27608734, 0.782190...\n", + " [ 0.03102289, -0.27608734, 0.782190...\n", " .\n", " \n", " \n", - " 307896\n", + " 307911\n", " train\n", " 539\n", " 760\n", @@ -1919,68 +2208,68 @@ " NaN\n", " NaN\n", " 14\n", - " [ -0.5088702, -0.22885968, 0.544944...\n", + " [ -0.50887, -0.22885998, 0.54494...\n", " \n", " \n", " \n", "\n", - "

243168 rows × 14 columns

\n", + "

243181 rows × 14 columns

\n", "" ], "text/plain": [ " fold doc_num token_id span input_id \\\n", - "64729 train 0 0 [0, 0): '' 101 \n", - "64730 train 0 1 [0, 2): 'Al' 2586 \n", - "64731 train 0 2 [2, 3): '-' 118 \n", - "64732 train 0 3 [4, 5): 'Z' 163 \n", - "64733 train 0 4 [5, 9): 'aman' 19853 \n", + "64731 train 0 0 [0, 0): '' 101 \n", + "64732 train 0 1 [0, 2): 'Al' 2586 \n", + "64733 train 0 2 [2, 3): '-' 118 \n", + "64734 train 0 3 [4, 5): 'Z' 163 \n", + "64735 train 0 4 [5, 9): 'aman' 19853 \n", "... ... ... ... ... ... \n", - "307892 train 539 756 [3152, 3154): 'my' 1139 \n", - "307893 train 539 757 [3155, 3158): 'car' 1610 \n", - "307894 train 539 758 [3158, 3159): ')' 114 \n", - "307895 train 539 759 [3159, 3160): '.' 119 \n", - "307896 train 539 760 [0, 0): '' 102 \n", + "307907 train 539 756 [3152, 3154): 'my' 1139 \n", + "307908 train 539 757 [3155, 3158): 'car' 1610 \n", + "307909 train 539 758 [3158, 3159): ')' 114 \n", + "307910 train 539 759 [3159, 3160): '.' 119 \n", + "307911 train 539 760 [0, 0): '' 102 \n", "\n", " token_type_id attention_mask special_tokens_mask postag \\\n", - "64729 0 1 True X \n", - "64730 0 1 False PROPN \n", - "64731 0 1 False PUNCT \n", + "64731 0 1 True X \n", "64732 0 1 False PROPN \n", - "64733 0 1 False PROPN \n", + "64733 0 1 False PUNCT \n", + "64734 0 1 False PROPN \n", + "64735 0 1 False PROPN \n", "... ... ... ... ... \n", - "307892 0 1 False PRON \n", - "307893 0 1 False NOUN \n", - "307894 0 1 False PUNCT \n", - "307895 0 1 False PUNCT \n", - "307896 0 1 True X \n", + "307907 0 1 False PRON \n", + "307908 0 1 False NOUN \n", + "307909 0 1 False PUNCT \n", + "307910 0 1 False PUNCT \n", + "307911 0 1 True X \n", "\n", " raw_span raw_span_id postag_id \\\n", - "64729 NaN NaN 14 \n", - "64730 [0, 2): 'Al' 0.0 2 \n", - "64731 [2, 3): '-' 1.0 5 \n", - "64732 [4, 9): 'Zaman' 2.0 2 \n", - "64733 [4, 9): 'Zaman' 2.0 2 \n", + "64731 NaN NaN 14 \n", + "64732 [0, 2): 'Al' 0.0 2 \n", + "64733 [2, 3): '-' 1.0 5 \n", + "64734 [4, 9): 'Zaman' 2.0 2 \n", + "64735 [4, 9): 'Zaman' 2.0 2 \n", "... ... ... ... \n", - "307892 [3152, 3154): 'my' 690.0 11 \n", - "307893 [3155, 3158): 'car' 691.0 4 \n", - "307894 [3158, 3159): ')' 692.0 5 \n", - "307895 [3159, 3160): '.' 693.0 5 \n", - "307896 NaN NaN 14 \n", + "307907 [3152, 3154): 'my' 690.0 11 \n", + "307908 [3155, 3158): 'car' 691.0 4 \n", + "307909 [3158, 3159): ')' 692.0 5 \n", + "307910 [3159, 3160): '.' 693.0 5 \n", + "307911 NaN NaN 14 \n", "\n", " embedding text \n", - "64729 [ -0.41927838, -0.22575253, 0.6648760... \n", - "64730 [ -0.36961424, -1.0804733, -0.283367... Al \n", - "64731 [ -0.9178737, -0.94624436, -0.808995... - \n", - "64732 [ -0.90530086, -0.97086835, -1.440879... Z \n", - "64733 [ -1.1586123, -1.149766, -1.194975... aman \n", + "64731 [ -0.41927913, -0.22575217, 0.6648752... \n", + "64732 [ -0.36961484, -1.0804743, -0.2833683... Al \n", + "64733 [ -0.9178743, -0.9462442, -0.808995... - \n", + "64734 [ -0.90530103, -0.97086823, -1.440878... Z \n", + "64735 [ -1.158612, -1.1497651, -1.194976... aman \n", "... ... ... \n", - "307892 [ -0.06984596, -0.4646067, 0.8547705... my \n", - "307893 [ 0.14624132, -0.46386197, 0.596684... car \n", - "307894 [ -0.090651065, -0.29592788, 0.597023... ) \n", - "307895 [ 0.031023545, -0.27608734, 0.782190... . \n", - "307896 [ -0.5088702, -0.22885968, 0.544944... \n", + "307907 [ -0.06984619, -0.4646066, 0.854770... my \n", + "307908 [ 0.14624149, -0.46386155, 0.596684... car \n", + "307909 [ -0.09065091, -0.29592815, 0.5970235... ) \n", + "307910 [ 0.03102289, -0.27608734, 0.782190... . \n", + "307911 [ -0.50887, -0.22885998, 0.54494... \n", "\n", - "[243168 rows x 14 columns]" + "[243181 rows x 14 columns]" ] }, "execution_count": 14, @@ -2010,29 +2299,120 @@ "id": "034a61f3-7fe8-4b02-b649-cea67e14ceb3", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUNNING THE L-BFGS-B CODE\n", + "\n", + " * * *\n", + "\n", + "Machine precision = 2.220D-16\n", + " N = 13073 M = 10\n", + "\n", + "At X0 0 variables are exactly at the bounds\n", + "\n", + "At iterate 0 f= 6.88984D+05 |proj g|= 6.62729D+04\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "/Users/freiss/opt/miniconda3/envs/pd/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + " This problem is unconstrained.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "At iterate 50 f= 2.44541D+05 |proj g|= 3.93970D+03\n", + "\n", + "At iterate 100 f= 1.63368D+05 |proj g|= 1.71818D+03\n", + "\n", + "At iterate 150 f= 1.32218D+05 |proj g|= 1.03361D+03\n", + "\n", + "At iterate 200 f= 1.18130D+05 |proj g|= 7.32021D+02\n", + "\n", + "At iterate 250 f= 1.09684D+05 |proj g|= 1.23366D+03\n", + "\n", + "At iterate 300 f= 1.05398D+05 |proj g|= 6.35734D+02\n", + "\n", + "At iterate 350 f= 1.02851D+05 |proj g|= 2.76671D+02\n", + "\n", + "At iterate 400 f= 1.01228D+05 |proj g|= 5.09281D+02\n", + "\n", + "At iterate 450 f= 1.00038D+05 |proj g|= 3.14557D+02\n", + "\n", + "At iterate 500 f= 9.92494D+04 |proj g|= 1.68499D+02\n", + "\n", + "At iterate 550 f= 9.88417D+04 |proj g|= 4.91916D+02\n", + "\n", + "At iterate 600 f= 9.85123D+04 |proj g|= 2.02593D+02\n", + "\n", + "At iterate 650 f= 9.82550D+04 |proj g|= 1.28953D+02\n", + "\n", + "At iterate 700 f= 9.81148D+04 |proj g|= 1.09533D+02\n", + "\n", + "At iterate 750 f= 9.80368D+04 |proj g|= 8.88012D+01\n", + "\n", + "At iterate 800 f= 9.79714D+04 |proj g|= 7.48262D+01\n", + "\n", + "At iterate 850 f= 9.79321D+04 |proj g|= 1.00950D+02\n", + "\n", + "At iterate 900 f= 9.79023D+04 |proj g|= 2.59398D+01\n", + "\n", + "At iterate 950 f= 9.78679D+04 |proj g|= 3.74091D+01\n", + "\n", + "At iterate 1000 f= 9.78449D+04 |proj g|= 3.24331D+01\n", + "\n", + " * * *\n", + "\n", + "Tit = total number of iterations\n", + "Tnf = total number of function evaluations\n", + "Tnint = total number of segments explored during Cauchy searches\n", + "Skip = number of BFGS updates skipped\n", + "Nact = number of active bounds at final generalized Cauchy point\n", + "Projg = norm of the final projected gradient\n", + "F = final function value\n", + "\n", + " * * *\n", + "\n", + " N Tit Tnf Tnint Skip Nact Projg F\n", + "13073 1000 1065 1 0 0 3.243D+01 9.784D+04\n", + " F = 97844.884007299028 \n", + "\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/freiss/opt/miniconda3/envs/pd/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 16.6min remaining: 0.0s\n", - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 16.6min finished\n" + " n_iter_i = _check_optimize_result(\n" ] }, { "data": { + "text/html": [ + "
Pipeline(steps=[('mlogreg',\n",
+       "                 LogisticRegression(C=0.1, max_iter=1000,\n",
+       "                                    multi_class='multinomial', verbose=1))])
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" + ], "text/plain": [ "Pipeline(steps=[('mlogreg',\n", - " LogisticRegression(max_iter=1000, multi_class='multinomial',\n", - " verbose=10))])" + " LogisticRegression(C=0.1, max_iter=1000,\n", + " multi_class='multinomial', verbose=1))])" ] }, "execution_count": 15, @@ -2041,12 +2421,13 @@ } ], "source": [ - "# now actually train a model, using sklearn \n", - "MULTI_CLASS= \"multinomial\"\n", + "# now actually train a model, using sklearn\n", + "MULTI_CLASS = \"multinomial\"\n", "\n", "# How many iterations to run the BGFS optimizer when fitting logistic\n", "# regression models. 100 ==> Fast; 10000 ==> Full convergence\n", "LBGFS_ITERATIONS = 1000\n", + "REGULARIZATION_COEFF = 1e-1\n", "\n", "base_pipeline = sklearn.pipeline.Pipeline([\n", " # Standard scaler. This only makes a difference for certain classes\n", @@ -2054,8 +2435,9 @@ " #(\"scaler\", sklearn.preprocessing.StandardScaler()),\n", " (\"mlogreg\", sklearn.linear_model.LogisticRegression(\n", " multi_class=MULTI_CLASS,\n", - " verbose=10,\n", - " max_iter=LBGFS_ITERATIONS\n", + " verbose=1,\n", + " max_iter=LBGFS_ITERATIONS,\n", + " C=REGULARIZATION_COEFF\n", " ))\n", "])\n", "\n", @@ -2165,11 +2547,11 @@ " NaN\n", " NaN\n", " 14\n", - " [ -0.37686592, -0.14841378, 0.7398001...\n", + " [ -0.37686658, -0.14841351, 0.739800...\n", " \n", " 14\n", " X\n", - " [ 3.681475919382054e-11, 8.766155854203454e-1...\n", + " [1.8453993737147312e-09, 7.2817536336665424e-0...\n", " \n", " \n", " 1\n", @@ -2185,11 +2567,11 @@ " [0, 4): 'What'\n", " 0.0\n", " 11\n", - " [ -0.23266968, -0.40546328, 0.617192...\n", + " [ -0.23266977, -0.40546313, 0.6171927...\n", " What\n", " 5\n", " PUNCT\n", - " [ 4.480117969135689e-05, 0.000492260661933639...\n", + " [0.00018662917618329718, 0.002463643966812593...\n", " \n", " \n", " 2\n", @@ -2205,11 +2587,11 @@ " [5, 7): 'if'\n", " 1.0\n", " 13\n", - " [ -0.8156859, -0.04782569, 0.08148429...\n", + " [ -0.81568515, -0.047825783, 0.0814849...\n", " if\n", " 13\n", " SCONJ\n", - " [ 0.00458089489431613, 1.0102614181540655e-0...\n", + " [ 0.0041733565145387315, 1.600001587508807e-0...\n", " \n", " \n", " 3\n", @@ -2225,11 +2607,11 @@ " [8, 14): 'Google'\n", " 2.0\n", " 2\n", - " [ 0.78967804, -0.8511879, -0.4881262...\n", + " [ 0.7896778, -0.85118735, -0.4881255...\n", " Google\n", " 2\n", " PROPN\n", - " [2.0128060688355368e-13, 4.3664010704307723e-1...\n", + " [1.9368418908888587e-11, 2.3583553447853555e-0...\n", " \n", " \n", " 4\n", @@ -2245,11 +2627,11 @@ " [15, 22): 'Morphed'\n", " 3.0\n", " 3\n", - " [ -0.25935018, 0.5710723, -0.0910664...\n", + " [ -0.25935066, 0.57107216, -0.0910669...\n", " Mo\n", - " 2\n", - " PROPN\n", - " [ 0.004772281895284574, 3.990804066047649e-0...\n", + " 4\n", + " NOUN\n", + " [ 0.019704268908089885, 4.618509095536987e-0...\n", " \n", " \n", " 5\n", @@ -2265,11 +2647,11 @@ " [15, 22): 'Morphed'\n", " 3.0\n", " 3\n", - " [ -0.3267119, -0.10905984, 0.053087...\n", + " [ -0.32671162, -0.10906017, 0.0530867...\n", " rp\n", - " 2\n", - " PROPN\n", - " [ 4.133346131920443e-14, 3.0715492927999484e-0...\n", + " 4\n", + " NOUN\n", + " [ 7.710227050759564e-11, 5.44760536137293e-0...\n", " \n", " \n", " 6\n", @@ -2285,11 +2667,11 @@ " [15, 22): 'Morphed'\n", " 3.0\n", " 3\n", - " [ -0.9018082, -0.16881262, 0.4379902...\n", + " [ -0.9018081, -0.16881368, 0.4379903...\n", " hed\n", " 3\n", " VERB\n", - " [ 0.0003547861848056146, 2.0943022199837429e-1...\n", + " [0.00028466818218936664, 5.74427599535707e-0...\n", " \n", " \n", " 7\n", @@ -2305,11 +2687,11 @@ " [23, 27): 'Into'\n", " 4.0\n", " 0\n", - " [ 0.09566124, -0.109931074, -0.1493219...\n", + " [ 0.095660955, -0.10993134, -0.149321...\n", " Into\n", " 0\n", " ADP\n", - " [ 0.98593362749934, 2.223312204453196e-1...\n", + " [ 0.937516572524648, 5.276906149141363e-1...\n", " \n", " \n", " 8\n", @@ -2325,11 +2707,11 @@ " [28, 36): 'GoogleOS'\n", " 5.0\n", " 2\n", - " [ -1.2022994, -0.29254374, 0.2236384...\n", + " [ -1.2022991, -0.29254493, 0.2236394...\n", " Google\n", " 2\n", " PROPN\n", - " [ 8.802423148364236e-22, 9.098724786449631e-2...\n", + " [ 4.637041881505099e-16, 2.9507503915558095e-1...\n", " \n", " \n", " 9\n", @@ -2345,11 +2727,11 @@ " [28, 36): 'GoogleOS'\n", " 5.0\n", " 2\n", - " [ -0.78180003, -0.20742358, -1.288184...\n", + " [ -0.7818, -0.20742272, -1.288183...\n", " OS\n", " 2\n", " PROPN\n", - " [ 6.859296955972293e-14, 1.3584745823663452e-1...\n", + " [ 6.662825566299148e-09, 7.615507893740757e-1...\n", " \n", " \n", " 10\n", @@ -2365,11 +2747,11 @@ " [36, 37): '?'\n", " 6.0\n", " 5\n", - " [ -0.34068698, -0.4208277, 0.674408...\n", + " [ -0.3406865, -0.42082712, 0.674408...\n", " ?\n", " 5\n", " PUNCT\n", - " [ 4.419303709134792e-06, 2.2879619521678678e-0...\n", + " [2.1774277583327972e-05, 8.000939232471684e-0...\n", " \n", " \n", " 11\n", @@ -2385,11 +2767,11 @@ " [38, 42): 'What'\n", " 7.0\n", " 11\n", - " [ -0.39101043, -0.33632284, 0.6353156...\n", + " [ -0.3910109, -0.3363229, 0.6353158...\n", " What\n", - " 13\n", - " SCONJ\n", - " [ 1.089099756150613e-05, 4.301330361203966e-0...\n", + " 5\n", + " PUNCT\n", + " [ 4.431027855005785e-05, 0.0001618364833338211...\n", " \n", " \n", " 12\n", @@ -2405,11 +2787,11 @@ " [43, 45): 'if'\n", " 8.0\n", " 13\n", - " [ -0.68665487, -0.16331403, 0.2546722...\n", + " [ -0.6866545, -0.16331364, 0.2546724...\n", " if\n", " 13\n", " SCONJ\n", - " [ 0.00056745829301006, 1.0575813370745938e-0...\n", + " [ 0.0002872959609098302, 3.591134341517693e-0...\n", " \n", " \n", " 13\n", @@ -2425,11 +2807,11 @@ " [46, 52): 'Google'\n", " 9.0\n", " 2\n", - " [ 0.57027435, -0.9182296, -0.1871779...\n", + " [ 0.57027406, -0.9182299, -0.1871781...\n", " Google\n", " 2\n", " PROPN\n", - " [ 5.256030568823519e-06, 0.000141057983398473...\n", + " [1.5862060169266657e-06, 0.00870008781376279...\n", " \n", " \n", " 14\n", @@ -2445,11 +2827,11 @@ " [53, 61): 'expanded'\n", " 10.0\n", " 3\n", - " [ -0.48126522, -0.1581611, 0.4039635...\n", + " [ -0.48126468, -0.15816039, 0.4039639...\n", " expanded\n", " 3\n", " VERB\n", - " [ 4.050874025777886e-07, 1.9460546573814385e-1...\n", + " [2.2494319522580332e-06, 1.3830784723467198e-0...\n", " \n", " \n", " 15\n", @@ -2465,11 +2847,11 @@ " [62, 64): 'on'\n", " 11.0\n", " 0\n", - " [ -0.17011818, -0.37733135, 0.745948...\n", + " [ -0.17011856, -0.37733135, 0.7459479...\n", " on\n", " 0\n", " ADP\n", - " [ 0.994939087225643, 1.8480376986013427e-0...\n", + " [ 0.9969812735277428, 2.038596982401045e-0...\n", " \n", " \n", " 16\n", @@ -2485,11 +2867,11 @@ " [65, 68): 'its'\n", " 12.0\n", " 11\n", - " [ -0.34582123, -0.3814539, 0.539305...\n", + " [ -0.34582132, -0.38145372, 0.5393058...\n", " its\n", - " 11\n", - " PRON\n", - " [ 0.14933916780906895, 1.9661816857805298e-0...\n", + " 0\n", + " ADP\n", + " [ 0.3528985046235023, 0.0004074385035340905...\n", " \n", " \n", " 17\n", @@ -2505,11 +2887,11 @@ " [69, 75): 'search'\n", " 13.0\n", " 4\n", - " [ -0.1650713, -0.54526025, 0.648461...\n", + " [ -0.16507219, -0.5452602, 0.648461...\n", " search\n", " 4\n", " NOUN\n", - " [ 5.089421239766719e-07, 5.0023756350866345e-0...\n", + " [ 2.736910426420035e-06, 2.578768500234103e-0...\n", " \n", " \n", " 18\n", @@ -2525,11 +2907,11 @@ " [75, 76): '-'\n", " 14.0\n", " 5\n", - " [ -0.16116095, -0.44251364, 0.7121795...\n", + " [ -0.16116115, -0.44251344, 0.712179...\n", " -\n", " 5\n", " PUNCT\n", - " [ 0.0004927920586926724, 6.359739270183003e-0...\n", + " [ 0.005427808445130677, 4.262439649575787e-0...\n", " \n", " \n", " 19\n", @@ -2545,11 +2927,11 @@ " [77, 83): 'engine'\n", " 15.0\n", " 4\n", - " [ -0.35368297, -0.47415957, 0.4551175...\n", + " [ -0.35368297, -0.47415996, 0.4551170...\n", " engine\n", " 4\n", " NOUN\n", - " [ 1.888161183325207e-08, 1.225134189182207e-1...\n", + " [3.6459129481986373e-06, 2.963439538826619e-1...\n", " \n", " \n", "\n", @@ -2601,48 +2983,48 @@ "19 1 False NOUN [77, 83): 'engine' \n", "\n", " raw_span_id postag_id embedding \\\n", - "0 NaN 14 [ -0.37686592, -0.14841378, 0.7398001... \n", - "1 0.0 11 [ -0.23266968, -0.40546328, 0.617192... \n", - "2 1.0 13 [ -0.8156859, -0.04782569, 0.08148429... \n", - "3 2.0 2 [ 0.78967804, -0.8511879, -0.4881262... \n", - "4 3.0 3 [ -0.25935018, 0.5710723, -0.0910664... \n", - "5 3.0 3 [ -0.3267119, -0.10905984, 0.053087... \n", - "6 3.0 3 [ -0.9018082, -0.16881262, 0.4379902... \n", - "7 4.0 0 [ 0.09566124, -0.109931074, -0.1493219... \n", - "8 5.0 2 [ -1.2022994, -0.29254374, 0.2236384... \n", - "9 5.0 2 [ -0.78180003, -0.20742358, -1.288184... \n", - "10 6.0 5 [ -0.34068698, -0.4208277, 0.674408... \n", - "11 7.0 11 [ -0.39101043, -0.33632284, 0.6353156... \n", - "12 8.0 13 [ -0.68665487, -0.16331403, 0.2546722... \n", - "13 9.0 2 [ 0.57027435, -0.9182296, -0.1871779... \n", - "14 10.0 3 [ -0.48126522, -0.1581611, 0.4039635... \n", - "15 11.0 0 [ -0.17011818, -0.37733135, 0.745948... \n", - "16 12.0 11 [ -0.34582123, -0.3814539, 0.539305... \n", - "17 13.0 4 [ -0.1650713, -0.54526025, 0.648461... \n", - "18 14.0 5 [ -0.16116095, -0.44251364, 0.7121795... \n", - "19 15.0 4 [ -0.35368297, -0.47415957, 0.4551175... \n", + "0 NaN 14 [ -0.37686658, -0.14841351, 0.739800... \n", + "1 0.0 11 [ -0.23266977, -0.40546313, 0.6171927... \n", + "2 1.0 13 [ -0.81568515, -0.047825783, 0.0814849... \n", + "3 2.0 2 [ 0.7896778, -0.85118735, -0.4881255... \n", + "4 3.0 3 [ -0.25935066, 0.57107216, -0.0910669... \n", + "5 3.0 3 [ -0.32671162, -0.10906017, 0.0530867... \n", + "6 3.0 3 [ -0.9018081, -0.16881368, 0.4379903... \n", + "7 4.0 0 [ 0.095660955, -0.10993134, -0.149321... \n", + "8 5.0 2 [ -1.2022991, -0.29254493, 0.2236394... \n", + "9 5.0 2 [ -0.7818, -0.20742272, -1.288183... \n", + "10 6.0 5 [ -0.3406865, -0.42082712, 0.674408... \n", + "11 7.0 11 [ -0.3910109, -0.3363229, 0.6353158... \n", + "12 8.0 13 [ -0.6866545, -0.16331364, 0.2546724... \n", + "13 9.0 2 [ 0.57027406, -0.9182299, -0.1871781... \n", + "14 10.0 3 [ -0.48126468, -0.15816039, 0.4039639... \n", + "15 11.0 0 [ -0.17011856, -0.37733135, 0.7459479... \n", + "16 12.0 11 [ -0.34582132, -0.38145372, 0.5393058... \n", + "17 13.0 4 [ -0.16507219, -0.5452602, 0.648461... \n", + "18 14.0 5 [ -0.16116115, -0.44251344, 0.712179... \n", + "19 15.0 4 [ -0.35368297, -0.47415996, 0.4551170... \n", "\n", " text p_id p_postag raw_output \n", - "0 14 X [ 3.681475919382054e-11, 8.766155854203454e-1... \n", - "1 What 5 PUNCT [ 4.480117969135689e-05, 0.000492260661933639... \n", - "2 if 13 SCONJ [ 0.00458089489431613, 1.0102614181540655e-0... \n", - "3 Google 2 PROPN [2.0128060688355368e-13, 4.3664010704307723e-1... \n", - "4 Mo 2 PROPN [ 0.004772281895284574, 3.990804066047649e-0... \n", - "5 rp 2 PROPN [ 4.133346131920443e-14, 3.0715492927999484e-0... \n", - "6 hed 3 VERB [ 0.0003547861848056146, 2.0943022199837429e-1... \n", - "7 Into 0 ADP [ 0.98593362749934, 2.223312204453196e-1... \n", - "8 Google 2 PROPN [ 8.802423148364236e-22, 9.098724786449631e-2... \n", - "9 OS 2 PROPN [ 6.859296955972293e-14, 1.3584745823663452e-1... \n", - "10 ? 5 PUNCT [ 4.419303709134792e-06, 2.2879619521678678e-0... \n", - "11 What 13 SCONJ [ 1.089099756150613e-05, 4.301330361203966e-0... \n", - "12 if 13 SCONJ [ 0.00056745829301006, 1.0575813370745938e-0... \n", - "13 Google 2 PROPN [ 5.256030568823519e-06, 0.000141057983398473... \n", - "14 expanded 3 VERB [ 4.050874025777886e-07, 1.9460546573814385e-1... \n", - "15 on 0 ADP [ 0.994939087225643, 1.8480376986013427e-0... \n", - "16 its 11 PRON [ 0.14933916780906895, 1.9661816857805298e-0... \n", - "17 search 4 NOUN [ 5.089421239766719e-07, 5.0023756350866345e-0... \n", - "18 - 5 PUNCT [ 0.0004927920586926724, 6.359739270183003e-0... \n", - "19 engine 4 NOUN [ 1.888161183325207e-08, 1.225134189182207e-1... " + "0 14 X [1.8453993737147312e-09, 7.2817536336665424e-0... \n", + "1 What 5 PUNCT [0.00018662917618329718, 0.002463643966812593... \n", + "2 if 13 SCONJ [ 0.0041733565145387315, 1.600001587508807e-0... \n", + "3 Google 2 PROPN [1.9368418908888587e-11, 2.3583553447853555e-0... \n", + "4 Mo 4 NOUN [ 0.019704268908089885, 4.618509095536987e-0... \n", + "5 rp 4 NOUN [ 7.710227050759564e-11, 5.44760536137293e-0... \n", + "6 hed 3 VERB [0.00028466818218936664, 5.74427599535707e-0... \n", + "7 Into 0 ADP [ 0.937516572524648, 5.276906149141363e-1... \n", + "8 Google 2 PROPN [ 4.637041881505099e-16, 2.9507503915558095e-1... \n", + "9 OS 2 PROPN [ 6.662825566299148e-09, 7.615507893740757e-1... \n", + "10 ? 5 PUNCT [2.1774277583327972e-05, 8.000939232471684e-0... \n", + "11 What 5 PUNCT [ 4.431027855005785e-05, 0.0001618364833338211... \n", + "12 if 13 SCONJ [ 0.0002872959609098302, 3.591134341517693e-0... \n", + "13 Google 2 PROPN [1.5862060169266657e-06, 0.00870008781376279... \n", + "14 expanded 3 VERB [2.2494319522580332e-06, 1.3830784723467198e-0... \n", + "15 on 0 ADP [ 0.9969812735277428, 2.038596982401045e-0... \n", + "16 its 0 ADP [ 0.3528985046235023, 0.0004074385035340905... \n", + "17 search 4 NOUN [ 2.736910426420035e-06, 2.578768500234103e-0... \n", + "18 - 5 PUNCT [ 0.005427808445130677, 4.262439649575787e-0... \n", + "19 engine 4 NOUN [3.6459129481986373e-06, 2.963439538826619e-1... " ] }, "execution_count": 17, @@ -2653,13 +3035,14 @@ "source": [ "def infer_on_df(df: pd.DataFrame, id_to_class_dict, predictor):\n", " result_df = df.copy()\n", - " raw_outputs = tp.TensorArray(predictor.predict_proba(result_df[\"embedding\"]))\n", + " inputs = result_df[\"embedding\"].to_numpy()\n", + " raw_outputs = tp.TensorArray(predictor.predict_proba(inputs))\n", " result_df[\"p_id\"] = np.argmax(raw_outputs, axis=1)\n", " result_df[\"p_postag\"]= result_df[\"p_id\"].apply(lambda p_id: id_to_class_dict[p_id])\n", " result_df[\"raw_output\"] = raw_outputs\n", " return result_df\n", "\n", - "test_results = infer_on_df(corpus_df[corpus_df[\"fold\"] == \"test\"],upostags_list,base_model)\n", + "test_results = infer_on_df(corpus_df[corpus_df[\"fold\"] == \"test\"], upostags_list, base_model)\n", "test_results.head(20)" ] }, @@ -2755,8 +3138,8 @@ " 0\n", " VERB\n", " 3\n", - " 2\n", - " PROPN\n", + " 4\n", + " NOUN\n", " \n", " \n", " [23, 27): 'Into'\n", @@ -2832,7 +3215,7 @@ "[0, 4): 'What' test 0 PRON 11 5 \n", "[5, 7): 'if' test 0 SCONJ 13 13 \n", "[8, 14): 'Google' test 0 PROPN 2 2 \n", - "[15, 22): 'Morphed' test 0 VERB 3 2 \n", + "[15, 22): 'Morphed' test 0 VERB 3 4 \n", "[23, 27): 'Into' test 0 ADP 0 0 \n", "... ... ... ... ... ... \n", "[307, 309): 'of' test 2 ADP 0 0 \n", @@ -2846,7 +3229,7 @@ "[0, 4): 'What' PUNCT \n", "[5, 7): 'if' SCONJ \n", "[8, 14): 'Google' PROPN \n", - "[15, 22): 'Morphed' PROPN \n", + "[15, 22): 'Morphed' NOUN \n", "[23, 27): 'Into' ADP \n", "... ... \n", "[307, 309): 'of' ADP \n", @@ -2884,27 +3267,27 @@ "text": [ " precision recall f1-score support\n", "\n", - " ADJ 0.793 0.783 0.788 1782\n", - " ADP 0.916 0.919 0.917 2030\n", - " ADV 0.788 0.749 0.768 1147\n", - " AUX 0.939 0.952 0.946 1509\n", - " CCONJ 0.971 0.961 0.966 738\n", - " DET 0.959 0.958 0.958 1898\n", - " INTJ 0.876 0.708 0.783 120\n", - " NOUN 0.864 0.893 0.878 4136\n", - " NUM 0.839 0.893 0.865 541\n", - " PART 0.943 0.938 0.940 630\n", - " PRON 0.967 0.965 0.966 2158\n", - " PROPN 0.844 0.831 0.837 1985\n", - " PUNCT 0.987 0.963 0.975 3098\n", - " SCONJ 0.852 0.828 0.840 443\n", - " SYM 0.646 0.604 0.624 106\n", - " VERB 0.906 0.901 0.904 2640\n", - " X 0.482 0.686 0.566 137\n", + " ADJ 0.796 0.775 0.785 1784\n", + " ADP 0.911 0.923 0.917 2033\n", + " ADV 0.784 0.748 0.765 1181\n", + " AUX 0.945 0.961 0.953 1525\n", + " CCONJ 0.975 0.966 0.971 737\n", + " DET 0.959 0.959 0.959 1898\n", + " INTJ 0.850 0.708 0.773 120\n", + " NOUN 0.863 0.891 0.877 4137\n", + " NUM 0.809 0.906 0.854 541\n", + " PART 0.947 0.940 0.944 649\n", + " PRON 0.963 0.967 0.965 2162\n", + " PROPN 0.846 0.834 0.840 1981\n", + " PUNCT 0.984 0.964 0.974 3098\n", + " SCONJ 0.857 0.781 0.817 384\n", + " SYM 0.639 0.495 0.558 107\n", + " VERB 0.911 0.900 0.905 2624\n", + " X 0.503 0.689 0.581 135\n", "\n", - " accuracy 0.899 25098\n", - " macro avg 0.857 0.855 0.854 25098\n", - "weighted avg 0.900 0.899 0.899 25098\n", + " accuracy 0.898 25096\n", + " macro avg 0.855 0.847 0.849 25096\n", + "weighted avg 0.899 0.898 0.898 25096\n", "\n" ] } @@ -2940,7 +3323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/notebooks/Sentiment_Analysis.ipynb b/notebooks/Sentiment_Analysis.ipynb index 60d1bab4..bd98e934 100644 --- a/notebooks/Sentiment_Analysis.ipynb +++ b/notebooks/Sentiment_Analysis.ipynb @@ -458,7 +458,7 @@ " 5632\n", " 5807\n", " 494\n", - " 5682\n", + " 5681\n", " 6512\n", " 32\n", " \n", @@ -494,7 +494,7 @@ " 6833\n", " 7106\n", " 829\n", - " 7203\n", + " 7202\n", " 7984\n", " 33\n", " \n", @@ -539,7 +539,7 @@ " 15781\n", " 16254\n", " 2760\n", - " 16501\n", + " 16500\n", " 19334\n", " 33\n", " \n", @@ -557,7 +557,7 @@ " 11611\n", " 10646\n", " 1704\n", - " 11142\n", + " 11141\n", " 12559\n", " 33\n", " \n", @@ -566,7 +566,7 @@ " 16145\n", " 15483\n", " 2328\n", - " 15914\n", + " 15913\n", " 18553\n", " 33\n", " \n", @@ -584,7 +584,7 @@ " 4958\n", " 4960\n", " 495\n", - " 4997\n", + " 4996\n", " 5529\n", " 33\n", " \n", @@ -593,7 +593,7 @@ " 6781\n", " 7373\n", " 1173\n", - " 7463\n", + " 7462\n", " 8460\n", " 33\n", " \n", @@ -620,7 +620,7 @@ " 16908\n", " 17136\n", " 3261\n", - " 17719\n", + " 17718\n", " 20576\n", " 33\n", " \n", @@ -782,7 +782,7 @@ " 6063\n", " 6542\n", " 804\n", - " 6639\n", + " 6638\n", " 7308\n", " 33\n", " \n", @@ -791,7 +791,7 @@ " 3002\n", " 3002\n", " 291\n", - " 3038\n", + " 3037\n", " 3355\n", " 33\n", " \n", @@ -818,7 +818,7 @@ " 10729\n", " 10025\n", " 1735\n", - " 10402\n", + " 10401\n", " 11760\n", " 33\n", " \n", @@ -902,25 +902,25 @@ " Review_Date Author_Name Vehicle_Title Review_Title Review \\\n", "Car_Make \n", "AMGeneral 5 5 2 5 5 \n", - "Acura 5632 5807 494 5682 6512 \n", + "Acura 5632 5807 494 5681 6512 \n", "AlfaRomeo 77 76 22 77 77 \n", "AstonMartin 82 89 31 89 89 \n", "Audi 5069 5389 753 5467 6006 \n", - "BMW 6833 7106 829 7203 7984 \n", + "BMW 6833 7106 829 7202 7984 \n", "Bentley 150 146 39 141 150 \n", "Bugatti 9 9 4 9 9 \n", "Buick 3406 3242 374 3334 3615 \n", "Cadillac 3539 3531 457 3593 3902 \n", - "Chevrolet 15781 16254 2760 16501 19334 \n", + "Chevrolet 15781 16254 2760 16500 19334 \n", "GMC 4327 4425 1261 4415 4964 \n", - "Honda 11611 10646 1704 11142 12559 \n", - "Toyota 16145 15483 2328 15914 18553 \n", + "Honda 11611 10646 1704 11141 12559 \n", + "Toyota 16145 15483 2328 15913 18553 \n", "Volkswagen 8260 8219 1577 8481 9334 \n", - "chrysler 4958 4960 495 4997 5529 \n", - "dodge 6781 7373 1173 7463 8460 \n", + "chrysler 4958 4960 495 4996 5529 \n", + "dodge 6781 7373 1173 7462 8460 \n", "ferrari 156 159 47 156 161 \n", "fiat 394 380 68 391 391 \n", - "ford 16908 17136 3261 17719 20576 \n", + "ford 16908 17136 3261 17718 20576 \n", "genesis 78 75 16 78 77 \n", "hummer 537 541 35 531 559 \n", "hyundai 7679 7032 943 7250 8156 \n", @@ -938,11 +938,11 @@ "maybach 24 24 6 24 24 \n", "mazda 7165 6830 938 7036 7820 \n", "mclaren 1 1 1 1 1 \n", - "mercedes-benz 6063 6542 804 6639 7308 \n", - "mercury 3002 3002 291 3038 3355 \n", + "mercedes-benz 6063 6542 804 6638 7308 \n", + "mercury 3002 3002 291 3037 3355 \n", "mini 1033 977 127 997 1036 \n", "mitsubishi 3982 4382 601 4222 4773 \n", - "nissan 10729 10025 1735 10402 11760 \n", + "nissan 10729 10025 1735 10401 11760 \n", "pontiac 5066 5294 345 5239 5927 \n", "porsche 1636 1646 280 1657 1774 \n", "ram 564 505 281 551 553 \n", @@ -1059,11 +1059,11 @@ { "data": { "text/plain": [ - "Review_Date 7239\n", - "Author_Name 7410\n", - "Vehicle_Title 5198\n", - "Review_Title 7648\n", - "Review 8341\n", + "Review_Date 7321\n", + "Author_Name 7434\n", + "Vehicle_Title 5292\n", + "Review_Title 7665\n", + "Review 8338\n", "Rating\\r 33\n", "Car_Make 50\n", "dtype: int64" @@ -1153,17 +1153,6 @@ " \n", " \n", " 0\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", - " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 1\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -1174,7 +1163,7 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 2\n", + " 1\n", " on 12/18/05 19:55 PM (PST)\n", " Clayton\n", " 2000 AM General Hummer SUV 4dr SUV AWD\n", @@ -1185,6 +1174,17 @@ " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", + " 2\n", + " on 01/19/06 19:46 PM (PST)\n", + " REUBEN\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " AWESOME HUMMER\n", + " Hummer is unstoppable. May only get 12 mpg bu...\n", + " 5.000\n", + " AMGeneral\n", + " AWESOME HUMMER: Hummer is unstoppable. May onl...\n", + " \n", + " \n", " 3\n", " on 08/23/03 00:00 AM (PDT)\n", " Bobby Keene\n", @@ -1197,14 +1197,14 @@ " \n", " \n", " 4\n", - " on 01/19/06 19:46 PM (PST)\n", - " REUBEN\n", - " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", - " AWESOME HUMMER\n", - " Hummer is unstoppable. May only get 12 mpg bu...\n", - " 5.000\n", + " on 08/30/02 00:00 AM (PDT)\n", + " bluice3309\n", + " 2000 AM General Hummer SUV 4dr SUV AWD\n", + " a true ride\n", + " this beast can go through just about \\ranythi...\n", + " 4.625\n", " AMGeneral\n", - " AWESOME HUMMER: Hummer is unstoppable. May onl...\n", + " a true ride: this beast can go through just ab...\n", " \n", " \n", "\n", @@ -1212,32 +1212,32 @@ ], "text/plain": [ " Review_Date Author_Name \\\n", - "0 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "1 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "2 on 12/18/05 19:55 PM (PST) Clayton \n", + "0 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "1 on 12/18/05 19:55 PM (PST) Clayton \n", + "2 on 01/19/06 19:46 PM (PST) REUBEN \n", "3 on 08/23/03 00:00 AM (PDT) Bobby Keene \n", - "4 on 01/19/06 19:46 PM (PST) REUBEN \n", + "4 on 08/30/02 00:00 AM (PDT) bluice3309 \n", "\n", " Vehicle_Title Review_Title \\\n", - "0 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "1 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "2 2000 AM General Hummer SUV 4dr SUV AWD HUMMER NOT A bummer \n", + "0 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "1 2000 AM General Hummer SUV 4dr SUV AWD HUMMER NOT A bummer \n", + "2 2000 AM General Hummer SUV Hard Top 4dr SUV AWD AWESOME HUMMER \n", "3 2000 AM General Hummer SUV Hard Top 4dr SUV AWD H1 Review \n", - "4 2000 AM General Hummer SUV Hard Top 4dr SUV AWD AWESOME HUMMER \n", + "4 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", "\n", " Review Rating\\r Car_Make \\\n", - "0 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "1 I have owned this car for a year and a \\rhalf... 1.000 AMGeneral \n", - "2 Vehicle is a beast. I don't recommend HUMMER ... 5.000 AMGeneral \n", + "0 I have owned this car for a year and a \\rhalf... 1.000 AMGeneral \n", + "1 Vehicle is a beast. I don't recommend HUMMER ... 5.000 AMGeneral \n", + "2 Hummer is unstoppable. May only get 12 mpg bu... 5.000 AMGeneral \n", "3 The truck is incredible. I have a long histo... 4.500 AMGeneral \n", - "4 Hummer is unstoppable. May only get 12 mpg bu... 5.000 AMGeneral \n", + "4 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", "\n", " Review_Content \n", - "0 a true ride: this beast can go through just ab... \n", - "1 What a waste: I have owned this car for a year... \n", - "2 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "0 What a waste: I have owned this car for a year... \n", + "1 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "2 AWESOME HUMMER: Hummer is unstoppable. May onl... \n", "3 H1 Review: The truck is incredible. I have a ... \n", - "4 AWESOME HUMMER: Hummer is unstoppable. May onl... " + "4 a true ride: this beast can go through just ab... " ] }, "execution_count": 9, @@ -1265,7 +1265,7 @@ { "data": { "text/plain": [ - "'a true ride: this beast can go through just about \\ranything you through at it. water, \\rfire, brick wall, glass ,ice , you name \\rit. i like my toys to be tough enough \\rto handle what i through at them. and \\rthis toy has NOT let me down and i do \\rnot think it ever will!!!'" + "'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall. I have driven it through \\reverything and it stalls on me all the \\rtime. I would never buy this car \\ragain. and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'" ] }, "execution_count": 10, @@ -1355,95 +1355,63 @@ { "data": { "text/plain": [ - "{'usage': {'text_units': 1, 'text_characters': 272, 'features': 1},\n", + "{'usage': {'text_units': 1, 'text_characters': 284, 'features': 1},\n", " 'language': 'en',\n", - " 'keywords': [{'text': 'brick wall',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.929032,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", - " 'count': 1},\n", - " {'text': 'true ride',\n", - " 'sentiment': {'score': 0.863267, 'label': 'positive'},\n", - " 'relevance': 0.900583,\n", - " 'emotion': {'sadness': 0.219645,\n", - " 'joy': 0.593742,\n", - " 'fear': 0.087546,\n", - " 'disgust': 0.037523,\n", - " 'anger': 0.08835},\n", + " 'keywords': [{'text': 'waste',\n", + " 'sentiment': {'score': -0.875215, 'label': 'negative'},\n", + " 'relevance': 0.685741,\n", + " 'emotion': {'sadness': 0.192383,\n", + " 'joy': 0.024961,\n", + " 'fear': 0.313145,\n", + " 'disgust': 0.08332,\n", + " 'anger': 0.277825},\n", " 'count': 1},\n", " {'text': 'fire',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.678235,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", - " 'count': 1},\n", - " {'text': 'toys',\n", - " 'sentiment': {'score': -0.738776, 'label': 'negative'},\n", - " 'relevance': 0.632428,\n", - " 'emotion': {'sadness': 0.308881,\n", - " 'joy': 0.49626,\n", - " 'fear': 0.157411,\n", - " 'disgust': 0.011575,\n", - " 'anger': 0.090552},\n", + " 'sentiment': {'score': -0.934513, 'label': 'negative'},\n", + " 'relevance': 0.598326,\n", + " 'emotion': {'sadness': 0.360925,\n", + " 'joy': 0.002355,\n", + " 'fear': 0.26649,\n", + " 'disgust': 0.069938,\n", + " 'anger': 0.442759},\n", " 'count': 1},\n", - " {'text': 'toy',\n", - " 'sentiment': {'score': -0.941767, 'label': 'negative'},\n", - " 'relevance': 0.567418,\n", - " 'emotion': {'sadness': 0.31777,\n", - " 'joy': 0.483067,\n", - " 'fear': 0.163651,\n", - " 'disgust': 0.011704,\n", - " 'anger': 0.094782},\n", + " {'text': 'car',\n", + " 'sentiment': {'score': -0.844774, 'label': 'negative'},\n", + " 'relevance': 0.581432,\n", + " 'emotion': {'sadness': 0.144346,\n", + " 'joy': 0.150177,\n", + " 'fear': 0.246102,\n", + " 'disgust': 0.06176,\n", + " 'anger': 0.203999},\n", + " 'count': 2},\n", + " {'text': 'hell',\n", + " 'sentiment': {'score': -0.934513, 'label': 'negative'},\n", + " 'relevance': 0.577011,\n", + " 'emotion': {'sadness': 0.360925,\n", + " 'joy': 0.002355,\n", + " 'fear': 0.26649,\n", + " 'disgust': 0.069938,\n", + " 'anger': 0.442759},\n", " 'count': 1},\n", - " {'text': 'glass',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.557307,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", - " 'count': 1},\n", - " {'text': 'beast',\n", - " 'sentiment': {'score': 0.863267, 'label': 'positive'},\n", - " 'relevance': 0.550077,\n", - " 'emotion': {'sadness': 0.219645,\n", - " 'joy': 0.593742,\n", - " 'fear': 0.087546,\n", - " 'disgust': 0.037523,\n", - " 'anger': 0.08835},\n", - " 'count': 1},\n", - " {'text': 'ice',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.54733,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", + " {'text': 'year',\n", + " 'sentiment': {'score': -0.875215, 'label': 'negative'},\n", + " 'relevance': 0.563676,\n", + " 'emotion': {'sadness': 0.192383,\n", + " 'joy': 0.024961,\n", + " 'fear': 0.313145,\n", + " 'disgust': 0.08332,\n", + " 'anger': 0.277825},\n", " 'count': 1},\n", - " {'text': 'water',\n", + " {'text': 'time',\n", " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.539073,\n", - " 'emotion': {'sadness': 0, 'joy': 0, 'fear': 0, 'disgust': 0, 'anger': 0},\n", - " 'count': 1},\n", - " {'text': 'name',\n", - " 'sentiment': {'score': -0.738776, 'label': 'negative'},\n", - " 'relevance': 0.539073,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", + " 'relevance': 0.466983,\n", + " 'emotion': {'sadness': 0.266573,\n", + " 'joy': 0.401314,\n", + " 'fear': 0.08908,\n", + " 'disgust': 0.024027,\n", + " 'anger': 0.065767},\n", " 'count': 1}],\n", - " 'analyzed_text': 'a true ride: this beast can go through just about \\ranything you through at it. water, \\rfire, brick wall, glass ,ice , you name \\rit. i like my toys to be tough enough \\rto handle what i through at them. and \\rthis toy has NOT let me down and i do \\rnot think it ever will!!!'}" + " 'analyzed_text': 'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall. I have driven it through \\reverything and it stalls on me all the \\rtime. I would never buy this car \\ragain. and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'}" ] }, "execution_count": 13, @@ -1470,7 +1438,7 @@ { "data": { "text/plain": [ - "'a true ride: this beast can go through just about \\ranything you through at it. water, \\rfire, brick wall, glass ,ice , you name \\rit. i like my toys to be tough enough \\rto handle what i through at them. and \\rthis toy has NOT let me down and i do \\rnot think it ever will!!!'" + "'What a waste: I have owned this car for a year and a \\rhalf now and it is not reliabile at \\rall. I have driven it through \\reverything and it stalls on me all the \\rtime. I would never buy this car \\ragain. and trying to sell it is like \\rtrying to sell fire in hell, just wont \\rhappen.'" ] }, "execution_count": 14, @@ -1490,91 +1458,59 @@ { "data": { "text/plain": [ - "[{'text': 'brick wall',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.929032,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", - " 'count': 1},\n", - " {'text': 'true ride',\n", - " 'sentiment': {'score': 0.863267, 'label': 'positive'},\n", - " 'relevance': 0.900583,\n", - " 'emotion': {'sadness': 0.219645,\n", - " 'joy': 0.593742,\n", - " 'fear': 0.087546,\n", - " 'disgust': 0.037523,\n", - " 'anger': 0.08835},\n", + "[{'text': 'waste',\n", + " 'sentiment': {'score': -0.875215, 'label': 'negative'},\n", + " 'relevance': 0.685741,\n", + " 'emotion': {'sadness': 0.192383,\n", + " 'joy': 0.024961,\n", + " 'fear': 0.313145,\n", + " 'disgust': 0.08332,\n", + " 'anger': 0.277825},\n", " 'count': 1},\n", " {'text': 'fire',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.678235,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", - " 'count': 1},\n", - " {'text': 'toys',\n", - " 'sentiment': {'score': -0.738776, 'label': 'negative'},\n", - " 'relevance': 0.632428,\n", - " 'emotion': {'sadness': 0.308881,\n", - " 'joy': 0.49626,\n", - " 'fear': 0.157411,\n", - " 'disgust': 0.011575,\n", - " 'anger': 0.090552},\n", - " 'count': 1},\n", - " {'text': 'toy',\n", - " 'sentiment': {'score': -0.941767, 'label': 'negative'},\n", - " 'relevance': 0.567418,\n", - " 'emotion': {'sadness': 0.31777,\n", - " 'joy': 0.483067,\n", - " 'fear': 0.163651,\n", - " 'disgust': 0.011704,\n", - " 'anger': 0.094782},\n", - " 'count': 1},\n", - " {'text': 'glass',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.557307,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", + " 'sentiment': {'score': -0.934513, 'label': 'negative'},\n", + " 'relevance': 0.598326,\n", + " 'emotion': {'sadness': 0.360925,\n", + " 'joy': 0.002355,\n", + " 'fear': 0.26649,\n", + " 'disgust': 0.069938,\n", + " 'anger': 0.442759},\n", " 'count': 1},\n", - " {'text': 'beast',\n", - " 'sentiment': {'score': 0.863267, 'label': 'positive'},\n", - " 'relevance': 0.550077,\n", - " 'emotion': {'sadness': 0.219645,\n", - " 'joy': 0.593742,\n", - " 'fear': 0.087546,\n", - " 'disgust': 0.037523,\n", - " 'anger': 0.08835},\n", + " {'text': 'car',\n", + " 'sentiment': {'score': -0.844774, 'label': 'negative'},\n", + " 'relevance': 0.581432,\n", + " 'emotion': {'sadness': 0.144346,\n", + " 'joy': 0.150177,\n", + " 'fear': 0.246102,\n", + " 'disgust': 0.06176,\n", + " 'anger': 0.203999},\n", + " 'count': 2},\n", + " {'text': 'hell',\n", + " 'sentiment': {'score': -0.934513, 'label': 'negative'},\n", + " 'relevance': 0.577011,\n", + " 'emotion': {'sadness': 0.360925,\n", + " 'joy': 0.002355,\n", + " 'fear': 0.26649,\n", + " 'disgust': 0.069938,\n", + " 'anger': 0.442759},\n", " 'count': 1},\n", - " {'text': 'ice',\n", - " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.54733,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", + " {'text': 'year',\n", + " 'sentiment': {'score': -0.875215, 'label': 'negative'},\n", + " 'relevance': 0.563676,\n", + " 'emotion': {'sadness': 0.192383,\n", + " 'joy': 0.024961,\n", + " 'fear': 0.313145,\n", + " 'disgust': 0.08332,\n", + " 'anger': 0.277825},\n", " 'count': 1},\n", - " {'text': 'water',\n", + " {'text': 'time',\n", " 'sentiment': {'score': 0, 'label': 'neutral'},\n", - " 'relevance': 0.539073,\n", - " 'emotion': {'sadness': 0, 'joy': 0, 'fear': 0, 'disgust': 0, 'anger': 0},\n", - " 'count': 1},\n", - " {'text': 'name',\n", - " 'sentiment': {'score': -0.738776, 'label': 'negative'},\n", - " 'relevance': 0.539073,\n", - " 'emotion': {'sadness': 0.29279,\n", - " 'joy': 0.230829,\n", - " 'fear': 0.175379,\n", - " 'disgust': 0.180316,\n", - " 'anger': 0.225414},\n", + " 'relevance': 0.466983,\n", + " 'emotion': {'sadness': 0.266573,\n", + " 'joy': 0.401314,\n", + " 'fear': 0.08908,\n", + " 'disgust': 0.024027,\n", + " 'anger': 0.065767},\n", " 'count': 1}]" ] }, @@ -1613,29 +1549,21 @@ " 'entity_mentions': Empty DataFrame\n", " Columns: []\n", " Index: [],\n", - " 'keywords': text sentiment.label sentiment.score relevance emotion.sadness \\\n", - " 0 brick wall neutral 0.000000 0.929032 0.292790 \n", - " 1 true ride positive 0.863267 0.900583 0.219645 \n", - " 2 fire neutral 0.000000 0.678235 0.292790 \n", - " 3 toys negative -0.738776 0.632428 0.308881 \n", - " 4 toy negative -0.941767 0.567418 0.317770 \n", - " 5 glass neutral 0.000000 0.557307 0.292790 \n", - " 6 beast positive 0.863267 0.550077 0.219645 \n", - " 7 ice neutral 0.000000 0.547330 0.292790 \n", - " 8 water neutral 0.000000 0.539073 0.000000 \n", - " 9 name negative -0.738776 0.539073 0.292790 \n", + " 'keywords': text sentiment.label sentiment.score relevance emotion.sadness \\\n", + " 0 waste negative -0.875215 0.685741 0.192383 \n", + " 1 fire negative -0.934513 0.598326 0.360925 \n", + " 2 car negative -0.844774 0.581432 0.144346 \n", + " 3 hell negative -0.934513 0.577011 0.360925 \n", + " 4 year negative -0.875215 0.563676 0.192383 \n", + " 5 time neutral 0.000000 0.466983 0.266573 \n", " \n", " emotion.joy emotion.fear emotion.disgust emotion.anger count \n", - " 0 0.230829 0.175379 0.180316 0.225414 1 \n", - " 1 0.593742 0.087546 0.037523 0.088350 1 \n", - " 2 0.230829 0.175379 0.180316 0.225414 1 \n", - " 3 0.496260 0.157411 0.011575 0.090552 1 \n", - " 4 0.483067 0.163651 0.011704 0.094782 1 \n", - " 5 0.230829 0.175379 0.180316 0.225414 1 \n", - " 6 0.593742 0.087546 0.037523 0.088350 1 \n", - " 7 0.230829 0.175379 0.180316 0.225414 1 \n", - " 8 0.000000 0.000000 0.000000 0.000000 1 \n", - " 9 0.230829 0.175379 0.180316 0.225414 1 ,\n", + " 0 0.024961 0.313145 0.083320 0.277825 1 \n", + " 1 0.002355 0.266490 0.069938 0.442759 1 \n", + " 2 0.150177 0.246102 0.061760 0.203999 2 \n", + " 3 0.002355 0.266490 0.069938 0.442759 1 \n", + " 4 0.024961 0.313145 0.083320 0.277825 1 \n", + " 5 0.401314 0.089080 0.024027 0.065767 1 ,\n", " 'relations': Empty DataFrame\n", " Columns: []\n", " Index: [],\n", @@ -1722,132 +1650,80 @@ " \n", " \n", " 0\n", - " brick wall\n", - " neutral\n", - " 0.000000\n", - " 0.929032\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " waste\n", + " negative\n", + " -0.875215\n", + " 0.685741\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", " \n", " \n", " 1\n", - " true ride\n", - " positive\n", - " 0.863267\n", - " 0.900583\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", + " fire\n", + " negative\n", + " -0.934513\n", + " 0.598326\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", " \n", " \n", " 2\n", - " fire\n", - " neutral\n", - " 0.000000\n", - " 0.678235\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", + " car\n", + " negative\n", + " -0.844774\n", + " 0.581432\n", + " 0.144346\n", + " 0.150177\n", + " 0.246102\n", + " 0.061760\n", + " 0.203999\n", + " 2\n", " \n", " \n", " 3\n", - " toys\n", + " hell\n", " negative\n", - " -0.738776\n", - " 0.632428\n", - " 0.308881\n", - " 0.496260\n", - " 0.157411\n", - " 0.011575\n", - " 0.090552\n", + " -0.934513\n", + " 0.577011\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", " \n", " \n", " 4\n", - " toy\n", + " year\n", " negative\n", - " -0.941767\n", - " 0.567418\n", - " 0.317770\n", - " 0.483067\n", - " 0.163651\n", - " 0.011704\n", - " 0.094782\n", + " -0.875215\n", + " 0.563676\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", " \n", " \n", " 5\n", - " glass\n", - " neutral\n", - " 0.000000\n", - " 0.557307\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " \n", - " \n", - " 6\n", - " beast\n", - " positive\n", - " 0.863267\n", - " 0.550077\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", - " 1\n", - " \n", - " \n", - " 7\n", - " ice\n", - " neutral\n", - " 0.000000\n", - " 0.547330\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " \n", - " \n", - " 8\n", - " water\n", + " time\n", " neutral\n", " 0.000000\n", - " 0.539073\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1\n", - " \n", - " \n", - " 9\n", - " name\n", - " negative\n", - " -0.738776\n", - " 0.539073\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " 0.466983\n", + " 0.266573\n", + " 0.401314\n", + " 0.089080\n", + " 0.024027\n", + " 0.065767\n", " 1\n", " \n", " \n", @@ -1855,29 +1731,21 @@ "" ], "text/plain": [ - " text sentiment.label sentiment.score relevance emotion.sadness \\\n", - "0 brick wall neutral 0.000000 0.929032 0.292790 \n", - "1 true ride positive 0.863267 0.900583 0.219645 \n", - "2 fire neutral 0.000000 0.678235 0.292790 \n", - "3 toys negative -0.738776 0.632428 0.308881 \n", - "4 toy negative -0.941767 0.567418 0.317770 \n", - "5 glass neutral 0.000000 0.557307 0.292790 \n", - "6 beast positive 0.863267 0.550077 0.219645 \n", - "7 ice neutral 0.000000 0.547330 0.292790 \n", - "8 water neutral 0.000000 0.539073 0.000000 \n", - "9 name negative -0.738776 0.539073 0.292790 \n", + " text sentiment.label sentiment.score relevance emotion.sadness \\\n", + "0 waste negative -0.875215 0.685741 0.192383 \n", + "1 fire negative -0.934513 0.598326 0.360925 \n", + "2 car negative -0.844774 0.581432 0.144346 \n", + "3 hell negative -0.934513 0.577011 0.360925 \n", + "4 year negative -0.875215 0.563676 0.192383 \n", + "5 time neutral 0.000000 0.466983 0.266573 \n", "\n", " emotion.joy emotion.fear emotion.disgust emotion.anger count \n", - "0 0.230829 0.175379 0.180316 0.225414 1 \n", - "1 0.593742 0.087546 0.037523 0.088350 1 \n", - "2 0.230829 0.175379 0.180316 0.225414 1 \n", - "3 0.496260 0.157411 0.011575 0.090552 1 \n", - "4 0.483067 0.163651 0.011704 0.094782 1 \n", - "5 0.230829 0.175379 0.180316 0.225414 1 \n", - "6 0.593742 0.087546 0.037523 0.088350 1 \n", - "7 0.230829 0.175379 0.180316 0.225414 1 \n", - "8 0.000000 0.000000 0.000000 0.000000 1 \n", - "9 0.230829 0.175379 0.180316 0.225414 1 " + "0 0.024961 0.313145 0.083320 0.277825 1 \n", + "1 0.002355 0.266490 0.069938 0.442759 1 \n", + "2 0.150177 0.246102 0.061760 0.203999 2 \n", + "3 0.002355 0.266490 0.069938 0.442759 1 \n", + "4 0.024961 0.313145 0.083320 0.277825 1 \n", + "5 0.401314 0.089080 0.024027 0.065767 1 " ] }, "execution_count": 18, @@ -1951,184 +1819,116 @@ " \n", " \n", " 0\n", - " brick wall\n", - " neutral\n", - " 0.000000\n", - " 0.929032\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " waste\n", + " negative\n", + " -0.875215\n", + " 0.685741\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 1\n", - " true ride\n", - " positive\n", - " 0.863267\n", - " 0.900583\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", + " fire\n", + " negative\n", + " -0.934513\n", + " 0.598326\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 2\n", - " fire\n", - " neutral\n", - " 0.000000\n", - " 0.678235\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " a true ride: this beast can go through just ab...\n", + " car\n", + " negative\n", + " -0.844774\n", + " 0.581432\n", + " 0.144346\n", + " 0.150177\n", + " 0.246102\n", + " 0.061760\n", + " 0.203999\n", + " 2\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 3\n", - " toys\n", + " hell\n", " negative\n", - " -0.738776\n", - " 0.632428\n", - " 0.308881\n", - " 0.496260\n", - " 0.157411\n", - " 0.011575\n", - " 0.090552\n", + " -0.934513\n", + " 0.577011\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 4\n", - " toy\n", + " year\n", " negative\n", - " -0.941767\n", - " 0.567418\n", - " 0.317770\n", - " 0.483067\n", - " 0.163651\n", - " 0.011704\n", - " 0.094782\n", + " -0.875215\n", + " 0.563676\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 5\n", - " glass\n", - " neutral\n", - " 0.000000\n", - " 0.557307\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 6\n", - " beast\n", - " positive\n", - " 0.863267\n", - " 0.550077\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", - " 1\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 7\n", - " ice\n", - " neutral\n", - " 0.000000\n", - " 0.547330\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 8\n", - " water\n", + " time\n", " neutral\n", " 0.000000\n", - " 0.539073\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 9\n", - " name\n", - " negative\n", - " -0.738776\n", - " 0.539073\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " 0.466983\n", + " 0.266573\n", + " 0.401314\n", + " 0.089080\n", + " 0.024027\n", + " 0.065767\n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " text sentiment.label sentiment.score relevance emotion.sadness \\\n", - "0 brick wall neutral 0.000000 0.929032 0.292790 \n", - "1 true ride positive 0.863267 0.900583 0.219645 \n", - "2 fire neutral 0.000000 0.678235 0.292790 \n", - "3 toys negative -0.738776 0.632428 0.308881 \n", - "4 toy negative -0.941767 0.567418 0.317770 \n", - "5 glass neutral 0.000000 0.557307 0.292790 \n", - "6 beast positive 0.863267 0.550077 0.219645 \n", - "7 ice neutral 0.000000 0.547330 0.292790 \n", - "8 water neutral 0.000000 0.539073 0.000000 \n", - "9 name negative -0.738776 0.539073 0.292790 \n", + " text sentiment.label sentiment.score relevance emotion.sadness \\\n", + "0 waste negative -0.875215 0.685741 0.192383 \n", + "1 fire negative -0.934513 0.598326 0.360925 \n", + "2 car negative -0.844774 0.581432 0.144346 \n", + "3 hell negative -0.934513 0.577011 0.360925 \n", + "4 year negative -0.875215 0.563676 0.192383 \n", + "5 time neutral 0.000000 0.466983 0.266573 \n", "\n", " emotion.joy emotion.fear emotion.disgust emotion.anger count \\\n", - "0 0.230829 0.175379 0.180316 0.225414 1 \n", - "1 0.593742 0.087546 0.037523 0.088350 1 \n", - "2 0.230829 0.175379 0.180316 0.225414 1 \n", - "3 0.496260 0.157411 0.011575 0.090552 1 \n", - "4 0.483067 0.163651 0.011704 0.094782 1 \n", - "5 0.230829 0.175379 0.180316 0.225414 1 \n", - "6 0.593742 0.087546 0.037523 0.088350 1 \n", - "7 0.230829 0.175379 0.180316 0.225414 1 \n", - "8 0.000000 0.000000 0.000000 0.000000 1 \n", - "9 0.230829 0.175379 0.180316 0.225414 1 \n", + "0 0.024961 0.313145 0.083320 0.277825 1 \n", + "1 0.002355 0.266490 0.069938 0.442759 1 \n", + "2 0.150177 0.246102 0.061760 0.203999 2 \n", + "3 0.002355 0.266490 0.069938 0.442759 1 \n", + "4 0.024961 0.313145 0.083320 0.277825 1 \n", + "5 0.401314 0.089080 0.024027 0.065767 1 \n", "\n", " 0 \n", - "0 a true ride: this beast can go through just ab... \n", - "1 a true ride: this beast can go through just ab... \n", - "2 a true ride: this beast can go through just ab... \n", - "3 a true ride: this beast can go through just ab... \n", - "4 a true ride: this beast can go through just ab... \n", - "5 a true ride: this beast can go through just ab... \n", - "6 a true ride: this beast can go through just ab... \n", - "7 a true ride: this beast can go through just ab... \n", - "8 a true ride: this beast can go through just ab... \n", - "9 a true ride: this beast can go through just ab... " + "0 What a waste: I have owned this car for a year... \n", + "1 What a waste: I have owned this car for a year... \n", + "2 What a waste: I have owned this car for a year... \n", + "3 What a waste: I have owned this car for a year... \n", + "4 What a waste: I have owned this car for a year... \n", + "5 What a waste: I have owned this car for a year... " ] }, "execution_count": 19, @@ -2197,290 +1997,182 @@ " \n", " \n", " 0\n", - " brick wall\n", - " neutral\n", - " 0.000000\n", - " 0.929032\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " waste\n", + " negative\n", + " -0.875215\n", + " 0.685741\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 1\n", - " true ride\n", - " positive\n", - " 0.863267\n", - " 0.900583\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", + " fire\n", + " negative\n", + " -0.934513\n", + " 0.598326\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 2\n", - " fire\n", - " neutral\n", - " 0.000000\n", - " 0.678235\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " car\n", + " negative\n", + " -0.844774\n", + " 0.581432\n", + " 0.144346\n", + " 0.150177\n", + " 0.246102\n", + " 0.061760\n", + " 0.203999\n", + " 2\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 3\n", - " toys\n", + " hell\n", " negative\n", - " -0.738776\n", - " 0.632428\n", - " 0.308881\n", - " 0.496260\n", - " 0.157411\n", - " 0.011575\n", - " 0.090552\n", + " -0.934513\n", + " 0.577011\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 4\n", - " toy\n", + " year\n", " negative\n", - " -0.941767\n", - " 0.567418\n", - " 0.317770\n", - " 0.483067\n", - " 0.163651\n", - " 0.011704\n", - " 0.094782\n", + " -0.875215\n", + " 0.563676\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 5\n", - " glass\n", - " neutral\n", - " 0.000000\n", - " 0.557307\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", - " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 6\n", - " beast\n", - " positive\n", - " 0.863267\n", - " 0.550077\n", - " 0.219645\n", - " 0.593742\n", - " 0.087546\n", - " 0.037523\n", - " 0.088350\n", - " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", - " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 7\n", - " ice\n", - " neutral\n", - " 0.000000\n", - " 0.547330\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", - " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", - " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 8\n", - " water\n", + " time\n", " neutral\n", " 0.000000\n", - " 0.539073\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", - " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", - " \n", - " \n", - " 9\n", - " name\n", - " negative\n", - " -0.738776\n", - " 0.539073\n", - " 0.292790\n", - " 0.230829\n", - " 0.175379\n", - " 0.180316\n", - " 0.225414\n", + " 0.466983\n", + " 0.266573\n", + " 0.401314\n", + " 0.089080\n", + " 0.024027\n", + " 0.065767\n", " 1\n", - " on 08/30/02 00:00 AM (PDT)\n", - " bluice3309\n", - " 2000 AM General Hummer SUV 4dr SUV AWD\n", - " a true ride\n", - " this beast can go through just about \\ranythi...\n", - " 4.625\n", + " on 06/15/02 00:00 AM (PDT)\n", + " mike6382\n", + " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", + " What a waste\n", + " I have owned this car for a year and a \\rhalf...\n", + " 1.0\n", " AMGeneral\n", - " a true ride: this beast can go through just ab...\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " text sentiment.label sentiment.score relevance emotion.sadness \\\n", - "0 brick wall neutral 0.000000 0.929032 0.292790 \n", - "1 true ride positive 0.863267 0.900583 0.219645 \n", - "2 fire neutral 0.000000 0.678235 0.292790 \n", - "3 toys negative -0.738776 0.632428 0.308881 \n", - "4 toy negative -0.941767 0.567418 0.317770 \n", - "5 glass neutral 0.000000 0.557307 0.292790 \n", - "6 beast positive 0.863267 0.550077 0.219645 \n", - "7 ice neutral 0.000000 0.547330 0.292790 \n", - "8 water neutral 0.000000 0.539073 0.000000 \n", - "9 name negative -0.738776 0.539073 0.292790 \n", + " text sentiment.label sentiment.score relevance emotion.sadness \\\n", + "0 waste negative -0.875215 0.685741 0.192383 \n", + "1 fire negative -0.934513 0.598326 0.360925 \n", + "2 car negative -0.844774 0.581432 0.144346 \n", + "3 hell negative -0.934513 0.577011 0.360925 \n", + "4 year negative -0.875215 0.563676 0.192383 \n", + "5 time neutral 0.000000 0.466983 0.266573 \n", "\n", " emotion.joy emotion.fear emotion.disgust emotion.anger count \\\n", - "0 0.230829 0.175379 0.180316 0.225414 1 \n", - "1 0.593742 0.087546 0.037523 0.088350 1 \n", - "2 0.230829 0.175379 0.180316 0.225414 1 \n", - "3 0.496260 0.157411 0.011575 0.090552 1 \n", - "4 0.483067 0.163651 0.011704 0.094782 1 \n", - "5 0.230829 0.175379 0.180316 0.225414 1 \n", - "6 0.593742 0.087546 0.037523 0.088350 1 \n", - "7 0.230829 0.175379 0.180316 0.225414 1 \n", - "8 0.000000 0.000000 0.000000 0.000000 1 \n", - "9 0.230829 0.175379 0.180316 0.225414 1 \n", + "0 0.024961 0.313145 0.083320 0.277825 1 \n", + "1 0.002355 0.266490 0.069938 0.442759 1 \n", + "2 0.150177 0.246102 0.061760 0.203999 2 \n", + "3 0.002355 0.266490 0.069938 0.442759 1 \n", + "4 0.024961 0.313145 0.083320 0.277825 1 \n", + "5 0.401314 0.089080 0.024027 0.065767 1 \n", "\n", - " Review_Date Author_Name \\\n", - "0 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "1 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "2 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "3 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "4 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "5 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "6 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "7 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "8 on 08/30/02 00:00 AM (PDT) bluice3309 \n", - "9 on 08/30/02 00:00 AM (PDT) bluice3309 \n", + " Review_Date Author_Name \\\n", + "0 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "1 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "2 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "3 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "4 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "5 on 06/15/02 00:00 AM (PDT) mike6382 \n", "\n", - " Vehicle_Title Review_Title \\\n", - "0 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "1 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "2 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "3 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "4 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "5 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "6 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "7 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "8 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", - "9 2000 AM General Hummer SUV 4dr SUV AWD a true ride \n", + " Vehicle_Title Review_Title \\\n", + "0 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "1 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "2 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "3 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "4 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "5 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", "\n", " Review Rating\\r Car_Make \\\n", - "0 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "1 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "2 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "3 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "4 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "5 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "6 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "7 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "8 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", - "9 this beast can go through just about \\ranythi... 4.625 AMGeneral \n", + "0 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "1 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "2 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "3 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "4 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "5 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", "\n", " Review_Content \n", - "0 a true ride: this beast can go through just ab... \n", - "1 a true ride: this beast can go through just ab... \n", - "2 a true ride: this beast can go through just ab... \n", - "3 a true ride: this beast can go through just ab... \n", - "4 a true ride: this beast can go through just ab... \n", - "5 a true ride: this beast can go through just ab... \n", - "6 a true ride: this beast can go through just ab... \n", - "7 a true ride: this beast can go through just ab... \n", - "8 a true ride: this beast can go through just ab... \n", - "9 a true ride: this beast can go through just ab... " + "0 What a waste: I have owned this car for a year... \n", + "1 What a waste: I have owned this car for a year... \n", + "2 What a waste: I have owned this car for a year... \n", + "3 What a waste: I have owned this car for a year... \n", + "4 What a waste: I have owned this car for a year... \n", + "5 What a waste: I have owned this car for a year... " ] }, "execution_count": 20, @@ -2578,370 +2270,370 @@ " \n", " \n", " \n", - " 0\n", - " count\n", - " emotion.anger\n", - " emotion.disgust\n", - " emotion.fear\n", - " emotion.joy\n", - " emotion.sadness\n", - " relevance\n", + " text\n", " sentiment.label\n", " sentiment.score\n", - " text\n", + " relevance\n", + " emotion.sadness\n", + " emotion.joy\n", + " emotion.fear\n", + " emotion.disgust\n", + " emotion.anger\n", + " count\n", + " 0\n", " \n", " \n", " \n", " \n", " 0\n", - " a true ride: this beast can go through just ab...\n", + " waste\n", + " negative\n", + " -0.875215\n", + " 0.685741\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1.0\n", - " 0.225414\n", - " 0.180316\n", - " 0.175379\n", - " 0.230829\n", - " 0.292790\n", - " 0.929032\n", - " neutral\n", - " 0.000000\n", - " brick wall\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 1\n", - " a true ride: this beast can go through just ab...\n", + " fire\n", + " negative\n", + " -0.934513\n", + " 0.598326\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1.0\n", - " 0.088350\n", - " 0.037523\n", - " 0.087546\n", - " 0.593742\n", - " 0.219645\n", - " 0.900583\n", - " positive\n", - " 0.863267\n", - " true ride\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 2\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.225414\n", - " 0.180316\n", - " 0.175379\n", - " 0.230829\n", - " 0.292790\n", - " 0.678235\n", - " neutral\n", - " 0.000000\n", - " fire\n", + " car\n", + " negative\n", + " -0.844774\n", + " 0.581432\n", + " 0.144346\n", + " 0.150177\n", + " 0.246102\n", + " 0.061760\n", + " 0.203999\n", + " 2.0\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 3\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.090552\n", - " 0.011575\n", - " 0.157411\n", - " 0.496260\n", - " 0.308881\n", - " 0.632428\n", + " hell\n", " negative\n", - " -0.738776\n", - " toys\n", + " -0.934513\n", + " 0.577011\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", + " 1.0\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 4\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.094782\n", - " 0.011704\n", - " 0.163651\n", - " 0.483067\n", - " 0.317770\n", - " 0.567418\n", + " year\n", " negative\n", - " -0.941767\n", - " toy\n", + " -0.875215\n", + " 0.563676\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", + " 1.0\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", " 5\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.225414\n", - " 0.180316\n", - " 0.175379\n", - " 0.230829\n", - " 0.292790\n", - " 0.557307\n", + " time\n", " neutral\n", " 0.000000\n", - " glass\n", + " 0.466983\n", + " 0.266573\n", + " 0.401314\n", + " 0.089080\n", + " 0.024027\n", + " 0.065767\n", + " 1.0\n", + " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 6\n", - " a true ride: this beast can go through just ab...\n", + " 0\n", + " Top speed\n", + " negative\n", + " -0.537564\n", + " 0.881037\n", + " 0.509224\n", + " 0.199172\n", + " 0.038777\n", + " 0.065161\n", + " 0.044472\n", " 1.0\n", - " 0.088350\n", - " 0.037523\n", - " 0.087546\n", - " 0.593742\n", - " 0.219645\n", - " 0.550077\n", - " positive\n", - " 0.863267\n", - " beast\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 7\n", - " a true ride: this beast can go through just ab...\n", + " 1\n", + " OK cause\n", + " positive\n", + " 0.647515\n", + " 0.786985\n", + " 0.063022\n", + " 0.432975\n", + " 0.107965\n", + " 0.016918\n", + " 0.090944\n", " 1.0\n", - " 0.225414\n", - " 0.180316\n", - " 0.175379\n", - " 0.230829\n", - " 0.292790\n", - " 0.547330\n", - " neutral\n", - " 0.000000\n", - " ice\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 8\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.539073\n", + " 2\n", + " HUMMER H\n", " neutral\n", " 0.000000\n", - " water\n", + " 0.639671\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 9\n", - " a true ride: this beast can go through just ab...\n", - " 1.0\n", - " 0.225414\n", - " 0.180316\n", - " 0.175379\n", - " 0.230829\n", - " 0.292790\n", - " 0.539073\n", + " 3\n", + " seat cushion\n", " negative\n", - " -0.738776\n", - " name\n", + " -0.537564\n", + " 0.593566\n", + " 0.509224\n", + " 0.199172\n", + " 0.038777\n", + " 0.065161\n", + " 0.044472\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 0\n", - " What a waste: I have owned this car for a year...\n", - " 1.0\n", - " 0.457683\n", - " 0.244124\n", - " 0.130232\n", - " 0.015328\n", - " 0.494477\n", - " 0.685741\n", + " 4\n", + " HUMMER\n", " negative\n", - " -0.875214\n", - " waste\n", + " -0.913874\n", + " 0.582162\n", + " 0.172604\n", + " 0.221188\n", + " 0.146469\n", + " 0.022002\n", + " 0.029588\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 1\n", - " What a waste: I have owned this car for a year...\n", + " 5\n", + " speed\n", + " positive\n", + " 0.305110\n", + " 0.548092\n", + " 0.286123\n", + " 0.316074\n", + " 0.073371\n", + " 0.041039\n", + " 0.067708\n", " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.598326\n", - " negative\n", - " -0.934512\n", - " fire\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 2\n", - " What a waste: I have owned this car for a year...\n", - " 2.0\n", - " 0.488137\n", - " 0.223132\n", - " 0.195960\n", - " 0.014601\n", - " 0.407834\n", - " 0.581432\n", + " 6\n", + " thing\n", " negative\n", - " -0.875214\n", - " car\n", + " -0.949193\n", + " 0.534867\n", + " 0.645165\n", + " 0.010028\n", + " 0.216581\n", + " 0.022772\n", + " 0.055427\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 3\n", - " What a waste: I have owned this car for a year...\n", + " 7\n", + " Vehicle\n", + " negative\n", + " -0.961235\n", + " 0.531410\n", + " 0.180207\n", + " 0.061332\n", + " 0.192902\n", + " 0.008274\n", + " 0.046232\n", " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.577011\n", - " neutral\n", - " 0.000000\n", - " hell\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 4\n", - " What a waste: I have owned this car for a year...\n", - " 1.0\n", - " 0.457683\n", - " 0.244124\n", - " 0.130232\n", - " 0.015328\n", - " 0.494477\n", - " 0.563676\n", + " 8\n", + " beast\n", " negative\n", - " -0.875214\n", - " year\n", + " -0.961235\n", + " 0.524488\n", + " 0.180207\n", + " 0.061332\n", + " 0.192902\n", + " 0.008274\n", + " 0.046232\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 5\n", - " What a waste: I have owned this car for a year...\n", + " 9\n", + " thats\n", + " positive\n", + " 0.647515\n", + " 0.462435\n", + " 0.063022\n", + " 0.432975\n", + " 0.107965\n", + " 0.016918\n", + " 0.090944\n", " 1.0\n", - " 0.345149\n", - " 0.351359\n", - " 0.333530\n", - " 0.024940\n", - " 0.245249\n", - " 0.475221\n", - " negative\n", - " -0.665741\n", - " reliabile\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 6\n", - " What a waste: I have owned this car for a year...\n", - " 1.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.475221\n", + " 10\n", + " bummer\n", " negative\n", - " -0.813165\n", - " time\n", + " -0.961235\n", + " 0.360189\n", + " 0.180207\n", + " 0.061332\n", + " 0.192902\n", + " 0.008274\n", + " 0.046232\n", + " 1.0\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", - " 7\n", - " What a waste: I have owned this car for a year...\n", + " 11\n", + " average\n", + " negative\n", + " -0.857270\n", + " 0.341687\n", + " 0.165002\n", + " 0.381043\n", + " 0.100036\n", + " 0.035730\n", + " 0.012944\n", " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.475221\n", - " neutral\n", - " 0.000000\n", - " wont\n", + " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", " \n", " \n", " 0\n", - " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", + " AWESOME HUMMER\n", + " positive\n", + " 0.734682\n", + " 0.833177\n", + " 0.032499\n", + " 0.493942\n", + " 0.116809\n", + " 0.009257\n", + " 0.024046\n", " 1.0\n", - " 0.134550\n", - " 0.289156\n", - " 0.079885\n", - " 0.033319\n", - " 0.170084\n", - " 0.881037\n", - " negative\n", - " -0.537564\n", - " Top speed\n", + " AWESOME HUMMER: Hummer is unstoppable. May onl...\n", " \n", " \n", " 1\n", - " HUMMER NOT A bummer : Vehicle is a beast. I do...\n", + " mph\n", + " neutral\n", + " 0.000000\n", + " 0.635404\n", + " 0.499977\n", + " 0.151388\n", + " 0.039640\n", + " 0.036049\n", + " 0.064654\n", " 1.0\n", - " 0.053211\n", - " 0.021603\n", - " 0.467839\n", - " 0.038405\n", - " 0.115986\n", - " 0.790468\n", - " negative\n", - " -0.857269\n", - " HUMMER H1\n", + " AWESOME HUMMER: Hummer is unstoppable. May onl...\n", " \n", " \n", "\n", "" ], "text/plain": [ - " 0 count emotion.anger \\\n", - "0 a true ride: this beast can go through just ab... 1.0 0.225414 \n", - "1 a true ride: this beast can go through just ab... 1.0 0.088350 \n", - "2 a true ride: this beast can go through just ab... 1.0 0.225414 \n", - "3 a true ride: this beast can go through just ab... 1.0 0.090552 \n", - "4 a true ride: this beast can go through just ab... 1.0 0.094782 \n", - "5 a true ride: this beast can go through just ab... 1.0 0.225414 \n", - "6 a true ride: this beast can go through just ab... 1.0 0.088350 \n", - "7 a true ride: this beast can go through just ab... 1.0 0.225414 \n", - "8 a true ride: this beast can go through just ab... 1.0 0.000000 \n", - "9 a true ride: this beast can go through just ab... 1.0 0.225414 \n", - "0 What a waste: I have owned this car for a year... 1.0 0.457683 \n", - "1 What a waste: I have owned this car for a year... 1.0 0.665425 \n", - "2 What a waste: I have owned this car for a year... 2.0 0.488137 \n", - "3 What a waste: I have owned this car for a year... 1.0 0.665425 \n", - "4 What a waste: I have owned this car for a year... 1.0 0.457683 \n", - "5 What a waste: I have owned this car for a year... 1.0 0.345149 \n", - "6 What a waste: I have owned this car for a year... 1.0 0.000000 \n", - "7 What a waste: I have owned this car for a year... 1.0 0.665425 \n", - "0 HUMMER NOT A bummer : Vehicle is a beast. I do... 1.0 0.134550 \n", - "1 HUMMER NOT A bummer : Vehicle is a beast. I do... 1.0 0.053211 \n", + " text sentiment.label sentiment.score relevance \\\n", + "0 waste negative -0.875215 0.685741 \n", + "1 fire negative -0.934513 0.598326 \n", + "2 car negative -0.844774 0.581432 \n", + "3 hell negative -0.934513 0.577011 \n", + "4 year negative -0.875215 0.563676 \n", + "5 time neutral 0.000000 0.466983 \n", + "0 Top speed negative -0.537564 0.881037 \n", + "1 OK cause positive 0.647515 0.786985 \n", + "2 HUMMER H neutral 0.000000 0.639671 \n", + "3 seat cushion negative -0.537564 0.593566 \n", + "4 HUMMER negative -0.913874 0.582162 \n", + "5 speed positive 0.305110 0.548092 \n", + "6 thing negative -0.949193 0.534867 \n", + "7 Vehicle negative -0.961235 0.531410 \n", + "8 beast negative -0.961235 0.524488 \n", + "9 thats positive 0.647515 0.462435 \n", + "10 bummer negative -0.961235 0.360189 \n", + "11 average negative -0.857270 0.341687 \n", + "0 AWESOME HUMMER positive 0.734682 0.833177 \n", + "1 mph neutral 0.000000 0.635404 \n", "\n", - " emotion.disgust emotion.fear emotion.joy emotion.sadness relevance \\\n", - "0 0.180316 0.175379 0.230829 0.292790 0.929032 \n", - "1 0.037523 0.087546 0.593742 0.219645 0.900583 \n", - "2 0.180316 0.175379 0.230829 0.292790 0.678235 \n", - "3 0.011575 0.157411 0.496260 0.308881 0.632428 \n", - "4 0.011704 0.163651 0.483067 0.317770 0.567418 \n", - "5 0.180316 0.175379 0.230829 0.292790 0.557307 \n", - "6 0.037523 0.087546 0.593742 0.219645 0.550077 \n", - "7 0.180316 0.175379 0.230829 0.292790 0.547330 \n", - "8 0.000000 0.000000 0.000000 0.000000 0.539073 \n", - "9 0.180316 0.175379 0.230829 0.292790 0.539073 \n", - "0 0.244124 0.130232 0.015328 0.494477 0.685741 \n", - "1 0.238400 0.116199 0.010869 0.283251 0.598326 \n", - "2 0.223132 0.195960 0.014601 0.407834 0.581432 \n", - "3 0.238400 0.116199 0.010869 0.283251 0.577011 \n", - "4 0.244124 0.130232 0.015328 0.494477 0.563676 \n", - "5 0.351359 0.333530 0.024940 0.245249 0.475221 \n", - "6 0.000000 0.000000 0.000000 0.000000 0.475221 \n", - "7 0.238400 0.116199 0.010869 0.283251 0.475221 \n", - "0 0.289156 0.079885 0.033319 0.170084 0.881037 \n", - "1 0.021603 0.467839 0.038405 0.115986 0.790468 \n", + " emotion.sadness emotion.joy emotion.fear emotion.disgust \\\n", + "0 0.192383 0.024961 0.313145 0.083320 \n", + "1 0.360925 0.002355 0.266490 0.069938 \n", + "2 0.144346 0.150177 0.246102 0.061760 \n", + "3 0.360925 0.002355 0.266490 0.069938 \n", + "4 0.192383 0.024961 0.313145 0.083320 \n", + "5 0.266573 0.401314 0.089080 0.024027 \n", + "0 0.509224 0.199172 0.038777 0.065161 \n", + "1 0.063022 0.432975 0.107965 0.016918 \n", + "2 NaN NaN NaN NaN \n", + "3 0.509224 0.199172 0.038777 0.065161 \n", + "4 0.172604 0.221188 0.146469 0.022002 \n", + "5 0.286123 0.316074 0.073371 0.041039 \n", + "6 0.645165 0.010028 0.216581 0.022772 \n", + "7 0.180207 0.061332 0.192902 0.008274 \n", + "8 0.180207 0.061332 0.192902 0.008274 \n", + "9 0.063022 0.432975 0.107965 0.016918 \n", + "10 0.180207 0.061332 0.192902 0.008274 \n", + "11 0.165002 0.381043 0.100036 0.035730 \n", + "0 0.032499 0.493942 0.116809 0.009257 \n", + "1 0.499977 0.151388 0.039640 0.036049 \n", "\n", - " sentiment.label sentiment.score text \n", - "0 neutral 0.000000 brick wall \n", - "1 positive 0.863267 true ride \n", - "2 neutral 0.000000 fire \n", - "3 negative -0.738776 toys \n", - "4 negative -0.941767 toy \n", - "5 neutral 0.000000 glass \n", - "6 positive 0.863267 beast \n", - "7 neutral 0.000000 ice \n", - "8 neutral 0.000000 water \n", - "9 negative -0.738776 name \n", - "0 negative -0.875214 waste \n", - "1 negative -0.934512 fire \n", - "2 negative -0.875214 car \n", - "3 neutral 0.000000 hell \n", - "4 negative -0.875214 year \n", - "5 negative -0.665741 reliabile \n", - "6 negative -0.813165 time \n", - "7 neutral 0.000000 wont \n", - "0 negative -0.537564 Top speed \n", - "1 negative -0.857269 HUMMER H1 " + " emotion.anger count 0 \n", + "0 0.277825 1.0 What a waste: I have owned this car for a year... \n", + "1 0.442759 1.0 What a waste: I have owned this car for a year... \n", + "2 0.203999 2.0 What a waste: I have owned this car for a year... \n", + "3 0.442759 1.0 What a waste: I have owned this car for a year... \n", + "4 0.277825 1.0 What a waste: I have owned this car for a year... \n", + "5 0.065767 1.0 What a waste: I have owned this car for a year... \n", + "0 0.044472 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "1 0.090944 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "2 NaN 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "3 0.044472 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "4 0.029588 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "5 0.067708 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "6 0.055427 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "7 0.046232 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "8 0.046232 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "9 0.090944 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "10 0.046232 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "11 0.012944 1.0 HUMMER NOT A bummer : Vehicle is a beast. I do... \n", + "0 0.024046 1.0 AWESOME HUMMER: Hummer is unstoppable. May onl... \n", + "1 0.064654 1.0 AWESOME HUMMER: Hummer is unstoppable. May onl... " ] }, "execution_count": 23, @@ -3002,16 +2694,16 @@ " \n", " \n", " \n", - " count\n", - " emotion.anger\n", - " emotion.disgust\n", - " emotion.fear\n", - " emotion.joy\n", - " emotion.sadness\n", - " relevance\n", + " text\n", " sentiment.label\n", " sentiment.score\n", - " text\n", + " relevance\n", + " emotion.sadness\n", + " emotion.joy\n", + " emotion.fear\n", + " emotion.disgust\n", + " emotion.anger\n", + " count\n", " Review_Date\n", " Author_Name\n", " Vehicle_Title\n", @@ -3024,59 +2716,17 @@ " \n", " \n", " \n", - " 10\n", - " 1.0\n", - " 0.457683\n", - " 0.244124\n", - " 0.130232\n", - " 0.015328\n", - " 0.494477\n", - " 0.685741\n", - " negative\n", - " -0.875214\n", + " 0\n", " waste\n", - " on 06/15/02 00:00 AM (PDT)\n", - " mike6382\n", - " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", - " What a waste\n", - " I have owned this car for a year and a \\rhalf...\n", - " 1.0\n", - " AMGeneral\n", - " What a waste: I have owned this car for a year...\n", - " \n", - " \n", - " 11\n", - " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.598326\n", " negative\n", - " -0.934512\n", - " fire\n", - " on 06/15/02 00:00 AM (PDT)\n", - " mike6382\n", - " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", - " What a waste\n", - " I have owned this car for a year and a \\rhalf...\n", + " -0.875215\n", + " 0.685741\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", " 1.0\n", - " AMGeneral\n", - " What a waste: I have owned this car for a year...\n", - " \n", - " \n", - " 12\n", - " 2.0\n", - " 0.488137\n", - " 0.223132\n", - " 0.195960\n", - " 0.014601\n", - " 0.407834\n", - " 0.581432\n", - " negative\n", - " -0.875214\n", - " car\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3087,17 +2737,17 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 13\n", + " 1\n", + " fire\n", + " negative\n", + " -0.934513\n", + " 0.598326\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.577011\n", - " neutral\n", - " 0.000000\n", - " hell\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3108,17 +2758,17 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 14\n", - " 1.0\n", - " 0.457683\n", - " 0.244124\n", - " 0.130232\n", - " 0.015328\n", - " 0.494477\n", - " 0.563676\n", + " 2\n", + " car\n", " negative\n", - " -0.875214\n", - " year\n", + " -0.844774\n", + " 0.581432\n", + " 0.144346\n", + " 0.150177\n", + " 0.246102\n", + " 0.061760\n", + " 0.203999\n", + " 2.0\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3129,17 +2779,17 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 15\n", - " 1.0\n", - " 0.345149\n", - " 0.351359\n", - " 0.333530\n", - " 0.024940\n", - " 0.245249\n", - " 0.475221\n", + " 3\n", + " hell\n", " negative\n", - " -0.665741\n", - " reliabile\n", + " -0.934513\n", + " 0.577011\n", + " 0.360925\n", + " 0.002355\n", + " 0.266490\n", + " 0.069938\n", + " 0.442759\n", + " 1.0\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3150,17 +2800,17 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 16\n", - " 1.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.475221\n", + " 4\n", + " year\n", " negative\n", - " -0.813165\n", - " time\n", + " -0.875215\n", + " 0.563676\n", + " 0.192383\n", + " 0.024961\n", + " 0.313145\n", + " 0.083320\n", + " 0.277825\n", + " 1.0\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3171,17 +2821,17 @@ " What a waste: I have owned this car for a year...\n", " \n", " \n", - " 17\n", - " 1.0\n", - " 0.665425\n", - " 0.238400\n", - " 0.116199\n", - " 0.010869\n", - " 0.283251\n", - " 0.475221\n", + " 5\n", + " time\n", " neutral\n", " 0.000000\n", - " wont\n", + " 0.466983\n", + " 0.266573\n", + " 0.401314\n", + " 0.089080\n", + " 0.024027\n", + " 0.065767\n", + " 1.0\n", " on 06/15/02 00:00 AM (PDT)\n", " mike6382\n", " 2000 AM General Hummer SUV Hard Top 4dr SUV AWD\n", @@ -3196,65 +2846,53 @@ "" ], "text/plain": [ - " count emotion.anger emotion.disgust emotion.fear emotion.joy \\\n", - "10 1.0 0.457683 0.244124 0.130232 0.015328 \n", - "11 1.0 0.665425 0.238400 0.116199 0.010869 \n", - "12 2.0 0.488137 0.223132 0.195960 0.014601 \n", - "13 1.0 0.665425 0.238400 0.116199 0.010869 \n", - "14 1.0 0.457683 0.244124 0.130232 0.015328 \n", - "15 1.0 0.345149 0.351359 0.333530 0.024940 \n", - "16 1.0 0.000000 0.000000 0.000000 0.000000 \n", - "17 1.0 0.665425 0.238400 0.116199 0.010869 \n", + " text sentiment.label sentiment.score relevance emotion.sadness \\\n", + "0 waste negative -0.875215 0.685741 0.192383 \n", + "1 fire negative -0.934513 0.598326 0.360925 \n", + "2 car negative -0.844774 0.581432 0.144346 \n", + "3 hell negative -0.934513 0.577011 0.360925 \n", + "4 year negative -0.875215 0.563676 0.192383 \n", + "5 time neutral 0.000000 0.466983 0.266573 \n", "\n", - " emotion.sadness relevance sentiment.label sentiment.score text \\\n", - "10 0.494477 0.685741 negative -0.875214 waste \n", - "11 0.283251 0.598326 negative -0.934512 fire \n", - "12 0.407834 0.581432 negative -0.875214 car \n", - "13 0.283251 0.577011 neutral 0.000000 hell \n", - "14 0.494477 0.563676 negative -0.875214 year \n", - "15 0.245249 0.475221 negative -0.665741 reliabile \n", - "16 0.000000 0.475221 negative -0.813165 time \n", - "17 0.283251 0.475221 neutral 0.000000 wont \n", + " emotion.joy emotion.fear emotion.disgust emotion.anger count \\\n", + "0 0.024961 0.313145 0.083320 0.277825 1.0 \n", + "1 0.002355 0.266490 0.069938 0.442759 1.0 \n", + "2 0.150177 0.246102 0.061760 0.203999 2.0 \n", + "3 0.002355 0.266490 0.069938 0.442759 1.0 \n", + "4 0.024961 0.313145 0.083320 0.277825 1.0 \n", + "5 0.401314 0.089080 0.024027 0.065767 1.0 \n", "\n", - " Review_Date Author_Name \\\n", - "10 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "11 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "12 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "13 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "14 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "15 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "16 on 06/15/02 00:00 AM (PDT) mike6382 \n", - "17 on 06/15/02 00:00 AM (PDT) mike6382 \n", + " Review_Date Author_Name \\\n", + "0 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "1 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "2 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "3 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "4 on 06/15/02 00:00 AM (PDT) mike6382 \n", + "5 on 06/15/02 00:00 AM (PDT) mike6382 \n", "\n", - " Vehicle_Title Review_Title \\\n", - "10 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "11 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "12 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "13 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "14 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "15 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "16 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", - "17 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + " Vehicle_Title Review_Title \\\n", + "0 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "1 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "2 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "3 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "4 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", + "5 2000 AM General Hummer SUV Hard Top 4dr SUV AWD What a waste \n", "\n", - " Review Rating\\r Car_Make \\\n", - "10 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "11 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "12 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "13 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "14 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "15 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "16 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", - "17 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + " Review Rating\\r Car_Make \\\n", + "0 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "1 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "2 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "3 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "4 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", + "5 I have owned this car for a year and a \\rhalf... 1.0 AMGeneral \n", "\n", - " Review_Content \n", - "10 What a waste: I have owned this car for a year... \n", - "11 What a waste: I have owned this car for a year... \n", - "12 What a waste: I have owned this car for a year... \n", - "13 What a waste: I have owned this car for a year... \n", - "14 What a waste: I have owned this car for a year... \n", - "15 What a waste: I have owned this car for a year... \n", - "16 What a waste: I have owned this car for a year... \n", - "17 What a waste: I have owned this car for a year... " + " Review_Content \n", + "0 What a waste: I have owned this car for a year... \n", + "1 What a waste: I have owned this car for a year... \n", + "2 What a waste: I have owned this car for a year... \n", + "3 What a waste: I have owned this car for a year... \n", + "4 What a waste: I have owned this car for a year... \n", + "5 What a waste: I have owned this car for a year... " ] }, "execution_count": 24, @@ -3274,14 +2912,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As we mentioned above, Watson NLU assigns the sentiment to the keywords based on their context within the sentence. Hence, all keywords within one sentence get the same sentiment score. Thus, to get the aggregated sentiment of each review we calulate the mean sentiment score of its sentences by considering the sentiment assigned to one keyword in each sentence. More specifically, we first drop duplicate sentiment scores for each review and then we calculate the average sentiment and emotion score for each review:" + "merged_keywords_review_dfAs we mentioned above, Watson NLU assigns the sentiment to the keywords based on their context within the sentence. Hence, all keywords within one sentence get the same sentiment score. Thus, to get the aggregated sentiment of each review we calulate the mean sentiment score of its sentences by considering the sentiment assigned to one keyword in each sentence. More specifically, we first drop duplicate sentiment scores for each review and then we calculate the average sentiment and emotion score for each review:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { - "scrolled": true + "tags": [] }, "outputs": [ { @@ -3305,12 +2943,11 @@ " \n", " \n", " \n", - " emotion.anger\n", - " emotion.disgust\n", - " emotion.fear\n", - " emotion.joy\n", " emotion.sadness\n", - " relevance\n", + " emotion.joy\n", + " emotion.fear\n", + " emotion.disgust\n", + " emotion.anger\n", " sentiment.score\n", " Rating\\r\n", " \n", @@ -3323,372 +2960,328 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " 2010 M 35 Acceleration/Braking Defects\n", - " 0.114714\n", - " 0.072652\n", - " 0.143729\n", - " 0.053823\n", - " 0.339398\n", - " 0.647531\n", - " -0.496327\n", - " 2.375\n", - " \n", - " \n", - " A Dream\n", - " 0.172335\n", - " 0.059397\n", - " 0.092631\n", - " 0.076986\n", - " 0.145674\n", - " 0.732942\n", - " -0.440443\n", + " 1 sweet R32\n", + " 0.151543\n", + " 0.532162\n", + " 0.067859\n", + " 0.018501\n", + " 0.112994\n", + " 0.649825\n", " 4.875\n", " \n", " \n", - " A Wonderful Ownership Experiance\n", - " 0.117943\n", - " 0.016000\n", - " 0.101099\n", - " 0.243949\n", - " 0.113873\n", - " 0.646053\n", - " 0.245085\n", - " 4.750\n", + " 2002 Trans Am/Sunset Orange Metallic\n", + " 0.176322\n", + " 0.465210\n", + " 0.257064\n", + " 0.032842\n", + " 0.038908\n", + " 0.148035\n", + " 4.625\n", " \n", " \n", - " Best truck ever\n", - " 0.042779\n", - " 0.014326\n", - " 0.029521\n", - " 0.277188\n", - " 0.179243\n", - " 0.998194\n", - " 0.969981\n", - " 5.000\n", + " 42 days of driving 8 days in the shop\n", + " 0.206478\n", + " 0.563466\n", + " 0.114506\n", + " 0.010082\n", + " 0.082325\n", + " -0.054126\n", + " 3.375\n", " \n", " \n", - " Even better than the Chevy\n", - " 0.044507\n", - " 0.045185\n", - " 0.054851\n", - " 0.284697\n", - " 0.098830\n", - " 0.664315\n", - " -0.075949\n", - " 5.000\n", + " A great little car\n", + " 0.278575\n", + " 0.470586\n", + " 0.063823\n", + " 0.015218\n", + " 0.039688\n", + " 0.503785\n", + " 4.875\n", " \n", " \n", - " I LOVE MY SLK\n", - " 0.070950\n", - " 0.036633\n", - " 0.064407\n", - " 0.694037\n", - " 0.104404\n", - " 0.645773\n", - " 0.926009\n", + " AWESOME FUN MY LITTLE TIGER\n", + " 0.007629\n", + " 0.628312\n", + " 0.013015\n", + " 0.001452\n", + " 0.024782\n", + " 0.986029\n", " 5.000\n", " \n", " \n", - " I love my Caliber\n", - " 0.057248\n", - " 0.040379\n", - " 0.049315\n", - " 0.604041\n", - " 0.187137\n", - " 0.639690\n", - " 0.919963\n", + " I LOVE my Focus\n", + " 0.074019\n", + " 0.589196\n", + " 0.111722\n", + " 0.008124\n", + " 0.066092\n", + " 0.621983\n", " 4.750\n", " \n", " \n", " Looks Good But Hunk Of Junk\n", - " 0.158059\n", - " 0.097350\n", - " 0.173938\n", - " 0.283959\n", - " 0.441097\n", - " 0.819306\n", - " -0.999568\n", + " 0.144671\n", + " 0.061358\n", + " 0.060613\n", + " 0.050494\n", + " 0.116835\n", + " -0.984622\n", " 2.875\n", " \n", " \n", - " Small Reliable Gas Saver!\n", - " 0.106193\n", - " 0.034896\n", - " 0.076201\n", - " 0.215213\n", - " 0.193688\n", - " 0.620210\n", - " 0.069819\n", - " 4.500\n", + " Mr TACOMA\n", + " 0.122766\n", + " 0.825653\n", + " 0.034777\n", + " 0.023124\n", + " 0.030344\n", + " 0.633803\n", + " 5.000\n", " \n", " \n", " Veracruz\n", - " 0.077888\n", - " 0.017731\n", - " 0.054831\n", - " 0.660735\n", - " 0.153267\n", - " 0.797108\n", - " 0.591815\n", + " 0.106981\n", + " 0.524371\n", + " 0.091482\n", + " 0.012344\n", + " 0.054493\n", + " 0.591816\n", " 4.750\n", " \n", " \n", - " hfgh\n", - " 0.018069\n", - " 0.007672\n", - " 0.040851\n", - " 0.866982\n", - " 0.023953\n", - " 0.860490\n", - " 0.971604\n", - " 5.000\n", + " You will pay for that warranty\n", + " 0.396306\n", + " 0.110458\n", + " 0.056980\n", + " 0.021192\n", + " 0.119030\n", + " -0.373583\n", + " 2.750\n", " \n", " \n", - " i'm on my second one\n", - " 0.367577\n", - " 0.156578\n", - " 0.418364\n", - " 0.043710\n", - " 0.366614\n", - " 0.790430\n", - " -0.825715\n", - " 5.000\n", + " everyday rSx\n", + " 0.038486\n", + " 0.515852\n", + " 0.133419\n", + " 0.008035\n", + " 0.033998\n", + " 0.677286\n", + " 4.000\n", " \n", " \n", - " \"Acceleration failure\" - Genesis phraseology\n", - " 0.141623\n", - " 0.103167\n", - " 0.200245\n", - " 0.063420\n", - " 0.281382\n", - " 0.626660\n", - " -0.643850\n", - " 3.000\n", + " got new weel\n", + " 0.108507\n", + " 0.348390\n", + " 0.079194\n", + " 0.034643\n", + " 0.239177\n", + " 0.654034\n", + " 4.625\n", " \n", " \n", - " \"FUN\"\n", - " 0.079389\n", - " 0.033952\n", - " 0.083648\n", - " 0.535024\n", - " 0.236222\n", - " 0.629725\n", - " 0.086591\n", + " i'm on my second one\n", + " 0.063124\n", + " 0.024840\n", + " 0.053951\n", + " 0.026402\n", + " 0.165089\n", + " -0.973446\n", " 5.000\n", " \n", " \n", - " \"First Ride\" Impressions when I visited Tesla's Factory\n", - " 0.089593\n", - " 0.035031\n", - " 0.061062\n", - " 0.353900\n", - " 0.148852\n", - " 0.623073\n", - " 0.664976\n", - " 4.875\n", + " ! un happy Camper\n", + " 0.424926\n", + " 0.219506\n", + " 0.066627\n", + " 0.036578\n", + " 0.084982\n", + " -0.388182\n", + " 2.625\n", " \n", " \n", - " \"Free\" Green on green lightning\n", - " 0.099639\n", - " 0.032288\n", - " 0.087812\n", - " 0.351336\n", - " 0.215824\n", - " 0.582427\n", - " 0.236840\n", - " 5.000\n", + " \"\"\"I can't believe it \"\"\n", + " 0.244671\n", + " 0.025868\n", + " 0.053133\n", + " 0.067597\n", + " 0.165597\n", + " -0.904022\n", + " 1.000\n", " \n", " \n", - " \"Grin\"\n", - " 0.147565\n", - " 0.086040\n", - " 0.092980\n", - " 0.440214\n", - " 0.043312\n", - " 0.669190\n", - " 0.665514\n", + " \"06\" GTO\n", + " 0.102005\n", + " 0.632400\n", + " 0.113796\n", + " 0.055768\n", + " 0.067348\n", + " 0.759998\n", " 5.000\n", " \n", " \n", - " \"Hemi-ness Is A Warm Run\"\n", - " 0.055350\n", - " 0.025375\n", - " 0.089810\n", - " 0.528417\n", - " 0.072373\n", - " 0.617704\n", - " 0.727694\n", - " 4.750\n", + " \"Acceleration failure\" - Genesis phraseology\n", + " 0.139895\n", + " 0.143780\n", + " 0.259497\n", + " 0.049344\n", + " 0.086211\n", + " -0.632639\n", + " 3.000\n", " \n", " \n", - " \"It's Still in the Shop\"\n", - " 0.213748\n", - " 0.064518\n", - " 0.144874\n", - " 0.147013\n", - " 0.540048\n", - " 0.604038\n", - " -0.716944\n", - " 1.500\n", - " \n", - " \n", - " \"Jack-of-all-trades\" yet master of many\n", - " 0.068786\n", - " 0.030514\n", - " 0.149626\n", - " 0.283489\n", - " 0.241629\n", - " 0.661193\n", - " 0.077671\n", - " 4.375\n", + " \"Cry wolf\" tire light and redundant warning screen\n", + " 0.304694\n", + " 0.165782\n", + " 0.128762\n", + " 0.043923\n", + " 0.172683\n", + " -0.744237\n", + " 3.000\n", + " \n", + " \n", + " \"Downgraded\" to an LS 430 but best upgrade ever!\n", + " 0.381772\n", + " 0.401842\n", + " 0.028101\n", + " 0.018007\n", + " 0.026035\n", + " 0.480924\n", + " 5.000\n", + " \n", + " \n", + " \"First Ride\" Impressions when I visited Tesla's Factory\n", + " 0.231870\n", + " 0.443138\n", + " 0.036262\n", + " 0.023640\n", + " 0.051412\n", + " 0.610619\n", + " 4.875\n", " \n", " \n", "\n", "" ], "text/plain": [ - " emotion.anger \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.114714 \n", - 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" Best truck ever 0.014326 \n", - " Even better than the Chevy 0.045185 \n", - " I LOVE MY SLK 0.036633 \n", - " I love my Caliber 0.040379 \n", - " Looks Good But Hunk Of Junk 0.097350 \n", - " Small Reliable Gas Saver! 0.034896 \n", - " Veracruz 0.017731 \n", - " hfgh 0.007672 \n", - " i'm on my second one 0.156578 \n", - "\"Acceleration failure\" - Genesis phraseology 0.103167 \n", - "\"FUN\" 0.033952 \n", - "\"First Ride\" Impressions when I visited Tesla's... 0.035031 \n", - "\"Free\" Green on green lightning 0.032288 \n", - "\"Grin\" 0.086040 \n", - "\"Hemi-ness Is A Warm Run\" 0.025375 \n", - "\"It's Still in the Shop\" 0.064518 \n", - "\"Jack-of-all-trades\" yet master of many 0.030514 \n", + " 1 sweet R32 0.151543 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.176322 \n", + " 42 days of driving 8 days in the shop 0.206478 \n", + " A great little car 0.278575 \n", + " AWESOME FUN MY LITTLE TIGER 0.007629 \n", + " I LOVE my Focus 0.074019 \n", + " Looks Good But Hunk Of Junk 0.144671 \n", + " Mr TACOMA 0.122766 \n", + " Veracruz 0.106981 \n", + " You will pay for that warranty 0.396306 \n", + " everyday rSx 0.038486 \n", + " got new weel 0.108507 \n", + " i'm on my second one 0.063124 \n", + "! un happy Camper 0.424926 \n", + "\"\"\"I can't believe it \"\" 0.244671 \n", + "\"06\" GTO 0.102005 \n", + "\"Acceleration failure\" - 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Genesis phraseology 0.143780 0.259497 \n", + "\"Cry wolf\" tire light and redundant warning screen 0.165782 0.128762 \n", + "\"Downgraded\" to an LS 430 but best upgrade ever! 0.401842 0.028101 \n", + "\"First Ride\" Impressions when I visited Tesla's... 0.443138 0.036262 \n", "\n", - " emotion.sadness \\\n", + " emotion.disgust \\\n", "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.339398 \n", - " A Dream 0.145674 \n", - " A Wonderful Ownership Experiance 0.113873 \n", - " Best truck ever 0.179243 \n", - " Even better than the Chevy 0.098830 \n", - " I LOVE MY SLK 0.104404 \n", - " I love my Caliber 0.187137 \n", - " Looks Good But Hunk Of Junk 0.441097 \n", - " Small Reliable Gas Saver! 0.193688 \n", - " Veracruz 0.153267 \n", - " hfgh 0.023953 \n", - " i'm on my second one 0.366614 \n", - "\"Acceleration failure\" - Genesis phraseology 0.281382 \n", - "\"FUN\" 0.236222 \n", - "\"First Ride\" Impressions when I visited Tesla's... 0.148852 \n", - "\"Free\" Green on green lightning 0.215824 \n", - 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" relevance \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.647531 \n", - " A Dream 0.732942 \n", - " A Wonderful Ownership Experiance 0.646053 \n", - " Best truck ever 0.998194 \n", - " Even better than the Chevy 0.664315 \n", - " I LOVE MY SLK 0.645773 \n", - " I love my Caliber 0.639690 \n", - " Looks Good But Hunk Of Junk 0.819306 \n", - " Small Reliable Gas Saver! 0.620210 \n", - " Veracruz 0.797108 \n", - " hfgh 0.860490 \n", - " i'm on my second one 0.790430 \n", - "\"Acceleration failure\" - Genesis phraseology 0.626660 \n", - "\"FUN\" 0.629725 \n", - "\"First Ride\" Impressions when I visited Tesla's... 0.623073 \n", - "\"Free\" Green on green lightning 0.582427 \n", - "\"Grin\" 0.669190 \n", - "\"Hemi-ness Is A Warm Run\" 0.617704 \n", - "\"It's Still in the Shop\" 0.604038 \n", - "\"Jack-of-all-trades\" yet master of many 0.661193 \n", + " emotion.anger \\\n", + "Review_Title \n", + " 1 sweet R32 0.112994 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.038908 \n", + " 42 days of driving 8 days in the shop 0.082325 \n", + " A great little car 0.039688 \n", + " AWESOME FUN MY LITTLE TIGER 0.024782 \n", + " I LOVE my Focus 0.066092 \n", + " Looks Good But Hunk Of Junk 0.116835 \n", + " Mr TACOMA 0.030344 \n", + " Veracruz 0.054493 \n", + " You will pay for that warranty 0.119030 \n", + " everyday rSx 0.033998 \n", + " got new weel 0.239177 \n", + " i'm on my second one 0.165089 \n", + "! un happy Camper 0.084982 \n", + "\"\"\"I can't believe it \"\" 0.165597 \n", + "\"06\" GTO 0.067348 \n", + "\"Acceleration failure\" - Genesis phraseology 0.086211 \n", + "\"Cry wolf\" tire light and redundant warning screen 0.172683 \n", + "\"Downgraded\" to an LS 430 but best upgrade ever! 0.026035 \n", + "\"First Ride\" Impressions when I visited Tesla's... 0.051412 \n", "\n", " sentiment.score Rating\\r \n", "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects -0.496327 2.375 \n", - " A Dream -0.440443 4.875 \n", - " A Wonderful Ownership Experiance 0.245085 4.750 \n", - " Best truck ever 0.969981 5.000 \n", - " Even better than the Chevy -0.075949 5.000 \n", - " I LOVE MY SLK 0.926009 5.000 \n", - " I love my Caliber 0.919963 4.750 \n", - " Looks Good But Hunk Of Junk -0.999568 2.875 \n", - " Small Reliable Gas Saver! 0.069819 4.500 \n", - " Veracruz 0.591815 4.750 \n", - " hfgh 0.971604 5.000 \n", - " i'm on my second one -0.825715 5.000 \n", - "\"Acceleration failure\" - Genesis phraseology -0.643850 3.000 \n", - "\"FUN\" 0.086591 5.000 \n", - "\"First Ride\" Impressions when I visited Tesla's... 0.664976 4.875 \n", - "\"Free\" Green on green lightning 0.236840 5.000 \n", - "\"Grin\" 0.665514 5.000 \n", - "\"Hemi-ness Is A Warm Run\" 0.727694 4.750 \n", - "\"It's Still in the Shop\" -0.716944 1.500 \n", - "\"Jack-of-all-trades\" yet master of many 0.077671 4.375 " + " 1 sweet R32 0.649825 4.875 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.148035 4.625 \n", + " 42 days of driving 8 days in the shop -0.054126 3.375 \n", + " A great little car 0.503785 4.875 \n", + " AWESOME FUN MY LITTLE TIGER 0.986029 5.000 \n", + " I LOVE my Focus 0.621983 4.750 \n", + " Looks Good But Hunk Of Junk -0.984622 2.875 \n", + " Mr TACOMA 0.633803 5.000 \n", + " Veracruz 0.591816 4.750 \n", + " You will pay for that warranty -0.373583 2.750 \n", + " everyday rSx 0.677286 4.000 \n", + " got new weel 0.654034 4.625 \n", + " i'm on my second one -0.973446 5.000 \n", + "! un happy Camper -0.388182 2.625 \n", + "\"\"\"I can't believe it \"\" -0.904022 1.000 \n", + "\"06\" GTO 0.759998 5.000 \n", + "\"Acceleration failure\" - Genesis phraseology -0.632639 3.000 \n", + "\"Cry wolf\" tire light and redundant warning screen -0.744237 3.000 \n", + "\"Downgraded\" to an LS 430 but best upgrade ever! 0.480924 5.000 \n", + "\"First Ride\" Impressions when I visited Tesla's... 0.610619 4.875 " ] }, "execution_count": 25, @@ -3697,7 +3290,13 @@ } ], "source": [ - "agg_merged_keywords_review_df = merged_keywords_review_df.drop(['count'], axis=1).drop_duplicates(['Review_Title','sentiment.score']).groupby('Review_Title').mean()\n", + "sentiment_cols = [str(c) for c in merged_keywords_review_df.columns\n", + " if c.startswith('emotion.')] + ['sentiment.score']\n", + "agg_merged_keywords_review_df = (\n", + " merged_keywords_review_df[sentiment_cols + ['Review_Title', 'Rating\\r']]\n", + " .drop_duplicates(['Review_Title','sentiment.score'])\n", + " .groupby('Review_Title')\n", + " .mean())\n", "agg_merged_keywords_review_df.head(20)" ] }, @@ -3717,273 +3316,234 @@ "data": { "text/html": [ "\n", - "\n", + "
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\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 26, @@ -3993,7 +3553,7 @@ ], "source": [ "corr = agg_merged_keywords_review_df.corr(method ='pearson')\n", - "corr.style.background_gradient(cmap='coolwarm').set_precision(2)" + "corr.style.background_gradient(cmap='coolwarm')" ] }, { @@ -4059,6 +3619,9 @@ "outputs": [ { "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "LinearRegression()" ] @@ -4105,8 +3668,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean Squared Error = 0.5905008481781794\n", - "R-Squared = 0.3859872132354025\n" + "Mean Squared Error = 0.6260140152618712\n", + "R-Squared = 0.37732446794693686\n" ] } ], @@ -4164,12 +3727,11 @@ " \n", " \n", " \n", - " emotion.anger\n", - " emotion.disgust\n", - " emotion.fear\n", - " emotion.joy\n", " emotion.sadness\n", - " relevance\n", + " emotion.joy\n", + " emotion.fear\n", + " emotion.disgust\n", + " emotion.anger\n", " sentiment.score\n", " \n", " \n", @@ -4180,96 +3742,82 @@ " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " 2010 M 35 Acceleration/Braking Defects\n", - " 0.114714\n", - " 0.072652\n", - " 0.143729\n", - " 0.053823\n", - " 0.339398\n", - " 0.647531\n", - " -0.496327\n", - " \n", - " \n", - " A Dream\n", - " 0.172335\n", - " 0.059397\n", - " 0.092631\n", - " 0.076986\n", - " 0.145674\n", - " 0.732942\n", - " -0.440443\n", - " \n", - " \n", - " A Wonderful Ownership Experiance\n", - " 0.117943\n", - " 0.016000\n", - " 0.101099\n", - " 0.243949\n", - " 0.113873\n", - " 0.646053\n", - " 0.245085\n", - " \n", - " \n", - " Best truck ever\n", - " 0.042779\n", - " 0.014326\n", - " 0.029521\n", - " 0.277188\n", - " 0.179243\n", - " 0.998194\n", - " 0.969981\n", - " \n", - " \n", - " Even better than the Chevy\n", - " 0.044507\n", - " 0.045185\n", - " 0.054851\n", - " 0.284697\n", - " 0.098830\n", - " 0.664315\n", - " -0.075949\n", + " 1 sweet R32\n", + " 0.151543\n", + " 0.532162\n", + " 0.067859\n", + " 0.018501\n", + " 0.112994\n", + " 0.649825\n", + " \n", + " \n", + " 2002 Trans Am/Sunset Orange Metallic\n", + " 0.176322\n", + " 0.465210\n", + " 0.257064\n", + " 0.032842\n", + " 0.038908\n", + " 0.148035\n", + " \n", + " \n", + " 42 days of driving 8 days in the shop\n", + " 0.206478\n", + " 0.563466\n", + " 0.114506\n", + " 0.010082\n", + " 0.082325\n", + " -0.054126\n", + " \n", + " \n", + " A great little car\n", + " 0.278575\n", + " 0.470586\n", + " 0.063823\n", + " 0.015218\n", + " 0.039688\n", + " 0.503785\n", + " \n", + " \n", + " AWESOME FUN MY LITTLE TIGER\n", + " 0.007629\n", + " 0.628312\n", + " 0.013015\n", + " 0.001452\n", + " 0.024782\n", + " 0.986029\n", " \n", " \n", "\n", "" ], "text/plain": [ - " emotion.anger emotion.disgust \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.114714 0.072652 \n", - " A Dream 0.172335 0.059397 \n", - " A Wonderful Ownership Experiance 0.117943 0.016000 \n", - " Best truck ever 0.042779 0.014326 \n", - " Even better than the Chevy 0.044507 0.045185 \n", + " emotion.sadness emotion.joy \\\n", + "Review_Title \n", + " 1 sweet R32 0.151543 0.532162 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.176322 0.465210 \n", + " 42 days of driving 8 days in the shop 0.206478 0.563466 \n", + " A great little car 0.278575 0.470586 \n", + " AWESOME FUN MY LITTLE TIGER 0.007629 0.628312 \n", "\n", - " emotion.fear emotion.joy \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.143729 0.053823 \n", - " A Dream 0.092631 0.076986 \n", - " A Wonderful Ownership Experiance 0.101099 0.243949 \n", - " Best truck ever 0.029521 0.277188 \n", - " Even better than the Chevy 0.054851 0.284697 \n", - "\n", - " emotion.sadness relevance \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.339398 0.647531 \n", - " A Dream 0.145674 0.732942 \n", - " A Wonderful Ownership Experiance 0.113873 0.646053 \n", - " Best truck ever 0.179243 0.998194 \n", - " Even better than the Chevy 0.098830 0.664315 \n", + " emotion.fear emotion.disgust \\\n", + "Review_Title \n", + " 1 sweet R32 0.067859 0.018501 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.257064 0.032842 \n", + " 42 days of driving 8 days in the shop 0.114506 0.010082 \n", + " A great little car 0.063823 0.015218 \n", + " AWESOME FUN MY LITTLE TIGER 0.013015 0.001452 \n", "\n", - " sentiment.score \n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects -0.496327 \n", - " A Dream -0.440443 \n", - " A Wonderful Ownership Experiance 0.245085 \n", - " Best truck ever 0.969981 \n", - " Even better than the Chevy -0.075949 " + " emotion.anger sentiment.score \n", + "Review_Title \n", + " 1 sweet R32 0.112994 0.649825 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.038908 0.148035 \n", + " 42 days of driving 8 days in the shop 0.082325 -0.054126 \n", + " A great little car 0.039688 0.503785 \n", + " AWESOME FUN MY LITTLE TIGER 0.024782 0.986029 " ] }, "execution_count": 32, @@ -4278,7 +3826,7 @@ } ], "source": [ - "X_df = agg_merged_keywords_review_df.dropna().iloc[:, :7]\n", + "X_df = agg_merged_keywords_review_df.drop(columns='Rating\\r').dropna().iloc[:, :7]\n", "X_df.head()" ] }, @@ -4290,7 +3838,7 @@ }, "outputs": [], "source": [ - "X = X_df.values.reshape(-1, 7) # values converts it into a numpy array\n", + "X = X_df.values.reshape(-1, 6) # values converts it into a numpy array\n", "Y = agg_merged_keywords_review_df.dropna()['Rating\\r'].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column" ] }, @@ -4310,6 +3858,9 @@ "outputs": [ { "data": { + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "LinearRegression()" ] @@ -4342,8 +3893,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean Squared Error = 0.5800027622737277\n", - "R-Squared = 0.39690330082744185\n" + "Mean Squared Error = 0.6149240275777909\n", + "R-Squared = 0.3883553136042148\n" ] } ], @@ -4377,14 +3928,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Feature Coefficients = [[-0.81706333 -0.75366695 0.18783079 0.00182372 -0.8290045 -0.07412579\n", - " 0.95410364]]\n" + "Feature Coefficients = [[-0.22073291 0.32887635 0.37191993 -0.32082964 -1.60607343 1.01721109]]\n" ] }, { "data": { "text/plain": [ - "array([4.44054646])" + "array([4.0674602])" ] }, "execution_count": 38, @@ -4439,28 +3989,28 @@ " \n", " \n", " 0\n", - " 5.235334\n", - " 2.375000\n", + " 4.300783\n", + " 4.875\n", " \n", " \n", " 1\n", - " 4.735796\n", - " 4.875000\n", + " 3.895731\n", + " 4.625\n", " \n", " \n", " 2\n", - " 4.567478\n", - " 4.750000\n", + " 4.342326\n", + " 3.375\n", " \n", " \n", " 3\n", - " 4.404560\n", - " 5.000000\n", + " 4.705346\n", + " 4.875\n", " \n", " \n", " 4\n", - " 5.188259\n", - " 5.000000\n", + " 3.256459\n", + " 5.000\n", " \n", " \n", " ...\n", @@ -4468,50 +4018,50 @@ " ...\n", " \n", " \n", - " 1524\n", - " 4.547005\n", - " 5.000000\n", + " 1527\n", + " 4.623339\n", + " 3.125\n", " \n", " \n", - " 1525\n", - " 4.129847\n", - " 5.000000\n", + " 1528\n", + " 3.747142\n", + " 5.000\n", " \n", " \n", - " 1526\n", - " 4.019317\n", - " 1.886364\n", + " 1529\n", + " 3.874102\n", + " 4.500\n", " \n", " \n", - " 1527\n", - " 4.591441\n", - " 3.500000\n", + " 1530\n", + " 3.468226\n", + " 3.875\n", " \n", " \n", - " 1528\n", - " 4.195468\n", - " 4.125000\n", + " 1531\n", + " 4.406095\n", + " 5.000\n", " \n", " \n", "\n", - "

1529 rows × 2 columns

\n", + "

1532 rows × 2 columns

\n", "" ], "text/plain": [ " Predicted Rating Actual Rating\n", - "0 5.235334 2.375000\n", - "1 4.735796 4.875000\n", - "2 4.567478 4.750000\n", - "3 4.404560 5.000000\n", - "4 5.188259 5.000000\n", + "0 4.300783 4.875\n", + "1 3.895731 4.625\n", + "2 4.342326 3.375\n", + "3 4.705346 4.875\n", + "4 3.256459 5.000\n", "... ... ...\n", - "1524 4.547005 5.000000\n", - "1525 4.129847 5.000000\n", - "1526 4.019317 1.886364\n", - "1527 4.591441 3.500000\n", - "1528 4.195468 4.125000\n", + "1527 4.623339 3.125\n", + "1528 3.747142 5.000\n", + "1529 3.874102 4.500\n", + "1530 3.468226 3.875\n", + "1531 4.406095 5.000\n", "\n", - "[1529 rows x 2 columns]" + "[1532 rows x 2 columns]" ] }, "execution_count": 39, @@ -4541,19 +4091,17 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(Y_test, Y_pred)\n", + "plt.scatter(Y_test, Y_pred, alpha=0.2)\n", "plt.xlabel('Rating From Dataset')\n", "plt.ylabel('Rating Predicted By Model')\n", "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n", @@ -4581,6 +4129,9 @@ "outputs": [ { "data": { + "text/html": [ + "
RandomForestRegressor(random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "RandomForestRegressor(random_state=0)" ] @@ -4634,8 +4185,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean Squared Error = 0.5311930091091184\n", - "R-Squared = 0.44765650914942323\n" + "Mean Squared Error = 0.572035684052662\n", + "R-Squared = 0.43101493698694837\n" ] } ], @@ -4671,19 +4222,17 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(Y_test, Y_pred)\n", + "plt.scatter(Y_test, Y_pred, alpha=0.2)\n", "plt.xlabel('Rating From Dataset')\n", "plt.ylabel('Rating Predicted By Model')\n", "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n", @@ -4713,8 +4262,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Mean Squared Error = 0.5024629864248751\n", - "R-Squared = 0.47753047350796296\n" + "Mean Squared Error = 0.5519593529266892\n", + "R-Squared = 0.4509842026276053\n" ] } ], @@ -4751,19 +4300,17 @@ }, { "data": { - "image/png": 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\n", 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tttF1HcuygDhUZnx8nCAIOP/887n//vuP+f0bO6QQe6iy2Sw7d+4EIAxD7rrrLq6++moGBgaa7U455RSuuOKKBb+P4zj84Ac/mPbzkY98ZFqb//k//+e037/zne8AzFBne9e73gXEu/xT2b59OxdffHHz9wsvvBCA3/zN32T9+vUzjjc+47GQTqcpFovN3xOJRPP/fd9nbGyMU045hY6OjgV/V1P7KBaLjI6Ocumll1KpVHj88ccBmh6lO++8k0qlMms/X//614miiFe96lWMjo42f/r6+jj11FOnhXq2yu/+7u9imua08KQf/ehHHDhwYFqYXiKRwLIs7rnnHiYmJhb1Xlu3bqW3t5eNGzfyxje+kVNOOYXvfve7JJPJZhvbtpvewzAMGRsbI51Os3Xr1mO+Rn73d393mner4alqzJ+DBw/y0EMP8drXvnaap+6yyy7jrLPOWvD7PP744/T29tLb28vpp5/ObbfdxpVXXjkjlO2yyy5j+/btzd+llHzta1/jqquuQko57bt+0YteRD6fb56D73znO/T390/L2Usmk03v0NH42te+hhCC97///TNemy9k93iNcSp/93d/17zXfP7zn+fyyy/nxhtv5Otf/3qzzWmnncaFF17IF77wheax8fFxvvvd73LdddctOBRZ13Ve9apX8cUvfhGIRSHWrVvXnCtTOXToEL/+9a95/etfP83LdfbZZ/M//sf/aN73wjDkzjvv5Oqrr552/zr99NNnhP+181pXKJ4pqFA9heIZwN/93d9x2mmnkc/n+cxnPsN//ud/Ytv2jHaf/exn+chHPsLjjz+O7/vN45s2bTqm95/6QG7Q2dnZXOQODw9TrVY55ZRTZrSb7dhc6Lo+p2RvgyM/y549e9A0bcb79PX10dHRwZ49e6YdP/KzNIyLdevWzXp8sQv5qZRKJTKZTPP3arXKrbfeyj/90z9x4MCBZsghMCP3aC4eeeQR3ve+93H33XdTKBSmvdboY9OmTbzzne/kr//6r/nCF77ApZdeyste9jKuv/765ufbsWMHUkpOPfXUWd/nWJL/u7u7edGLXsQ3vvENPvnJT+I4DnfccQeGYUwTNbFtm7/4i7/gXe96F6tXr+aiiy7ipS99Ka997WtnKJDNxde+9jWy2SwjIyN87GMfY9euXdOMSzice/P//t//Y9euXdPywRohp4vlyHnVMKIa86cxD+e6RhZquG3cuLEpX+04DqeeeiqrVq2a0e7I62RkZITJyUk+/elP8+lPf3rWvoeHh5tjPeWUU2YYBFu3bp13fE8//TQDAwPTFvsL5XiNcSrPec5zpolDvPrVr+bZz342b33rW3npS1/a3Ix67Wtfy1vf+lb27NnDhg0b+MpXvoLv+/ze7/1eS+/3mte8ho997GM8+OCD3HHHHVx77bWzGl6N+TLb5zn99NO58847KZfLFItFqtXqrNfv1q1bmwYWtPdaVyieKSjDSaF4BjD14X711VfzG7/xG7zmNa/hiSeeaO5ef/7zn+f1r389V199Ne9+97tZtWoVuq5z6623TkuQXwxT81OmMnXBf7w4cjHcoJVd31aOH+tn9H2fJ598kjPPPLN57A//8A/5p3/6J97xjndw8cUXk8vlEEJw7bXXLkjaeXJykssuu4xsNssHPvABtmzZguM43H///fyf//N/pvXxkY98hNe//vV861vf4vvf/z5ve9vbuPXWW/nZz37G2rVriaIIIQTf/e53Zz0HR8tjWgjXX389//Zv/8a//du/8bKXvYyvfe1rzXyMqbzjHe/gqquu4pvf/CZ33nknf/Inf8Ktt97K3XffzbOf/ex53+d5z3teU1Xvqquu4qyzzuK6667jV7/6VdPLdMstt/Anf/InvPGNb+SDH/wgXV1daJrGO97xjmOWGD9e10gqlZp3cwFmXieNz3f99dfzute9bta/mS/Xsd0shzFqmsbll1/O3/7t37Jjxw7OOOMMAK699lr+6I/+iC984Qu8973v5fOf/zznn39+y4bahRdeyJYtW3jHO97Brl27puX5tZt2X+sKxTMBZTgpFM8wGsbQ5Zdfzsc//vFm3aWvfvWrbN68ma9//evTjIgjQ2aWQuHuSFatWoXjODz11FMzXpvt2FKyYcMGoihix44dTeEEiJPMJycn2bBhQ1vffz6++tWvUq1Wp4XNfPWrX+V1r3vdtDBE13VnFGyd67u65557GBsb4+tf/zrPe97zmsd37do1a/uzzjqLs846i/e9733ce++9PPe5z+WTn/wkH/rQh9iyZQtSSjZt2sRpp5121M+ymLnzspe9jEwmwx133IFpmkxMTEwL05vKli1beNe73sW73vUuduzYwTnnnMNHPvIRPv/5z7f0nul0mve///284Q1v4Mtf/jLXXnstEJ/3yy+/nH/8x3+c1n5ycrJpdEF7rpHGPDwR1whAb28vmUyGMAznNbw2bNjAww8/jJRy2rl44okn5n2fLVu2cOedd84QUjiS2c7x8RrjfARBAMSe4gZdXV1ceeWVfOELX+C6667jv/7rvxZUDHo2Xv3qV/OhD32I008/fUadsAaN+TLb53n88cfp6ekhlUrhOA6JRIIdO3bMaHfk37ZyrSsUJysqx0mheAby/Oc/n+c85zl89KMfbRZNbOwgTt3h/vnPf85Pf/rTaX/byPk4cpF+LDRC7L75zW9y8ODB5vGnnnqK7373u0v2PrPxkpe8BGDGIuav//qvAbjyyivb+v5H48EHH+Qd73gHnZ2dvOUtb2ke13V9hifitttumyEl3qhRdeR3Ndt37Xke/+///b9p7QqFQnMR2OCss85C07SmbPPv/M7voOs6N99884wxSSkZGxubNp6FhhI2SCQSvPzlL+c73/kOn/jEJ0ilUjOknSuVyozin1u2bCGTySxaXvq6665j7dq1/MVf/EXz2Gzn/Stf+QoHDhyYdmyu834sDAwMcOaZZ/LP//zP0xbkP/rRj3jooYeW7H3mQtd1rrnmGr72ta/x8MMPz3i9IWUO8TV18OBBvvrVrzaPVSqVOcPnpnLNNdcgpWwW2Z7K1HOfSqVmndfHY4xHw/d9vv/972NZ1rSNGIDf+73f49FHH+Xd7343uq43DfJWufHGG3n/+98/I39zKv39/Zxzzjl89rOfnXaeHn74Yb7//e8373u6rvOiF72Ib37zm+zdu7fZ7rHHHuPOO++c1mcr17pCcbKiPE4KxTOUd7/73bzyla/k9ttv53/+z//JS1/6Ur7+9a/z8pe/nCuvvJJdu3bxyU9+ku3bt09bqCUSCbZv386XvvQlTjvtNLq6ujjzzDOnhZIthptuuonvf//7PPe5z+V//a//RRiGfPzjH+fMM8/k17/+9TF+2rl51rOexete9zo+/elPN0PYfvGLX/DZz36Wq6++mssvv7xt7z2VH//4x7iu2xQd+K//+i++/e1vk8vl+MY3vjEtV+elL30pn/vc58jlcmzfvp2f/vSn3HXXXTPybM455xx0Xecv/uIvyOfz2LbNb/7mb3LJJZfQ2dnJ6173Ot72trchhOBzn/vcjMXQ3XffzVvf+lZe+cpXctpppxEEAZ/73OeaC1SIDZQPfehDvOc972H37t1cffXVZDIZdu3axTe+8Q3+4A/+gD/+4z8G4LzzzuNLX/oS73znO7ngggtIp9NcddVV856b66+/nn/+53/mzjvv5LrrrptRtPjJJ5/kBS94Aa961avYvn07hmHwjW98g6GhoUUvTk3T5O1vfzvvfve7+d73vseLX/xiXvrSl/KBD3yAN7zhDVxyySU89NBDfOELX2Dz5s3T/nbLli10dHTwyU9+kkwmQyqV4sILLzzmXMFbbrmF3/7t3+a5z30ub3jDG5iYmGheI1Ov0Xbx53/+5/zwhz/kwgsv5Pd///fZvn074+Pj3H///dx1112Mj48D8Pu///t8/OMf57WvfS2/+tWv6O/v53Of+9w0oY25uPzyy/m93/s9Pvaxj7Fjxw5e/OIXE0URP/7xj7n88st561vfCsRz6a677uKv//qvGRgYYNOmTVx44YXHZYxT+e53v9sUUxkeHuaOO+5gx44d/N//+3/JZrPT2l555ZV0d3fzla98hSuuuGLW3LKFsGHDhmbtvKPx4Q9/mCuuuIKLL76YG264oSlHnsvlpv39zTffzPe+9z0uvfRS3vzmNxMEAbfddhtnnHEG//3f/91s18q1rlCctBw3/T6FQrHkzCZR2yAMQ7llyxa5ZcsWGQSBjKJI3nLLLXLDhg3Stm357Gc/W/7bv/3brLKy9957rzzvvPOkZVnTpMnnkiN/y1veMuP9N2zYMENO+D/+4z/ks5/9bGlZltyyZYv8h3/4B/mud71LOo4z72d93eteJ1Op1JyvN8Y2MjIy4zXf9+XNN98sN23aJE3TlOvWrZPvec97mrLcU8d85ZVXzvj72T5jQ2Z6Ponshhx548c0Tdnb2yuf97znyT/7sz+Tw8PDM/5mYmJCvuENb5A9PT0ynU7LF73oRfLxxx+f9Zz+/d//vdy8eXNTWrghs/xf//Vf8qKLLpKJREIODAzI//2//3dTWr3RZufOnfKNb3yj3LJli3QcR3Z1dcnLL79c3nXXXTPG9LWvfU3+xm/8hkylUjKVSslt27bJt7zlLfKJJ55otimVSvI1r3mN7OjoWLCEtpSxVH5/f78E5He+850Zr4+Ojsq3vOUtctu2bTKVSslcLicvvPDCaVLTc3G0eZHP52Uul2tK2ruuK9/1rnfJ/v5+mUgk5HOf+1z505/+VF522WXNNg2+9a1vye3btzfl9BvS5HPJkc82T6ZeWw3+5V/+RW7btk3ati3PPPNM+e1vf1tec801ctu2bfN+1ssuu0yeccYZ87ab65qVUsqhoSH5lre8Ra5bt06apin7+vrkC17wAvnpT396Wrs9e/bIl73sZTKZTMqenh759re/XX7ve9+bV45cyvj7/vCHPyy3bdsmLcuSvb298oorrpC/+tWvmm0ef/xx+bznPU8mEgkJTJv3Sz3G2ZhNjtxxHHnOOefIT3ziE3NKjL/5zW+WgLzjjjuO2v9U5rrvzDaeI+/1d911l3zuc58rE4mEzGaz8qqrrpKPPvrojL//0Y9+1Lynb968WX7yk5+c9X4u5cKudSVHrjhZEVKegOxthUKhqHP11VfzyCOPzBqDr1AoaBan/sEPfnCih6KYhz/6oz/iH//xHxkcHGzZu6VQKJY/KsdJoVAcN6rV6rTfd+zYwXe+8x2e//znn5gBKRTLCN/3Z+Sc3XPPPTz44IPqGlkBuK7L5z//ea655hplNCkUz1CUx0mhUBw3+vv7ef3rX8/mzZvZs2cPn/jEJ6jVajzwwANz1g5RKE4Wdu/ezQtf+EKuv/56BgYGePzxx/nkJz9JLpfj4YcfPuZaUor2MDw8zF133cVXv/pVvvnNb3L//ffPqYanUChWNkocQqFQHDde/OIX88UvfpHBwUFs2+biiy/mlltuUUaTQkFcFPe8887jH/7hHxgZGSGVSnHllVfy53/+58poWsY8+uijXHfddaxatYqPfexjymhSKJ7BKI+TQqFQKBQKhUKhUMyDynFSKBQKhUKhUCgUinlQhpNCoVAoFAqFQqFQzMNJl+MURREHDx4kk8kghDjRw1EoFAqFQqFQKBQnCCklxWKRgYEBNO3oPqWTznA6ePAg69atO9HDUCgUCoVCoVAoFMuEffv2sXbt2qO2OekMp0wmA8QnJ5vNnuDRKBQKhUKhUCgUihNFoVBg3bp1TRvhaJx0hlMjPC+bzSrDSaFQKBQKhUKhUCwohUeJQygUCoVCoVAoFArFPCjDSaFQKBQKhUKhUCjmQRlOCoVCoVAoFAqFQjEPynBSKBQKhUKhUCgUinlQhpNCoVAoFAqFQqFQzIMynBQKhUKhUCgUCoViHpThpFAoFAqFQqFQKBTzoAwnhUKhUCgUCoVCoZgHZTgpFAqFQqFQKBQKxTwow0mhUCgUCoVCoVAo5kEZTgqFQqFQKBQKhUIxD8pwUigUCoVCoVAoFIp5UIaTQqFQKBQKhUKhUMyDcaIHoFAoFAqFQqFQKJYXUkrKXkgQRhi6RsrSEUKc6GGdUE6ox+mmm25CCDHtZ9u2bUf9m6985Sts27YNx3E466yz+M53vnOcRqtQKBQKhUKhUDzzyVd9HjqQ55e7xvnl7gl+uWuchw7kyVf9Ez20E8oJD9U744wzOHToUPPnJz/5yZxt7733Xl796ldzww038MADD3D11Vdz9dVX8/DDDx/HESsUCoVCoVAoFM9M8lWfhw/kGcy7pGyD3oxNyjYYzLs8fJIbTyfccDIMg76+vuZPT0/PnG3/9m//lhe/+MW8+93v5vTTT+eDH/wg5557Lh//+Mfn/JtarUahUJj2o1AoFAqFQqFQKKYjpWTPWJlyLaA/l8AxdTQhcEyd/lyCci1g73gZKeWJHuoJ4YQbTjt27GBgYIDNmzdz3XXXsXfv3jnb/vSnP+WFL3zhtGMvetGL+OlPfzrn39x6663kcrnmz7p165Zs7AqFQqFQKBQKxTOFshcyXvLoTFqzvt6ZtBgrepS98DiPbHlwQg2nCy+8kNtvv53vfe97fOITn2DXrl1ceumlFIvFWdsPDg6yevXqacdWr17N4ODgnO/xnve8h3w+3/zZt2/fkn4GhUKhUCgUCoXimUAQRgSRxDJmNxFMXSOIJEEYLfo9pJSUagGTFY9SLVhR3qsTqqp3xRVXNP//7LPP5sILL2TDhg18+ctf5oYbbliS97BtG9u2l6QvhUKhUCgUCoXimYqhaxiawAsiHFOf8bofRhiawNAX53vJV332jJUZL3kEkcTQBF1piw3dKXIJ81iH33ZOeKjeVDo6OjjttNN46qmnZn29r6+PoaGhaceGhobo6+s7HsNTKBQKhUKhUCiesaQsna60xUTFQyKp+AFF16fiB0gkExWP7oxFypppVM3HM0F0YlkZTqVSiaeffpr+/v5ZX7/44ov5j//4j2nHfvCDH3DxxRcfj+EpFAqFQqFQKBTPWIQQbOhOAfCzp8d4cM8kDx/M8+CeSX729BgA67tSLddzmio60Zd1iKSkXAuIpKQv66wY0YkTGqr3x3/8x1x11VVs2LCBgwcP8v73vx9d13n1q18NwGtf+1rWrFnDrbfeCsDb3/52LrvsMj7ykY9w5ZVX8i//8i/88pe/5NOf/vSJ/BgKhUKhUCgUCsUzCwGS2MsSCdCOofZtQ3TC0jWeHimRr/gEUmIIQS5pThOdSNsn1Dw5Kid0ZPv37+fVr341Y2Nj9Pb28hu/8Rv87Gc/o7e3F4C9e/eiaYedYpdccgl33HEH73vf+3jve9/Lqaeeyje/+U3OPPPME/URFAqFQqFQKBSKZwQNzxDARZu6qfohYSTRNUHC1BksuOwdL3PmQK4lr1MQRhRcn8mqj+tFZBMGpq7hhxEjRY+CG9CZNI9JdOJ4IORy94ktMYVCgVwuRz6fJ5vNnujhKBQKhUKhUCgUy4JSLeCXu8ZJ2cas4hCuH1KuBZy/qaslz1DR9fnqL/dT8QL6cg5FN8API0xdI+PEeU4py+Ca89eScY6vSEQrtsHy9YUpFAqFQqFQKBSK40Z75cglk1WfA5MVxss+QSgxdEFXyiRlm6Ts1gUnjjfLShxCoVAoFAqFQqFQnBimypHPxmLlyMNIIoHdo2V2jVSxDY3utIVtaOwaqbJ7tEwk43bLGWU4KRQKhUKhUCgUimly5LOxWDlyTcDBySqmLtjcm0QIgeuHCCHY0pvC1AWH8u4xCVAcD1SonkKhUCgUCoVCoWjKkRfdgEP5Kp1JqyniMFHxSNnGouTIK15IxQvIJay4flMtwJdgCkjaBqGUVGo+FS8km2jTh1sClOGkUCgUCoVCoVAoAMglTM5ck2PPWJnxkkcQSQxN0N/hsL4rRS7RunhDLYhImSa+jNg5UkJKEBrICISAzpSNpWnU5ggRXC4ow0mhUCgUCoVCoVA0ySVMzlqTo+yFBGGEoWukLL1lT1MDx9QxDUGpEiIRyHo3jX+9ICSV1GdV8ltOKMNJoVAoFAqFQqFoM1LKJTNEjke/QoglK0bbkzKxTY2JcsDp/Rn8UBJKiS4Epi547FCRgY4EPanjK0XeKspwUigUCoVCoVAo2ki+6s8IfetKW2zoXlzoW7v7XWqqgaS/I8FIyWPfeJlswsTQNWphRKHq05O1WJ1zqAaS9DJ2OinDSaFQKBQKhUKhaBP5qs/DB/KUawGdSQvL0PCCiMG8S9ENOHNNblFGTrv6bQdBGNGVtDl/Qyc/2znGk8Nl/CDENHTWdzmcv76TtGMusj7U8UPJkSsUCoVCoVAoFG1ASsmesTLlWkBfziFCUq4FREj6cg7lWsDe8TJStla/aGq//bkEjqmjCYFj6vTnEovudypRFDFUcNkzVmao4BJFizdqDF2j5occmHCxNOhI6HQkLToSOoaAA5MuNT9suT7U8UZ5nBQKhUKhOElpV26EQjGVlZbbs5SUvZDxkoepazw1XKJQ8QmkxBCCbNKkM2kxVvQoe2FL+USNfjuT1qyvL7bfBvsmKvz86VGeGCxR9UMSps7WvjQXbulhXWey5f6SpsZk1ef+PeO4XkCxFjRDCydKBgcma3SmTJKmMpwUCoVCoVAsM1ZKboRiJlEUMVLycP0Qx9TpTVto2vJccLYzt2fXSIHHBkuU3YCUY3B6X5pNvdllNX+DMGLS9SlUfcpVj4of4YcRpq5R9XxKtYBsovUQtSCMCCKJZWhIJFU/JAwlui5ImDqmrhFEcs5+j2Z07puo8MWf7+Hxg3mqoURKiRCCp4cL7Bwt8+oLN7RsPJW9kL3jZfaOlRgr16gFEgkIwDYE3SmbvWNpyl5IxlmecxmU4aRQKBQKxUnHSsqNWMm0wyOyb6LCz54a5r7dE5RqIWlb54KNnVx0yqpFeQLaSTtze/79oYPcu2OYsXJAGEbousZ/pQwuOXUVV541cEzzdykNU10TTJRqPDlUYNdIhf2T1abhtLYjwabeJFv7suhaa/PC0DUMTTBe9hiaLPHzPRPkKyG5pM6FGzpZ3ZHG0MSsoW9HM2Yzts73HjrEvU+PUSxXyddC/FBi6oKcrTNeDehKW7zxkk0tnZPJsseDeyc5VHApedPDB8u+xAtdHtw7yWTZI+Ms33uPMpwUCoVCoTiJODI3okEjN+JQvsre8TJnDuSWXdjTSqIdnpZ9ExVu+48n+fnOMUpuQBSBpsHPd41z355J/vAFpy0b46ld80xKyT1PDPHtBw5Q9kJSpo5lagSBZM+4y+gDB0jbBledPbCo+btvosJ9u8bYP17FCySWIVjbleCCTd2LPrd7Rkv88IkRClUfGUkiCZqA8XKN3eNlHKN1oyxl6dimzqd+9CS/3jdJpRYREYsX/PDxYc5Z18GbLjuNlDVdom4+Y7Y3bfKfT47y9FCeojf9PUfLIZmSx4+eMLnyzH76OxZ+PkYKVR4+mJ9hNDUoeZKHD+YZKVRZ151q8WwcP5avL0yhUCgUCsWS00puhGJxNBang3mXlG3Qm7FJ2QaDeZeHD+TJV/2W+4yiiM/+105+8MgQYyWPKJJoOkSRZKzk8YNHhvjcvTuPKYF/KZk6z6SUVLyAoutT8QKklIueZ4Wqx50PDzJR9ulMWKQTJinLJJ0w6UxYTJR97nxokELVm7+zI9g3UeE7/32IHUNlOpIWG3qSdCQtdgyV+c5/H2LfRKXlPmuez317JhgrevihjI0bDSLADyVjRY9f7J6g5rU+J+585AD3Pj1OoRohAEvEoW+FasS9T4/z/UcOTGu/EEGJB/ZO8MCesRlGU4OiB7/eO87+iXJLY90/UaLkHX1uFr2I/ROllvo93ijDSaFQKBSKk4ipuRGzMV9uhOLotEvt7OBEme8/MoTrBzimgWMZ0/51/YA7HxniYIsL2nbRmGdeGPH0SInHDhZ49FCBxw4WeHqkRC2IFjXPdo6W2TteIesYJG0DQ9MQQmBoGknbIOMY7Bkvs3O0tfMQRRH37RpjsuJz2uoMGcfE0DQyjslpqzNMVnx+uXu8ZcN0x1CRPWMVIgmGJtA1DU3T0LU41C6SsGe8wo6hYkv9jhViYy4IIWEKhCaIRPxvwhQEIfz7fx9irHD4PCxk02TXcJ6Sf3huiik/DYq1iENjhZbGe/evdy5puxOFMpwUCoVCoTiJaORGeMHsC0A/jObMjVDMT7s8er/aO8FYycPUBY6poWkCCWha/LupC0aLHr/aO7HosUspKdUCJisepVpwTFLWDfnpJwYLjBQ9HEunM2nhWDojRY8nhwqLkp8uVH1cPyLp6EgZUaoF5KuN8UakbB3Xj4uqtsJIyWP/eJX+nEMYhgwXquwbKzNcqBKGIf05h31jFUZKM10xRztvT42UcP0ITYvDFC1Dw9Q1LEOLjWoNXC/iqZHWPC3fe3SYiXKArlE3QOOfMJL10FCYKAd879Hh5t8sZNPkscHp45BTfqby8935lsZ755PVJW13olA5TgqFQqFQnESkLJ2utMVg3p2We9JgouLR3+HMyI1QLIypi9NIRkxUfLwgwjI0OpPmoj16QwWPMIpwTINaEBKEh1XJDF1g6jpeEDBUaD1EDRoqdUV2jVWo1EKSts6m7iSbejOLyslKmhq1MGSk6LGlN8lo2cfzQyxTpydl8vRIhc6U1bL8dNYxcUyN0UKNiueTr4WEkUTXYvGCpBW/nm1RYMD1Q7xAMlmp8aO94+wdd/EiiaUJ1nc5nLWuiyCM201lvly2khu3NzXw/IBQ0sxx0kX9eAgjxdjoWqiAyJ7RMv5sFk39d404HHBw0m2+NHXTxDFnXt9+GFHxFzYvDxXcptreQlioObS8zSZlOCkUCoVCcVIhhGBDd4qiG3BwsoJj6egIQiSuF5J2TNZ3pZalMMRKqNvTWJzun6jw1HCR4UKtqUq2KmtzyqoMKcto2dOyOmMhBJRrAbquEUeMxaaTFkIYRggRt2uVfNXnh48P8eDeSSpeSIREQ/Dfeyd51voOLt+2umXjqeJHWIZOKCO+9esD5CsBoYzQhUYuaXD6QA5T16j4EWl74edic2+KjqTF/bvH8EMQjTgyCcVKgKnXOHdjD5t7WxMYcEydyWqNnz49znC+jC/rnSIYzFfYPV7j4i1d0wyOhagGbuxOYOjgBrEhMxuGANcP+OWu8QUJiOSrsYz50Wi8Vy55eKm/kE2TdZ1J7tszfxhesRbys53jbB9YmPx7RkBxAQ7MzPK6nGegDCeFQqFQKE4ycgmT9d1J7ts1xhODxaZ62LquJNuXqRT5Sqk7lbJ0/Cji7seGEULQk7awTY2aH7Fv3GXvWJUrzu5v2aN37voOUpbBaCXAiCI00bAXJH4IgYSepMG56zta6ldKyX27xvnxk6MIAd1pG8fUcP2IsVKNHz85Sto2ecHpq1oyUoMwYrjg8uiBPIfyLgINISRSRlQ8FymhL+u07HlLWTqeH+AFsVljaPXzICCMwAvA94OWz29XQueRA5PsGSujaSCQdbtJUpOwZ6xMR0KnKxH3O5dqoG1q5JImByeqPKFrnLkmh2nEhtNcWBpcsKm7KSByNKn2xvuuTi9sCX9Wf7r5d2UvpCNpMlyscXCyQlfKxtQ1/DBiouKRsg2uP7+Pr/96cN5+t69K8tCBScIo4sw1OQxdO+qGxivPSvGZ/54/7+yVZy1fRT1QhpNCoVAoFCcd+arP3rEKKcvgnHWdscpXBFU/YO9YhaxjHpMxstSeoXbXnVrK8UopGS64eGFETyZemAoEpq6RSRiMFmsMtxjmBJBJWPRmbCYqAVE9Rq/hE4kk6MCqrEMm0ZrHqej6/HznKKGM2NSdxg8jvLqHbF1Xkl2jJX6xa5TnbOok20LfAskvd48zVKyRcQyiSCKFQEjQNIOhYo3794xzzblrWhrvzpESByddbEtD1wR+ECHrHrKUoxFGkgP5KjtHSpzW39FSv3vHYxEHGU6PgGuc591jZXaOlNg60DlrLlupFnAoX6VQ8an4IfvGK5y1OkGtdvT39kJIG5IISdo2GC3V2DNW4qw1HTPmSON9Q29hIZmj+dKMTYeaH1ILQ7wwQtRlH3qzFltXZ3ngyT0L6jeKAixNY/9EhZGSS0fCIoyYc0PjkT0LE+tYaLsThTKcFAqFQqE4iZi6Uz5wRB2WTqxjruOUr/rsHo0Xt7UgwjY0BjocNvakF2XcTB1vX86h6of1cDVBX85hMO8e83iX0pM1UvIYK3mcuSZHzQ8pugGRlGhC0JWw6Ms6jBZrjJQ8VmedBfc7XKiRcUx6sxYTZY8whJA4l8XSoSNlkbYNhgs1OlIL73cw7zJcrNGRNBkquFSnhOolLJ1cwmSoUGMw77ZkOI0UaxyYqBKEEtBwLIGmCaJI4gWSIAzYN1FlpFgjl7QX3O8jh4pUvJBVGRtNQDWQRKFE0wUJQxBKKFR8HjlUbMlwun/vJIVa2DSYROM/8rA4QqEWcv/eSbYOdM4QWijVAp4eKVH1AjKOScoxGCnW+NZDQ8xRuqhJAHzp/kHOWddFIOPPM1aq0ZN2GOiYHlLXeN979y/MwPjqgyN0dnbN2HTYP1FmKF8jmzRJmBqVWsje8Qofuefggvr90Y48q7u7KNWgFkacv9GmK2XNuaHx8wVqSSy03YlCGU4KhUKhUJxEtKL6lrZbWybkqz4/3znG7rEysp4AH8m4Ps5QocaFm7tbNkYa4zV1jaeGSxQqPoGUGEKQTZrHPN6l9mQ1RAb6Oyw0oOgGeGGEpWtkHINQwt6xygyRgfko1AKCCNbkHNKmTskP41pOmiBt6mSTJkEUt2uFMJJU/RBZid1Yjqmja4IwkvU8GonrR4RRawp7Y6UaVT/ENrXDVois/ysktqlR9ULGSjVOWd3aeCNiYQXb0NE0iZRxrpOlCWpBSFRv1wr5qkdQ9zQlTA0p4zBIgUCIOGcrCON2MF1owTY1DuWrVL2AnnRstNaCkKSpU5tDvfJI9o+XuWhLL6auUQtC9k9U+MWucc7d0ElXymp6QRvvW6wu7HseKnozwgmDSFKuRYyVPdIJnXVdafxAMph3OZRfmDzDpOtTqHhkErF32tS0abL7z9RC2spwUigUCoXiJKJddZyklDxyMM9jhwokTINcwmjmTxSqAY8dKpBJGFy0qbvlXJlJ16dQ9an5IRnHbPY7VqpRqgVkE+aixjtbjsqxLvxiyWnBeNnD9QLGyz5hFKFrGl0pE8cysAwxq6rZ0cjYBpGMkBFs7k1TCULCEHQdkobOUKGG0CWZFo3HjqSJBkyUfdZ1JgkiiR9INAFpy2DfRIWUrdORbM2AjBBoQpCzDTRNo+qHTTW5hKljaQYFNyCitUX1qatSpG2DiYpHwtSp+BFRFBeVTZrx+6Rtk1NXtZYrY2qHxxFGEtn0PUmEFDPaTRVayCVNChWfzBQlv0I1oDdjkZtjg+JIutI2thHPiZof4QeSxw4VGC25bOlN05222dCdIusYdKUtEgtUI0yY+rRNEiklg/kqFS9kQ3cStxZSCyKSpkF/LoFtLMzg1AVIIcglTMJ6va6i66Provmei93QWM48cz6JQqFQKJ5xtEtFbSWos7WLhUgSL6aOU6kW8MShIoYm6M0cDr2yDZ3ejM7ByZAnDhY5cyA3bYE5H7ommCjVqHjhtNBC29Cx0zoHJytEUYSutfb9tcvz1pu26E5b/HjHKKZGbDAQh9SNl3X8CJ53Wi+96dZykbIJk85UvFAfLLjUwsOeFlsX+JFkIOOQbdFDlrINVmUdHj9UZO94GdePmiGLjqnhBZLNvSlSLS5+1+RscgmTvOuRsw1q4WEPma3HnrGOpMWa3MLD9ADOGMixuTfFz3eOka+GaKKhfQeFKuganLW2gzMGci31u7YjgSHAl+CFMi50Wg/Vi+pGlCHidnCEOuVElYofknJiqfhCNSBp6fTlEly0Ps2//HIBn2tV7KmqeAFPDZewdI2NnQnC+jmb6gXd0J3ionUpHjw4f7jes/sT0zZJqn5IvuKTTcTFgwMZEIYS6tNmS6fFwaI7R2+HWZ3SyTkmk5W4XtaukRKhpOkJXp1xpm3ANOTR52O5V49ThtMJ4mR+aCsUCsVCaJeK2kpRZ2sX7arjVKj6TFbnztvpSFoMF10KVb8lwykmfj5KJLUgJIziBXJjhx7Z+vOzXZ43IQRpJxaBmKh46JpohiyGkaQzaZF2jJaf+Y6pc0pvij1jFQbLLkFE02IwNOhMOWzuTbfsyYokbOpJ8eihAo8PlYlk1Cw0pAuNDd1JNnanaDHyjc60w/b+DD94bJjhQg1DF2gIIiRDoSRpGVy8OUNneuH5WACapjXnbVSvidQQb4BYJKMvl0DTWluC93el6EwZjJQCJHH+2FSFCAF0pgz6uw57snIJkzPX5HhcE+wbrzBSrJE0dXozFn25BGnb4HFPoDf6OwoFX8f1A54aKRJJyamr01iGzmTVw9Q0unL2NC/o+r5uYHieXiE0bGp+SMKKl/xhJAmkbHptDSHQ9cNzMZFMAvMbTj2daQquT9kL6cvaJCxjmid4suKzKms3N2AG0jr7S/OHpw6kl3f9OGU4nQBO9oe2QqFQzEe7VNTarc62Epi6U34oX6Uzac2QJF50HaejLK6FOPrrcxFGku60hZePeOjAJE33Tf3fvkyCrrTVck5LOz1vO4fLaJpoGmWC+PPrWiyQ8PRwiVItaMmANOrfkeuFBFFsiUVINCkIELheiB+FLY9X1wSDk1VK1QBHB4Rx2DsgI0rVgKG827JHL2XpdKdtTE2gaxBFkkjKpqFnaoKedOsG+mC+ytMjJbKOQRiBG0RN8Q3H1NAFPD1cYjBfZaBz4eF66zoTdKUcxkulpmekMdWo/9uddljXOX2zIZcwuWBjJxLJoUmXgY4ECfPwZnjFrc1rNAGMFCr0ZhOYQmP96hQJK/ZeTTVspnpBrQV+zWNFl1/vn+SsNR2kbQNdExhC4NdD63oyNokp8z8xx0bCkaRNnbRtEEQRa6ec54Yn+MmhIp0ps1ngeH2nuSDDaX3n8r7/KsPpOKMe2gqFQnF02pV70q5+VyKNnfIjN/H6OxzWdy1uEy+bMOlImkyUPRIdM5cXE2WPzpTZciiZoWtodbECISESolm3R5OSUMahTK0aDO3yvE2Wazx6qEDC1NmyKoXrxcIKuiZwLI2DEy6PHSowWa61ZDiFYciTQ2W8SJIwBa4PQsaGsGMKvEjy1FCFMAxpxl0tsN/Hh0sEUnLGmg5qftQ0GGxT4+mRMo8PF1vut1QLGCq6dKZtsqFO2YuansKUpaHrBsPFaj1HbeFhi08NlxguuHSnbXKOSTkICUOJrgtShk7e9RkquDw1XGrJcCrVQtK2TtrR8YN6CGCsY0EkwTR00pZOqRbSmZ7+t5qmcXp/jjCK13maEM3NiCeHF6Z+55g6p65Ks1OjeY0cadhM9YKOlxYm4mBpEVUv5Be7xnjW2g5yCRPH0tkzVmFNZ4K+bKIpSQ5Mye06OmtyDlt6UwwVY/XAbMLA1DT8KGrmd1na4QLHA8mF3VMX2u5EoQyn44h6aCsUCsX8tCv3pJ1qciuRXMLkrDW5JQsbT9sG2/pz/GLXGKMll4xjxjkUUbyzHUSSrX25ls9t0tTwgrC+udhBLThsiNiGxo7hEn4YNXe2F0q7PG9j5VjIYnXWRkMjeYRrIJcwGCrUGCv7rOteeL+7RysMFVyCICSIYg+WJgQSqHgSQws5lK+ye7TCOS3Ike8ei42XnrRJ1Y+wDA1dCEIpqfoRPWmTohuwe6zaUr+DeZeRkkdX2qLm6SQdaCRlGQJsS2eo6LUsc17xQvxQxop6uk5Gn27Y2kZEvhpQ8VpTLRwrexi6xml9GQ5MVMlX/aYnqzNp0t+ZQBeCsZJHZzqYcc3MtRkhF+gJHS1VMPVYma5cC6gFcXjdVMNmqhf0qaHigvodLwe8YlM3D+2fZPdomTWdCTK2wZpOh7RtYNQ3JRrzvlxbWGhqwfWxTYOtfTbDBZd8xackAwwh6M1Y9GYcXD9shro+MDRPMas6C213onjmPxmWEeqhrVAoFPPTrtyTqf1KYgnmxk51wtQX3e/xoF15sUKIJXveCCHYPpCl6PrsHi1TqPjTQupO78+yfSDb8rgrfoStx3kjoyWPbMIgYer4UcRoyZuxs90K7fC8WYbANHS8MC7MGkSyqSZnaIJaEGEasfJeK4yXa0xWPIIowtB1BIdPrwSCMGSy4jFebm3hWQtCNGBVxqEWRJS9oDnelG2Qc5xYjCJozRAJQknJDUjbOqu6kgThlPOgC8bKNUpuUK/ztHBWZWwSpkapFpAwNdwgIpCxcINjxMdtU2u2Wei1YtUNMNcLSFgaoTSaan2OpVHzAizLYPdYhclqMGuqxWybET945MCCPpdpmoRRhJQwUqqxuTdFXzbRvD6llBzKV+nJWBQqHrvGF/Y9F+trynPWdzBW9tg+kCWXtAjDiD3jlRnzPowWdv8bLfsYmsDSNbb0puP7aX1DI1GXYW+cA4DR4gL7XWC7E4VanR9H2rUYUCgUimcSR9ZHOdLAWWzuSaPf8bLHRMWbtR7QYvptNyspLzaXMLlwczerszYH8y41P/4O13Q4bOheXAHcIIywTZ2tfdkF7WwvZsxnDmQZKXm4fohj6vSmrZbFBRp0pWz6sjaDeZdyrUIg5WERByGQEvpzDl2p1tTkQimbNYHSttEsyirqP5OVWFY6lK0ZIj1pm6RlMFn1SZgGsTlW71kK8q5P0jboSbc2XtvUMITAC+NaSKY+3Xjxw/jas1v0FG5ZleaUVRl+vX+CyWqAjA7LnCM0IinZ1JNksurzy13jC75WOlMmAsGhfA0hBKahYWqxYVqohkyUfVblHFw/IGUn50y1EEKQsnTKXjx3+7ML86adu66DCzZ3s2V1mieHSoT1+1wYSSYrHk8OF/FDSdkLeGBvnvICPWpZO/7clq5jahop22gaY2clzBkbMqkFWgYJXU4LdU1a0//wyFBXTWN+hYxGu2WMMpyOI+1KRFUoFIpnEo3ck50jZSIpZxg4mhBsWZVqOfckZenYps4vdo2RtPQZ9YD2jVd4zqbulvttJ+3Oi22HJyuXMDl7bQdbVi1Nv41n50J3tlslX/XZPVri4KRLLYiwDY2BDoeNPYsz9FZlbLb1ZXl6uIwbBESRbBo4miZwDINt/VlWZVozRCxNYOiCIIzXC5qIP6+EWAmvbpxYLYo4bOxOsmVVkp88OU42EZK0dExDEIaSsVKNQjXgead1s7E7OX9nU+hMmqztdtg9WqXg+iQs/XAIoBfiBREbe5J0tlgfKpuwOGd9jl/tnaBS89B1DU1AEIIX+FiGxun9WTZ0p/FDueBrJWFqRDL2Xjm61hT0EBL0eh2qIAhZ15VsruFmS7UouMG0jQ7TMOZV1TOBc9d3ka4bNbmE1eyj4PrsH69iGIJtq9McKrhUvYD+jMVjw/PnOW3tixOyZltjzuZxzqQcID9vv9mU01Koa3cCJvx5u6V7ZrrhskIZTseRdiWiKhQKxTMJIQSdKYuhp8eYrPj05xxylkHVC9kxVKIjaXLexs5FLcQbic+yIV9d35xv/i4WIfvWJtqdF9tOT9ZShgAe+eycb2e7FfJVn5/vHGP3WBlZ91pEEvZNVBgq1Lhwc3fL50IIwaqsja7JehichtAkUSTwo5CUpdObsY/6nc1m0KYck66kXc9DCZj651LGBmZn0iLVotS7pmk8a10n/72vyGTVo1AL0AWEMlbC60hYnL2uo2UPnGUaPGtNJ74Pw6UqricQ9ZyfQErW5BI8a00HltnaPImiiKIbkHMMwjCkUhedEIBlxoZ7HP4nW7pWhgo1ogg6k0Y95ycOs4yFHiBlGeiaxnjFJ5uYbvQ2Ui0O5V12jZanbXTUvCRJW6N4lNyhzpRBf8fha7wR8leqBTy0PzZiejI2e8bKPHaggK5rTXnx+dhQN3gXep1sXKDlsrE70VKoq9AWVslJLHOXkzKcjiNtlYBVKBSKZwhSSibKHquzNqsyNvmKT77qYwjBaavTCAGTFY+1HYmW7pdlL8TzI561toOJijcj5KszmabmRcsmz7SdebErSeG1Xc9OKSWPHMzz2KECCdMglzhch6ZQDXjsUIFMwuCiTd0t9V2qBRyYqLA66+CYGlVPEskQTegkLY1swuTAZGVOOfK5DNpswmRNp0PZCwijWCBDSokQoimUsbbLYU1na56hsheiaxrnbujg0YOTjJcDIhlhCI2urMn2gdhoanWepSydrf1ZCm6ANgz7Jlz8wMc0DDZ0Jdjcm2HbQLZlg3fPeJXHh4r4YWxEWYYWS50TFwQOItg5VmawUGNNvWDyQq6VUi0ADbqSJnvGKpS8qHl+k6ZGR9JE08SsohONebN79PBGRyOPsuhFZG2Tcq02q9lgCcgl7Rmhd0IIhIijlLKOyc7hMuPlGroRi1XsH1tYaOqe8Rpd2eqCrxNdX9h132i3UJEZXdNZiOEUt1u+nPgnw0lGOxJRFQqF4plEw2BY05HENrRZQ7MWYzA08kx7MzZdKWtGv5GE0VJt2eSZtisvdiUqvLbj2VmqBTxxqIihCXqnhM3Zhk5vRufgZMgTB4ucOZBrSTY8X/HYM1ahI2Vz6uoMlVqIH0lMTZC0dUZKHntGK+Qr3ox+j2bQJkxBfy7JU0MlkqaGEFozBFDKCAFs6E61HALoB7EaX9o2OG9DFwfzNWpBiG3oDORsNE1jMF/FD0Jo4XpreI6DSJJ1DM5d34FAIhHU/FhcoSNptTzHCpUaO4fLTFZdhNDRkKCBDOP3LLo1DoxDyfWA2HBayLWSdkw8P2LfRAWkpDNpNj2Q5VrAYKFGf9ae4fGEOAwuiCR516cnZVOqxUZ+oeKzc7RINQgwNfAimsLfgtjDaVuCIIoo1GYaZEEYxYa861PxQnqzNiU3IIxgshYs6HztHi/xknPWLPg6SVsL+z6mtluIhznt6MD8sXppZ3mbJst7dM9QlloCVqFQKJ5JTDUYhBAzFiqLNRiOzDM9sl8/CJdVnmm78mJXqsLrUos4FKo+k1WP1dnZJbY7khbDRZdC1W/JcKp4IVU/ZHXCRBMaaWf6+JKWznDBn+G5mM+g3TlSRNOgp64u6AURoQRdgGVo9Q0Bm4ofzZDoPhpeKBkve1RqIbomWJ116rlUkmItIIwC3EDHa1H9ruE5TjsGrm8wXKjhhxJTF6zO2aQdY1Ge44mKz0SpRhBJbFNg6DpSSjxNEkURQSgpVDyK7mHDYiHXSm/KiGXA/ZCedHwO6mYphqYxXHCp+CFdzsw+JioeuaRB2Q3xwohdo2WqXuxRzNoGQRCfO1OPBUIQ8fdm6DqhlLh+iD6LN8ao3+tGSx65hIllaKQcg3zVJ/AXZjh1JfTWNkHEAq+nhbYj3hAIwoWNN73Qyr4niOVzRzzJWMr4b4VCoXgm0S6DYaXlmbZrvCtV4XW2ELahY83JOootIMTRX5+LpBXLpVe8gLQ9c1wVLyAxi+E+n0FrGBqjxbjmkR9K3CAiCiWaLnCMOL8pX/FbNvRMDWp+SNn1WdsVq9H5foRpanQkTPaNVzC0uF0rlL2QveMVan5EwjQ4tc9qikN4fkTNj9gzVmFTT7ql9VAYBfhRFBel1TRkXV5Qr/vfIgkBkjA6bJgu5Fo5kK+haYJMwsSPJKGk7iGDSEpySRMNwSODRZ69fma46MbuFE8MFtg7HhtNPenYIBd1sY7GVBJaXBwX4ty0MIqIovrnOIKUpZNNGBSqPj1pG4GgJ23jBhHuAustGZrWkmEaLbDu1ELbNbyoJX9h4621qAp5vFErd4VCoVAsK9plMKy0PNN2jXeqYWoZgomKX/9/jc6kGctELyPPG7QnJyubMOlImkyUPRIdM5dDE2WPzpRJtsV+c0mT9T0pdo+Umt+ToQmCSFKuBfhBxKbeNLkj1OTmM2gNYLTkI0TsDZus+oRaLNPfkTCp+iEHJ6tEC6zD08CPwDF0RoIa9zwxQmVKHaekZbCuM4Ft6Cxw3Xu433oIoIwkvZkjrmMHhovVRYUAVnyBqesEYUip5qNpAiKIhCSse8V0Iaj44Prhgq4VKSXDhRqWrrM2Z1JwPVw/lnbXhSBp6XQkHSp+RNKKPVNHhotmHYN94xUOTrqsmSL0gBRoWqzYFyuy1wubEUvMU1fti+TMsQkh2NST5tf78gzmq3SnbWxDZ1XaxpxlU2k2Cm5Avuov+PqINMF8ewai3m4+pnpRLd0E5q89FQTL574zG8pwUigUCsWyop0GzkrLM23HeBuG6UMH8owW3WkhVKuyNj0Zh7PX5paN523q4qsv51D1Q8q1AF0X9OUcBvPuonKy0rbBtv4cv9g1xnChWpfUo15VNlZU29qXazk6JG0bPHtdJ8VqgBuElGpBcyGqCehOO5yzrnNGv/N5WitBSNn3CYI4fC9tGxhCEEhJvhrgBSEaouWQOksXeGHIrrEyVT8gYR429Io1j11jEatzNpbe2vXmhZJqLaRjDg9awoxD9Vodb1/WJmlpFKpxjqIXyObXpovDoYuGiHOT5rtWGp7MfeMV/DBCaGAaGhJRVysUdCQNHMtA0yKeta6DTb2ZWVMt+jscLE0jX/XJOpJIxuGOALYuME29WfBXF+BYOmEksEwNfY7z259zOHd9Jw/tn8T1QoK6oM2GnjSPDFXmP2FC5+ED+QVvLnQ65ryOVllvNx9Tvaj2Ag29tLM8Nq7mQhlOCoVCoVh2tNPAWWl5pks9XiHiekAP7c8zWfZZ0+nQldap1EIeO1iiI1Xj2es7jul8LGV9qMbiy9Q1nhouzVq4eDE5WUIItg9k2TVa4qdPjzJZ8YkiiabFqmUXbelh+0C25XE3+i26PjtHipTciFBG6EIj42hs6s3M2u98ntZixUNDIKMQ27TiOk4CNAS2CVXPxwtDRIvxhZqAg5MuQSQZyCYxNNHUZQ8ii5FyjcG8S4vlobD0OD+x6oekHYNaEBJGoGuxAEfVD0naRssG2drOBLmExVjZJ4hig6nho/Al6MCqtM3l21axrjt91Lk31ZO5tS/NQIfNE4MlsonYeEzZOqahowvYN1blvA0dnN6XwTBmn2ddKZvNvSmGizV2jpQpuj4T5RqGDgINTUikFnvEdI24PplpkLENUnPIizfmUxhFjJc90rZBwtLRpMd3Hhk+6rkSwIUbs5RrwYI3F6JoYYV1F9LOr28cGLrA1hY2L2MRieWLMpxOEO0oOqhQKBTPJJZaDGAqKy3PVMo4zKtxHmJVtcU9M6Io4vFDBZKWzkDOoegGVGohmhCcMZBlourxxGCRbaszizrXS10fKggjJt04d6fmhzMKF5dqAdmEuaicrILrM1KsYes6Ax1GU0UtDCUjxRoFd+EhTlPJJUxOH8hSqHqMlUtUvZCEpbMpk+D0geysfc7nadW0OC8qZekEUcBo9XDh34yt0ZWy0HSNSosxdaOlWBhiVcYmYeqUvYjQj9A1QdrWEZpNyQ1jgYLkwhX7TEOnP+ewe6zCf++boOqFBBIMAQlLpzeTYG1HAtNobaHcKNDbiEg88tNKCbal0Z9zjnqNHynGIZGcsSbH0yNlhoseAolR1TBF7H3LJUzO3dCJfhThjZSl05m22DFcJJsw6e9wqNQc9o5XOJh3cWshhqEjkEQSakGIo5kMdCZJHWWsuYTJWWs7mtdVuRbiSQ2rrtQ3F6YWy4a3srmwf3L+cLqFtMtXfZ4cKrFzpISha5T8hd2vTGN5ef2PZOU8NZ5BtLPooEKhUDxTaIsYwApk30SF+3aNsX+8ihdILEOwtivBBZu6WddizR6AkZLH/vEqG7tTpG2DWhA1ZdltQyOXNNk3VmGkNLfi3Fy0IxdJ1wQTpRoVL6Q/l6AWRFS9WAGuO2VzKB/n9egtukSiKOK+XWO4fsTFW7opugFeGGHpGhnH4KmRMr/cPc6anNOyAZmv+jx2sMBYOZYcz9gGCMFoyeOxgwWyjjnreTiap7Uva3PPE0PsG6swVKxR9kKkjEUsUpbO6ozNuu5kyyGWFS/ENASWoZOvBvX6UBBGkpIXS4n7YTRr/aKjEXvQbO7dOcqesTIlN2zWRco4Bn4kOXdDZ8vjHcy7FKsBhlY3muI0oVjmW8Tep2I1YDDv0pGae/4eKcZR9eN6Vv05mz1jFao+eF6IbWikTJ313Qn0BdSzEsT1lxxTI2kZWJpGyjYRooYk9sIEIWhCwzE1dA26ksa8uXRHep79wGPemrICim7QkuDLVDXCxbZrCkK4Pr1Zm2LVx5kjd+9I7BYN6eONMpyOMyup6KBCoThxnOxeaXWvjNk3UeE7/32IyYpPf84hYelUvZAdQ2VGih4vObu/ZePJ9UO8QJKoz6kj82kcU8cLYonkVmhvfSiB64fsn6hQdAMiKdEaC/AwmjPM6Wg0DMiOpMmByeqMfjsWaUBKKXn0YIHH6l69VZnDnqOi68eFdR2TizZ3zXoejlwgNwzCQ5NVamHE3okKQQSOoWFqAj+K7xV7JyoMdDokZ1HyOxop28DSdVwvQiNWfRMi9twIoOZLHFM/qkdkLoaLVfaMVQgCyUDOwTQ1fD8OOdszVmGkVG25z70TFYpeQNLWCSOJH0ZNMQtT19A1QcEL2DtRYdtAx5z9HCnG4Qchu0bLaJrG+s4EQ0WXagBZW2djVxJd03h8sMALtvXOKWZR9kJqfsjZ9SLbhYpPqebjBXE+1tTzqiEJohDX0/D8hYWxTfWUT1YC5tMBiUIYr3gtKZF2Jhf2Pc/Vbup9YKAjSTZh8fRICX+BKnzGMn/MKcPpOLISiw4qFHNxsi/s28nJ7pVW98qYhkdksuJz2upM83jG0cg4Jk8OFRflEXFMHcsQVL2QzCw1aVw/xDJmGlTz0a76UGEkSVgaxfGAUjWgO2OTNHTcIGLvWIWUY7CmM0G4wIVZA9cPKVYDSrUAP5KkrcOiCBNVH8MNQNKyAVmqBTx+KI+hiaYkNcQ76XZa5+BkhScG85y5JjunbHhjgZyv+uwaje8FpUqVJwcLhBFkEwZhKAmIPXIdCZOCG7B3vEpu9tM/J+s7HbJJgwMHS2wfyBCEh8UWDF3w6MEiZ6xJs76zNe9j0fV5eH+erGPQkbSo1D1klqmzZVWaiYrHw/sKPP+0VWQTCx+0H0qCIAQRGwKaEE2Pk6bFxWQJIvx5RCeOFOMo1UKG8y4jZY9qza8bJZJaIHlqtEI6YRJFMFkN6ErP3ufUItvd6bjI9qGJCn4kmwIhjWkqAT0CPww5mK+Sr3gtnQdDSIJ5DKcA0CJ5VCXSI5/l9gIve2cOC+fI+0DaNtjSm8bS5i9+CyBDb2EDOEEow+k4slKLDioUR3KyL+zbifK0qHtlg4ZHpD83+4K1P+csyiPSm7ZY25Vgx1B51oX7obzLaX1petOtrcDbVR9K1wRVLyLjmHQlLUpuQMkL0BGs707iRxLXaz1UzzY0yr6PlGKagW7qgs6ExaF8FU1I7AWGGDUoVH0mKz6r5/jeOlMWQ/n5C+seeS/YORLgBfG4/SDC1HUM4li9UEoyjk7ZDfn5njyXbV34fHBDOL0vx3Chxs7hhjCCRhBFFKoB3WmTratzuCGkW7jcBvMuw8UaazuTJE2dihfhRxGmppG0NJK2wVDBZTDvtmQw5BwDoQlqXoTWUKOrWyVhCJ4fYVsaOefog50hxiEjxkoeE+UatqkjRayEZxkaYQTDeRddAnL2+SulpBZEuH7AZFXQmbRImgZeGPfrR7E4RmP6ayKuEUUgOTBRZaRUY133HBbZLNiGtiAZkGItmFOJdLZn+VMj7oLev+jN/u6z3QfStkFpgfbQjtGF5VidKJ65T5xlyEotOqhQTEUt7NuH8rTEqHtlzNSQusaibGou0mJD6jRN44JN3YwUPZ4cKtKfc3BMHdcPOZR36UianL+xq+W8nnYVLo6ROIZWz3EKCWUs52wbOofyVRCtF81MWrHYwmhx9hVd1QvpzdokFyPLfpTLU8qjvx63mXkvKLoBEknaMeoeghBN19CkxNQECUunUt90aIUgjBjoSHDJlh5+8MggTw4VCSIwNOjPJbhkcw8DHYmWr7cwkvWfiOFS0PQ4CRGfe8fUmm1aoTttkTIMXM8jkBFBIKYkOcXGSMoy6Z7H8D9SjGOi4lP0PAIpCWp+c45pWoClCSIhyNd8SrWZuT0NA2SsWGMwX+OJoRKbelL05xJUXJ+y51Hzp9dGiiT4AfhETGo+xWpr35uma2jAfFd/JmGyriuJrolmjlljzLM9y/O1hXmGgjniBOe6D1SOpmIxhUJ1YTlWJwplOB1H2vtQUSjaj1rYtxflaYk5HvfKlRBq2gipGy971PxwRg6OXX+91ZA6gHWdSV5ydv8M0YnT+tKcv7FrUaITU3fwG/WWwjAu0Jow9UUXLg4jSWfaRq/6jJZcbCP+rkIpKbo+mURcpLbVBXgkYcuqNH5YZM9YmVzCxNAFQSjjOjxJk829KVrsNi6sm7DmLKw7WfHoTFhHFQOY7V6QdQwEcahaytKo+rERbWiCpKlTqxvQnanWNq4MXWO8UmPXWJn+nM1Al4OMBEKTyFCya7xMJmm0fL11piwcQ+Pp0TJZ2yRh6uiaiEUn3ICDeZ/elEVnqjXPpqbp9GZtKn6IF0o0cTj2LZKQaKpvzj/Ppopx7BotEoRQ88L4Oxfxj5DgitiQdAyd2hHxcUcaIKcPZHlisMCOoRKTFZ/Jso/rHdZw0AGhHR5vRGykV1sU3wiiOK/raBGJOlANQp4cLE6LDMk6xpzP8tXZmVL4s5Gdw1s6l6y+scCNmOW+Bn7mPnmXIfPVaFjsQ0WhOF6ohX17UZ6WmHbfK1dKqGlv2qI7bfGzneP0ZOwZOTijwyUu3tLdckhdg3WdSdbknCWTe2/s4A/mXX729Fi94ivxylDAxp7UogoXG7pGh2NiCsFTZY89oxU8KbGEYHWHw5oOi5Td+sLe0DX6swkMTXD/3gl2jpaahYAHOh3OWpOlN+203G/aNtjan+G+XeOMFGtkEwampuHXQ9+CSLJ1IHPUe+Rs94JTe1OkLYPhYhxKFkYSGQkCAV4gqXo+aztTnL0m29J4E4bg0GSVQxMuq3IWRTeoXxeQcQwOTbisztgkWsza701brMrGcuQZ28CLIkQUTwuJpFQNOKM/2/L8TZoanRmbYi2g7IaU/ZCGKytr6yRtna6MTdJc2PfWEOOYLLsI4lA6yXSRAj+MDRU/jJjqN5ptM9ExdU7vz9GZrLJztMzu0WLTK1S3l5rRfqIeYhhEUJnFk3U0LEObz3GJADKORW/GnhYZsqknNeezvD9rN/Ox5sIANnanZn/POWT1B7IGT0/M71U7rXfhkvcnArWyOY7MV6NhrhhUhWK5oBb27UV5WmLaea9cSaGmQghWZR0sXaNYDbB1DSHq6mzVAEvXWJV1jun70zStZcnxBSGm2E31eqqLJWXpWKbGg/tLJEyd0/ozCBGHHdX8iKdGSjxnc1fLhnSj34OTLhu7U2zqTtOI+ZJIDk66rO1qXd5bCMEZAzlKbsDusTL5qt9ciGoCTu/Psr3/6F752e4FQtdZ25loSpFbOuiaQSAlJddHF7C5N4VYgKdlKqPluEZWSMSOoRKWrqFpEEVxnlLaMZiseIyWfVZnF953NZBs6k1zcLLKgYlqvBCvG9JCxIVsN/akqQaSdAtDNg2dnGVgGzqhJTH02Puo1ws724ZO1jZaqg8lhCBj63hBCCLOc4vkYZ1z04g9ka4fTDOo5tpMbAgidKdsdg7lm8frewjNH1m3TgQwWV1YiFyDhKEh54vV0yBj62h19cxGZMie0TJ+GM36LO9O2zg6VI/Sb8LkqPeN2WT113alYU9l3s915tqueducSJThdJw5Wo2G9V3La7dToTgSFW7aXpSn5TDtuFdO3R1uhJKVawG6LujLOQzm3WUValr2QkxN4zdPX8VTw0WGC7WmR2R9d4ItvWkMIZaNh7dxfgEu2tQdh+rVc7ISps5gYfHnV9T31kU9r6khXuA1wqbk4r6vRr9x+KPZ7Lfo+sfUby5hcuHmblZnbQ5OutSCCNvQWNPpsKE7Pe/8ne1eUPMjpNDoz9qMuwF+EFHxQzQEXUmLzqSJEKLlnLeqFzBa8hBA0tSJiOXINQ2SdYt3rORR9VrziARh/Jk39aYJpaRUC4miCE3TyNg6m3rT2IbW8kabpQssKxZGSFoGmqjH08nYmI6IlfssvbXvruAGzRA9KeVhY7/+OyI2JgtT6hcdbTNRCIGmCWpBNNXxijji/3Xic92q3HsQxXbo0dA4LEbRoDNpMVqugWTWZ7lfD689WgygYeh483xvR8rq7x+dhAeG5xkxOIsoLXA8Wd6je4Zy5GRarru+ipXPUnsX2pXDoIhRnpbpLPW9srE7bOoaO4aKjJS8WJ3M0OhNW3Sl7GUVatpYlK3tTLK2M8FExccL4l3izqSJlILRUm3ZeHin7r4LIUgesQBabCjvbLVxAhlgCEFPxqYjYVHzw2XTb4NcwuTstR1sWdX6/J3tXlB0fap+SEfKYlNPikAKwjDCtnRWZywmKgFVL6DgBvS3MM4wkuQrPpoeF1YOQtmsi2TogsGCy2TVbzmHTNcEYyUPQxM8f+sqim6AH0aY9QLDg3mX8ZLXshqiF0pkGItEiCgWUQlkHFrnmHrshYkk3jxy5EdS9iW6FgtCRHE8YUz9X12ArsXtGhxtM1FKyf6JCmnHJGEKXF82jaZGyJ5GbDylLJ31c4S+zYUmQG/E+h2lzZHTzdQ1DCFIOvVn9hGbdAU3QEqBgZxRW1fUz4MQgvHy/Ib01LpTC6yrS95dHvezuTjxT4aTlKmTSaFoB+3wLrQrh2Els9TGabs9LStN1GMp75VBGDHp+gznqxzKxzuuQovzDYbyLn05m9W51tXD2sWRi7Lu1PTYfzcIl5WHt12hvLPVxpm6YRNFLMqAbFe/UzmW+XvkvWCs7KELSCUsHMskl4i9LZGUVPwQy9DQqIeYtYBlaJhGPM8EAvMIT00YRtiGPuf3enTisWhCIzeb5Pgi1BCDUGLoGjnHZLzokXcDwihC1zQMXaMraaFrGkGLhlPO1kFo6CL+vH4km7lTlibwwxAhtLhdnaNFCVT9kAOTVc4cyPLQwTz7xqrU1eNj6uIQhg7ru5OcumrhUuQAkZRIIZvGVwM55afpLZtCw3jd1JNm12h5xibdSLEGAiwjHl8j50sQj7UxPfw5ZNmPNt6lbHeiWDYr9z//8z/nPe95D29/+9v56Ec/Omub22+/nTe84Q3Tjtm2jesuTHNecfKwEvI42snx8C5IJLUwQgbxg8BZ1EN1ZdOu0Ld2eVpOdlEPXRMcmnTZNVIi7ZgkbaOpolbxAp4eLhFJ0fIOeLtYaYJC7QrlPbLfpGnAlMvLDxdnQE7t155FSGA55BROvRf052weHywSRvF5KHthU146Z5tU/ZBcQsx5nc+FJgRrOpMMFqoM5qvkkhaWLvBCSb7ikbB0+nKJOCSuBaaqIY4UXSxTQ0cQIvH8aNFqiEKA0ATDpRquF9CbtZvjLVRiOfFc0prhaZmPzpRFwtSo1Z9plq5NEUmQSAEJU5umAni0KIGDk1VMXbChN83p/Vkmyn4stR0d9hEJYrnwZ6/vxLZae2ZYho6pa4Rh1AwjrEcUoon4//VZ5m/jvtGfc0jZxoxn2KZuB1sXVML484l6val6sCx+JHGAnkRrz4qcs7D71ELbnSiWxRPyvvvu41Of+hRnn332vG2z2SxPPPFE8/eTaTGsWBgrKY+jHbTTu9Dou+KF9KRtRos1vFBi6YLutE3FC5et52KpabdxutSeFiXqEc/f8ZKLF0Z0TNn9tnSBlbAYL9eYKLszdmhPFFMXZQcnKziW3lx4ul5I2jGP2cO7lJtM00J5s86MHKfFGnrt7nfnSJkwimaEbuqaxpZVqROeU9i4F5y6OsOz1uX42c5xso7AShhEQqDV52vejThvYzerMq2pkmUTJhu6UliaRtkLGCt5FKTEEIL+nEPSMhjoSBxVPn02mmqImuCpcpE9Y4dz9FZnHdZ0JkhZrashpm2dai0gimBNR5KCG1AJQ3ShsbYjyXDZw62FpO3WvjdD01iVieeXH0oEsmk4SeLcut6MM0NWe64ogYFOB9PQsHSdtH24rpQX1HuUcSjk6oxNR4vGLkDCNkiZBjXfQwjQjcOGXhRFhBFYmoamxfWyZgv5nm2Tbt+oiWPq5KthvSpWTAQEUXxOLEMj5bQ25oXurS73PdgTbjiVSiWuu+46/v7v/54PfehD87YXQtDX13ccRqZYiazEPI6lpp3ehbIXsne8wnjZI4wiclN218bLXhwqMSbY1JN+RnsuVlromxL1iCm6AboQdKdsJio1LF2nsdLwwpDulI2GoOgGZGcLKzoB5BIm67uT3LdrjCcGi816S+u6kmw/xvvZUm8yTQvl3TXW3P2WxDv4G7sXF8rbzn47UxZ7Hh9m31gFx9QxDEEQSJ4aLLKuO8l5GzuPKaew5PokTAPHFEQRHJqsLvpZpGkaz9u6iv0TVR4/VMCPoBFEZWmwtT/Lpaf1tiwn35BPL7g+3RmLDV0pJBKBQAqJ60fT5NMXamynLB3b1Hlw/yQJ0+C01XZTra8WhDw1XOI5m7pbNkyrfhSr5+mCoaJLxYuaohNVP4jrRRlxu1wL/SZsg86URaHqUfFCKn5IFNWFG0ydZN3QTszybJvNAEmaGg8fLLBjsMBk1SdpGqzqswkiSVi/52QsAwSMlWv4Qcg0V+o8dCZMUk7saQxlhB9ETdEJyxAIBCnHRNfikNO5Qr5nbNKJWBxEcljEokEzL0scXa58NgZLC1MNXGi7E8UJX9m85S1v4corr+SFL3zhggynUqnEhg0biKKIc889l1tuuYUzzjhjzva1Wo1ardb8vVAoLMm4j5WTPZSsHay0xWy7aKd3wQ9CDuWryEjSmzl8jm1Dx07rDBfjUA8/COEZbDittNC3lRby1U5sSyejaxyYrHBo0iWIIgxNoztt0p9L4kfLy+uWr/rsHauQsgzOWdfZXHhW/YC9YxWyjrkoI6cdC/tpyDh9RUyVE1sKlrBfKSX7x6u4fkjG0fFDiR/Ieu0bHdcP2T9eZW1HoqVnRuNZNFJ0kZJ4ntU9OLlkvNDdO24s6lmUdUzWdSXZN1ZmrOI3c3uSifj4XEVJj8aR8umxKSbq8ulimnx6q8a2nBKUZptas55Vs4jsInKcakFsJFW8IM770rR4IR+EVDyf7pSFJrQZhWrnw9Q1cgkDxzSIIFaWa8iR6xqOaZB1DMw5NphmixLY0J3iycEiE6VaXDoASdEN8MKIpGHQn0vgmAYlN2CyGtDVQpqTYxn0pE0myjWINHSLWAVSxN4sXROs6XA4f0MXq3OJBa81KzWfqh+hC7C0urBk/TyIes5T1Y+o1FozcBY605f76uyEPtH/5V/+hfvvv5/77rtvQe23bt3KZz7zGc4++2zy+Tx/9Vd/xSWXXMIjjzzC2rVrZ/2bW2+9lZtvvnkph33MnOyhZO1ipS1m20U7vQteKKnWwjnDChJmXO+jVTWjlcZKC31TNeRisgkTx9DZNVomaets6LbQ6gpatTD2pm7qSbUcktQupm4GDXQkp73WibXozaB2LeynyZFv6Z4htrBYufd29VuqBTx+KI+hCTqSFuPlw4ZIR9IkiCRPDOY5c02WTAsGSdkL2TcWe+aDkLgAbjPx3sPQwRyttOyZl1LyyME8I8UaZ63LUQtARhFC07ANGCnWePRQnos2dbd8LS9EPr3ViI6yF+L5Ec+qqxbmKz6lumphb8aiM5mm5kUtP5MtXTBWcvECSXfKwo9oeshMLQ6FGyu5LcuRW7rAMmIvdEPmvBn6Vg+HtE2jpX5zCZOtfWkyjhUbchJCKXFMnaxjYhl6/CzRtZZrnYl6/ynHoOoFCBrjjeuQJSyDzqRJyjZaCgUcLcUKirYlMDWd6HA5KzQBfhSHyo626Bmav1xva+1OFCds9bhv3z7e/va384Mf/ADHWVjxvYsvvpiLL764+fsll1zC6aefzqc+9Sk++MEPzvo373nPe3jnO9/Z/L1QKLBu3bpjG/wxoELJ2sdKW8y2i3Z6Fyw9lhiu+uGsC4mqH5K0W3uwrERWYuibqiEXXxuZhEHVD0jZBqYRf49BJPHCiKofkEsay8bz1q7NoHYt7Ns53qbMOWKGOMRi+y1UfYbyLn4Uyy5nE2ZzPky6ARrgT7oUqn5LhlPsmXcJpWRV5vD6Js6R0RkuuhwquC175ku1gCcOFQkiSc2HkhsQItER4BixoXewyJkDuZbG26ARbtaXS+D6IY6p05u20DRtUREdU1ULu1LWjNy0SC5OtVBKScUNiaQkacX9NuTTE6aO69eo1MKWcxW9UCKlpDtlI4nwQ4hkhCY04luChoyiljcGV2UTrO9y2DNWwQslfZkECStejwwWXQwhWNeVINFi/aJYxEawKp3A0GC84tUVB+O6XkEEiNbFbprFhOuFlCNJ3f8o0ATYuo6sG4Ct0JVa2OdbaLsTxQkb3a9+9SuGh4c599xzm8fCMOQ///M/+fjHP06tVkPXj/7wMk2TZz/72Tz11FNztrFtG9tuLVGyXahQsvayEhez7aCd3gXT0OnPOQwVXYYLLo6pNeVwXT/CNASrM05LFdtXIis19O1kryFX8SM6khbb+rKMlGpMVDxkJBGawNIF2/qyZBMWFT8ibZ/4+0S7NoPatbBvjNcLI/aOlxkpek3p496MRV82cUxy5Et9HqSUTLo+uqaxesp5MHVBZ8JiqOBSltGiFuAVLziKZ15flGe+UPUZKlRjQy+KC6Y2VCEnKj5CxN9tq4Zeg9miYYbq0TC6Jlo2imeoIR5hGPiLlNPPVwN0Iy4uO1KskUkYJHSBF0lGijVMQ6Drgnw1YKBz4f0GYYSua/SkLSp+yFipYYhIkkkrzp3SWy/Y25MyySUtGHNZ22VRrAbkayGGgL6szVDeoyNp0ZNq7TsLI0nKMqjaEWEUkrENgigWV9D1uAhw0jJaVi3sy9qkbINSzUcTdXn6elKTlJJQSjK2SV+2tbW1Jhb2PFxouxPFCTOcXvCCF/DQQw9NO/aGN7yBbdu28X/+z/+Z12iC2NB66KGHeMlLXtKuYS4pKpSsvazUxWw7aJd3IWXprOtOknd9Sm6NvWNlPCmxhGB1ziGTsFnfk3zGn+OVHPp2MteQC8IIx9A5dXUGP4w4MOk2F/arMg6nrsqgaWLZeKXbtRnUroW9oWu4QcgTgwVGyx6y7gWIJAwVXQbzLht6UscsR34kxyJzriEI58hrC2WETuv9WrogYeu4fjCHZz4gaeste+alhMmKj6ELejMOfiCp+RG6EHQkTYaLLpWaZDGikPNFw6zrSrZsvLZLDVETce5Vd9oiCKFcC/CCEE1o9GZtdC1+veXQNyFwTI2xohsbosSqdwLJWKlGR9KkI2W2fF+vBpKBjiQHJqoMTlbrQYXgAuWaT2/GoS+XoBpI0i2cCiEE2YTBWNllvBQgRZyfVwuh4kX0ZgVZx2h5vJt70/TnHJ4cCpCinqcWcrhabyQYyCXY3Nta3alM0sQAjlYH16y3W86csKdnJpPhzDPPnHYslUrR3d3dPP7a176WNWvWcOuttwLwgQ98gIsuuohTTjmFyclJPvzhD7Nnzx5uvPHG4z7+xaBCydrLSl7MtoN2eBcaKlRFN6DqhWzsSWMaAj+QjJdrFN14MXYynGMV+rbyMHSNmh8yVHRJWgZnrskihEDKeAG6f7LC6oyzbLzS7doMatfCPmlqTFY8Hh8s0JN2SE/xiJRrAY8PFuhMmSRnqZl0NNp1HmxDo7/D4eBktfmMaITqlWvx8q6/08FuUR859swnGC7UGC25ZBwTQ9MIooii62PqOquydsueeUOPjYGqFzJccKfVcUpZej28TsNo2SCbPxrmUL6CrtGS8douNcRswiRZr2O1qSeB60fNHD3H1Ng/USVp6S3nKmYcAykF4xWPKJKUalFsPAuNtK0xUfFY25ki47S2dA7CWOK+J21zcLJKvhoQEaGhkUvodKdtTKN1T1bGMYikpOpLutMmXhg1QxYtXcP1JRLZ8nh1Xef0gQyH8lXKtSCu31X/iqIo7u/0gfSCHBxTSVsmlgHBUSwn04jbLWeW9bbj3r17p8lqTkxM8Pu///sMDg7S2dnJeeedx7333sv27dtP4CgXjgolaz9qMTudpfYuSCmZKHuszjr0ZmwKFZ8giutynNafQSCYrHgtq1AdD9qhZHmyh76tNJKmRi0MGSl6nLY6M/1FB54cKtKZslpe2LeLdm0GtWthX/ZCitWAhBlLLAuIVfAARCwek68ElL2QjLPwc9zO87CpN40mBKNlj1ItaC7sNQHrOpNs6Em1fB5Sls76riRBKImkjO+TdVGEnoyNQLChu3XPvG1odKYtHjtYqNfgqXuGQslgIa4/dvpAtmVDbyHRMCXXJ2kbsZG6CONVRhIviJoiA9YxXGOOqXPKqiyPHJxkuFgjl7BI1c/DcDGW3T6lNzvrOms+an7IZCVWlQtCiZQRQmiUPVEvjhu23KeuCQ5OVjk4WaUjZeGYUbNfx9I4NFklmzAXWXi7LntXz0mKkLG4QrNyb+vnOQgjejMOp/Vl2DNWplgNCEWcS9eRjmt+9WQSLRt6KbvxbJzbJSqEINVi/a3jzbIynO65556j/v43f/M3/M3f/M3xG9ASo0LJjg9qMds+Gg/YNR0JbFOboW5V86NlGW7aTiXLkzn0baVR8SMsQ6c3Y89qMPRmbExdWzY5TtCezaB2LewLVR83CNnal6FcCyi6AZGUaELQlbBYk0tQrPmLysFp93noyznTcrJWZWNJ68Wch6mGXrHqkepwYm+LiDdwMglrUYaeoWukLZNswiIIIwo1HxlGCF0jWc+/SVtmy5uvU6NhpJQzQupMXSOU0N/hEERywcbr4YLpAT0Zi/2TLjU/xDb1OI/ICxaV120aOtsGMmiaZNdImWLVbxoMSUtnU0+K0/oyLRu8hWrASNGlXAsIQolt6ujCIJSSmh8SRREjRZdCtbU6b1JKBicrjJRqdKdsulMWuiYIo/hcj5Rq5CarLefSFd0ATRNkHI2CG2DqOoYmmnWyGmF6rdal80JJpRayKp3A1jVGSx5eEGEZca5i1rGp1IKWQ3krXkAwT75VEMVhxMsZ9bQ/jqhQsuOHWsy2h6kP2NnUrZZjuKlSslQ0aOQ4be3LMJivzlgor84kqPrhspq/sPSbQe1a2AMgIWHpdCRNiq6PF0osXZBxTGpBbKAulnaeh5Lr05Ox0RGESFwvJO2Yiz4PhwsXV9k/Xp1WuPiMtclF33McU6MjYXAw7zJaqDXPw6qczUDaImG1bvA3omEmyl5TNnyqPH1n0oo3m1I2uYS1YOO1od54cLLKSMFrOEaoeBGTJZ/erIWpaS2rN041eNd3Jdg9VqXqhSQsnY3dCQzdWJTBW3JrHJhwsfRYTdALo2YB3O6UzWTF48CES8mtAcl5+2tQqPqUa7FKoYyF8GKfi4idRbapU6oFFKp+ywZZ1Q/oSVuszjkU3LApp59zDPwwxPWDlg0yUwM3CBkuVSlVA4ZLteZ1LIBqEJG0NVp1Gg6XanEoIXEJtiPRiPMhh0u1WV5dPqiV5XFGhZIpVjIrLdxUKVkqpjJ1/gKHKy2KOPHeW2bzdypLvRnUjoV9NmHSkTQZzLvomqA8RS475QSEkaQrZR5Tnax2nIepz2Q3iufAQGfimJ7J7ShcHEZxOdkDky7Fqk9vxsLSNbwwolD2iSJY351qWUUtZelYpsZ9u8ZJmMYMefq94xWes7mraaQu1Hj1g5Bdo2X2T1ZJWDoZ63AOWckL2DteJZJwSYvqjVNzp3aNVrF0HTMRb+aNlX029diLMnjHKz4VPyRl66Tr+UONnCFNCPwwjKMuKq0Z/xUvJIgkazsTeH44TTa8O2nRlTSZrPhUvNbCAA1dQxcgNZ3VWYeuZEQI6IBpaAzmXTQhW76f+REUawE7BotMun5dygLKwHi5Rodj0p228VvcX/ICicbc4hCS2HjyguVdB1IZTicAFUqmWKmstHBTpWSpmErK0rFNnV/sGiNp6XQkzOYCcbzssX+iynM2dS+b+dtO2rGwT9sGa7uSPHxgECEEPWmrGcK7bzwORTp7XccxXWsrIVexXYWLNQGHJqsIAbmkyUTZI5BgCOhKWUTAYL7aspoc0FwcQ0TND+N8JClp+gbk4U4XarzWgoiD+QqCWN69QUPu3fWqHJqsxIVhF4kQAtsUTfXGRaUJ1dGERsLUEELgBhGWrmFocb9uXBSJhKmhidYMkaRlkDB18nXDbKLiNw0nSaximbBmyrXPh21oDOSS7J+sMln1SVsGdj0EcLLqI4H+jmTLOW86EU8NlRgrewgtLsx7uBCwYKzssXOohD6r32hu1uTsWNJ8jtcb+W9rcsujhNBcqJXCCUKFkilWIist3FQpWSqORNYf27KxEKw/xZu/i+W927kUtGthD3FtodU5m6ofUvNDamEIEWQSOglTP6bE75WSq9iuDZuKF3srqnXPhKFr6MSLzXL92HjZo+KFZGfuax11vDU/5JTeNE+NlNgzWjlcZqLD4ZTeNDU/bHm8QRgRSuYM6dI0CCMWVX9rz1gZgIs2dc/IyRosuIuav70Zm960Tb7mg5R4QcgUlQUMQ5CzbXozrS3sc0mTjqTFvU+PIIm/f9vRqIURh/JVBHDJll5yLcpwm0acz6VpMFLwKE4RNxES1ncl2NDVurjJgbqQBcShoYbQEBrICAIZUa1FHJiscmCySk924SGLvWlrXlMrrLdbzqiVu0KhaImVFG660kILFe2l7IV4fsSz1nY0czlKdVGE3oxFZzJNzYue8R7Idi3sG+f3ORu7GS/XGC56+GGIqeusztp0Jq1Fn9+VlKvYrg0b1w+peiEVL0BogqxtNMVNCrWAKJJUPQPXby3kKwgjCq7PZNUnYeqxQuoUmf7RskcoWx9vrPxnEoaSyapHckqoXsULsA09rpPUonE+df4KIWZ4ahY7fzd2Jzl7XY6f7hwnY+kEkjiGV2gYQlD0As5Zn2Nj98KNBYjVPONwP70ueQ5uKEEKOpI2xWqArrEomf513Un8KKIv6zBSPJyL1Jux0TSxqLqKByaquH6AY+o4hk4oYzeR0MARejzP/IADE1Wetb6FfvM15hHVQ9TbbV/X0pCPK8/cJ4NCoWgbKyXcdKWFFiraS2NB25ux6UpZM3aqIwmjpdoz3gPZroX9ked3TefSnN+VlqvYrg2bMJJUvJC0bZKo122qBRFCQG/KoeLFUu+t5jjpmmCs5FHxAvo7EtSCkDACvS7qcWiyiqx/j62QTZj0Zx0mKn4z561az3nrSJoE4eJy3to1f3Vd56pz1jBU8Dg4USXpaOhCEEpJtRaxuSfNlc8aaLl+0WjZRwrY1p+mVAsIImiosZgarOlwiOrtVmcX3vcMcZO0Uxd4iUNuFytuUgsjhBBYhoYmRL0uWBxIF0VxiGDVl9RaPL8H8tV5PU5Rvd1yRhlOCoViUayEcNOVFlqoaC9HLmiP3Kn2g/Ck8EC2a2HfrvO70nIV21m42NI13CCiM2ni+lozx8kxdcpegG1oLRcujpHUgoh945UjRD0MwkiSslsPYU3bBtv6c/xi1xgJUyeXNJuqhZ4fUZUhW/tyLX9n7Ywk2N6f48bnbeL7Dx9ix3AZz49ImDrnrE3zwjP62N6fa7lP1w/R0dg+kGO8XGOyEjQ3HDuTBp1Jm+FCrWVPIcwSARLKYxY32dCVJGnpBKEkaWuxKIkUCAGmIXD9gJSls6GrNc+bhkBGsVepYYo18pqav0dxu+XMib/DKBQKRRtZSaGFivaykj2QSymK0K7z0K5+V1quYrs2bDRNY6DDYf9ElR3DJSxda4p6eGFER8JkIOegaa17shxLJz9WoVwLZ4h6JG2d/g6nZU+WEILtA1mKrs/u0TKeFx3Wotbg9P4s2weyrefRtfk63t6fY+uqNHvGq5RrASnbYENXomVPUwPH1LGMWIN8fVeK1dm6R08D29ApuQGWIRZVsBeWPgJky6oMW/uyPLQ/j+v7JA0DTRdEkaTi+0QSTuvLsmVVZv7OpjCQtQ7nYM3yeuP4QFblOCkUCsUJZaWEFiray0r1QC61KEK7zkO7+l2JuYrt2LDJJkxW5RIMF2s4Rux5Cv14AZ4wdCxDY3Uu0XLom64JXC8klzDoTlmU3IBSLUBHsL4rQRDFSnuthupBfB4u3NzN6qzNwbxLzY+wTY01HQ4butPLav5ORdd1NvemF/33U+lNW6ztSrBjqMwpVoqaF+GFsWqfpWkcyruc1pc+JlGEpYwAySYsXv7stUxUPEYLNSp+2HQJCQRruxxe/uy1LdWcAkg6No4JJX96HaeG5wnAMQVJR6nqKRQKxQlnJYQWKtpPY0G7e7TEwUmXWhBhGxprOhe/kGsn7RJFaJcnth39rlRP4VJv2KQsnZxjEkWCnGNieiEBEQYaSUsnjGIxhsWdB4FtxDljtSAklKCL2CNyKF+dJkfeKrmEydlrO9iyauk2rtp9HS+lh1fTNC7Y1M2OoRL//tAgkgiBQCIRaGztS3P+xq6WPYXtGq8Qgsu2rqJUC/ivHcMczNeoBSG2oTOQs3nuqau4bOuqlvtP23q8uXGUAlCGIUgfg/Lm8UCtIhQKhUJxcnJEAdzlRrtFEdrliV3qfleqpxCWdsOm4keYhsCxBPvHa0gkmpBEMqJUC1nT5WDogoofkbYXvggPI0l32kKvCkZLHtmEQULX8KOI0ZJH2jHpTJoth+pNpX0bVxI3CKnW4qwsuQQXcrtk74UQICJEBELUCxpp0THP23aMN5cwufS0XkwNHjlQpOz7pEyTM9dmuHBL76L6LXshEoEOs4pEaMRlIRrS+ssVZTgpFAqF4qRhqgenJ2U3PThDhRqlWrisZK2PhyhCuxa0S92vylWMxTWGCi66EPR32AShbOaFGLpAE4LhgosfhNDCuTd0jaxjknXMOWT6rWa75UK+6vPznWPsHi03pbJLbsBIocZQwePCzd2LmhPt8PBGUcR9u8aQEq48a4CiG+CHEaaukXEMnhou88vd46xZRH5aY7wl1ydhGjhmrHx3aLJ6TB7pRoHstG3w7A2dBFGEoWlYOosukB0btwJDA00TsfFUVxfUoG6YC6o1ZTgpFAqFQnHCOR6y1ksZMrPSRBHazcmeq1gLIg5NVrEMnfVdKfxQEkqJLgSmLhgsuByarFILWpsPU0Mht/SmZy0ou5xCIaWUPHqwwGOHCiQtnUzCbHogi67PY4cKZByTizZ3tTQ32nV/GCl57B+v0p9z0IRG7ojcoP6cw76xCiMlj9VZp+XxjhRdpIRDky6BlBhCkEuaVP2QveNGy+Nt9LtnrMxoscZQ3j1cEDnn0JMJyCZa7zeQEl0DyxBINEQUIYVAEIczGlqEpsXtljPKcFIoFArFScFUD45ExgvEUKLr8QLxWD04Sx0ysxJFEdrNyZyrGIQRUb2+0mxomiBchCE9NRRysODSmbSwLR0/jBgsuMsuFLJUC3j8UB5DE/SkDxsatqFjp3UOTlZ4YjDPmWuyZJyFX3ft8vC6fogXSBJzGJ6OqeMFsmU58rIXsm+swnjZIwghmzCaBuRI0cPQwRytsKkn3XIh6ycOFXj0UB63FpFNGKQNDT+I2DtWYbjkYmqi5X670xaOISgHAtMQhJFGCOiArmkEgSRhCLqPQSTjeHBy3n0UCoVCsSJohwfHCyP2TVQoVPzmDm02abI64yzag9OOEJ+VKoqwElnKedYuhIjFH8pewI7hEmEUNSKd0DWNXNIg55iLzndrii0skfpduyhUfSYrPqtzDhJ5uGBvXd67M2UxlHcpVP2WDKd2eXgbcuRVLyTjzOzb9cNFyZH7QcihvEsoJasy0w3I3ozOcNHl0CJCNz0/4LFDRfIVvx6+6RPFPiKSlk6+7PP4YJEXnB5M63e+a6g3bdOddih7VcIoQgB6ff6GUQiaRlfaoTetVPUUCoXiGctKWHCtVNrhwXGDkL3jFcIoIuMcDvEZK9WYrPisytote3DaFeKzkkURVhLtEgNYarIJk46kyf6JKkXXx/WjujKbwDE1pJSs7WxdjnwG8vC/yzZqSoDrR4wUazMK9mZsc/ZCQfPQLg/vVDny2Qy5xcqRe6Gk4gV0zOEhS5g6kxUPL2ztS5ysBgwVXEIZUXADEpaOXvdmFtwAP4o3hCarAV11xfaFXEOWabCuK8losUa+GhBKDhv+AnKJ+HXLXN6myfIenUKhUCxjVsqCayXSDg9O0tTwgpCRYo3TVh8u3tgI8XlyqEhnyiRptrYwaqeIgxJFaC/tkntvBykrlnMeK3ukLJ2OpNVc0JZrAWNlD9PQF+WBnCaakl7eoinZhIllaDw+WCBtGyQtA0MXBKEkX/U5MFllY3eyZQOyXR7ehhz5SNHjyaEi/TkHx9Rx/dhj1JE0FyVHbumChK3j+sGsBlnVD0jaOtYcoZ1zIZpePEl36rAnS9MEpqNxcNJH1yWibmEv9BqydIGta2gi9t7pUh4WZRcCrS5/3+p4jzfKcFIoFIpFsJIWXCuNdnlwKn6Erev0ZixGirU4J0CLZZcL1YDejIWlaS3LObdbxOFkF0VoF1PnWV/OoeqHlGsBui7oyzkM5t1jFgtZSspeiB9J+nIOoYyQkYwT6SUknVgkwQ8iynOEhM3F8RBNWUpSlk5HwuRxLyRl6fhBiB8JiCRSSqpeSEfSatnAaaeHd11nkpec3c8vdo6xa7SM64c4ps5pq1Ocv6mbdZ3Jlvs063W3hgs1RksuGcfE0DSCKBbJMHWdVVkb02jtPEgElqkTRRHFmo9j6uhCEMo4D8s2NTRNQyJamju1IGKsUkPXdTpTGmKKW1AiiaRgvFxrWdzkeKMMJ4VCoWiRlbbQWGm0y4MThBG2qbO1L8twwZ0hu9ybcXD9sGUD53iIOJzMogjtojHPTF3jqeHSjJy3pZB7X0oKVZ+aF3L2mhwF12Ok5OMFAZZhsCptkXFM8lW/5dye4yF7v5RU/IiOhMWm7hS7xkpUvbAZspiwdTZ1p8k5ZssbINBeD2/WMdnSm0ITgooXkrR0NvUkybbwXU0lZems70oShJJIyvr8je9nPRkbgWBDd7JlA7IjYdCXcRgpuaRtg6oXUqvnOGUcg0JFsirr0JEwWpo7hapH0Q3JJXRSpk7BC4lCiaYLspZB2Q8ougGFqkd/R+uG5PHixF8BCoVCscJYaQuNlUa7PDgNA8fStVlll2tB1PTotIIScTh+LLVYyKQbGxo1P5yR81aqBWQT5vKSexfUazdpmJoAw4j/RRA1ijq1yEqTvQ/CiFBKUgmD7qSNn4g9TUIITCFIOgahXF4e3qkRCms7k80IheGiR9nLLypCYaqHrOT6cRFjBCES1wtJO+aiPGSWaXB6fwbvQIhbi+hMmRi6RhBGFCoBuZTJtr4Mlmm0NHeKXoQuBLquMV718QMZ5zgF8XeatQ0QgqK3PObZXKgnukKhULTISltorDTa5cE50sBJWtMfgYs1cKYuYA5OVnAsfUkWMMeDlSRustQ5hbommCjVqHghA1N2uKfKWkdRhK4tj/OR/f/Z+/MoS876vh9/PbXf/fbes2pmtIzQxiYWEWKcQGIQAeMv5mCbWHJCODgnJiTEHMAxibEx2E4cwPHXgA1eYh+CA7b5/ZKQH1++mMXsYhdIGoE0oxlpunt6vVvd2p/fH9W35/Z0z6jv7Vs9VT31OnBGfbun5ulanvqs709Bx9JUTs23YhEES0dTBEEkWXPi3p7jk6WBe3v6nzdTU7YEFNIme68qguW2RxBE3HGkjhtEG+s1NYW5RpeVtrer6zbKDG+SFQqXZsicKL5WB8cKQ2fISobKyQNV/EhuzHFqyRBDxBmsiYrJzQerlAyVjseO9+qqoaICi3aAlPF9pwAREASSlTBgqmxQTXmAKXeccnJycgYkn6+TLEllcC51cAq6hhCxslPXD3bl4NQKOkcnitx3eplT8y28QGJogiPjRW5Jab9blsRNkuspfJJrLdPhNEH8XFQKGl0/oGRqSNj4P+v3cK2oDfxc9J63RxfbSAmNvpLFWlFHCLh+upyyjGksTCDEZWS8xe7kAKMoYrHtbfQiTZWNgcUbeiRdoVAr6Nx2sDqy9fbvk2MFjesmSvhhhK4qGCpUi+bGPjnIXj1dNdE0BS8MmSpbqIrY2H/DSLLYdjA0helqLkeek5OzD8lSpHrU5KVZyZJkk/ZlHZyJwq4cnEbX5+yyTcnQeNqRMRQFoig2Zs8u21QtPVXOSM8RaTs+BV3D0gVRBHNr3dSJmyQVsQ8jGZc3dcW2YiFlS2esqBNG6dDktv2IetHg5tkqi22XVdtDRhKhCAxVcPNslWrBGLi3RwgRzz56JJbkP1CzqOoajh/y8EKbelHnmcfGU7O/h5FkrGyidv1tRREqBZ1qYfjrdm7V5r7Tyzy+0t3YHw6PF3jWkCIOSVcoNLp+PH9rzcFdzxoerFscmxx+/tbGPtnsbjoPRyYK3DZR3DjuIHu1E0gmKyZNx8cNQvR1hb1IxsHGsaLBRNnECdLxvF2O3HHKyckZmCxFqpMgn6+TPEk1afccnKKuctOBSq9hBCI5tIPTb9gfvKSpeQwjdWIhvfUuthykhLk1Z1OGoeuHnF3RUrPepCL2mqpQtfT1IZ/eFrGQ3r+XlsxxEEZYmsqNMxX8MOKJNWcjEzBdsbhxuoKiiIENcCklqx2PmarFVMWkafs0HR9NCG6aLSMQrNkeh+uFVNwPmqpQt3Rq69ftUlGEesFAMNx1O7dq88nvzW04kAVDpeuF/HChw2LL4+47DgzsPCVZodDo+nzt0WXOLHeQkg1H5NyqzULT5TknJobaK/sDQU89Wr+oqueFW/bJne7VbhAxXTEpGwpnV7o0uwFeFKEJhYlSPOuqbOq5ql5OTs7+Ipfhjsnn6yTPqJu0L3UYLi1JcgJnKIcha2IhHS/k3LLNSscjCCWmrmCIeIDqYstFUwX6ks3xyXIq1ptUxL4/c7ydWMh800lV5lhTFVw/ZKHlUDQ0bjtURYhYEtr1Ix5fs5mpWAMb4L3791C9gKEJVm0fL4gwNIWxoo4XyFTdv/3X7Ybp9esWSlR1/bo1hrtuURRx3+ll1mx/05y3iqVQsXQeXmjxjTMrHKpZA5XBJVWhIKXkB+cbPDjXpKBr1AraRgCv2Q14cK5JpaDx3OMTA+1nVwoEUWTbQNBO9mpLV6laOrqmUGi5rHQi/ECCFlHUFcZKJgVN3b70MkVc/ScgJycnM+Qy3JvJ4nydrJVYjrJJOymHIWtiIX4QD97seAEASy2XEImKoGRp4EEkHfwghBQYyklF7Pszx/NNh7GigWnEYgjzTSd1meOiruCGIYstjxuny/GQUhmLJVRMnR9eaDNWMgYe4Ny7f70w4tyqs0mWfaWoM1OxUnX/brpujUuuW2P467bY9nh8pcuBmhU7o73zuz6Y9UDN4tyyzWI7zs4Ns95RVii03YBTcy00RTBVudgXZGoqUxWV82shp863uO1gbU/k6Z9sr54qGxQMlS88vAgCKpa+0eO00g1YeXSFF99xgKny9v9uWrj6O2JOTk5myFpkfS/I0nyda73Est9hkEgeW3E3avenKibCE0M5DJtUyXRlSwQ8bWIhXihZtl1sN0RVBEVDQ1MFQShpdH3CSOKEIV6Yjl6DJHsKs5Q5tv0IQ1Mpmyr3n2/EUs6sV5sKmCzFBvmgPU6aquAEIWdXbMIo2iLLvmb7cWN/Su5fSOa6OX6IF8Qn9fFVm7YTbAQUypZGrWjgBfEQ2DSst9n1Wete3omrFw0utJyB53olGQhy/ZC2F6CrgvGigaEqeGHEiu3hhxLXG/zc7jXZeNvn5OSkgqxF1nMukpdYXnQYFhsOCy2XrhcSSYkiBGeXbWYqJlM1a2CH4aIqWYdwXY3LDyJ0TWGqbKAqCtdPl1JT8qUr4PoRbSfguonSxueGKjAKBo8td+LsW0rs5KR7CketSpYUvf1XV+NhTkKC6Ok5C2Lnd4j9t6greEHIYsvdVKLWk2V/eKHFWEkfOJPVTxKZ7lFn/C1dJZIRj1xooQiFknkxoLBq+yx3XEqGNnQpWSIVClfYqoS48vcvR1IZ3gstl/ONLjdOVfDCiEbXp+sHKEJwqF5EVxTOr9lcaLnMbhMgSQu545STk7NjchnubJKXWMboSlyW9v3zDRRFoKrxXBFfSuyOy2LL4XZVGdhh6KmSPfbQBc4t21i6iqYJgkDyo/kWRyaKPPPYWGrOrR+BpSmElrrhePTmAXXcgLKlYqoKforiH0lmhrbLxC6kMBPbmzvlh5LbDlVpOcGGg16xNOYbDmsdd+D5RbYfYaoqUxWDxZaDqSubeqemKgaGMngmq0eSme5RZvwnSzqmrvDoosctB6sbnxuqwCgaPHC+ya2HTCZLw695lOutFnTqRZ3VjkehvvWYqx2PsZI+8FyvpDK8q7ZHsxtydKKAJmC54+MEIZamMlHS8SM4t9Jl1fZyxyknJ2d/kMtwZ5O8xDLGDSIWGi5dP6Ro6JiquOgwBIKuH3Ch4Qys6iSl5PGVLo4fUi1oRMTzSTRNUNUEjh/y+Eo3NapkhioYL5mYuooA2k5Ad70kaayoE0koWxqGevXX2k8SEfvsZWIFThDxxGp3SymZH0lKxuDPbxBGmLrK4bEiD841eWypgyclhhDM1CxunqgOpdYH2Tq/3UByoF5gse1xdqXDRNnE1BTcIC5ZnKwazNQsuoGknIJXXNnUuPlAja+fXt5Wlj2IJCdnawPv6UkN9FYVBVWB1bbPsh3L6YcRqEr8DhovmqhK/HNpZv++IXNyckZOLsOdTfISy5hG16fl+JRNjaql44URXiARAupFHUVIWl2fRtfnQH3nx227AQ/NNSibGjdOV3CDaEOdzdQU5hpdTs03uO1QdaBeg6TQ1xvdF1oOQSipFXSEIpCRxA0iNFUwU7HQtRRYh5cwyoh91jKxYSQpGAqtFZ92N2CiYlLSVVw/iqWjLY1DdWvg+UW9HqcLTZeCrnDTbCxrHkWxQMLja92hepyydn6DMGK8aPJjN07xwPkG59e6eKHEUAVHxwvcPFsb2oFMAiEEtxys0nJ8zix1aNo+9Eo3FXjKgSq3HKymZqD3bNWkoGt86+wKiiJQUBCKREaCtmPz2FKHZ1w3zmw+ADcnJ2c/kaVm6pyYvMQypuMGICT1goGiCCxdRwiJlIIgiqgVDLp+EP/cADS7Pmu2z0zNQgix5RyPlQwWGoM3aSdFyVA5MlHEj6INWXY/iNbnF5kIAUcni/s+c5y1TKyqCLpeRMXUGC8atJyAjhP3iFw3UcILQxwvGrhUr7/HKVbrix1/Q4tluGO1vsF7nLJ2fnv7JAiOjBcQisD1Q0xd5VDd2gg87WafHHWvV62g85wTE8xUTc43HFw/Fqg5VLe4bmL4AbhJDPQuGSqqKml2A1RVYGrqxtwpN1gX1NFk6vedq3+n5uTkZI4synBfy+QlljFlU6Ni6XEmSFVxgpAoAiEkJVPDDUI0VR/OiLvCrS/llb+/1/RnjtuOz0TJ3JAF7vrB0KU4WSObmVi5kbHZLrOJGFwNoNfjVLFU7n+8gRR9an0SpqrD9Tj1n1+J3KI2OYrzO0pHpGSoGLrCfadXKOgq9YKOKBpIKVlu+zy+6vDsE+ND75NJ9XrVCjp3HK5z/fRo592NeqD3Ytuj3Q2oF3WcICSUET0dHkMVmKZGyw5YbOc9Tjk5OfuQLMlwX+vkJZYxB+oFjk0UeWSxQ9nQKBrGhoXohxGtIOKG6RIH6oO9tKsFnXrBuGyT9prtMVYwBm7STpItmeMwNuQOjhWumcxx1jKxYSQZK5uo3VjhrWLpWLpKEEXx14VYCGDQUr0gjAiljHtLFImI+tT6FFCEQigHd3B653el48XCAH3zoapFnbGisavzm4QjIhA4fsSa7ceeY59qoaWrIIfbI5Pu9Rr1vLskMoUrbZdl2+dg3UIVguWOjx9F6IrCRDm+b5c7PivtXFUvJydnH5K1QarXOnmJZTxw8cdummG18wQdL8DU4+GWoQTPDxkvmvz9G2cGLqcrmxonD1S47/QKiy2XakFDVxT8KKLZDeIm7YOV1AUarvXMcdYysZqqULd0apbe54gEaEIwWTGpFwwEg5eSqYpgue0RhBG3H6xvGfw61+iy0vYGLgEsGSqmrvL108sUDXXLfKhzKzbPPj4x1PntOSJtx8cyVCw1du7m1rpDOyIdL2S541I2Vbp+QBBKolCiINA1QdlUWe64AzsMWez1SiIT663/HVPVaToeHS9Yfw9F6F0omQZB6OMN6PjvNenaxXNycjLBtT5INatc64ayEIJnHR+n7fp857FVmk5A0FMPqxg87egYzzo+PvD5EEJw68EabSfgzHKHRtffKHVSxHqT9oF0GEWXci1njrOWie139G6YLm8pfZtvOLtw9GJjNe7R2+Z+GKIEMD5q/PekjIefemGEjGRcvjrkcXuOyGLLJZKSuTVnUyar60dDOSLxgOwuYRQxVtRZ7fgEEWiKoF7UCaKI+UZ34AHZe9HrNcpAZlKZ2ImijioEP1xsoSBQlbiG2Q8j5psRES5Hx4pMFNNtQ1ybu2VOTs7QZEleNmcr17KhDLHz+A9unuHYRJHTyza2G1I0VU5MFjk2WRn63t3UpL0WS5qbmsKhsd01aeckS5Yysf2O3nzDYaxoYBpqbHg2nKEdvf4SwO1krYctAex4IZ4fccNUmUcW25tkzmdrFjdMlXG9aGCHoeOFnF2xWel4hFG0JZOlKgrasuD4ZHmg43qhZLUdZ0IUoVC29I0BuGt2QCQjXD8aeEB20r10ow5kJpWJrRZ0dF2h0w0QSjywWZEQCQhCiYzAmBKpKmnejmv37ZmTkzMwWSs5yMnZjlpB56lHxrhhpjrSzNuom7Rz9oYsZWKTcPSSKgEMwoim47PW9TE1dZPMueNHLHW8oXqnepkhGUmmKhffQ6amYpZVLrS6Q2WGdAUcP6LthFw3YW183huA+9hyB00ZfEB2kr10SQQyk8rE2l5IEEWYejzbzgkFUkiEFChCrvfrSWwvpFZ88uNdLXLHKScnZ8dkTV42J+dyJJV5u9YzelklS9dt1I5eUiWAvd4p29uqzlYtwPk1G7muCjgIXijpuiH1y7yHCrrGmu0NnBnyI7B0hUhqrHY9yoaGqgjCSNL2AiqWhqkp+AMmhpLK4CQZyEzCQV+1PRw3xNQUImJ584i4h0xRwNAUbDdYPx/p9ZyysUvk5OSkgmzK9+bk5OTsL0bp6CVVAhjzJM7LED1OhiooGhpdP9xWyKXrhxRNDUMdbL2GKhgrG1hugJSw0vEJowhVUZgoGcj1sQWDHjepDE5/IFPKdbn3dUe0oKu7DmSO2kF3/Yi2G6IoClVLJZKx8yeEQBGCMIq/7w7qme4xueOUk5OzY7Im35uTk5OT8+QkkWFIqndK11QO1CwWWs62Kpa6KpipWOjaYBmc+LgFzi7bLDa7hJEkkkAUsWq7TFULzNYKAx8Xkjm/vUCmF0Y8vmrT6JN7rxV1pirWrgOZo3TQC7qISzMjmK6ahFEsHSKIs5Pn1xxURVLQ01ci20/uOOXk5OyYrMn35uTk5OTsjFFnGJLqnSoZKkcmivhRhJTQsH3a68edqhgIAUcniwO/h0qGynjJ4IHzTVAEOgKhgFyfZ9V2AybKxtDvtyTOr+uHnF3pEIQSU1cwhIKUksWWy6rtMVOxUhPIVEQ8VHi167HWcbEMDRUIgFY3QNegVtRRRLrth9xxysnJ2TH9JQfn12wsQ0VFECJxvJCypadKvjcnJydnP5LUHL1RZhiS6p3qfw+1HZ+JkokQseR51w929R4SCExNoV4oYOjKxvvN8yO6fjj0ANwkKOoKbhjy+GqXWkFnqeUSIlERlCyNRtNnrGRQHFTNIiGKpsbh8SLKimSlG7DYcjcyTiVDZaKgc2isSDHlvYbpXl1OTk7qqBV0jk4Uue/0MqfmW3iBxNAER8aL3JJLkefkJEI+cDqnx27kp/fyPkqyd2pL6VsYn4eDY4WhS986Xojrh9xxuL6RIXNlFGeyqnGGzPXDoXuGRi0bbvsRYQRuEHF22aZW0NE1gR9Izi7Hgc0glNh+RNm8+s5TrRg7tE+s2qhCoiusz8kCoUikEBwZL1HL5zjl5OTsJxpdn7PLNiVD42lHxlCUWB2n6wecXbapWnruPOXkjJB84HTyZMUx3Y389NW4j3oOzpml9sjnm4269K3XMzRVMRkv6azaPl4QYWgKY0UdKQVLbXeonqEkZMP9IGSt61GzVKSMOL/WJZARmlAYL+tULZVG1xtYlj0pSoZK2VTp+iFOIHGDuNQyiEAKga6GVApa6kv9r/6ZzMnJyQz98qeXysuOYeRznHJyRkxWB05nxRGB7Dimu5GfTsV9JC7+KQcX09v+kCMsLeyJH610vL6erFhsYaWoM1Y0hhI/Sko2vDew1/YiTE3lugkdoQjkumBE14tYaQ8uy54UHS9kqe3iBxFBIFGEWFdVFASBxA8illoOHS+kYl39DNnlyB2nnJycHZPPcdpKlgzEnGzRb3DNVi26fkjHDVAVwWzVYr7ppDJQkRVHBJJ3KEa5P2ySn0Zu6Rm63P57NQeX95/fyZK5cX4Xmi5tN0yV418yVExd5eunlykaKhVL35ANX267nFuxefbxiYEzIkm9N3sDe1tOwHUTpc2/C/DYcgdVEQMP7E2KNdvj1HwLKaFgxiWbkQRFxKNMpIRT8y3WbG9bmfm0cG1YNjk5OSMhn+O0mSwZiDnZo2dwGarCI4vtLXLDaQxUpCKzsUOSdihGvT/0y0+fW7U3ZUSqRZ2Zy8hPX62A19V02IZFrs+dkj0RiPVkzcbXQ8ydSuq92RvYG0p1Yx6UpgiCSNJxA8qWOtTA3qRYbjnMNxwioGTEa0UAknjNXsBcw2G55XBkvPRkh7tqpGOnzcnJyQT5HKeLZMlAzMkmQRjRdHzWuj6OF8XzatYj4Istj6YTMFbUUxOoyJqhnKRDkcT+oKkKThBydsUmjKItGZE122e6am7Zf69WwCtrFQodL8TzI566Lg5xqcz5WLGM60UDrzep9+bFgb0KIGg7Ad11Vb2xoo5kuIG9SeFFEicI0YWgYGw+fzrQcXzcMMIbcK7XXnP179ScnJzMkM9xitkLAzEvAcxRFcFy28P2NvcUmprKVEXl/JqNjCSqko77ImuGclIORVL7Q1FX8IKQxZbLjdNl3CCk64eoAiZKJj+80GaspG+Rn75aAa+sVShsFocw4lLI9eeroKtEkqHEIZJ6b/YG9l5oxmuqFYwNWXY3CNFUhemqOdTA3iQwFIGlq0SRpOvFJce99YbrNXuWomCkZD+7HFd/58rJyckM/fKyc40uY0VjI+LZKxW4FuY4JW0g5iWAyZMdx/RJoq9DlA4lRdYM5aQciqT2B9uPMFWViqlx//k1WB/M2vtzqmRhKMoW+emrFfDKWoXCpestXpIV8YNwqPUm9d4sGSpHx4sEoSSSkqbt40dyQz5dILhuYvBBwEkxUTaZrVrMrXWxvRA/kkgpEUKgKwIFmKlaTJTNq73UK7KjJ/bpT3/6ji/ot771rV0tKCcnJ91smZ+xbtgfqFtDz8/IGkkaiHkJYPJkxTENI8lY2UTt+iy1HSqWjqYoBFFEy/GpFHSqBT2O1qaArBnKSTkUSe0PQRgRSomqCYgEstciIkBEoKiCUG497iCG+ygDClmrUEhyvUm8N7cMAi4bqR5IXy+Z3DxTZrHlYrs+uqoiVIGMIAgCCqbOzbNl6qV94Di94hWvSHgZ8Fu/9Vu87W1v441vfCPvfe97L/tzH/vYx3j729/OmTNnuPHGG/nt3/5t7r777sTXl5OTc5FRz8/IGkkZiFnrEckiWXJMNVWhbunULL1PHjnuuZisxAM5xfrPpYGsGcpJZQKS2h96pZtBEHH74RpuEG2Ukpmawlyjy0rb27Z0cyeG+6gDCv3n9/xaPJA1zYZ90hUVtYLObQerLLY9HD/E0lWmygaKMvzze+l1daJo14OAk6JkqByZKDMx30RTBR03JAzi+7daNKgWNI5OlFOzP1yOHTlO//E//sdEF3HffffxwQ9+kDvuuOOKP/flL3+Zn/3Zn+Xd7343/+Sf/BM+8pGP8IpXvIJvfetb3HbbbYmuMScnZzOjnJ/RTxZKqJIyELPWI5I1suaY9t9nN0yXt8hPzzeca8IRSZIkMgHJOpBxdlEIsa1TdqXSzSsFvHoBhVbXi/WhJQQSzq/auwoo1Ao6RyeK3Hd6mVPzLbxAYmiCI+NFbklRkKJHkhUV2zmmCyPIdGclkGn7EbommK0WUYXDRFEgRYSQChLJVMVCU8WWUtO0MdSbd21tjY9//OM88sgjvPnNb2Z8fJxvfetbzMzMcOjQoYGO1W63ec1rXsMf/dEf8c53vvOKP/u+972PF7/4xbz5zW8G4Dd+4zf49Kc/ze///u/zgQ98YJhfJScnJ0VkpYQqKQMxaz0iWSNrjmn/fTbfcBgrGphGPP9kvuFcM45IP0kEVmoFnVsPVHhspUvHDSiZGteNF1DV4RzSpPaHUZRubhfw6gUUHlvusNhyudBw8KTEEILpmsVUxaRa0IYKKDS6PmeXbUqGxtOOjKEoEEXQ9QPOLttULT1Vezsk44gknelOKpA5SvwgpGkHHBorcKBustT28YIQQ1OZKusIFJrdAD8IIcW/y8Ar+973vseLXvQiarUaZ86c4XWvex3j4+P89V//NWfPnuW//bf/NtDx/tW/+le89KUv5UUvetGTOk5f+cpXeNOb3rTps5/4iZ/gE5/4xGX/juu6uK678XWz2RxofTk5OXtDlkqoIBkDMWs9Ilkji45pFnsKk4qAJxVYObdqc9/pZR5f6W5kRA6PF3jW8QmOjBWf/ADb0LtuZ5banG84uH6EqSscqltcN1Eeen9IonSz44U8NN/kgbkmjhcyUTaxdAXHjzi3YrPYdtFUwfHJ8kDGeX+Gt18VEmAM45pRH81VWGO8UGJ7AZau0vUkfVOzAIFlqNhugBemo2fzcgzsOL3pTW/iF37hF/id3/kdKpXKxud33303P/dzPzfQsT760Y/yrW99i/vuu29HPz8/P8/MzMymz2ZmZpifn7/s33n3u9/NO97xjoHWlZOTs7dkrYSqx6gNxKz1iGSNrDqmWSnF6WfUEfCkAivnVm0++b051myfAzWLgqHS9UJ+uNBhseVx9x0HhnaeNpAX/5S7sAn794frp0qs2j5eEGFoCmNFnYWmO9T+4PkBD801cdyAoxPlvn9PoTSu8dhym1NzTV508/RAmYCsqo+O+rhZPQ+jxlAFQoGH55sbzpEAXD+i2Q3QVcGJqVJq5k5djoGvUK8f6VIOHTp0RQfmUs6dO8cb3/hGPv3pT2NZ1qDL2DFve9vbNmWpms0mR44cSezfy9nfZCGqk0WyVkLVzygNxCz2iGSJLDumWSjFSYqkAitRFHHf6WXWbJ+bZi4GgiuWQsXSeXihxTfOrHCoZg3cwN/v6E2WzQ1Hb6Hp0nbDoRy93v4w33D4yo+W6LghgYzlp0umyonpylD7w1o3YKXjM1Hafv+tF2Kn7OyKjaFrO37vZVF9NInjZvE8JIGmxmtb6sQCJvWCjqEqeGHEWtcnjCSHxwqpC1xdysC7sGma25a7Pfzww0xNTe34ON/85je5cOECz3jGMzY+C8OQL3zhC/z+7/8+rutuqS+enZ1lYWFh02cLCwvMzs5ecb2mmW5pw5xskJWoThbJYglVUiRR4pMTkzum2SSpwMpi2+PxlS4HatsHbw/ULM4t2yy2PWaqOw/wJp1BX2q7/GCuyaodIGWEEArjRY3qZc7Pk6GIi3vspThByHzToeEEfP+JJmt2sOP3XtbUR5M6btbOQ1JIua6maGrUCjpdL6QbhCgIDtQs1mwfxw+Ru0nL7gEDu3Uvf/nL+fVf/3V83wfiF9HZs2d5y1vewitf+codH+eFL3wh999/P9/5znc2/n/nnXfymte8hu985zvbNmXeddddfOYzn9n02ac//WnuuuuuQX+NnJyB6EV1eg3ZUxWTkqkx33D4/hMNGl3/ai8x0/S/WLYjrSVUiTOiEp8sI6Wk7Qas2R5tN9j1S7XnmM7WLDpuwFLbjQ2PupWq6GzORZIKrDh+iBdICpfJMFq6ihdIHD8c6LiDOHqDIKXk66eX+eaZVUxV5abpErcdqnHTdAlDVfnmmVXuO7M88DNSMDSmygYgWbU9vDAikpKW4/PoYgfbi5gsmRwcKwz03utleFdtb9vvr9oeExUjUfXRNBw3a+chKVpOgFAEszULQ1WoFXXGCga1oo6uKMzWLBCClhNc7aVekYEzTr/7u7/LT//0TzM9PU232+UFL3gB8/Pz3HXXXfzmb/7mjo9TqVS2SIiXSiUmJiY2Pr/nnns4dOgQ7373uwF44xvfyAte8AJ+93d/l5e+9KV89KMf5Rvf+AZ/+Id/OOivkZOzY7IW1ckiWS6hGjVJlPhklaSyvFnsGbqWSSpib+kqhiboeiEVa+vfdfwQQ7uM7PcVSMrRazk+X390mVBGnJi8WFqIAfWiyaNLLb7+yArPPjZOtbDz7NNU2eCGmTLff6JBvaDTdgJsKVnpeOiKwCyoXD9dYqJkoAhlx++9rKmPJnXcrJ2HJCloKqamcG7NZrXtE8gITSiMV3QOVYqXDZ6miYEdp1qtxqc//Wm++MUv8r3vfY92u80znvEMXvSiF418cWfPnt1UV/y85z2Pj3zkI/zqr/4qv/Irv8KNN97IJz7xiXyGU06iZLn/JivkJVQxuZN+kVy+N6dHf2BltmrF86zWB78WdHXowMpU2eDweIEfLnSoWFvvpbmGw02z5fVszM5JytGbbzhcaLmXLRucqlgsNB3mG85AjpOiKDzr+ASLLY+1jsdUxULKiI7r48iI8bLFUw7UUMTF9e70vZcl9dEkxWOydB6SolrQsXSF00s2JUvlukkdIQRSSrww4rFlm+NTRaopDwwO/dZ4/vOfz/Of//xRroXPfe5zV/wa4FWvehWvetWrRvrv5uRciSxGdbJIFmWXR03upMfkDmROP/2iCF89vYyUsRqXBISAYxOloQIr/Q7DwwstDtQsLF3F8UPmGg71os6dx8YHFoZIKoMeRpJwfV/cDk0RGz8zKEfGitx9x4ENWfam4+P4ESemytx6sL7FWRvkvZcV9dGkKx+ych6SomSoVAsGXb9J2dLQVRVNEQSRxA8lXT+gVhi8ZHGv2dGb9/d+7/d2fMB//a//9dCLyclJI1mL6mSZa72EKnfSY3IHcu9ISik0MQVSCUKCUICI2IPaBZc6DL05TjfNlrnz2PhQUuRJZdDHSgZVS2PF9jhY23rfr9oetYLG2GXU8Z6MI2NFDtUsFtseK22XhxZaHKxZFI2tQatB33tZUB/di8qHLJyHpLD9iFpR5+bZKkttj44TxEoLEaDAzbNVqgUd248om+m1p3Z09d7znvds+npxcRHbtqnX6wCsra1RLBaZnp7OHaecfUfWojpZ51ouocqd9JjcgdwbsjIHp5eBBHju9RNxqV4oUdW4VG++4ewqA9nvMDh+iKWrTJWNgTNN/SSRQZ+umNx6qM5XH11iretRNLSNiL3tBXTcgLuun2S6MrySsKIozFQtpismfiSZbzjbOk5X+72XVIVC1iofNqmwrjm4QYSpKRwaS58KaxBGWJrK046OMd/ostj28IMIXVOYrhjMVAp0/TD1+/qOrJPTp09v/PdHPvIR/uAP/oAPf/jDnDx5EoBTp07xute9jte//vXJrDIn5yqStahOTnZJqpcja+QOZPJkaQ5OfwZSICjqGvQdYhQZSCEEJVPD1BQ0VRnJfj7qDLqiKPzYySmW2i7nlm08P0LVBGEgcYKQG6Yr/P2bpnbl8PXIwnsvqQqFzFY+iIt/plGFtbevG6rCjTMVDo9vDoC4fhTv7Snf14UcULfy+uuv5+Mf/zhPf/rTN33+zW9+k5/+6Z/e5GSlkWazSa1Wo9FoUK1Wr/ZycjLEdlHUiYqRyihUTnZpdH2+9ugyZ5Y72/ZyPOfExL6/36SU3L8u/79dlneu0Y3lw/Mep6FI6vwmddw12+MbZ1aZqpgo2/y9MJIstV3uPDZGfYhZRlmb0Xdu1eZrjyzx/SeadPyAkq5xx+EqzzoxOVRp4ZXI33vp5nKBip5zmyYV1jTv64P4BgOHZubm5giCrRrrYRhuGU6bk7OfyGwUKiebjLiXI0tkIdqdZZLqIUvquElmIJNWb0wOiaEqBKGKoSpECaUY8vdeesmaiM5+2dcHdpxe+MIX8vrXv54PfehDPOMZzwDibNO//Jf/MhFJ8pycNHEt99/kJE/SvRxZImu9Blkia3NwkuozzZrhCXG26ZPfm2PN9jk8XqRgqHS9kEcWuyx35rj7jgMjzzrl77100h+okMgt74s0iujsh3194DP5x3/8x9x7773ceeed6Hr8CwZBwE/8xE/woQ99aOQLzMnJyblW2ItejiyRR7uTIWtzcJKKVGdNvTGKIu47vcya7XPTzMUBuBVLoWLpPLzQ4htnVjhUs0bS55STbnqBCi+MOLdq07R9AinRhKBa1JmpWKkU0cn6vj7wTjA1NcUnP/lJHn74YR588EGEENx8883cdNNNSawvJycn55ohV5PbSh7tHj1ZnIOTRKQ6a8/bYtvj8ZUuB2oWUkrcICSUoAowNZUDNYtzyzaLbe+yQ3Jz9g+aquAEIWdXbMIoomLpGwGF5bbLmu0zXTVTKbaQ5X196FXfdNNN3HjjjQCZ8RJzcnJy0kyuJpezF2R1Ds6oI9WqIjaMzJKpUdA3Hyttz5vjh3hBrBjz+KpN2wkIkagIypZGrWjgBRLHD6/2UnP2gKKu4AUhiy13UwbS1FTMssrDCy3GSjpFPR33735hqLP53/7bf+P222+nUChQKBS44447+PM///NRry0nJyfnmqIXsV+1vW2/v2p7TFTSP1k9J/30MjizNYuOG7DUduNen7q1K0GE3nFnqiZLbZczyx2W2i6zNXMkQgu9SHW9aFA2taGdpkbX59HFNvONLt98bJUHzzd5ZLFN270ofpW2583SVSIZ8ciFFisdD4RAVxUQgpWOx6OLLSK5fdBlp0gpabsBa7ZH2w0YUHg5Zw+x/QhTVZmqGCy2XNwgJIriTORiy2WqYmAoCrY/fMY0vx+2MnDG6b/8l//C29/+dn7pl36Jv/f3/h4AX/ziF/nFX/xFlpaW+Lf/9t+OfJE5OTk51wL7RXUoZ/RIKbM3ryalc2X6lfSOTZZRVZtW16fjBTSdgOvGi3hhlLrnbbKkY+oKp+YdpqsGi22XKAJFgaqlcaHp8bSjNSZLwzmnWZNlv9YJwghTVzk5W+VC06Fh+7RlgCYEUxWDqYqFs4uBsvn9sD0DO07/9b/+V97//vdzzz33bHz28pe/nFtvvZVf+7Vfyx2nnJycnF2wH1SHckZLkgbMqHsN+p2SyZK5Ie+90HRpu+FVl/feTknP0lXmGl2ats+FpoOMJM84Vue6iXKqnrduIKkXDYIo5PvnmxR1FV0V+KHk7IrNeFGnWtDpBpLygEmn7MqyX7v0D5S9fqq8ZWC6G0QbAZFBye+HyzPUHKfnPe95Wz5/3vOex9zc3EgWlZOTk5MVMpkJyMkMSRswo7x/+52S2apF1w/puAGqIpitWsw3r76c/nZKemVT44bp2PA86gS4YcTxyTIVK12GoR+EtLoB1YJBKCVdN8QL456LsYJG1TJoOQF+EMIAznAWZdmzyiift0vFWIrG5mueZpn+JN6be8XAjtMNN9zA//gf/4Nf+ZVf2fT5X/7lX26IReTk5ORcC2QpE5CTPZI2YEZ9//acEkNVeGSxTaNPHrlW1FMh7305Jb2e/L+pqiy1XcIoRbWF67hBxPmGjaEqHK4XWWq7+IFE1wSTZRM/lMyt2bjBYKVZm+YBSbklc5GG67YfGPXzllWZ/qyXAA78G7/jHe/g1a9+NV/4whc2epy+9KUv8ZnPfIb/8T/+x8gXmJOTk5NG8lKGnKRJ0oBJ4v4Nwoim47PW9XG8iGpB2zDkFlseTSdgrKhfVXnvLCtXBmFE149YabtEEsJIIgHfgyfWuggBk2Vz6MHFXhjx+Kq9xeGdSuk8oCQZdUYkqfdF1mT698N7c2DH6ZWvfCVf+9rXeM973sMnPvEJAJ7ylKfw9a9/nac//emjXl9OTk5O6shLW3L2gqQMmKTuX1URLLc9bC/gYL248bmpqUxVVM6v2cj1TMbV4nKzpiRxpuX8apdDY8WUSjjHDt+q7eGHsROoCghlfK/oKlQtnYuqHDtDUxVcP+TsSocgZIvDu2p7zFSsVDqTSTDqjEjS74tRl3YnFVzYL+/NoXKuz3zmM/mLv/iLUa8lJycnJxMkXcqQc5Es18LvlqQMmGTv3ycpcRNXtwRuu/ImL4g4u9rh/JqDoSgYqsL3zzdTVzqkKvEsJ9+XmOv9LEKACqi6iusGdP2QQf2boq7ghiGLLW/LPKCpSm8ekJFSZ3K0JJER2Yv3xShLu/uDC7O1uFcxDCWqGpduDts7tV9KQtO7spycnJyUkmQpQ85Fsl4Lv1sulx3pMawBk9T9G0aSsbKJ2vVZajtULB1NUQiiiJbjUynEqm9Xu3+ov7zp7IrNo4tt/FByuF7g8HgRQ1VSWToUhBFhKNF0QcXS0NTYYJZSEoQQRvEcn0Gvm+1HGJrKVMXc9rpNVUx0NZ4HVDb3r/OUVEYka++LXnBhvuHw1UeW41iIAkSAgGOTpaF6p/ZLSeiOHSdV3dnGHIb5xOqcnJz9TZb7JLLCfqiF3y1JNX8ndf9qqkLd0qlZOqu2R9P2CdbnykxWTOoFA7H+c1ebWkHntoNVbC/AD4ocrBco6BezmWksHeoGkqKpYqgFnCAkCEEIiZRxcd5spYCuCbrBYI5pEEZYmsrJ2QoLTWfLdZsux1mHtBu0uyWpzFCm3xeiz28SsJsq2/1SErrjKy+l5LrrruPee+/Ne5lycnKuaZLKBOTE7EUtfBRFLLY9HD/E0lWmygaKkr4Xdi87cmapzfk1BzeIMDWFQ2PW0HOGkrp/+4/bk/fuL/GZbzipei5sP8LxIg6PFbc1aNNWOlTUFaqWTihhQoVWNyCQoInYCPXDuJxv0JK6/nlA2103149i4z7lBu1uSSozlLX3RW//BXju8YktJXXDjhXoLwm9cbqMG4RxaamAybLBDy+0M1ESuuOd4Otf/zof/vCHed/73sfx48f55//8n/Oa17yGsbGxJNeXk5OTkzqSygTkxCTdE3Bu1ea+08s8vtLFCySGJjg8XuBZxyc4MlZ88gNcLcTFP+Uuqt2Sun/7jzvfcBgrGpiGih/GmcK0PRdZK6GqFQ2Ojhc5s9LBVDUqdQNFQCTBC0KCKOC68RK1yzw3l2PLPCBdgz5/PG2GfVIklRnK2vuif/8VQmyZDzXs/tsrCS2bKt97Yg0/jJARCCV+1qbK2SgJ3fHK7rzzTt7//vczNzfHm970Jv7mb/6Gw4cP8zM/8zN8+tOfTnKNOTk5OamjlwmYrVl03IClthtnSOpWqsvIpJS03YA126PtBsjdWOAJkaRBe27V5pPfm+OHCx3qRYPrJovUiwY/XOjwye/NcW7V3u3yR0qvZHGh6TJZMjk+WWKyZLLQdPn+Ew0aXX+o4yZ1/2bpueg3lLcjbSVUZVPjadeNMVk2EUo8ENcNQ/wgRCixFPnTrhsbOJjQM+xLpsZco4uznmFw/JC5Rjd1hn1S9BzIVdvb9vurtsdExRjKgczSc5HU/ts7bhhFLLVcnljt8sRalydWuyy1XIIoSlWg4nIMHKqzLIt/+k//Kf/0n/5TTp8+zWtf+1pe/OIXs7i4yPj4eBJrzMnJyUklo5aBTZqsiC0kFfmNooj7Ti+zZvvcOBOXirhBhK4Jbpwp88OFNt84s8KhmpWKsr2syRgnfdxRk7USKiEEtx6s0XYCTi+18XxJiERFYOqCY5Nlbjkw/L0w6nlAWSPpzFBWnouk9l9VEZxf6/LoUgehgGVo9FJOQoHTSx0MTb2q4wp2wlBFu48//jh/+qd/yp/+6Z9i2zZvfvObqVaro15bTk5OTuoZpQxskmRJbCEpg3ax7fH4Spd6Qefcik3HCTYMz5KlxZ8v2yy2PWaq1qh+naHJmozxXhx3lGSthApi4/s5JyaYqZoj63nrP3YWDPsk2dRT2HBw/QhTVzhU3/35zQpJ7b9SSubWbBZbLuMlk6oVO0lhJOl6IYttl1rBTmUVRD873tU8z+Nv/uZv+PCHP8zf/d3f8ZKXvIT3vve9vOQlL9mx4l5OTk5Ozt6TtcGDSRm0jh/SdHxUJcAPJUVDQ1MFQShpdH06akC0XqKUBrLWg5NFsphpqRV07jhc5/rp0Ts4WXB49wx58c9R2PJZyfj377/n12wsQ0VFECJxvJCypQ+1/za7Ph0nFuMRrI90k/Gfgvh91HJCml2famGwPr29ZMdPx4EDB6hUKtx77738wR/8AdPT0wB0Op1NP5dnnnJycnLSRRYH9iZh0Jqagu0GRMDB2kURCEMVGAWD8w0bVcQ/lwYyLWOcIbKYackdnGToz8xPls2NzPxC06XthkNn5nvHbTs+lqFiqQqhlMytdUeS8R/1oPBaQefoRJH7Ti9zar61IaJzZLzILUOu1fYjQik5MlbACyW2FyKDeIhzxdIYK+ms2T62n+5A0I6futXVVVZXV/mN3/gN3vnOd275vpQSIUQ+xyknJycnZWQ1czFqg7ZoqJRMjcW2u+33HT9kumxRTElPS9Z6cLJM7ohkk1E6DEll5nvHXWy5RFIyt+ZsDH6tFnW6frSrjH8SmaxG1+fssk3J0HjakTEUBaIIun7A2WWbqqUPfOyirmAZKkEkmS6b2F6EH0XoikLRUFjsuBQMdf/IkX/2s59Nch05OTk5OQmR5czFKA3aSML102X8SHJ2ucNExcTUFNwgYrnlUisYnJguEaWkxD6LPTg5Odsx6owIjN5hSCoz3/FCzq7YrHQ8wiiiYukbz/Fy20VVFLRlwfHJ8sB7XRK9q/0O5MH65vEMYxhDO5A9Of0fLrRpdjtExCV6vQG7iiK4aaY8sJz+XrPjK/SCF7wgyXXk5GSCJDb/nJykyTMXMZqqMFstUDF1Hllss9BwWJESQwiumyhyYqpMydRS5UBmsQcnJ7tkwcHpHXPUpW9JZeb9IJZ1l5FkqnJx/zU1FbOscqHVZb7RxQ9CGMBxSipDlpQDWTY1bpqpcGq+Rdv1URUFRUgiKQijiLKlc9NsJfWZ33SvLicnRWSlsTMn51LyzEVMz4H0woi/f+MEZ1a6dJyAkqVxbLzAYtsfek5LkmSxBycneyTp4CSRERl16VtSmXkvlHTdkPplHJGCrrFme3jhYKnupBycJEu7y6bOZNkgiuJ5gpEERUgqpsZk2aBkpN+Wyh2nq0SeucgWWZJyzsnZjjxzcdGBfHSxzf/n24s0nWDD4PquFQ8XTasDmffg5PQzahsi6ZKvUWdEkih9Syozb6iCoqHR9UMq1tZz2PVDiqaGoQ52/ZJycJJyIDteyHLHZaJkEkWxqmkkJYoQFA2FiZLJcsdNlUjRdqR3ZfuYPHORLbIm5ZyTczmymLkYtYHYdHxOzbc4u9olQqIKCCWsdX0KpsYzj43n+3BOqhm1DZG1kq+kSt+SyszrmsqBmsVCy2Gx5VItaOiKgh9FNLsBuiqYqVjo2mAOWVIOTlIOZO+6hVHEWMkAERCEIZqqMlbUCKJoqOu216R3ZfuUPHORPbIo5ZyTczmylLlodP0tgygP1iyOTQ43iDKKIr5w6gKPr3a5bqKIoSoIRSAjiRdGPL7a5e8eXuRnnnUERUlPn1NOTo8kbIislXwlVfoGyWTmS4bKkYkifhQRRZLFlosXSgxVMFUxURTB0cniwI5IUg5OUg6kF0pW2x4dL0ARCmVTQ1N1glCyZgdEMsL1o6Gu214y8NvzT/7kT3j1q19NsVh88h/O2USeucgmWZVyzsnJMo2uz9ceXebMUicekqiAjODcss1C0+U5JyYGNmIutFx+cL5B2dQYK5qbvlcCHD/iB0+sceHmaWa3MURycq4mSdkQWSv5Sqr0rceoM/M9R2S+4XB6sUXLjQiiCFdRAIfjU5WhHJEke1eTcCB1Jd5j207IdRPWxueGKjCKBo8td9AUhZSrkTPw8t761rcyOzvLa1/7Wr785S8nsaZ9yyBRnZz00L/5b0eapZxzcrKIlJIHzjd5cK6JEFAt6oyXTKpFHSHgwbkmD5xvIuVgkclV26PZDRkrbf/Srxd1Gt2QVdsbxa+RkzNSkrIhknrH9TIil3ueVm1vKDGWXumbrgoWWy5uEBJFEjcIWWy56KrgQHXw0rd+epn5etGgbGojCWbbfsh8y2Gx2WW55bLY7DLfcrD94W2+noMzW7PouAFLbTd2rOvWriuYeg7kncfHufPYGHceH+e2g8Mf04/A0hUqlsZq16Xj+XT8gI7ns9p1qVgapqaQ8vm3g2ecnnjiCf7n//yf/Omf/ik//uM/zokTJ/hn/+yfce+99zI7O5vEGvcNeeYim+RSzjk5e0vbDXhoroGmCCbLFyOTvR6G82s2p+Yb3Haoum3E+XKoioKqQBjKbd9+YSRRlfjncnLSRr8NEcmIVdvH8yMMXWGsqA9tQ2St5Ku/9E1KaNg+bRmgCcFUxUAIhip9S4peIOixpQ4HqkWMCQVVCEIp8fyIx5Y6PFBp8twT40Nnh7LQu2qogrGyAW2P1a7H3KpDICM0oTBe1qkVDMbLxtCZwr1iYMdJ0zR+6qd+ip/6qZ9iYWGBv/iLv+DP/uzPePvb386LX/xiXvva1/Kyl70srw/fhiwPobyWyaWctydXhsxJimbXZ832malZ235/rGSw0HBodv2BHKfZqslUxWSh6XJiauvfu9B0ma5azFbNbf52Ts7gjHKf7NkQj6/Y/GixzYWGg7c+h2y6ZnHDkHPIslby1b/eVtejMGZBRFxDFUkqBSNV7+T+QNBU5ZI9zWLoQFA/SfSujlqERNdU6gWDM0s2ni85WC+gawI/kDS6Pk0RcGKqvKtM4V6wq7M8MzPD85//fB5++GEefvhh7r//fu69917Gxsb4kz/5E378x398RMvcH+SZi+ySSzlvJleGzLmUkTvSV/irUl75+5ejYuk858QE/7/75zm7YjNZvthcv9T2UBXBc05MDG28xGvLAwo5MaPeJ0uGShBJ/vahCwgBE2UTS1dw/IhzKzZnl21ecvuBoWyIJN9xSWREagWdoxNF7jvd5fGVLl4gMTTBkfEitx4upuo9lFQgKEmSECEp6nHG39QE05UCq3ZAxw3QVJXrJoqsdT00VVBMeZPTUI7TwsICf/7nf86f/Mmf8Oijj/KKV7yC//W//hcvetGL6HQ6/Pqv/zr33nsvjz322KjXm2nyzEW2yUo6PGlyZcicSxm1gVgt6NQLBqsdj0J962tqzfYYKxhUBzy2EIJnH5+g44V899wKqx2XUIIqoFLQeOqRMZ51bLhyGcgDCntBVhzTpOYizTe7eGHIZNlCVxUEAl1VqFg6S22HC60uUsrUlXyNOiPS6PqcXbYpGRpPOzKGokAUQdcPOLtsU7X0dD1zCQSCkiIpERLbjzBVlYmSxVK7ix9IJLFM+aodMVkuYCgKth9RNtPrPA18F7/sZS/jU5/6FDfddBOve93ruOeeexgfH9/4fqlU4t/9u3/Hf/pP/2mkC90v5JmLbJMlKeckyJUhcy4lCQOxbGqcPFDhvtMr2849CSLJyYOVoZ7FWkHnH5yc5vhEgUeXbLpuSMFUOTFZ5NhkZeg9OA8oJE9WHNP+fXK2atH1QzpugKoIZqsW801nqH1yse2x0va4/WAdJwhpOwFdJCqCsaLOgarFUstjse0xU90+u/FkZOEd139+D9Y3KzyPYYzkPTRKBz2pQFBS9IuQSCnp+uF6/6egoKu7kqf3pUTTROzwawpCxI6jIP7cl+nv8x/46Zienubzn/88d91112V/ZmpqitOnT+9qYfuZPHORk1XymVY5/STlSAshuPVgjbYTcGa5Q6PrIwAJKAKecqDKLQeGN4riPbjObK2I44dYuspU2Ri6NzcPKCRPlhzT3j5pqAqPLLZp2D6BlGhCUCvqQ++Tjh/iBZIDdQNVCNwg3MiYmlpcxnd22cbZhUpbFkj6PTRqBz3JQFAS9ERIvDDi3GqHCy0PPwjRNZXpisF0pTCUCImqCFbbLkEQcduhOm4QbThkpqYw1+iy1nFRlXTvkQNfpQ9/+MNP+jNCCK677rqhFpSTk5NecmXInH6SNGBqhbgfaaZqcn7NwQ0iTE3h0JjFdRPDDcDtsZ1htLALwygPKCRL1hzTIIxoOj5rXR/Hi2JDeb0sf7Hl0XQCxor6wPukpasYmqDrxfOLLH3zveT4AYYmthWf2k8k+R5KwkFPOhA0ajRVwfVDTs23WOo4EF2co7fQ7DJZcjk2URxSyCz+HYW4zH0q03EOrsRAO3ir1eLhhx/m5MmTlMtlvvWtb/He976XbrfLK17xCl7zmtcktc59RVbKDXJyLiVXhszpJ2lHulbQueNwneunR5edT8IwynJAIQs9Q1lzTFVFsNz2sL3NpWSmpjJVieX05XqkfRCmygaHxwv8cKFD2dS2ZJzmGg43zZaZKm9/nvYLSb2HknTQkwwEjZqirrDW9XlovsFUxaJoaWiqIAglthfw0HyDsZI+sIhDGEkmygZqV2ybeStbOmNFnTAabD7fXrPjHeYLX/gC/+Sf/BPa7TZjY2P89//+3/npn/5pDh06hKqq/PVf/zW2bfO6170uyfVmniyVG+Rkn1EbRf3KkLO1uHY/DCWqGtc+58qQ1xZ74UiPsuciKcMoqwGFrATxsumYPonxJwY3DhVF4VnHJzi30uXLjyxh6iqaIggiieuHHBkvcuex8X0/DiYpheKkHfQkAkFJ0PFCGo5PQVfZmDG+/qeUUNBVGl2fjhdSsXZ+r2mqQtXSqVo6q7a3Zf5W77ynbZ+8lB2v7ld/9Vd51atexblz5/g3/+bf8OpXv5pf+qVf4sEHH+T73/8+73jHO/i//+//O8m1Zp5LX9qWrqKspysP1Ap03ICzKx2kTLe3nZMNGl2f+59o8I3TK3zjzCrfOL3C/U80aHT9oY/ZU4YE+Oojy3z3sTW+f77Bdx9b46uPLAPkypAjQkpJ2w1Ysz3abpDKfaFnwKza3rbfX7U9JipGahzpQQyjQcjaeYCLQbz5hkPJ1JiqmJRMjfmGw/d3uU+Mmn7HdDvS5piGkWSsbFIpxEp3bhA317tByFLboVLQqZfMoSLrVUvnuokiFVMjCCJsLyAIIiqWxnUTRaopkbNOkt57qGRqzDW6OOviBY4fMtfoDq1QvBcOei8QVC8alE0tle/KZtfH9UJunq0yVtTx/JCW6+P5IWNFnZtnqzheSHPAPaK3T3phxInJEscmSxwdK3JsssSJyRJeGKVun9yOHbvM3/ve9/jDP/xDDh06xFve8hZ+7dd+jVe/+tUb3/+Zn/kZfvu3fzuRRe4XslZukGWyUH6SJHuS2RTrNdpAJOJa7ZzRkJVMQP+IhfNrNgVd21BJ6vpx6UWaHOmkDKOsjZrIWs/QXsxAjKKIxbY3ErEQTVWoWzq19ch60/YJ1iPrkxWTesFAMHhkvXfdiobGK55xiFXbx/MjDF1hrKiz0HRTdd2SJAmF4qxmjhNBgGWo1IvGlpLQrh+CM3hgpbdPzjccvnZmZV1NL7YjhIBjE6VU7ZOXY8fWebPZ3JAdNwyDYrFIpVLZ+H6lUsG27dGvcB+RzXKD7JEVozMpkjSKescGeO7xiS0ypcPK7OZcJGvlvBcHUS5zar51cRDlRIFbUrbWJA2jniF3ZqnN+YaD60eYusKhevp6GLIWxEvaMT23anPf6eVNg1QPjxd41vEJjowVn/wAl9Dv6N0wXd5S0jzfcHZdSqYIhYmSuen7abtuSTNqheK8FD2mXz79YL24RYRkJPLpMq5WFQoQkao5Vk/Gjp8sIcSmm/HSr3OenDyakTxZMzqTIEmjqP/YQgiKxua/f629uEdN1jIBsHkQ5VOP1lGFIJQSxwtTN4hyLzIXUsYlQ7YbIlGJUtjonMUgXlIzEM+t2nzye3Os2T4HahYFQ6XrhfxwocNiy+PuOw4M7Dz1O3rzDYexooFpqPhhtFEamdZSsqwxyh7I/ozIVx9Z7iupAARxaVkGMiK7pV8+/ULTwdIVFCGIpMTxo6Hl0/sDr885MR5nTIMIQ8tWxnTHv7WUkhe+8IVoWvxXbNvmZS97GYYRG2dBECSzwn3EXry0r2WyaHQmQZIv1/zFnSxJDR5MiisNoqRI6p65JDMXja7P1x5d5sxSJ46mKtBxAhabLhdaHs85MZEaBzKrQbxRZxiiKOK+08us2T43zfRV0FgKFUvn4YUW3zizwqGaNXDZXhIZyP7rZmrKlv0hrdctk1zDpeg9+fQLTYfvnluj44VEkURRBGVD5Y4j9aHk03vvN11VeGSxs17CGs83W9nFfLO9Zscr+4//8T9u+vonf/Int/zMK1/5yt2vaB+TtTr4rJG18pOkSNIoyqrBlRX6Bw8+vmpvGZw5VbFS5Zhm8ZlLInMhpeSB800enGtSNFQqBX1jb285Pg/ONalYOs89MT70/j7Kvs0sB/FGmWFYbHs8vtLlQM1CSrmll+NAzeLcss1i22Omag3/D/Upk+1G46V33R5dbCMlW/YHIeD66XIqr1sW+o6zXIqexPktGhqzNQvXl6x3ImHpWytNdkoQRqw5Ps2uj+OFGLqCKRRCKVlqubTdgGph8Plme83QjlPOcCRVbpCTZ0N6bKrTrlpbNv/dGEVZNriyQG/w4NmVDkHIlsGZq7bHTMVKjWOa1WeuVtC57WB1ZGIAbTfgobkGmiKYLF80sE1NxSzHc3tOzTe47VCVyhCqZ6Pu28yDeDGOH+IFcYf646s2bScgLrAUlC2NWtHAC+LSy0HpLxufLJsbZeMLTZe2Gw49SHWsZLDwiLtRWljVNRw/5OGFNvWizjOPDe+cJ0VW+o6zWoo+6vPb70Dedf3ktj16wziQqiJYbbus2j6KAufX/I311ks6bTcgiqKB55vtNem58tcQoy43yInJsyEx/XXaX3l0CTeIYvUaAaamcHyyPLRRlGWDKwsRz6Ku4IYhiy1vU+lQb3DmwwstxkrGwIMHkyKrz9x2hsbCLgyNZtdnzfaZqW2flRgrGSw0HJpdf2DHqWeAtx0fy1Cx1DhCO7fW3VXfZh7Ei8u4IxnxyIUWilAomRcHfa7aPssdl5KhbXtvX4mkysallKx24uzXVMWkafs0HR9NCG6aLSMQrNkeh+uF1OxtWeo7zmIgKInzu8mBRFDUNeg7xG4cSNsPObtiE0UREWJDhXWl46IoCpZeHuh4V4PccbpKjLLcICcmz4ZsxvZD5ptdbCckknGNdtHSmNnm3AxCFg2urEQ8bT/C0FSmKmY878XS0RSFIIpLvqYqJrqqYPsRZfPqOyNZfOYSM+TW7dS45CvayPKamhKXZw1hx/YM8MWWSyQlc2vORmlWtajT9aNdlQ5d60G8yZKOqSs8uuhxy8HqxueGKjCKBg+cb3LrIZPJ0vCG53YMa3j2jnuoXsDUlS2ZANePUpUR2Yu+41EGxLIWCErq/CblQAZhPHes0fUQQNnQ0FSFIIhoewERxHPJUuSYbsdVfbLe//738/73v58zZ84AcOutt/If/sN/4CUvecm2P/+nf/qn/LN/9s82fWaaJo7jJL3UnAyQ5WzIKOn1Wzy21OFAtYg5rqIoEEXgBiGPLXV4oNLcVb9FlgyurEU8LU3l5GyFhaazZf7LdDkuvUzLi2UvnrlRGkZJGRo9+d65tS66qtByAiIpUYSgYmn4YcREyRxYvrfjxdHZlY5HGEVUrIu9U8ttF1VR0JYFxyfLQxvK13IQrxtIDtQLLLY9zq50mCibmJqCG8Tnd7JqMFOz6AaS8gC+f5KGZ++422UC0pYRSboHctQBsawFgpI6v0k5kG4QsdrxqOg63vrctFBGqEKhVtQxhMJqx8O9zKDrtHBVd8vDhw/zW7/1W9x4441IKfmzP/szfvInf5Jvf/vb3Hrrrdv+nWq1yqlTpza+TqOhlnP1yGI2ZNRs7rcwNxqeDQ0qlslco7urfoseWTC4sqa02HthGaqy7fwX14/il1ZKIp6Q7DM3asMoKUOjbGocHi/w/ScaCGCiYlLUVJwg4uyyjQSeerQ+8PPiByFzjS4ykkxVLt6/vd6pC60u840ufhBCyp/FNBKEEeNFkx+7cYoH55pcaDisSIkhBNdNFDk5U0VRxMCOSFKGZ9YyIkmWviUREMta8DWp85uUAxmEEY4f0QlDCprCbN3aKNXzg4i2H6L5gz9ve81V3Wlf9rKXbfr6N3/zN3n/+9/PV7/61cs6TkIIZmdn92J5iZKFfouskqVsSBL0+i1qBX3bhueyqbHa8Yfqt8gaWVN9u/SFdWlEOW0Rzx5JPHNJGEZJGnJlU2e6auEEIW4Q4QUREqgWdUxNpWQM/qx5oaTrhtQvc/8WdI0128ML0zcrKgv0HJGSqfPjJ6fiuTJ+hKHHc2W8QNJxg4EdkaQMz6xlRJJy9JIMiGUp+JrU+U3OgVzXeI8kYj1A2JsPFUQSIeW6AmW6bbUdWQq/93u/t+MD/ut//a+HWkgYhnzsYx+j0+lw1113Xfbn2u021113HVEU8YxnPIN3vetdl3WyAFzXxXXdja+bzeZQ6xslWem3yDJZyIYkiRuEPNEIiCK2NDyvdX0MNd0b06jIWrNv1iKe/YzymUvKMErK0Oh4Ia4f8uzj46x0XBZbHn4YoasK01WDsaKJ64cDO+iGGit7df1w2yBH1w8pmto18zyPmksdkYmSuen7q3Z3KEek/zk+v2ZT0LWNyHrXDyhb+lDPcVLHTYqkHL2kA2JZCb4m6Ugn4UBqqsDSFYTQsXSVjhcipUQIQc3UMZT4+1rK97Md3VHvec97Nn29uLiIbdvU63UA1tbWKBaLTE9PD+w43X///dx11104jkO5XOZv/uZvuOWWW7b92ZMnT/LHf/zH3HHHHTQaDf7zf/7PPO95z+MHP/gBhw8f3vbvvPvd7+Yd73jHQGtKkiz1W+Rkk4qlEUaS5bbLicmLymy9hudHl1pMly0q1v53LLNW2gLZingmRVKGUZIlKEEkmaqYTJQNDo9vLrGMIlhquwM76Pr6LKGFlsNiy43l6RUFP4podgN0VTBTsdC1dGQYskaSgYpaQefoRJH7Ti9zar6FF0gMTXBkosAtu3jP94779UeXuf+Jxoac/omp0q6O22OU1TBJnd+9CIhlIfiadKBt1A6kqSkcGCtwfs3B0BTKpoYQAinj2YUhKrP1AuZlrmta2NFdcfr06Y3//shHPsIf/MEf8OEPf5iTJ08CcOrUKV73utfx+te/fuAFnDx5ku985zs0Gg0+/vGPc++99/L5z39+W+fprrvu2pSNet7znsdTnvIUPvjBD/Ibv/Eb2x7/bW97G29605s2vm42mxw5cmTgdY6CrPVb5GQTIQTjJYNGN2C161E2NFRFEEaSthdgqCpjZf2auMeyVtrSIysRz6RIyjBKytDod9BNTdk08BQ5vINeMlSOTBTxo2hj4Gl7XSxkqmIgBBydLKbu/s0SSQUqGl2fs8s2JUPjqUfrqEIQSonjhZxdtqla+lDHbnR9HjzfZKntYqgqhqoAgsWWy4Pnm0Mft3fsUVfDJJO5yF5ALCmSDrSN0oHUNZUTE2UUFJY6Dl4QgQJEgAKH60WOTRRTHwga+Gy8/e1v5+Mf//iG0wSx8/Oe97yHn/7pn+Y1r3nNQMczDIMbbrgBgGc+85ncd999vO997+ODH/zgk/5dXdd5+tOfzo9+9KPL/oxpmpimednv7yVZ67fIySZhJDkwVkRRFRbWurTcYON7CpLrZ8rMVC3CaP/3ReSlb9kkScMoCUOj56A/utjecHB6suG1oo4QcP10eVclX23HZ6Jkpro0K6uMOlDRHyQ9WC9u/maRXc1xeuB8kwfnmhQNlenqxf2s5fg8ONekYulDKaYmWQ0z6vOb1YAYJNPfnpVAW38g6EDd5ELLww9CdE1lpmJmJhA08Ft5bm6OIAi2fB6GIQsLC7teUBRFm3qSrkQYhtx///3cfffdu/5394Ks9VvkZBNNVahbOjVLZ7JksNRy8UKJoQqmqiZjRROx/nPXAnnpW/ZI2jAataEhhIiH3D7ismb7HKhZVHUNxw95eKFNvajzzGPDyf9vuX/D+P49OFbI798RMspARVJB0s2KqReHLfdUFs+v2UMppu5FNcwoz29WA2JJ9rdnIdB2aSDollkrk4Gggc/yC1/4Ql7/+tfzoQ99iGc84xkAfPOb3+Rf/st/yYte9KKBjvW2t72Nl7zkJRw9epRWq8VHPvIRPve5z/GpT30KgHvuuYdDhw7x7ne/G4Bf//Vf57nPfS433HADa2tr/Kf/9J947LHH+Bf/4l8M+mtcFfL0cs5e0G903jhT4fB4cVO/xXzDGUk0LkvKkFmJyOXE7IVhNGoxi9WOx0zVYqpi0rR9mo6PJgQ3zZYRCNZsj8P1QuoG1WbpOc4KSQVJe4qpMzVr2++PlQwWGs7AiqlZrIbJWkAs72+P6V23M0ttzjccXD/C1BUO1S2umyhn4hwM/AT88R//Mffeey933nknuh7/gkEQ8BM/8RN86EMfGuhYFy5c4J577mFubo5arcYdd9zBpz71Kf7RP/pHAJw9exZFubjxrK6u8rrXvY75+XnGxsZ45jOfyZe//OXLikmkjSynl3OyQ7/ROd9wGCsamIaKH8ab9CiMziwqQ2YhIpdzkSwZRj3D81C9gKkr287f2q3hmcT9m8XnOAts6nnb5n7YVZD0Ctu2lFf+/uXIajVMVgJie5HRy2QApK8XVGaoc0BIOdxyH374YR566CEAbr75Zm666aaRLiwpms0mtVqNRqNBtVrd83//0qjDpVHUayXqkJM82xlFExVjJENJt4uc5fdwThJkwSBYsz2+cWaVqfU6/UsN5Z6q3p3Hxi47k2mvyZ/j5JBScv8TDR5d7BBJSbOv561a1FGE4Prp0sCGcsvx+fg3Hsf2tumdAs6vxWIUr7zz8EAZp7Yb8I3TK5RMbdtqGMcP6bgBdx4fz4NPQ5D0+U0yADLq/Tet+84gvsHQT8CxY8eQUnL99dejafmDtFOyFEXNyTZJRONyZcicvSYLmcJehmGl47Fqe1sM5bGikaoy7Pw5TpaLPW/LGz1vNUOj64X8cKPnbWzgc1s2NU4eqHDf6ZVt5emDSHLyYGXg5yWvhkmWJDN6SZYAjtoh2y/7zsBvI9u2ecMb3sCf/dmfAXHm6cSJE7zhDW/g0KFDvPWtbx35IvcbWUkv52SfURud/bXwErklsp7GWvicnKQpGSqmrvL108sUDZWKpW9UEyy3Xc6t2Dz7+ERqDM8s9rRkiYs9bybTFZOG7dPorve8zZQRgqF63oQQ3HqwRtsJOL3U5kLLQUYgFLA0haccqHLLgcGNzksH61qGioogJJZPz0rTflpJqr89SUek55C1HZ+CrmHpgiiCubXu0A7Zftl3Bl7Z2972Nr773e/yuc99jhe/+MUbn7/oRS/i137t13LHaYdkIYqak31GnWbvRc68MOLcqr0lsj5TsVJZC59zkSyUvmURuV6wL6XofbD5a5GeIv6s9rRkhYs9b0VMbb3HKZKoynrPWzB8z1utoPOUg1WaXY9T8226fkhBVzl0oMxTDlZ3PVh3y8De8eJIButeyySV0UvKEek5ZIstBylhbs3ZNF6h64ecXdEGdsj2y74zsOX+iU98gr/8y7/kuc997qYTduutt/LII4+MdHH7mdx4yUmaJOqeNVXBCULOrtiEUbQlsr5m+0xXzdSUJOVsJhcDSIaOF+L5EU89XGfV9rYMqh0rlnG9KDWR1CwrvGbh3dlvIAohKBqbr/luS7MePN9kueNRKehULA2EYKnt7WoAbv/A3qcdGUNRIIpimejdDOzNSU4lNClHpOOFnFu2Wel4BCFxSej6ehdbHpoK+pLN8cnyQPvZpYPCLw0opHnf6WfgHXxxcZHp6ektn3c6ndRtXmml0fVjKcY1B3f9BjpYtzg2mQ0pxpz0k1Tdc1FX8IKQxZbLDVPxi6AZ+hiqwnjR4EeLHcZKOkU93RvftUguh5scPQNmqmIyXjK2GASRjMUh0hJJzWpPS1Yc/yRLszYNwK2MZgDulQb2jmFkpvckzSTR357UfeYHIXMNh1BKpiub54VNVVQutBzmmg5+EMIAjlNSg8L3moEdpzvvvJP//b//N294wxsANh6iD33oQ9x1112jXd0+pNH1+dqjy5xeauMGEVKCEHB2pcNC0+U5JyZS9QLIyR5J1j3bfoSpqugqfO7UIl0/RCIRxAbi8akihqJg+xFlM3ee0sJ+acpNK5caMJdmGPwgTFUkNYsDRJPouUiKpBzTpAbg7pfek7Qz6v72pO4zL5TYXnBZBdCCrrJme3jhYOXHSQ4K30sGfgLe9a538ZKXvIQHHniAIAh43/vexwMPPMCXv/xlPv/5zyexxn2DlJIfnG/wnXNrdP0A1ps6ZQQo0OgGVAoazz0+kfobJye9JPkSDMKIlutzoeXR8X0URUEDQqDj+1xoehys+6mJrOfE5IZRsmQxg5Mlhdekei6SIinHtH8ArpQSNwgJJagidp6GHYC7X3pPssAo+9uTus8MVVAwVRw/2PY+6voBRVPFUAc7bv+g8MmywWLbo9H10TWFG2dKKELZ1aDwvWLgq/f85z+f73znO/zWb/0Wt99+O//P//P/8IxnPIOvfOUr3H777Umscd/QdgO+fXaVpbZLyVDj+mdFINeb7ZfaLt9+bJXbDtYG2vRycvpJ8iWoCHjkQpum4zNTsVi1ffwQdFUwVrFoOD6PXuigpHfPuybZC8MoC70nSZHFDA5kR+E1qZ6LJEnMMRXg+BFLLZe2ExAiURGULY2SqQ81ADfLPW/XOkncZ7oWVyJcaLostR0qlo6mKARRXBKqqyrTVRNdG07MolbQWem4Wwbg1op6JgJ4Q63s+uuv54/+6I9GvZZ9T8P2OLtiI2SE6wsWWxdv8qqlgYw4u2LTsL3cccoZmiRfgrYXstoNaNk+LSdAytiZcoN4UySSrBgathdS3Rp4zxmAUToiSRtGWek9SZIsZXD6yYLCa1I9F0kzase0WtAxVYWH5huUDJ2SqaGpgiCUrNo+j6/ZHJ8oUR3wXstixjTnIkmUAB4dLxKEsm+Acyx2M1kxEQiumygOfD8EYcSa49Ps+rh+SL14UVxqpRM7TNWCnvrM5sA7jKqqzM3NbRGIWF5eZnp6mjAMR7a4/YbtR/ENGElc32W9Qo8IaHY9TF1FUwS2n+6bJifdJPkSdPwQ2w2wvQBFEZQtHV0V+KGk7fhEkaTrBjh+vg/shlE7IkneE7noxEWyksHZC0bp+CfVc7EXjNIxLRlqXJo4H1Ey15NLcv1PAV0volY0Bn6Os5oxzblIUiWAbcdnomyMZK6XqghW2y62F3KgVsANQrp+iCpgomQy1+gSRRFqyktWBj7LUm6/Mbmui2Fsv6nlxBQ0gROELLU86iWDgqagCkEoJc66oTFZNSho6b5pctJNki/BMJLYbkDZ0igYcVOn60uEgMmyhe35dNyAMEqfAZMVknBEkronctGJrWQhg5M0o3b8k+q56CcLpaa2HztGTzlQZanl0HKDje8pSJ5yoEq1oA8lzpPVjGlSZOF+SJJL7wcniqsSDo4Vdnk/CJwg4vFVe0upqR9JSkb6984dr/D3fu/3gPil8KEPfYhyubzxvTAM+cIXvsDNN988+hXuIzRVQcj1QYnx/zb+z/rniiSvI87ZNUm9BA1VYOgKjh8xXjLWU/lxuZ6mCjpugGkouzJgrmWSdESSuCeyLDpxrRtGSZGE459Uz0X/mrNQahqEEZam8tQjdeYbXZZaLl4oMVTBVNVkplKg64dDlzplMWOaxHOclfshaUZ9P4SRpGAotFZ82t2AiYpJSVdx/SieIWZpHKpbqQ+87vhN9p73vAeIb9IPfOADqOrFDcowDI4dO8YHPvCB0a9wHxFGUC8aRDKuG3X9ECHipjgBTJQMakWDlJd35mSEJF6CiqJwqGYx13SZbzqMFXQ0VSEII5Y6PkVT5UDVRFFy538YknZERn1PZFWNK5+llwxJOf79PRdhFAtC+GGEripMVw0UoQzVcwHZKjXt9SoaqsKNMxUOjxcJQ4mqxuMgXD+K+xV3EXzNUsY0iec4S/fDXjDK+0FVBF0vomJqjBcNWk5AxwlQ1isivDDE8fZRqd7p06cB+Af/4B/w13/914yNjSW2qP2KEDBZNSlaKg07IAglUkqEEGiqoFbUKBoaKQ7u5GSMUb8EqwWdoxMldFWh7QWstH2CyEdTFA5U4x6Zg2OFgZuTc2L2whEZ5T2x12pco4gu92bpnVnubIibRBLOrdr5LL1dkpTj3ys1nW84PLpo03YjwihCVRRoRpyYqqS21HSUGZFLexWLugZ9t+q1JOKQxHOclx7vBXLjfLpBtDEo3NQU5hpdEOnONsEQPU6f/exnk1jHNUG1oHOgWmDF9pgsmax1fAIZoQmFsbJOEMFE0ciNzpzUUjY1bj5Qo+kETJQNjk2IjVI9KSVOIDk5W8tMxDJtZE0WeC/VuEZRPtObpffgXJOCrlHrk7VudgMenGvms/R2QdKOv+0FLDRdOl64EXS0XZXZbe69nZB0hnfUJV97IeKQhRLWpJ7jLJceZ4EwkoyVTdSuz3LHpWLpWLpKEEXx1wWdakHfP6V6PV75ylfy7Gc/m7e85S2bPv+d3/kd7rvvPj72sY+NbHH7jbKpcfJAhftOr1DQ42F1Qoh4kJ0f0fVDTh6s5A9kTmoRQnDLwSotx+fMUgckKOvSkEIRPOVAiVsOVlP3os0KWZMF3is1rlGVz7TdgFNzLTRFMFUxNz7vyVqfXws5db6Vz9IbkqQc/56h/NiyzYFaAbNvBqIbRDy2bPPAXGNgQzlJRy+pkq8kRRyy0tuT1HOc1dLjrKCpCnVLp2bprNreFpnzesFAkP4+/4FX94UvfIG77757y+cveclL+MIXvjCSRe1XhBDcerDGUw5UN4bYOV6I40cIAU85UOWWA3kKOCfd1Ao6zzkxwbOPjzFVMykZGlM1k+ccH8vLnHZJzxEpmRpzjS6OHxJGEscPmWt0UykL3DPkZmsWHTdgqe3GpS51ayT9AJeWz1i6iiLERrlHxw04u9K5rOJrP82uz1rXY6y0fUS5XjRY7Xo0u/6u1nyt0nP8V21v2++v2h4TlcHlsjcbyhbVgkHF1KkWDKYqFpoiOHW+RbtPZW4n9Dt627EbR29U9+x21Ao6tx2s8pSDVW6cKfOUg1VuPVDdtdP0/ScazK1119eqoAjB3FqX7z/RoJGiZyKp5zip+yEnprc/+GHEDdNlnnKoyi0HqjzlUJXrp8r4YTTU/rDXDJzaaLfb28qO67pOs9kcyaL2Mz2jc6ZqbmpoPDRmcd1E3pickx16tnt6TPj9QRZlgXuG3GLbw/FDLF1lqmyMRCRk5OUz67ZqJCNaTrAhMlCx1vtL010lkmqSykD2DOWZqrXt9+tFgwsth2bXHyjDkFSG92qUAC7sIjPUc/QWWw5SwtyaQyAlmhDx3Cg/5OyKlq7enis8p8M+x1nL+GeNTfvDWpeCHu+5QShZ63hDz4faawZ+Ym+//Xb+8i//kv/wH/7Dps8/+tGPcsstt4xsYfuZWkHnjsN1rp9Odx1xTs52bDTlrpfqCQXaTsBi02Wh6eVZpxGQNVngURty/YyyfKZa0KkXdU4vdei4PisdnyCUaKpgvKRTMnUO1a28z3QXJOb4J2AoJ+XoZa0EsOOFnFu2Wel4BCFU+3qGFlsemgr6ks3xyXIqWgl6z/Fqx6NQ37qe1Y7HWEkf+DnOBwEnT62gc3SiyNcfXeb+JxobgbYTUyVuyYhi4cBPwNvf/nb+r//r/+KRRx7hH/7DfwjAZz7zGf77f//veX/TAGRJ8jMnp4eUkgfON3lwrknRUKkU9I0XS8vx46ZcS+e5J8bzl8suycoekbR87yj7ZsqmxnjZ4DMPXSAMYbpqUCuoOH7I6cUuqtrl9kPVXZ33LDTX95PEekft+CdlKPfWOmpHL8leryRU3/wgZK7hEErJdOViVq/XM3Sh5TDXdPCDEFKwJ/VEir5+epnFVhdTUy/2iwchQTS8SFEWM/5ZotH1efB8k6W2i6GqGKoCCBZbLg+eb1K19NSf44Hvqpe97GV84hOf4F3vehcf//jHKRQK3HHHHfy//+//ywte8IIk1pgzAFl7aedki7Yb8NBcA00RTJRN3CCk64eoCkyUTby1LqfmG9x2qJo316eUUe4ReyHfO8ryGSklbSegbGoYqiCMJF0vIJIwXTFww4i2e1GxbVCy0lzfI8n1jtLx7zeUtxuAuxtDGUbv6F2NEkCJxNQVHlvqMF2xmK6YO16/F0psL6B+mdLCgq6yZnt4YTrqWHsiRQvNLt89u4bthYRIVARFMx4QvBuRoqxl/LPCpYHX6aqRycDrULvMS1/6Ul760peOei05uyRrL+0kyR3IZGh2fdZsn1pB59yKTccJNl5YJUujbGqsdvyBew1y9oZR7xF7Id87yvKZxbbHctvj6UfHcLyQVdvf6HEaL+qYhspSy2Wxffl+msuRtcGZvfW2HR/LULFUhVBK5ta6qVvvpWqeTduPpa0iQFkXVtqlmucoHb29LgFsu/G/s9bxWOn4gODoRHHHz7WhCgqmiuMH2+7bXT+gaKoYarreoUVDY7Zu4QYSpAQhsDRB0dj9dcxKxj9L9AdeJ8ubM5tmWeX8mp2JwGt+V+wTsvbSTpLcgUyWbhDSbgRIGb+4NFUQhJJG16fp+Kmf+n2t0m8oF3QNSxdEEbsylPdKvndU5TOOH+IFkgN1A7UsmK5uHsAYRJKzyzaOHw60vqwNzrwoBuASSblJDKBa1On6UarWC5cIKzUcXD/C1BUO1dMprNS7Z88stUcmBLVdCWDbDXhksU3XCzA1lfGyTtXSB3r361p8n15outtm9HRVZbpqomvpEEXo3b8Ad10/SdcPCUOJqgoKusp8w0nd/Zs0SQWLR3ncXuB1prZ9UGqsZLDQGFzkZa/ZkeM0Pj7Oww8/zOTkJGNjY1c8aSsrKyNbXM7OyNpLO0lyBzJZKpaGjCTLbZcTk5WNzw1VYBQMHl1qMV0xqVh5TCZN9KtmRVJyetnGD0J0TWW6YgytmrWXA3tHUT5j6SqGJuh64cbwxX4cP8DQxLa/y5XI2uDMjhdydiUWAwijiIp1sVdxue2iKgraskiNGECPLAorxYPBQ2w3zs1HuxjueWkJoEQy1+jS9QImyxaLLZepikG9qCOEseN3f8lQOTpeJAglkZRb5usIBNdNFFOjJtf/vAkERV2Dvtd62p63pEkqWJzIca/wqEp55e+nhR3dUe95z3uoVGIj6b3vfW+S68kZgqy9tJMidyCTRwjBeMmk0Q1Y7XqUDQ1ViXtF2l6AoaqMFXdeW5+zN/RUs55YdVjqOOsDi0FGsNDsMlmy0BVlYEN5r+V7d1s+M1U2ODxe4IcLnW0jmnMNh5tmy0yVt99LL8deZN5GGfmNxQC6yEgyVbl43XolMxdaXeYb3dSIAfSTlRKqDfXR5Q5SgiLi7NBiy+VCazj10UtLAE1dYa3jYWoqiy2XoqEyWyts3Bc7fff3H7ft+EyUDVQEIRLHC1MnE50Pqr1IUsHiJI5bLejUC8ZlRV7WbI+xgpF6VdMd7T733nvvtv+dkw7yTSSm34GMZMSq7eP5EYauMFbUrxkHMknCSHJorICiCuZWuqzYHjKSCEWgC8GJ6TIHaxbhLqKqOaPHD0IeXY5Lhgq6StG6WGJpewGPr9lESJ43oKGcNfleRVF41vEJFlseDy+0OFCzsHR1fcCwQ72oc+ex8YHnTyWdeRt15NcLJV03vIIYgJYqMYB+stC/KqXkB+cbPDjXpKBr1PrkvZvdIG6CL2g89/jEwGvvL1s9u9xhpeMzXtaZqhjM1gqb3m2DvPsvLYd1oviePThWSJ2a3F5mutNMUsHipI5bNjVOHqhw3+kVFltuLHuvKPhR/FwEkeTkwUrq7bMdrW6QwbbVanXoxeQMR76JxPQcyFXb48G5JhcaDp6UGEIwXbN4ymwVRRH73oFMEk1VqFo6qhA4bsATa128UGKogul6gSP1AiVT2/f3WtZwg4i51S4SqBcuGsu9Ekvbs5lfs3GDwZ+NrMn3HhkrcvcdB7jv9DKPr3TxAomhCW6aLXPnsXGOjBUHPmaSmbckIr+GGjfQd/3wMmIAIcV15cE0kZX+1bYbcGquhaYIpirmxuc9ee/zayGnzre47WBtqF6O3sDpsqnRWXeAZ6vmFod/0Hd/VtTk8kG1MUlVGyV1XCEEtx6s0XYCzix3aHR9eiPYFLEu8nIg/RVBO/qN6/X6jn+RMBysqfZaZZRRs3wTidFUhRXb5XuPN3C8kImyiaUrOH7EuRWbxbbLHYdruVG/C0qGiqErfPfxNpahcsN0ZUPNSCL50WKbZ58Y3/f3WtaIexdAu4whrCqCMJQEQ2YYsmJw9TgyVuRQzWKx7W0MYJwqGwNnmnoklXlLKvIbiwFYLLScbSO/uiqYqVipEQOAbPWvNrs+a91YmbE3WyiUoIrYeaoXDS60hm+C7zmQy22Xpuvz+FoX2yttyTgN8+7PQilk1jLdSdFfbSSRW0Qyhq02SrKKaZPIy4hEU/aaHT0dn/3sZzf++8yZM7z1rW/lF37hF7jrrrsA+MpXvsKf/dmf8e53vzuZVe4zRh01yzeRmIImmFvrstT0uOXgxcxnyVAojWs8cL7JQsOhoO3v85A0AoHjR6x2PIIw6ikCo6kKBUMDmZ/ftCEE1Is6/npGtmRqaIogiCQdN1bjMkzBbraILBhc/SiKMrDk+JVIIvOWVOS3ZKgcmSjiRxFSQsP2aa+LAUxVDISAo5PpEQPodyBnqxZdP6TjBqiKYLZqMd9MoYqaBMcLWWq5tPvGNpTXxzYwZBXkpQ7kLQdqnJpv8fBCm1Xb46aZKqam7Pt3f9Yy3UnQqzZa6Xis2t66qMdFdcyxojFUtVHSVUxZFHnpZ0c7bf9g21//9V/nv/yX/8LP/uzPbnz28pe/nNtvv50//MM/zHugnoSkomb5JgJLHR/Xj5ipGdsahzM1g64XstTxmammwyDIGh0vZLnjoqvweMel60cbGaeirnCsoLHccfM+spRRLejMVAus2B66Img7Ad11Q25s3aGaKO6uKTcLvSdJM+rMW1KR3y1iACUTIeJHuesHqRMD6DmQhqrwyGKbRp+BWEth/2q1EM8Ee2i+ScnU18uX457CVdvn8VWb41PlgZ+37TKQlq7ylANV6sUup5c6PDTf5PrJ0jXx7s9apnvUlAwVU1f5+ullioa6RR3z3IrNs49PDBwA2YsqpqwF2voZeNVf+cpX+MAHPrDl8zvvvJN/8S/+xUgWtV9JWvXtWt9EHD9EEQrXT1dY63hbjMNa0WCx6Q48oyXnIn4QcnqxzWLLZbJsYagCoQhkJPFCyYWWi6G18a+fSJ0a17Vs2Pc35Vq6Sq2gb1w3N4iQfrirptys9J7sBaM0CJKM/G4JtoUytWIAQRjRdHzWuj6OF8WlhesG4mLLo+kEjBX11KgWlgyVmqXT9UNKpo4XhgShIFpPM3X9kFpBH9jwvFwGsmxq3DBdZqJs0Or63HKoxnTl2lA3zbIBPgrk+j0le5Ue65nMja/F4KnNvIrpygx8tx05coQ/+qM/4nd+53c2ff6hD32II0eOjGxh+5G9kA2/ljeR3owWJBweK26pK2+7w81oybmIG0TMrTlI2HIfl4BuI2Bu1RlKZCBJrnXD/tKmXCeIRtaUm8Rg3ZyYpCO/WQm2qYpgue1hewEH6xfFOy6KLdjI9UHGwzDq/cH2I+oFnWOTZc4sten60Yb6aFFXODZZpmbp2H5E2dy503ulDKRAULMM/EBiakrqrmHO6Ol4IZ4f8dTDdVZtb0vJ7VixjOtFQ9mUSVcxZTmQObCF/Z73vIdXvvKV/J//8394znOeA8DXv/51fvjDH/JXf/VXI1/gfiKXDU+W/hktN81UsPTNt/ewM1pyLhL3NEnUyzTRq0IhlFGq7uEsNZUnSRJNuZsG60aSM0udDZXFqYo59GDdvSALL+7+yO/5NRvLUEc+Xyc7wbYniZwPEVmHZPaHIIzwpaRsaUyWTPxQIpEIBLoa9zn5cvB3fX8G0tBEPHIjiDC0eOSGv541zAWQrg16NuVUxWS8FA8yD9cDCAVdJZKw1HaHfh8nFVjJeiBz4N3y7rvv5uGHH+b9738/Dz30EAAve9nL+MVf/MU84/Qk5LLhyZLUjJaciwghqFvrIgPbDMC1dAVdSY8Bmsmm8gQZdVNub7Du+bUui00PKdjolZlvuExVjaEG6yZNll7ctYLO0Yki951e5tR8a0M+/ch4kVuuEac/jCRjZRO167PUdqhYOpqiEEQRLcenUtCpFvSB58clVT6vKoLVtksQRNx+uI4bRBsGrakpzDW6rHXcgTNkvQzk/U80WGo5XGi6+KFEVwXTVZPJisUdh2upEfXISZZLbcqisXmP9YNw1zblqAMr+yGQOdTZOHLkCO9617tGvZZ9Ty4bnjxJzGjJuUi1oDNTs1jpeOiqQssJiKREEYLxgoEXhkyU0zP5O2tN5XvBKF+EfhByeqnD42tdCoZKxbgoyNL2As6udIkkAw/WTZKkX9yjzmQ1uj5nl21KhsbTjoyhKBBFsYjD2WWbqqWn3tDYLZqqULd0apbepx4WlyRNVkzqBQOx/nODkGz5fHzNhbhMefgQ6qNCCDRVcP/jDdY6PofGLMbLKrYb8uD5NvWSy9OP7nx8TE62yZpNmXSf/14x1Jvs7/7u7/jgBz/Io48+ysc+9jEOHTrEn//5n3P8+HGe//znj3qN+4a9KLvIGf2MlpyLlE2Nmw/U+PrpZSxdoVYoIITYmFUSoXBytpYaJyRrTeVZww0izjdsBIKxvsG6uhp/7Xhd5oYcrJsESb+4R53J6l9vf28PwBhGZgyN3dJvIN4wXd4yr2a+4QxlICZVPh9GkomygdoV287JKls6Y8XBM2RRFPHQXJOioXKwZtFyAmw3RBGCWw9WWe16nJpvcfNMJX/fXQNkTcRhL/r894KBV/ZXf/VX/PzP/zyvec1r+Na3voXrugA0Gg3e9a538clPfnLki9xP5GUXe8OoZ7TkxAghuOVglZbjc2apg+tH8RCnCFBELDJwsJqajTprTeVZIwgjQgn6ZWw0RYEwIjU9b0m+uJPIZO0XQ2O39BuI8w2HsaKBaaj4YXx+hzUQkyqf11SFqqVTXc+QbW3aNzZ+bhAW2x6Pr3Q5NlGibGpbSgBrRZ1zyzaLbS9//10jZGkUzX7p8x94p33nO9/JBz7wAe655x4++tGPbnz+9/7e3+Od73znSBe3H8nLLnL2kiSyIZtEBhoOrh9h6gqH6mmd/J2dpvKsIYSgVtAJQ8la16PYV6pne/FgXU0VqXGkk3pxJ5XJ2i+GxihIwkBMqtSp/7jXT5W3NO3PN4fLkDl+iBdICuv7+KXOnqWreIHMR25cY2RFHXO/9PkP7DidOnWKH/uxH9vyea1WY21tbRRr2rfkZRc5e0mj63Nmqb1JQe1g3YqlcHdp0Gdl8nfWmsqzRrWgc6BqsWr7qIqg0zc7rV7UCULJeElPTc9bUi/upDJD+8XQGBWjNhCTKnW69Li9snw/ilizvaHL8nsjN7peSMmAlhPghRGGqlCxNBw/zEduXKNkQR0zaz1Zl2Pgszw7O8uPfvQjjh07tunzL37xi5w4cWJU69qX5GUXOXtFo+vztUeXObPcQcp4Vk8k4dyqzULT5TknJnbtPGVho85mU3l26O95K+gqtaK+0bfp+RFdGaaq5y2pF3dSmaH9YmiMklHvO0mVOiVRlt8bufHNx1aRUrLS9gmiCE1RGC/rCCG489h4PnIjJ5VkrSfrcgy8+7zuda/jjW98I3/8x3+MEILz58/zla98hV/+5V/m7W9/exJr3DfkZRdbuZYb65NCSskPzjd4cK5JQdeo9QkiNLsBD841qRQ0nnt8Yt+f66w1lWeNS3vePK+/543U9bwl9eJOKjO0XwyNtJNEqVMSZfmKonB4vMj/97vnWesEHKqbVAsGHTfggfNt6iWNnxwr5MIQOaklSz1Zl2Ngx+mtb30rURTxwhe+ENu2+bEf+zFM0+SXf/mXecMb3pDEGvcNednFZq71xvqkaLsBp+ZaaEo8hLTHRUGEkFPnW9x2sEbF2t/nOWtN5Vkkaz1vSffKzNasLQ76bjJD+8HQyAKjzGQlVZYfRRGPr9gcqhc5WItY6fisdHw0VXDroQogeGK1S3Q0Sp3zlAdJs0lSfdJZ6Mm6HAPtEmEY8qUvfYl/9a/+FW9+85v50Y9+RLvd5pZbbqFcLie1xn1DXnZxkazNUskSza7PWvfyqkr1osGFlkOz6+97xwmy1VSeVbLS89YjqV6Z+YbDVx9ZjvVIepk3AccmS7vKDGXd0LjWSKqUt6eqd3KmQslUaTkBfhihr/c4ddwwlap6eZA0myR53bJQ6n85Blq1qqr843/8j3nwwQep1+vccsstSa1rX5KXXcRkbZZKJrmC1oEQV/7+fiQrTeVZJmsvwsTWK/r8JhH3F47ksBk7v9cySZXy9qvqKUKhVtjsmFk6qVPVy9VHs0l+3S7PwLvwbbfdxqOPPsrx48eTWM++pxf9vlTt7NBYOstakiBrs1SyRrWgUy/qrHY8CvWt52+14zGWIqWzvSIrTeU52aMXDAJ47vGJbeWnrwWVxZyYpEp5+1X1KtbWv5s2Vb1cfTSb5NftygxcBPvOd76TX/7lX+Z//a//xdzcHM1mc9P/cwZAXPxTXkMZgL2apWLpKsr6rIsDtQIdN+DsSge5z092T+ksiCRLbQc3iI04NwhZajsEkUyV0lmW6WWy7jw+zp3Hxrjz+Di3Hdz/znnOZvqDQUIIioZGxdIpGhpCiE3BoJz9T6+Ud9X2tv3+qu0xUTEGLuXtqerNNZxtvz/XcDgyUUyNqt4gQdKc9JBftyszsOV09913A/Dyl798k6cppUQIQRhemydyp/RnRCZL5kZGZKHp0nbDayIjkrVZKnvBKHuyLlU6a9p+qpXOsk5eQpUsWehXzFUWc/rpL+U9v2ZT0DXEeoC06wdDz3FSFIVnHZ9gseXx8EKLAzULS1dx/JC5hkO9qHPnsfHUCEPkz0U2ya/blRn4bf/Zz342iXVcE+Tpz5iszVJJmiR6srKmdJaTsx1Z6VfcC5XFLDiQe0FWzsNl5zhNFIae4wRwZKzI3Xcc4L7Tyzy+0t047k2zZe48Ns6RseKTH2SPyNVHs0l+3a7MQI6TlJKDBw/ieR4nT55E0/Io6yBkOSMySrI2SyVJkuzJyprSWVZJypDLioGYFHvZr7jbc520ymJWHMikydJ56J/j9NSjdVQhCKXE8cKh5zj1ODJW5FDNYrHt4fghlq4yVTZSk2nqkauPZpP8ul2ZHVvnp0+f5uUvfzkPPPAAAIcPH+av/uqvuPPOOxNb3H4jqxmRJMglovcmA5mXkSVLUoZclgzEJNiLZ6PnLK10PM6v2XTdkFAy1LlOUmUxF7yJydJ5uNIcJ4qM5P5VFCVVkuPbkauPZpP8ul2ZHVtUb37zmwmCgL/4i7/Asiz+83/+z7z+9a/nm9/8ZpLr21f0Z0RMXdkyJDGNGZEkudYlovMM5PZkJdPSM+Tajk9B17B0QRTB3Fp3V4ZclgzEpEj62eg5pmdXbB5dbOOHkkP1AkfGixiqMtS5TiIYlJd3x2TtPPTfvxK55V1/Le3tufpoNkn6umXlPb8dO35iv/jFL/Lxj3+c5z//+QA897nP5fDhw3Q6HUqlUmIL3E/0MiKPLnaIpKRp+wRSoglBtaijCMH106XUZET2gmtZIjrPQG4lK5mWniG32HKQEubWnI1nuVbU6fohZ1e0gQ25rBmISZHks9Hv8LYcH0tTmK4YNLsBpxc7XD9dHvpcjzoYlAdXYrJ2Hnr3rxdGnFu1t7zrZyrWNbW35wOcs0lS1y0r7/nLsePUxoULF7jxxhs3vj5w4ACFQoELFy4M/Y+///3v54477qBarVKtVrnrrrv4P//n/1zx73zsYx/j5ptvxrIsbr/9dj75yU8O/e/vNUIIxkoGC02HHy60URRBraCjKIIfLrRZaDrU1+Vsc4YnKxLR/RnI7bjWMpA9g3a+4VAyNaYqJiVTY77h8P0nGjS6/tVe4gYdL+Tcss1Kx2Ox5WEZcRTZMlQWWx4rHY+zS/bAcq25DGxMUs9Gv2NaLxm4XkS1YGBqKlMVE9sLmW90kVIOfa57waB60aBsarvaz/PgSkz/eZBIbD+g5fjYfoBEpu48aKqCE4Scmm+x3HY37Q/LbZdTCy2cILxm9nYY7XORs3eM+rpl6T1/OXb81AohaLfbm2Y2KYpCq9Uaeo7T4cOH+a3f+i2++c1v8o1vfIN/+A//IT/5kz/JD37wg21//stf/jI/+7M/y2tf+1q+/e1v84pXvIJXvOIVfP/73x/o371aSClZ7XjMVE1umikTRZJG1yeKJDfNlJmpmqzZ3r6fM7QXZGGTTmrWRxbJ2gwuP4jlf/1QMlUxMbV4vT0D3A8lc00HPxjM6M4N5Zikno1+xzQMJYGMje4e1YJGw/bp+mEqznUeXInpnYeVjsePLrR58IkmD8w1efCJJj+60GbV9lJ1Hoq6gheELLZcJsvWpv1hsmyx2HLxw4iino715uTsBVl7z1+OHee0pZTcdNNNWz57+tOfvvHfg85xetnLXrbp69/8zd/k/e9/P1/96le59dZbt/z8+973Pl784hfz5je/GYDf+I3f4NOf/jS///u/zwc+8IEd/7tXi95L+1C9iKkpW6bLu0GUqnKDnGTJWk/WpYyyRjlrpTheKLG9gPpl1lvQVdZsDy8c7AWQRWXIJEjq2eh3TCPi0ik/jDC1+FzrikJbBoSRTMW5zprgTT+j3B9Khoqpq3z99DJFQ6Vi6Rv3w3Lb5dyKzbOPT6TmPNh+hKmqTFUMFlsu1YKGrij4UUSzGzBVMTAUBduPKJv7+1nOyemRtff85djxypKe3xSGIR/72MfodDrcdddd2/7MV77yFd70pjdt+uwnfuIn+MQnPnHZ47qui+u6G18PmhUbJXk0OedSstST1c+oa5Sz9mwYqqBgqjh+QMXa+vt2/YCiqWKogxmKWTaUR00Sz0a/Y1rQVapFneW2i1mOz6cfRWhCoCoiFec6q8GVJHoYJHEQQkrR+2Dz1yI9UeogjDB1lZOzVS40HRq2T1sGaEIwVTGYqlg4fpia/SwnZy/I2nv+cuzYcXrBC16QyALuv/9+7rrrLhzHoVwu8zd/8zfccsst2/7s/Pw8MzMzmz6bmZlhfn7+ssd/97vfzTve8Y6RrnlYei/t1Y7Hqu3R6GsYrRV1xorGVY9w5uw9WWucTUL1LWuZFl2LSwsuNF2W2g4VS0dTFIIoouX46KrKdNVE1wYzurNqKCfFqJ+NSx3TA7UCthduXMNG16de0FmzPcqWnopznbXgShL7Q8cL8fyIpx6ub7w7+x2RsWIZ14tSE6nu7WeGqnD9VHnb6pLe/ZyTc62wSVl6m6qrtL3nL8dV32FOnjzJd77zHRqNBh//+Me59957+fznP39Z52lQ3va2t23KUjWbTY4cOTKSYw9KyVAxdIX7Tq9g6QoCAVLiCcGFlsPZFZtnnxjfVYQzyxKP1zJZmbeUlOpbv0E7W7O2yPemIfrfT8lQOTpeJAhln0JmbMhNVkwEgusmikOtN2uGctKM8tnYzjE9PlHi7GqHJ9a6GIpCua5xcKyQqnOdleBKUvtDL1I9VTEZLxlbDK5IwlLbTU2k+lIHvWhsvn/Ttp/l5OwFF5Wl20jJluSBEHD9dDn1z8VVt9QMw+CGG24A4JnPfCb33Xcf73vf+/jgBz+45WdnZ2dZWFjY9NnCwgKzs7OXPb5pmpimOdpF7wKBoNH1+NGCi+NHREgUBJauMFU1QQ7/Isy6xGNO+kmqRrln0M43HL76yHJchqMAESDg2GQpFdH/Hv0GeNvxmSgbqAhCJI4X7jpbkRVDOYts55jOVi2un6owWzMZL5mpPNdZCK4ktT9cmpG+1BHxgzBVkeo8c5yTs5UNZelHXNZsnwM1i6qu4fghDy+0qRd1nnlsPPXPRep24SiKNvUk9XPXXXfxmc984eZ7RwAASmhJREFUhn/zb/7Nxmef/vSnL9sTlTY6XsjjqzZtJ8D2Q3RNxVLAj8D2Q9pOwOOrNh2vNvALMh+ambMX7EmNsujzmwQoKd1DLzXAnSguMxhVtiILhnJWyR3TZEhqf8hi71+eOc7J2cxFZWmLybLBYtuj0fXRNYUbZ0ooQmHN9jhcL6R6L76qb+W3ve1tvOQlL+Ho0aO0Wi0+8pGP8LnPfY5PfepTANxzzz0cOnSId7/73QC88Y1v5AUveAG/+7u/y0tf+lI++tGP8o1vfIM//MM/vJq/xo7x/IAH55qEEdxxuI4fRISACuiawtllm4fmm7zw5ikYwGDKh2bm7BVJ9SL17mGA5x6f2FKKM990UnkP5wZ4Ts5FktofsprByfeHnJyL9DLStYLOSsfdEHhBgpRQK+r7S1UvCS5cuMA999zD3NwctVqNO+64g0996lP8o3/0jwA4e/YsinJxg33e857HRz7yEX71V3+VX/mVX+HGG2/kE5/4BLfddtvV+hUGYq0bsNLxmCgZCATGJY3j9aLOcttjrRswXt75cfeLxGNO+kkq8tt/DwshtpTipPkezjND2SMva06GJDNDWc3g5PtDTk5MEEasOT7Nro/rh9SLF8cKrHTi93u1oKemV/FyDPw0/9RP/dS20RIhBJZlccMNN/BzP/dznDx58kmP9eEPf/iK3//c5z635bNXvepVvOpVr9rxetOEIsBQY+Wt7fDDCENVBi5N2i8SjznpZy/m60jkFnGI/B7OGRV5WXNyJJ0ZyjM4OTl7yygFx1RFsNp2sb2Qg/XixuempmKWVc6v2URRhJrW+vx1Bu6krNVq/O3f/i3f+ta3EEIghODb3/42f/u3f0sQBPzlX/4lT33qU/nSl76UxHozTcHQmCzH2abVrocfRkQyHrS42vVQhGCybFAwBvNn8+nyOXtJL/I7W7PouAFLbTcuE61bQxudvXt4pePxowttHnyiyQNzTR58osmPLrRZtb38Hs7ZNf1lzbNVi0hKOm5AJGOBiKxMrk8zSewPOTk5e0+j63P/Ew2+cXqFb5xZ5RunV7j/iQaNrr+Loz6JU7QLgbS9YuCM0+zsLD/3cz/H7//+72+U0UVRxBvf+EYqlQof/ehH+cVf/EXe8pa38MUvfnHkC84yU2WDG2bKfP+JJlVLY7Hl4IYSUxVMVywaTsCNsxWmytuX3F2OLDbO5mSbJObrmLrK108vUzRUKtbFFP5y2+Xcis2zj0/k93DOruiVhBqqwiOL7W1n6aW1JDQpkhhhkVRmKC+xzMnZG5LIzIeRjBVou4LFlku1oKErCn4U0ewGlC2dsaJOGKU7cDXwm+HDH/4wX/rSlzb1HimKwhve8Aae97zn8a53vYtf+qVf4u///b8/0oXuBxRF4VnHJ/jhQpvvnl0jkBFSxAPPz6863Hywyp3Hxjed252Q1cbZnGwz6tp9ud4pKnsRp/W9c+Nrke7NNCf9BGFE0/FZ6/o4XhS/uNf3ysWWR9MJGCumv8Z+VCTpiIx6f8hLLLNNPmMyOyQlOKapClVLp2rplxlkbWz8XJoZeFcLgoCHHnqIm266adPnDz30EGEYAmBZVv5AXAEhQCigoaIgiRAIJLs5ZVltnM030xyIMwGeH/HUw/XLbKhlXC+6pjIBSXEtP3OqIlhue9hesKXGfqoS19jLdTXH/U6WHJFcOXYrWXqO80xhtkhKcKy/Our6qfK26rlZqI4a2AL5+Z//eV772tfyK7/yKzzrWc8C4L777uNd73oX99xzDwCf//znufXWW0e70n1AFEXcd3oZKQUvvf0gLSfADyN0VaFiafxoscM3zqxwqGYNnHWC7DXO5ptpTo+eOMRUxWS8ZGzZUCMJS233mskEJEX+zAEbmU2JG0Qb95nZE9e5BjKbWXNE9kI5NndEYkZ9HrLkoOfEJCU41l8dNd90GCsamIaKH0bMN53MVEcNvMO85z3vYWZmht/5nd9hYWEBgJmZGf7tv/23vOUtbwHgH//jf8yLX/zi0a50H7DY9nh8pcuBdceodslL4EDN4tyyzWI7HhA2DFmRPs0305x+Lp3/cqkcuR+EuTjELsmfubjGfqxs4jUdvv/EGlFfo7KCZKZeoF4yU19jv1uyNsIiaeXYRtfnzFKb8w0H148wdYWDNYtjk+XUPRNJPsejdsiy5qDnxCQ1jw2yWx3Vz8A7oqqq/Pt//+/59//+39NsNgGoVqubfubo0aOjWd0+w/FDvEBSuEwa0tJVvEDi+OEer2xvyTfTnEvJBU6SJcvP3Cgj4JqqoAtBEMi4vzSSCAVkBFKBIJDoYv876FkbYZGkIdfo+nzt0WXOLHVAsnE/nFu2WWi6POfERGqMuSSf4yQcsqw56DkxSb+Ps1YddSm7ulMvdZhyroylqxiaoOuFlE2BG4SEElQR19g7foihiW1fDPuJfDPNuZT+FP75NRvLUFERhEgcL6Rs6ZlI4aeVrD5zo46AF3UFNwxpuwG3H6rHe3AEqhLvwT9caONFEUV9fztOSToiSZCUISel5IHzTR6ca8ZqnoWLap4tx+fBuSYVS+e5J8ZTsfck9Rwn5ZBlzUHPidkLwbGsVEdtx8C74sLCAj//8z/PwYMH0TQNVVU3/T/n8kyVDQ6PFziz3ObcaodT8y0emmtyar7FudUOZ5bbHJkoDixHnjXyzTRnO2oFnaMTRTpewHfPrvHVR1f47tk1bC/k6EQxNVHfLJLFZ64XAZ9vxLXvUxWTkqkx33D4/pCzRGw/wtBUpiomy20XgcDSVASC5bbLVMVEVxVsPz3nIQl6jsiq7W37/VXbY6JipCbD2zPkSqbGXKOLs94D6fghc43u0IZc2w14aK6BpggmyxampqIIgampTJYtNEVwar5B2w0S+s0GI6nneBCHbBDyGZPZJZ/HdnkGdvd+4Rd+gbNnz/L2t7+dAwcOpCIKkxUUReHmA1W+9MNlHjzfomCoaAoEEZyaDzlYL3BytjKUMESWyFq0M2dvaHR9zi7bFHWVk7NVJDLWm5QRZ5dtqpZ+TW/WuyFrz1ySEXBLUzk5W2Gh6dC0fYJ19cbJisl02aLrh6lyIJMgiyMskuiNaHZ91myfmdr2PcVjJYOFhkOz61Oxrv7ek9RznJRDlpdgZ5usl9QlxcCO0xe/+EX+7u/+jqc97WkJLGd/I6VkzfaxDAVVhZWORxRJFEVQtTQsQ6HR9ZFS7usbM8ubaZaUl7JEz1BebDlICQ3b3TSY1Akczq5oqezByQJZe+aSKknqGZ6GqnDD9LocbihR1Vi90fWj2PhMiQOZJFls0k7EkLvCX5Xyyt/fa5J6jpNyyLLooOdsJqmSuizbUgOfjSNHjiDl/lYcSoq2G/Dts6v4oeSGqUocTRcSIQUSie2HfPuxVW47WEtFdCspsrqZ5lLOydHxQs4t26x0PIKQLYNJNRX0JZvjk+XM1kVfTbL2zO1VBLyoa9D36KbNgUyaLEaUR2nIVQs69YLBasejUN96zDXbY6xgUE3J/p7Uc5xkYCWLDnpOsmTdlhp493nve9/LW9/6Vj74wQ9y7NixBJa0f2nYHmdXbAxVMF4yt3zfb3U5u2LTsL197ThB9jbTXMo5WfwgZK7hEErJdOVi2UxvMOmFlsNc08EPQsgdp6HI0jOXR8D3jiw3ae+Wsqlx8kCF+06vsNhy44CNouBHEc1uQBBJTh6spOr8JPEcJ/1cZNFBz3JGJM3sB1tq4N3g1a9+NbZtc/3111MsFtH1zb/gysrKyBa337D9CMcLqV9mRlPJ0FhoOvu+MblHVjbTLEs5ZwUvlNheQP0ypVkFXWXN9vDCayfbncSLOyvPXB4Bz9kLhBDcerBG2wk4s9yh0fURxCOSFQFPOVDllgPp29eTeI6Tfi6y5KBnPSOSVvaLLTVUxilnOIq6QkFXaXshJXPrw9fxQgqGmkop3KSiL1nYTLMq5ZwlDFVQMFUcP9g229r1A4qmiqGmdzMdJUm+uLPwzOUR8Jy9olbQec6JCWaqJufXHNwgwtQUDo1ZXDeRvgG4SZI/F/sjI5JW9ostNfDK7r333iTWcU1QKxocHS9yZqWz8fLXFEEQSTpugB+GHJ8oUbvMTXW1uNajL1mUcs4auhZHnC40XZbaDhVLR1MUgiiep6KrKtNVE13b/70n+Ys7Jo+A5+wVtYLOHYfrXD+dDYfhWg+sJMVeZESu5RLA/WJL7ejpaDabG8Num83mFX82H4p7ecqmxtOuG6PtBXT9kI7jx5O0IkCBybLJ064bS9WmlbQRl4VNJGtSzv0keX5HeeySoXJ0vEgQSiIpt8hECwTXTRR31bSfhXttv5QyjIo8Ap6zV2TFYcgDK8mRdEbkWg9CZ9mW6mdHV35sbIy5uTmmp6ep1+vbvrR6EtphONiAtGuJ/nrq00ttPF8SIlERmLrg2GQ5VfXUSRtxWdlEsibl3CPJ8zvqY/eXZrUdn4mygYogROJ4IWVL31VpVlbutf1SyjBKsmLQ5mwmC4GKrJFnRJIlyYxI7vBm15a6lB29jf72b/+W8fFxAD772c8muqD9TpbqqZM04rK0iWRRiSvJ85vUsS8tzXKiOPp0cKywq9KsLN1r+6WUIQtcywZi0mQlUJE18oxIsvRnRExd2TLnbdiMSF5JEJNFW2o7dvRkveAFL9j47+PHj3PkyJEtv5iUknPnzo12dfuUrNRTJ2XEZXETyZISV5LnN+lrN+rSrKzda/ullCHtNLo+Z5banG84uH5sJB2sWRyb3H3w6lp3yLIUqMgaeUYkWXoZkUcXO30l4/Eg9mpRRxGC66dLA2dE9qKSICv7TpZsqcsx8BU6fvz4RtlePysrKxw/fjwv1dshWSg/ScqIy2o5Ulb6LZI8v3tx7Ub5bGTtXtsvpQxpptH1+dqjy5xZ6oAEoYCM4NyyzULT5TknJnaV3byWI/ZZC1TsBaM0aJN6J/dft9mqFfdguwGqIpitWsw3nWuiBFAIwVjJYOGRZdZsnwM1i5qh0fVCfrjQpl7UeeaxsYHXnXQlQdb2nazYUpdjYEuh18t0Ke12G8vafj5RTjZJyojLcjlSFhzeJM9v1q5d1ta7X0oZ0oqUkgfON3lwrknRUKkU9I3z23J8HpxrUrF0nntifOBznEfssxeoSJpRG7RJvZN7181QFR5ZbNPoy7TUivo1UwIopWS14zFTNZmumDRsn0bXRxOCm2bKCAFrtsfhemGg/SHJSoKs7jtZsKUux45X/aY3vQmIf9m3v/3tFIvFje+FYcjXvvY1nva0p418gTlXj6SMuLwcKVmSPL9Zu3ZZWy/sj1KGtNJ2Ax6aa6ApgsnyxUCfqamYZZXzazan5hvcdqi67Tyxy5FnWmKyFqhIkiQM2qTeyUEY0XR81ro+jhdRLWgbx11seTSdgLGivu9LAHsO5KF6EVNb73GKJKoS9zi5QTSUA5mUw5vvO1eHHV/5b3/720B8oe6//34M42JEyTAMnvrUp/LLv/zLo19hzlUlCSMuL0dKliTPb9auXdbW2yPrpQxppdn1WbN9ZmrbV0eMlQwWGg7Nrj+Q45RnWmKyGKhIgiQN2iTeyaoiWG572F7AwfrFoLipqUxV4oCCXHcgBiFrhn2/4y+EoGhsflaHdfyTcnjzfefqsOMz2VPT+/+3d/fhUVV3HsC/d+6d95lMQiSvBER5EeVFpZYGdwutVizUJT5d6roq6FrXdqGC1bW25alan6f41iI++lisGnbtuqy6vlWtGrXACmgBoSUui6BIfEkChGSSeb9z79k/hhkS8p7MnZk78/08Tx7IzJ3kzJlzb87vnnN+57rrrsO6deu4X9MomWG+b1K6O3GcjmQsI+vXbJ+d2crbnZmnMuS0Ex+1EALRuJ66o2xXLBDi5PPDwZGWBLPeqEg3ozu0xtxYEQM/LQ3yfB/MlhTByMDfiICX153sGHZLra+vN6IcBcXIjE5GSXcnjtORjGVk/ZrtszNbeck4RU4rip02NHeEYZUt6IrEoQsBiyTB61CgajpK3XYUDbNNcKQlofuNii87QnBaFUgSIAQQVuOj3o/NLDLRoU3n32RNFyjx2CGHVRwLROB1WKFYLIjribV/XqcVRU4rNH14wZPZkiIYHfinO+DldSc7RnTW7dy5E8888wyampoQi8V6PPf888+npWD5ysiMTmbD6UjGMrJ+zfbZma28RjLTaHe6eewKxo1xovELPyQARa6Tazma2kIQAGaNLx52h5QjLSf5nFaML3Vhx6E27G/pQiwuYFMk1JQ6cXYOrWcxktk6tIpsQbHDCp/DivZQ7EQa7jgUScJpXjuKnTZIJ44b7s81U1KETMxQSGfAy+tOdgz709u4cSOWLl2KBQsW4M0338Qll1yCjz76CK2trbj88suNKGPeMDKjk1lxOpKxjKxfs312ZiuvEcyS3cpIHrsVPpeCo51RfNamQoeABRIcVgvGFtnhtg2/Hsw8JTTd/GEVTW0huG0KZo0vhixJ0IRAJKahqS2EIoc179ua2Tq03cs7qczTa+PXFn9kROU1Y1IEM81Q4HUnO4bdi/jVr36FtWvXYvny5fB6vVi3bh0mTpyIG2+8EZWVlUaUMW8YldGJKBsKeeTCjMyU3coowZiGtmAUpW47dJG4440T65pssgWlbjvagtERrbkwusNlhvOte4e2e5IBAIALOZcMwChm69B2L2+LP4ISlw12mwxVS1wfRlpesyZFMNMMBTMFevli2C3q448/xqJFiwAksukFg0FIkoSbb74Z3/zmN3HXXXelvZD5wqiMTpQ5Zui8ZAJHLswlE9mtzHBuqHENzf4wrLKEmdXFiMY1aAKQpcQNrKOBCFr8YahxDcihDpeR51s6Pzdm+TrJbB1ao8pr1qQIZpqhYKZALx8Mu1WUlJSgq6sLAFBdXY3GxkbMmDEDHR0dCIVCaS9g3hmgHY80oxNlBoOFBI5c9GSGgMHoDq1Zzo2YJhCOaih22SBJEhzWnu/VaVXQEYohpg0/g1hSujtcRp5v6f7cmOWrJ7N1aI0qL5MiGM9MgZ7ZDbuWv/71r6OhoQEzZszAkiVLsHLlSrzzzjtoaGjARRddZEQZ80Yyo1N7MAZnce+q7wjFUOK0DTujExmPwUKC2fblMJpZAgYjO7TJcyMQUeGwyXDIFmhCoLkjnHPnhk1O7M0SVrU+R/XDqgaXXYFNzo22a+T5ZsQ1jR3a3szWoTWqvEyKQPli2K344YcfRiQSAQD8/Oc/h9VqxbZt2/Dd734Xq1evTnsB84nHrmBqpRc7Dh3H0a4I7NbEJmtCCETVRMdmapXXVBfZQsBg4SROxTnJ6GDaDPuTJM+No11R6EKguSOCuBBQJAlFLivCqp5T54ZVkVHpc6C1K4IjnRE4rBZYJAm6EIioOqyKhHKvA1YlNzpcRp1vRl3T2KGlTDDbGjLKL8Pu2YwZMyb1f4vFgttvvz31fTgcTk+p8pQkSTinyocjnRHsaWpHR/jkHiLFTgXnji/B2ZW50cGgkxgsnMSpOAlGB9Nm2Z8kGNPQdDyE48EYNF2H13EyU2hbIArZYoHSJmHiaZ6cODfcNhk1pS74Iyq6whEcPBqBqmmwyjJqSpzwOu0Yf5orZzr2Rp1vRl3T2KE1PzNMPQbMt4aMejJLO+tLWv6SRaNRPPLII7jvvvvQ0tKSjh+Z94QQiOt6KuWnECOfU0/GYrBwEqfiJBgZTJtpf5JksgWhC4z1ngzIkplCj3SFR5VsId0kSUKJ24ajXVE0HQ9BkSU4FRk6EoGwAFLrn3KBUeebkdc0dmjNyyxTj5PMtoaMEszWzk415L9k0WgUd955JxoaGmCz2XDbbbehrq4O9fX1+PnPfw5ZlnHzzTcbWVbTE0Lgwy/92N/SBUlKbDiXzAchSRbsb+lCuc+Br00sLZgT3wx3HRgsnMSpOAlGdTzNtj9J92QLfUlHsoV0EkLg8+NhRFQdRU5bj03IYQciqo7Pj4cxrtiZE9cho843o69p7NCaj1nX8ZptDVmhM2s7627Ire0Xv/gF1q9fj4svvhjbtm3DkiVLcN111+G9997Db37zGyxZsgSynN+dpdEKROP4S1MH2gIxuB0KShx2KBYJcV0gEIujLRDDnsMdmF7lK4h05Ga568Bg4SROxUkwquNptv1JzJZsIbmXnscuY3K5J5GOXAdkS2KUrLkjnFN76Rl1vmXimsYOrXlwHS9lQr60syFf1Z599ln8+7//O/7u7/4OjY2NmDlzJuLxOP7yl7/k9BvMJf5QDIePB2FVLCh2WqHGBSJxHbIkodhpRUzVcfh4EP5QLCf+aBvJTHcdGCz0xKk4xnU8zbY/SfdkC0e7oihyKrBaLFB1HZ3hOKxybiVb6L6XngQJDqVnPeTiXnpGnG+8plF3XMdLmZAv7WzIJfv8888xe/ZsAMD06dNht9tx880388I6DKGYhnBMQ7HLgiOdEQRjGoQQkCQJbpsMRZYQCGkIxbRsF9VQZrzrwGChp0KfimNUx7P7SJbdakFY1VLrIJ1WOeemhSaTLai6DiEAf0hFQMShSBLGem2QJORUsgUAqb3yBESvEadc3UvPiPON1zRK4jpeyoR8aWdDDpw0TYPNdjJKVBQFHo/HkELlK5dNgSxJ+Px4GC6HApdVhixJ0IRAZzSOUCQOn8sKly13I+10MOtdh0IPFk5V6FNxjOh4JkeyPjkahC4EOkNqj/TeFknCmWXunAlEugeQgYiKUrcdkpTYzDusxuFxWHNq5CK5l16zPwLFIiEYiUODgAwJboeCuC5Q6srNvfSMON94TSPAfDdsyJx6tDPlRDvTBWSLudrZkK/CQghce+21sNvtAIBIJIIf/OAHcLvdPY57/vnn01vCPFLkVOBxWNHSGYHLLkON64if2McJQiCsahjncKHImd+dUTPfdSj0YIF6SnfHM5n1rfXjNnSEVFT6HPDZFIRjGg60BlDssmL26SU51bHtFUBqiQCyqsSZcyMXHruCmjEuNH7hhyQBpR473FYZUVXHZ8dDEAKYNa64oM5xXtPIbDdsyJxOtrNAaoZCsp35XFZIEnBmmSfn29mQr5bLli3r8f3VV1+d9sLkO0mSUO6zoy0YRUdIRSyuQ4eABRJsigUlbhvKi+w51SkyArPUUT5JZ8dTCIH2YAzlRXaUee3wh1T4wyoUScKUcg8kCegIxXIm61uSmUYuXHYZZUV2hKMaAtE4usICkkWCx6bAaZfhduT2H22idDPjDRsyn5PtLJpqZ0VWBRFVw0epdjYm59vZkP/a19fXG1mOgqDpAiVuG4ocCsKxOOw2K2QAGgBd0+F1KCh226DpuZG61yjMUkfUt+Q01upiV59TGaJxPSensQLmGLkIxjTEVB3Tq3w4eKQLX3SEEdMEbLKEsmInJpV5EY3pOVm/REYx6w0bMpeT7cyBsV47OkMqOiMn2lmFBxIkU7Qz/mXIINkiIRrTcJrHjgqfE+2hGFRNwCpLGOOyQdV0xFQNsiV3G0w6MKMTUd+6T2OVJKnXesdcnsZqBnFNR2dERUdYhdOm4JwqHySLBKELROM6jgVj0ATrlwqLmW/YkHmcbGfOPtfSRVVztLPcLVneklLZ48q8jtTFya5Y0OwPA6IwggVmdCLqjdNYjSVbJLQFYgjFEhk9o3ENmgBkRYLXYUWzPwxx4ppMVCh4w4YyoUc7gwSXVQG6dfXM0s4YOGWQpguUemyQwxKOBWIociqJTCK6jmOBGDwOK0pc1ryfqpdkpnURRJnAaayZkEhD/nl7CIFuWfU8DgVxXYfbzrqlwsIbNpQJ+dLOcrt0eUaRLShyWDG+xIWxXhsiMQ3t4RgiMQ1jvTZMGONCkcOa840mnZLrIopdNnjsCoMmKmjJaaxuu4JmfxiRE1NmIqqGZn+Y01hHSdMFHDYZ/nAcTcfDsFgkuG0KLBYJTcfD6AjHYbfKBXPzigg4ecOmPRTr8/n2UAylXhtv2NCo5Es744hTBnW/m3zmWE+vecQtnRHeTSbT0HUdRwMxRFQNDquMsR4bLJbCCfqNwmmsxpEtEiIxDT6nFSVuKzqCKgLROBSLhJoxDmg6EC2AdaZE3XHdMWVCvrQzBk4Z1L3RtHRGUOKywW5LbPrV0hkxTaMh+qw9hB2H2vD58TBicQGbImHcGCcumFiKmhJXtotnepzGaiQJAoAEAJIEQAekRMAvgIJZZ0rUXfKGzafHAvjSH0FUTWyGW13swIRSD2/YUFrkw41BBk4Zlg+NhgrbZ+0hvPbX5tQ+DE6bfGK/jyCOdsWwcGYlg6c0MEN6b7PRdAGnzYKu4yoC4ThKvXYUOZXEBrhtYbgdCqqLHZyqR4VNnPxX8FSgNDP7jUH+Vc4CszcaKly6rmPHocQmiVPKvanHvQ4LvA4rPmrtws5Pj6Pa5+C0Pco5skVCOKbDa1cwxmVDVySOYCQOy4nZADFNQySm5+RUPSEE/2aQYfxhFY1f+BGMxnGaxw6bYkEsrqO1M4pAVMP0ah9v7FLamPnGoDlLnQfM3GiocB0NxPD58TAqfY4+n6/0OfBZWwhHA4lN7ohyj0htCRGN6723hJBy7xa7P6z2mqUwxmPDhFLOUqDRE0LgcFsQwWi8RzbP5HnS7A+j6XgQ06t8DNap4GX1lvCaNWtwwQUXwOv1oqysDHV1ddi/f/+Ar9mwYQMkSerx5XCYr4MmhEAgGkdHKIZANA7B8XBTKPTPLaJqiMUFnP0kMHFYZcTiiSxwRLlG0wVKPHZ4nVYcC0QRi+vQhUAsruNYIAqv04pitz2npuolRwJa/Il1sGO9drjtClr8ETR+4Yc/rGa7iGRyyY1JS1y2Pp8vcdlSG5MSFbqsDnls3rwZy5cvxwUXXIB4PI6f/exnuOSSS/C///u/cLvd/b6uqKioR4BltjsgvHtoTvzcEoGRTZEQjmnwOnrfd4moGmyK1OceDUTZpsgWFDussFokHAx24XBbEKomYJUllPvsqC5xwm1TcmZLCI4EUCZ035i0L2bZmJQoE7IaOL3++us9vt+wYQPKysqwa9cufP3rX+/3dZIkoaKiwujiGaL7POISly01j7jFH0FXJM55xDmKn1vCWI8N48Y4caA1CK+j9/tt9kcwpcKDsZ6+71wSZZPbJsNulfGXzzvgtCqYXGGDLEnQhEBM1XHwSABfnViaM1tCDGckgFO/aaTyZWNSokzIqbPA7/cDAMaMGTPgcYFAABMmTEBNTQ0WL16MDz/8sN9jo9EoOjs7e3xly6l3Dx1WGRZJSt09DEbjaDoeLLjpX7mOn9tJFosFF0wsRbErkQiiK6JC1XR0RVR81NqFYpcVXzl9DBNDUM4SqZRhEhyKDJdVgUORcSJBeU6tceJIAGVCvmxMSpQJOdO70XUdq1atwoUXXojp06f3e9zUqVPx5JNP4qWXXsLvf/976LqOuXPn4vPPP+/z+DVr1sDn86W+ampqjHoLg+p+91AIgVAsjq6IilAssVaG84hzE+d/91RT4sLCmZWYXO5GRyiGprYQOkIxTKnwMBU55bRgTENM1TFrXDHGem2IxDS0h2OIxDSM9dowa1wxojE9Z87l7iMBfeFIAKVDco9Jt11Bsz+MiKpB0xNrVZv9Ye4xSdSNJHLkNvkPf/hD/PGPf8S7776LcePGDfl1qqpi2rRpuPLKK3H33Xf3ej4ajSIajaa+7+zsRE1NDfx+P4qKitJS9qHqCMWw89N2OG0yWjvDONIVgxrXYFVklHltKPM6EVE1fOX0EhT300nPlkJOhZv83MZ67bD08Z41XeBYIJqTn5uRdF3H0UAMEVWDwypjrMfGkSbKad3PZQlA+EQHUbZIcFpl6AI5dS4LIbD3RGKI7muckpr9YVQWO7jGidKir3W8pV4b95ikvNfZ2Qmfzzek2CAnJkWvWLECr7zyCrZs2TKsoAkArFYrzjvvPBw8eLDP5+12O+x2ezqKOWqKbEFU1bC/pQvHgpHUhvVCB1o7wzjNHcXppa6cu3tY6EkROP+7bxaLhSnHyVROPZddtp5/AtW4llPncnIkoCsSR7M/jBKXDVbZAlXT0R6K5fRIQCHfbDMr7jFJNLisBk5CCPzoRz/CCy+8gE2bNmHixInD/hmapmHv3r1YuHChASVML5fVgo6wiv9r8WOs1wGXQ4EiS4hriWl7/9fiR4nbCpc1N/5oA0yKAJyc/93fXd/2UAyVxQ7O/ybKcWY8l31OK6ZX+3rdvKosduTsSIAZb7Yx0EvgHpNEA8vq2bF8+XI8/fTTeOmll+D1etHS0gIA8Pl8cDoTf9SWLl2K6upqrFmzBgDwy1/+El/72tcwadIkdHR04P7778fhw4fx/e9/P2vvY6iCMQ3+iAqnVUZqguSJf4UAnFYZ/rCKYD+pnjONqXATzHzXl4hOMuu5bKaRADPebDNjoEdE2ZHVwOnRRx8FAMyfP7/H4/X19bj22msBAE1NTT3WTbS3t+OGG25AS0sLSkpKMHv2bGzbtg1nn312poo9Yp1hFdGYhrMqihCIxhGIxBGGgAwJJS4rakqc8IdVdIbVPlM9ZxpT4Z5kxru+RNSbWc9lM4wEmPFmmxkDPSLKnqxP1RvMpk2beny/du1arF271qASZYAEOGwyfE4ruiJxqJoOq2yB16EgEteBSO7sAs9UuD2Z6a4vUX84JYnnslHMdrPNjIEeEWVX9q9cBaTIaUWx04ZmfwRWi4RAJA7txIiTx6FA1QVKXTYU5cjdLSZF6M0Md32J+sMpSSfxXE4/s91sM1ugR0TZVzg93hzgsSsYN8aJL9vDaGoLQbJIcNsVSBYJTW0hfNkexrhSZ85coLkpHlH+SE5JavFH4LYrGOu1w21X0OKPoPELP/zh3BntJnPKxL5TQggEonF0hGIIROOj2njcbIEeEWVfbvTQC4jHbkVZkR2RWByBaBydYQGLRYLXIcNhU+C25c5dX7MupCainjgliTLB6KyF6R4x5awKIhouXg0yKBjTEFU1TK/yweeyQY1rCKsa1LiGYrcN06t8iKpazuxaD5xcSF3hcyAYjeNYIJrofBU7uGiWyCSGMyWJaKSSN9vcdgXN/jAiJzYYjqgamv3hUd1sM2LElLMqiGi4OOKUQXFNR2dERUdYhcum4JzqxN1dIQSiqo5jwRg0kXvTAriQmijz0pnEgVOSKFOMyFpo1IgpZ1UQ0XAxcMog2SKhLRBDKBZHVbGr55MO4MuOEIQuIFty7yLNhdREmcMpSWRm6b7ZZmQSB7Ompyei7GBPOOMGWcgqjXyhKxGZnxH7yhi99oToVOm82Wb0iClnVRDRUPH2YgZpukCJxw6v04pjgQii8cT872hcw7FABF6nFcVuOzSdwRNRITp1SpLDKsMiSakpScFoHE3Hg8POJGbk2hMio2UiW18y0Ct22eCxKzwXiKhPHHHKIEW2oNhhhc9hRXsohs6QiriIQ5EknOa1o9hpg3TiOCIyh3SuReKUJKLeOGJKRLmCgVMGdb/4TyrzIKxq0DQBWZbgtMqJPwq8+BOZRrrXInFKElFvTOJARLmCgVMGdb/4t/gjKHHZYLfJUDU9lWKVF3+i9EvnqFCSEWuRMpHEgYleyIw4YkpEuYB/PTOMF38qdEYEMQNJ96gQ0HMtUkWRA2FVQzAah2yRUFHkQEtnZETpkTklKXMy3Q5p9DhiSkTZxsApC3jxp0JlRBAz2O9L96gQcHItkk224OOjAfhDKuJCQJEk+FzWEa9F4pSkzMh0O6T04YgpEWUTrz5Zwos/FRqjgpj+GLVpJtBzM+tITEeRU0kFOEe7YuiMxFHiso5oLRJHpY2V6XZIRJTEkW7zY8+diAxnZBDTHyMz1PW3mbVdkTHWK496M2uOShsjG+2QiAjgSHe+YOBERIYzMojpj9EZ6ozezJqj0umXjXZIRMSR7vzBDYOIyHDGBzG9GblpJjezNqdstEMiKmxGbWxO2cHAKUuEEAhE4+gIxRCIxnnCUF4zMojpTzJDXXso1ufz7aEYSr22EWWoS25mXVPiQqnHjkhMQ0c4hkhMw2leO2pKXCh2WLmZdY7JRjskosI2nJFuyn2ci5AFnOdKhSYbabaNzFDHzazNieneiSjTONKdX3hbLcOS81ybO8KQLIDDaoFkAZo7wmj8wg9/WM12EYnSLhnEuO0Kmv1hRNTE1LaIqqHZHzYszXYyQ12Fz4FgNI5jgWhiukSxY1Rzyru/nxZ/BBZIcNkUWCBxM+sclq12SESFiyPd+YUjThmUnOd6tCsKXQg0d0RSe78UuawIqzozOlHeylaabaMy1Jk1bXihp8M16+dGRObEke78wsApg4IxDU3HQzgejEHTdXgd1tTUobZAFLLFAqVNwsTTPMzoRHkpW2m2jcpQZ7a04ZwmnGC2z42IzIsbm+cX9s4zSI0npoMIXWCs9+RdB7siw+6RcaQrjBZ/GGpcAxg4UZ7KtzTbZnk/TIfbk1k+NyIyP4505w/+1cigmCYQjmoo7iezitOqoCMUQ0xjhj0iSh9u/EpElF0c6c4PDJwyyCYnFpCH1cTmitG4Bk0AspQYdQqrGlx2BTaZJxFRupltbU86y8uNX4mIso8j3ebHTy+DrIqMSp8Dh9tC2PtlB6AjkdfwxL9j3Q6MK3bCqnCBYKExW6febMy2tifd5WU6XCIiotFj4JRBbpuMEo8NjV/6AV2CkAAJSPyrA11RFWNGuCEnmZfZOvVmY7a1PUaUt3s6XIe19/WF6XCJiIgGx7+SGSZBgsMqo8JnR5XPgXJv4t8Knz3RoREcZSgkyU5ycu+fsV57am8g7us1eqeu7XFYZVgkKbW2JxiNo+l4EELkxrpCo8qbTIfbHor1+Xx7KIbSArtpI4RAIBpHRyiGQDSeM22AiIhyF0ecMigY0xBVNUwq8+DgkS4c6YxC1QSssoRynx2TyryIqhrXGRQILtg3ntnW9hhVXqbD7YmjvERENBLZ7ykUkLimoyOiojOswmlVMLnCBlmSoAmBmKqjLRiDynUGBcNsnXozMtvaHiPLy3S4CWabuklERLmDvbEMki0S2gNRhGIaqopdPZ90AF92hKDrOmRLYdz1LXRm69SbkdnW9hhd3kJPh8tRXiIiGo3c6C0UlEH+GI9yjRPn7ZtH905yX3KtU29GZlvbk4nyJtPhFrts8NiVggoQhjPKS0REdCqOOGWQpguUemyQwxKOdkVR5FRgtVig6jo6w3F4HFaUuKzQ9JEFO5y3by7JTnKLP9Lj7ndSeyiGymJHznTqzchsa3vMVl6z4SgvERGNBgOnDFJkC4ocVhQ5rGgPxeAPqQiIOBRJwlivLXUXdCQjDJy3bz7sJGeG2db2mK28ZmK2qZtERJRbGDhlUPcRhjPHehBWNWi6gGyR4LTKaOmMjGiEgfP2zYud5Mww29oes5XXLDjKS0REo8HAKYO6jzC0dEZQ4rLBbpOhajpaOiMjHmFgdjZzYyc5M5Jre8zCbOU1A47yEhHRaPCvcoYZMcLAefvmZ8ZOshCCwR6ZDkd5iYhopMzVU8sT6R5h4Lx9yjQmIiEz4ygvERGNBAOnLEnnCAPn7VMmMREJ5QMzjvISEVF2cQgiDyTn7bvtCpr9YUROJJ2IqBqa/WHO26e0OTURicMqwyJJqUQkwWgcTceD3D+MiIiI8g5vt+UJztunTGAiEiIiIipU7NnkEc7bJ6MxEQkREREVKgZOeYbz9slITERCREREhYq9GyIasmQikvZQrM/n20MxlHptTERCREREeYeBExENGROREBERUaHinC4iGhYmIiEiIqJCxMCJiIaNiUiIiIio0DBwIqIRYSISIiIiKiRc40RERERERDQIBk5ERERERESDYOBEREREREQ0CAZOREREREREg2DgRERERERENIisBk5r1qzBBRdcAK/Xi7KyMtTV1WH//v2Dvu7ZZ5/FWWedBYfDgRkzZuC1117LQGmJiIiIiKhQZTVw2rx5M5YvX4733nsPDQ0NUFUVl1xyCYLBYL+v2bZtG6688kpcf/312L17N+rq6lBXV4fGxsYMlpyIiIiIiAqJJIQQ2S5E0tGjR1FWVobNmzfj61//ep/HXHHFFQgGg3jllVdSj33ta1/Dueeei9/+9reD/o7Ozk74fD74/X4UFRWlrexERERERGQuw4kNcmqNk9/vBwCMGTOm32O2b9+Oiy++uMdjCxYswPbt2/s8PhqNorOzs8cXERERERHRcORM4KTrOlatWoULL7wQ06dP7/e4lpYWlJeX93isvLwcLS0tfR6/Zs0a+Hy+1FdNTU1ay01ERERERPkvZwKn5cuXo7GxERs3bkzrz/3pT38Kv9+f+vrss8/S+vOJiIiIiCj/KdkuAACsWLECr7zyCrZs2YJx48YNeGxFRQVaW1t7PNba2oqKioo+j7fb7bDb7WkrKxEREVE2CSEQjGmIazoU2QK3TYYkSdkuFlHey+qIkxACK1aswAsvvIB33nkHEydOHPQ1tbW1ePvtt3s81tDQgNraWqOKSURERJQT/GEVe7/wY+eh49j5aTt2HjqOvV/44Q+r2S4aUd7L6ojT8uXL8fTTT+Oll16C1+tNrVPy+XxwOp0AgKVLl6K6uhpr1qwBAKxcuRLz5s3Dr3/9ayxatAgbN27Ezp078dhjj2XtfRAREREZzR9W0fiFH4GICqdVgcMqQdeB5o4wuiJxTK/2wee0ZruYRHkrqyNOjz76KPx+P+bPn4/KysrU13/913+ljmlqakJzc3Pq+7lz5+Lpp5/GY489hlmzZuG5557Diy++OGBCCSIiIiIzE0LgcFsQR7siCMU0fHI0gP9r6cInRwMIxTQc7Yqg6XgQObTLDFHeyal9nDKB+zgRERGR2QSicWz+vyNo7YogrgFFTgVW2QJV09EZjkORgXKvA/POKoPHnhNL2IlMwbT7OBUSIQQC0Tg6QjEEonHeISIiIqJ+qXENzf4IVE1grNcOuyLDIkmwKzLGeu1QNYHmzgjUuJbtohLlLd6SyAJ/WMXhtiCOB2KI6wKKRcIYjw0TSt2cm0xERES9xDSBUCyOYpetz+edVhkdoRhiGm/EEhmFgVOGJRd2BqNxlLhssCkWxOI6WvwRLuwkIiIaQCGn4bbJEpx2GRE1Dq+jdz8hrMbhssuwyYVRH0TZwMApg5ILO4PROCp9ztTjDquMSp8Tzf4wmo4HMb3KVzB/CIiIiIai0GdrWJVEX+FIZxTHAhF4HVYoFgviuo6uiAqrLKOsyA6rIme7qER5i4FTBgVjGo4HYijpZ5i9xGVDW1cMwZjGhZ1EREQncLYG4LbJGD/GhbgmoAuBzpCKuIhDkSSc5rVDgoQJpS64bQyciIzC3nkGxTUdcV3ApvSdk8MqWxDXBeKanuGSERER5SbO1kiQJAkTSt3oisQRiKgo9dggQ4IGgUhMg8dhxfgx7ryuA6JsY1a9DFJkCxSLhFi878BI1XQoFgmKzI+FiIgIGN5sjXznc1oxvdqHymInhA5E4jqEDlSVOAti1I0o2zjilEFum4wxHhta/JEed82S2kMxVBY7OMxORER0Amdr9ORzWjGj2lewSTKIsomBUwZ1H2Zv9odR4rKlNq9rD8XgtiscZiciIuqm+2wNh7X3jcVCnK0hSRLXQhNlQeFcZXJEcpi9wudAMBrHsUA0MW+72MFhdiIiolMkZ2u0h2J9Pt8eiqHUa+NsDSIyHG9XZAGH2YmIiIaGszWIKFcwcMoSDrMTEeWfQt6g1UjJ2Rqn7uNUWezA+DGFsY8TUabxetYbe+5ERERpUOgbtBqNszWIMofXs74xcCIiIholbtCaGZytQWQ8Xs/6x+QQREREo3DqBq0OqwyLJKU2aA1G42g6HoQQIttFJSIaEK9nA2PgRERENArcoJWI8gWvZwNj4ERERDQK3KCViPIFr2cDY+BEREQ0Ct03aO1LIW7QSkTmxOvZwArzXRMREaUJN2glonzB69nAmJomS5gbn4goP3CDViLKF7yeDYyBUxYwNz4RUX7hBq1ElC94PesfA6cMY258IqL8xA1aiShf8HrWNwZOGXRqbvykZG78Zn8YTceDmF7lK/iGSURkRtyglYjyBa9nvTE5RAYxNz4RERERkTkxcMog5sYnIiIiIjInBk4ZxNz4RERERETmxB56BjE3PhERERGROTFwyqBkbny3XUGzP4yIqkHTBSKqhmZ/uOBz4xMRERER5SqmysiwZG78T48F8GVHBNG4DrtiQXWJAxNKPUxFTkRERESUgzjilE3SyX+FyGpJiIiIiIhoABxxyrDuG+Ce5ranNsBt7YwiENW4AS4RERERUQ7iiFMGnboBrsMqwyJJqQ1wg9E4mo4HITj8RERERESUUxg4ZRA3wCUiIiIiMicGThnEDXCJiIiIiMyJgVMGcQNcIiIiIiJzYg89g7gBLhERERGROTFwyiBugEtEREREZE5MR55hyQ1wD7cFcTwQQ1wXUCwSKosdGD/GzVTkREREREQ5iIFTFvicVsyo9iEY0xDXdCiyBW6bzJEmIiIiIqIcxcApSyRJgsfO6iciIiIiMgOucSIiIiIiIhoEAyciIiIiIqJBMHAiIiIiIiIaBAMnIiIiIiKiQTBwIiIiIiIiGgQDJyIiIiIiokEwcCIiIiIiIhoEAyciIiIiIqJBMHAiIiIiIiIaBAMnIiIiIiKiQTBwIiIiIiIiGgQDJyIiIiIiokEwcCIiIiIiIhqEku0CZJoQAgDQ2dmZ5ZIQEREREVE2JWOCZIwwkIILnLq6ugAANTU1WS4JERERERHlgq6uLvh8vgGPkcRQwqs8ous6vvzyS3i9XkiSlNWydHZ2oqamBp999hmKioqyWpZ8xPo1HuvYWKxfY7F+jcX6NRbr11isX2PlUv0KIdDV1YWqqipYLAOvYiq4ESeLxYJx48Zluxg9FBUVZb3R5DPWr/FYx8Zi/RqL9Wss1q+xWL/GYv0aK1fqd7CRpiQmhyAiIiIiIhoEAyciIiIiIqJBMHDKIrvdjjvuuAN2uz3bRclLrF/jsY6Nxfo1FuvXWKxfY7F+jcX6NZZZ67fgkkMQERERERENF0eciIiIiIiIBsHAiYiIiIiIaBAMnIiIiIiIiAbBwImIiIiIiGgQDJwMtGXLFlx22WWoqqqCJEl48cUXB33Npk2bcP7558Nut2PSpEnYsGGD4eU0q+HW76ZNmyBJUq+vlpaWzBTYZNasWYMLLrgAXq8XZWVlqKurw/79+wd93bPPPouzzjoLDocDM2bMwGuvvZaB0prPSOp3w4YNvdqvw+HIUInN5dFHH8XMmTNTmyvW1tbij3/844CvYdsduuHWL9vu6Nxzzz2QJAmrVq0a8Di24ZEZSv2yDQ/dnXfe2auuzjrrrAFfY5a2y8DJQMFgELNmzcIjjzwypOMPHTqERYsW4Rvf+Ab27NmDVatW4fvf/z7eeOMNg0tqTsOt36T9+/ejubk59VVWVmZQCc1t8+bNWL58Od577z00NDRAVVVccsklCAaD/b5m27ZtuPLKK3H99ddj9+7dqKurQ11dHRobGzNYcnMYSf0CiV3Wu7ffw4cPZ6jE5jJu3Djcc8892LVrF3bu3IlvfvObWLx4MT788MM+j2fbHZ7h1i/AtjtSO3bswPr16zFz5swBj2MbHpmh1i/ANjwc55xzTo+6evfdd/s91lRtV1BGABAvvPDCgMfcdttt4pxzzunx2BVXXCEWLFhgYMnyw1Dq909/+pMAINrb2zNSpnxz5MgRAUBs3ry532O+973viUWLFvV4bM6cOeLGG280unimN5T6ra+vFz6fL3OFyjMlJSXi8ccf7/M5tt3RG6h+2XZHpqurS0yePFk0NDSIefPmiZUrV/Z7LNvw8A2nftmGh+6OO+4Qs2bNGvLxZmq7HHHKIdu3b8fFF1/c47EFCxZg+/btWSpRfjr33HNRWVmJb33rW9i6dWu2i2Mafr8fADBmzJh+j2EbHrmh1C8ABAIBTJgwATU1NYPe4acETdOwceNGBINB1NbW9nkM2+7IDaV+AbbdkVi+fDkWLVrUq232hW14+IZTvwDb8HAcOHAAVVVVOOOMM3DVVVehqamp32PN1HaVbBeATmppaUF5eXmPx8rLy9HZ2YlwOAyn05mlkuWHyspK/Pa3v8VXvvIVRKNRPP7445g/fz7ef/99nH/++dkuXk7TdR2rVq3ChRdeiOnTp/d7XH9tmOvIBjbU+p06dSqefPJJzJw5E36/Hw888ADmzp2LDz/8EOPGjctgic1h7969qK2tRSQSgcfjwQsvvICzzz67z2PZdodvOPXLtjt8GzduxAcffIAdO3YM6Xi24eEZbv2yDQ/dnDlzsGHDBkydOhXNzc2466678Ld/+7dobGyE1+vtdbyZ2i4DJyoYU6dOxdSpU1Pfz507Fx9//DHWrl2Lp556Kosly33Lly9HY2PjgHOUaeSGWr+1tbU97ujPnTsX06ZNw/r163H33XcbXUzTmTp1Kvbs2QO/34/nnnsOy5Ytw+bNm/vt3NPwDKd+2XaH57PPPsPKlSvR0NDABAQGGEn9sg0P3be//e3U/2fOnIk5c+ZgwoQJeOaZZ3D99ddnsWSjx8Aph1RUVKC1tbXHY62trSgqKuJok0G++tWvMhgYxIoVK/DKK69gy5Ytg95V668NV1RUGFlEUxtO/Z7KarXivPPOw8GDBw0qnbnZbDZMmjQJADB79mzs2LED69atw/r163sdy7Y7fMOp31Ox7Q5s165dOHLkSI/ZEJqmYcuWLXj44YcRjUYhy3KP17AND91I6vdUbMNDV1xcjClTpvRbV2Zqu1zjlENqa2vx9ttv93isoaFhwDnjNDp79uxBZWVltouRk4QQWLFiBV544QW88847mDhx4qCvYRseupHU76k0TcPevXvZhodI13VEo9E+n2PbHb2B6vdUbLsDu+iii7B3717s2bMn9fWVr3wFV111Ffbs2dNnp55teOhGUr+nYhseukAggI8//rjfujJV2812dop81tXVJXbv3i12794tAIjf/OY3Yvfu3eLw4cNCCCFuv/12cc0116SO/+STT4TL5RL/+q//Kvbt2yceeeQRIcuyeP3117P1FnLacOt37dq14sUXXxQHDhwQe/fuFStXrhQWi0W89dZb2XoLOe2HP/yh8Pl8YtOmTaK5uTn1FQqFUsdcc8014vbbb099v3XrVqEoinjggQfEvn37xB133CGsVqvYu3dvNt5CThtJ/d51113ijTfeEB9//LHYtWuX+Id/+AfhcDjEhx9+mI23kNNuv/12sXnzZnHo0CHx17/+Vdx+++1CkiTx5ptvCiHYdkdruPXLtjt6p2Z9YxtOr8Hql2146G655RaxadMmcejQIbF161Zx8cUXi9NOO00cOXJECGHutsvAyUDJ9Nenfi1btkwIIcSyZcvEvHnzer3m3HPPFTabTZxxxhmivr4+4+U2i+HW77333ivOPPNM4XA4xJgxY8T8+fPFO++8k53Cm0BfdQugR5ucN29eqr6TnnnmGTFlyhRhs9nEOeecI1599dXMFtwkRlK/q1atEuPHjxc2m02Ul5eLhQsXig8++CDzhTeBf/qnfxITJkwQNptNjB07Vlx00UWpTr0QbLujNdz6ZdsdvVM79mzD6TVY/bIND90VV1whKisrhc1mE9XV1eKKK64QBw8eTD1v5rYrCSFE5sa3iIiIiIiIzIdrnIiIiIiIiAbBwImIiIiIiGgQDJyIiIiIiIgGwcCJiIiIiIhoEAyciIiIiIiIBsHAiYiIiIiIaBAMnIiIiIiIiAbBwImIiIiIiGgQDJyIiGjENm3aBEmS0NHRke2iEBERGYqBExFRnrv22mshSRIkSYLVasXEiRNx2223IRKJDOvnzJ8/H6tWrerx2Ny5c9Hc3Ayfz5fGEveWDNBO/Vq9erWhv3coNmzYkCqPLMsoKSnBnDlz8Mtf/hJ+v39YP+vTTz+FJEnYs2ePMYXtx4YNG1BcXJzR30lEZDZKtgtARETGu/TSS1FfXw9VVbFr1y4sW7YMkiTh3nvvHdXPtdlsqKioSFMpB7d//34UFRWlvvd4PL2O0TQNkiTBYsncvcGioiLs378fQgh0dHRg27ZtWLNmDerr67F161ZUVVVlrCxERGQMjjgRERUAu92OiooK1NTUoK6uDhdffDEaGhpSz7e1teHKK69EdXU1XC4XZsyYgf/8z/9MPX/ttddi8+bNWLduXWp05dNPP+01VS85cvHGG29g2rRp8Hg8uPTSS9Hc3Jz6WfF4HDfddBOKi4tRWlqKn/zkJ1i2bBnq6uoGfR9lZWWoqKhIfXk8ntTvfPnll3H22WfDbrejqakJ7e3tWLp0KUpKSuByufDtb38bBw4cSP2s5OteeeUVTJ06FS6XC3//93+PUCiEf/u3f8Ppp5+OkpIS3HTTTdA0bcBySZKEiooKVFZWYtq0abj++uuxbds2BAIB3HbbbanjXn/9dfzN3/xN6r1/5zvfwccff5x6fuLEiQCA8847D5IkYf78+QCAHTt24Fvf+hZOO+00+Hw+zJs3Dx988EHqdUII3HnnnRg/fjzsdjuqqqpw0003pZ6PRqO49dZbUV1dDbfbjTlz5mDTpk0AEqN51113Hfx+f+qzvfPOOwf9LIiICg0DJyKiAtPY2Iht27bBZrOlHotEIpg9ezZeffVVNDY24p//+Z9xzTXX4M9//jMAYN26daitrcUNN9yA5uZmNDc3o6amps+fHwqF8MADD+Cpp57Cli1b0NTUhFtvvTX1/L333ov/+I//SI3GdHZ24sUXXxzVewqFQrj33nvx+OOP48MPP0RZWRmuvfZa7Ny5Ey+//DK2b98OIQQWLlwIVVV7vO6hhx7Cxo0b8frrr2PTpk24/PLL8dprr+G1117DU089hfXr1+O5554bdpnKyspw1VVX4eWXX04FXsFgED/+8Y+xc+dOvP3227BYLLj88suh6zoApOr7rbfeQnNzM55//nkAQFdXF5YtW4Z3330X7733HiZPnoyFCxeiq6sLAPDf//3fWLt2LdavX48DBw7gxRdfxIwZM1JlWbFiBbZv346NGzfir3/9K5YsWYJLL70UBw4cwNy5c/Hggw+iqKgo9dl2/7yIiOgEQUREeW3ZsmVClmXhdruF3W4XAITFYhHPPffcgK9btGiRuOWWW1Lfz5s3T6xcubLHMX/6058EANHe3i6EEKK+vl4AEAcPHkwd88gjj4jy8vLU9+Xl5eL+++9PfR+Px8X48ePF4sWL+y1L8ve43e4eX8eOHUv9zj179qSO/+ijjwQAsXXr1tRjx44dE06nUzzzzDP9lvXGG28ULpdLdHV1pR5bsGCBuPHGG/stW319vfD5fH0+9+ijjwoAorW1tc/njx49KgCIvXv3CiGEOHTokAAgdu/e3e/vE0IITdOE1+sVf/jDH4QQQvz6178WU6ZMEbFYrNexhw8fFrIsiy+++KLH4xdddJH46U9/Ouh7ICKiBK5xIiIqAN/4xjfw6KOPIhgMYu3atVAUBd/97ndTz2uahl/96ld45pln8MUXXyAWiyEajcLlcg37d7lcLpx55pmp7ysrK3HkyBEAgN/vR2trK7761a+mnpdlGbNnz06Nugzkf/7nf+D1elPfl5SUAEistZo5c2bq8X379kFRFMyZMyf1WGlpKaZOnYp9+/b1W9by8nKcfvrpPdZOlZeXp8o/XEIIAImpfABw4MAB/OIXv8D777+PY8eOpd5zU1MTpk+f3u/PaW1txerVq7Fp0yYcOXIEmqYhFAqhqakJALBkyRI8+OCDOOOMM3DppZdi4cKFuOyyy6AoCvbu3QtN0zBlypQePzMajaK0tHRE74uIqBAxcCIiKgButxuTJk0CADz55JOYNWsWnnjiCVx//fUAgPvvvx/r1q3Dgw8+iBkzZsDtdmPVqlWIxWLD/l1Wq7XH95IkpQKI0Zo4cWKf2d+cTmcqOBmOvsra12NDCer6sm/fPhQVFaUClMsuuwwTJkzA7373O1RVVUHXdUyfPn3Qel62bBna2tqwbt06TJgwAXa7HbW1tanX1dTUYP/+/XjrrbfQ0NCAf/mXf8H999+PzZs3IxAIQJZl7Nq1C7Is9/i5fSXXICKivnGNExFRgbFYLPjZz36G1atXIxwOAwC2bt2KxYsX4+qrr8asWbNwxhln4KOPPurxOpvNNmiShMH4fD6Ul5djx44dqcc0TeuR6CAdpk2bhng8jvfffz/1WFtbG/bv34+zzz47rb+rP0eOHMHTTz+Nuro6WCyW1O9fvXo1LrroIkybNg3t7e09XpNcd3ZqPW/duhU33XQTFi5ciHPOOQd2ux3Hjh3rcYzT6cRll12Ghx56CJs2bcL27duxd+9enHfeedA0DUeOHMGkSZN6fCUzIqbjsyUiyncMnIiICtCSJUsgyzIeeeQRAMDkyZPR0NCAbdu2Yd++fbjxxhvR2tra4zWnn3463n//fXz66ac9ppkN149+9COsWbMGL730Evbv34+VK1eivb19RCNG/Zk8eTIWL16MG264Ae+++y7+8pe/4Oqrr0Z1dTUWL16ctt+TJIRAS0sLmpubsW/fPjz55JOYO3cufD4f7rnnHgCJaYWlpaV47LHHcPDgQbzzzjv48Y9/3OPnlJWVwel04vXXX0dra2tqH6jJkyfjqaeewr59+/D+++/jqquugtPpTL1uw4YNeOKJJ9DY2IhPPvkEv//97+F0OjFhwgRMmTIFV111FZYuXYrnn38ehw4dwp///GesWbMGr776KoDEZxsIBPD222/j2LFjCIVCaa8jIiKzY+BERFSAFEXBihUrcN999yEYDGL16tU4//zzsWDBAsyfPx8VFRW90oPfeuutkGUZZ599NsaOHZtaXzNcP/nJT3DllVdi6dKlqK2thcfjwYIFC+BwONLwzk6qr6/H7Nmz8Z3vfAe1tbUQQuC1117rNRUvHTo7O1FZWYnq6mrU1tZi/fr1WLZsGXbv3o3KykoAiZG+jRs3YteuXZg+fTpuvvlm3H///T1+jqIoeOihh7B+/XpUVVWlgrwnnngC7e3tOP/883HNNdfgpptuQllZWep1xcXF+N3vfocLL7wQM2fOxFtvvYU//OEPqSmC9fX1WLp0KW655RZMnToVdXV12LFjB8aPHw8gsZHxD37wA1xxxRUYO3Ys7rvvvrTXERGR2UkiXRPPiYiIRkDXdUybNg3f+973cPfdd2e7OERERH1icggiIsqow4cP480338S8efMQjUbx8MMP49ChQ/jHf/zHbBeNiIioX5yqR0REGWWxWLBhwwZccMEFuPDCC7F371689dZbmDZtWraLRkRE1C9O1SMiIiIiIhoER5yIiIiIiIgGwcCJiIiIiIhoEAyciIiIiIiIBsHAiYiIiIiIaBAMnIiIiIiIiAbBwImIiIiIiGgQDJyIiIiIiIgGwcCJiIiIiIhoEP8PByzuDezY+YIAAAAASUVORK5CYII=", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "plt.scatter(Y_test, Y_pred)\n", + "plt.scatter(Y_test, Y_pred, alpha=0.2)\n", "plt.xlabel('Rating From Dataset')\n", "plt.ylabel('Rating Predicted By Model')\n", "plt.rcParams[\"figure.figsize\"] = (10,6) # Custom figure size in inches\n", @@ -4835,194 +4382,194 @@ " \n", " \n", " \n", - " 2010 M 35 Acceleration/Braking Defects\n", - " -0.496327\n", - " 0.339398\n", - " 0.053823\n", - " 0.143729\n", - " 0.072652\n", - " 0.114714\n", - " 2.375\n", - " infiniti\n", - " 2010 M 35 Acceleration/Braking Defects: I leased a new 2010 M 35. This was my 9th Nissan/Infinity vehicle since 1996. This vehicle has been a huge disappointment. The vehicle has demonstrated low speed and high speed rpm increases while downs shifting. At high speeds, this is causing additional wear and tear on the drive train as evidenced by a large clanking noise. At low speeds, this is causing excess wear and tear on brakes. In both situations, it poses a safety hazard. Nissan/Infinity claims this is within the normal operating range, but the problem is significant and widespread as evidenced by Nissan/Infinity's own service people and engineers. Infinity will be forced to address this very soon or face a Toyota situation.\n", - " \n", - " \n", - " A Dream\n", - " -0.440443\n", - " 0.145674\n", - " 0.076986\n", - " 0.092631\n", - " 0.059397\n", - " 0.172335\n", + " 1 sweet R32\n", + " 0.649825\n", + " 0.151543\n", + " 0.532162\n", + " 0.067859\n", + " 0.018501\n", + " 0.112994\n", " 4.875\n", - " BMW\n", - " A Dream: Bought this vehicle for myself with 97000 miles and fully loaded with no apparent deficits (one owner vehicle). Body, engine, paint, interior, exterior in immaculate condition. Have driven it on long and short trips and it continues to elicit stares from lexus and benz owners. This vehicle is made for driving!!\n", - " \n", - " \n", - " A Wonderful Ownership Experiance\n", - " 0.245085\n", - " 0.113873\n", - " 0.243949\n", - " 0.101099\n", - " 0.016000\n", - " 0.117943\n", - " 4.750\n", - " lincoln\n", - " A Wonderful Ownership Experiance: Purchased the car with 20,000 miles on \\rit. I drive every day and I am not \\reasy on a car. My 98 Town Car \\rSignature has been truly a wonderful \\rcar and most of all I have had not one \\rmajor problem with the car and any \\rvisit to the Lincoln service \\rdepartment has only given me great \\rtreatment.The car is always reliable \\rand built tough, I am approaching \\r110,000 miles on it and I have got the \\rurge to get something new, however \\rthis car has been so good to me I \\rdon't want to trade it for something \\rwith problems. I would recomend this \\rcar for anyone who desires a luxury \\rride and reliability.\n", - " \n", - " \n", - " Best truck ever\n", - " 0.969981\n", - " 0.179243\n", - " 0.277188\n", - " 0.029521\n", - " 0.014326\n", - " 0.042779\n", - " 5.000\n", - " Chevrolet\n", - " Best truck ever: very reliable well rounded truck.\n", + " Volkswagen\n", + " 1 sweet R32: I was looking into buying a Subaru WRX \\rSTI, but after two test drives in each \\rand reading as many \\rRoad&Track,Car&Driver,and any other \\rinfo I could find I desided to go with \\rthe R32. I traded in my 2003 GTI VR6 \\rthat had 29,000 miles on it. That was a \\rgreat car but this is a whole new \\rbeast. Once you own an all wheel drive \\rthere is just no going back. This car \\rhandles like a dream, the seats are the \\rbest I've ever been in. Cabin is put \\rtogether very well and the pipes are \\rcrazy. The climit control is awsome, \\rheated seats are so sweet on those cold \\rwinter days. I live in the central \\rvalley of California so these tire are \\rthe best. If there was one thing I \\rwould change(give me a spare tire)!!!!!\n", " \n", " \n", - " Even better than the Chevy\n", - " -0.075949\n", - " 0.098830\n", - " 0.284697\n", - " 0.054851\n", - " 0.045185\n", - " 0.044507\n", - " 5.000\n", - " GMC\n", - " Even better than the Chevy: This is the best truck I have ever \\rowned. This is my first Full Size, but \\rmy dad has a silverado. My GMC drives \\rjust a little bit better,is a little \\rbit quiter, and the exterior styling \\ris the classiest out of all the trucks \\rin the market. Nobody could talk me \\rout of this truck and into anything \\relse. Eventhough the 2004 Ford \\rinterior has a good design, you still \\rcouldn't pay me enough to own a FORD. \\rThe editor that rated the Sierra lost \\rthere mind, it should be alot higher \\rthan the low 8's. The only thing I \\rregret is not getting the Z71 pkg.\n", + " 2002 Trans Am/Sunset Orange Metallic\n", + " 0.148035\n", + " 0.176322\n", + " 0.465210\n", + " 0.257064\n", + " 0.032842\n", + " 0.038908\n", + " 4.625\n", + " pontiac\n", + " 2002 Trans Am/Sunset Orange Metallic: This Is Pontiac's most exciting vehicle \\rof all time.It has so much performance \\rthat it is a big disapointment that it \\rwill be discontinued this year.The only \\rarea that this vehicle does not excell \\rin would be the fuel economy \\rdepartment.I guess that if you can \\rafford one of these dream cars, you \\rreally dony worry about how far it will \\rtravel on a tankfull of gas.\n", + " \n", + " \n", + " 42 days of driving 8 days in the shop\n", + " -0.054126\n", + " 0.206478\n", + " 0.563466\n", + " 0.114506\n", + " 0.010082\n", + " 0.082325\n", + " 3.375\n", + " chrysler\n", + " 42 days of driving 8 days in the shop : I was given the sebring for my 20th wedding anniversary. I have been in love with it for years and finally got it. After 42 days I blew most of the electrical system. It has been at the dealer for 8 days and they can not find the problem. Right now I am not very happy.\n", + " \n", + " \n", + " A great little car\n", + " 0.503785\n", + " 0.278575\n", + " 0.470586\n", + " 0.063823\n", + " 0.015218\n", + " 0.039688\n", + " 4.875\n", + " kia\n", + " A great little car: Bought my Spectra about one year ago, currently has about 18,000 miles on it. I have had absolutely no problems with it. I had cruise control added at the time of purchase, other than that it's stock. This is my daily driver, it's comfortable, reliable and gets decent mileage. The Spectra happens to be my second Kia, I have a Sedona van that has been to the dealer several times (however everything was covered by the warranty) it currently has 58,000 miles on it. The Spectra's a great handling car.\n", " \n", " \n", - " I LOVE MY SLK\n", - " 0.926009\n", - " 0.104404\n", - " 0.694037\n", - " 0.064407\n", - " 0.036633\n", - " 0.070950\n", + " AWESOME FUN MY LITTLE TIGER\n", + " 0.986029\n", + " 0.007629\n", + " 0.628312\n", + " 0.013015\n", + " 0.001452\n", + " 0.024782\n", " 5.000\n", - " mercedes-benz\n", - " I LOVE MY SLK: It is fun driving top down or up but I will srive it top down more weather permitting. It get's pretty darned hot here in Phoenix AZ in July.\n", + " fiat\n", + " AWESOME FUN MY LITTLE TIGER: Abarth is ultimately more fun than my old mustang or Z a little power house that doesn't shy away from a fight love the engine growl and the kick more room than you think awesome bang for the buck .Fun the most fun than any car I have ever own worth every penny a pleasure to drive.\n", " \n", " \n", - " I love my Caliber\n", - " 0.919963\n", - " 0.187137\n", - " 0.604041\n", - " 0.049315\n", - " 0.040379\n", - " 0.057248\n", + " I LOVE my Focus\n", + " 0.621983\n", + " 0.074019\n", + " 0.589196\n", + " 0.111722\n", + " 0.008124\n", + " 0.066092\n", " 4.750\n", - " dodge\n", - " I love my Caliber: I just bought my Caliber this week So far it has been a blast to drive. I was so surprised how much pep it's got for a small engine. It's also very smooth, you don't even feel the gear shift, I love that. I also love the way it feels like an SUV but looks like a sporty car. So far I love everything about it.\n", + " ford\n", + " I LOVE my Focus: I LOVE my Focus. I've had it about 2 \\ryears. It drives great, looks good, \\rgets great gas milage and never slows \\rdown. I'm even thinking of getting \\ranother one on my next car purchase!\n", " \n", " \n", " Looks Good But Hunk Of Junk\n", - " -0.999568\n", - " 0.441097\n", - " 0.283959\n", - " 0.173938\n", - " 0.097350\n", - " 0.158059\n", + " -0.984622\n", + " 0.144671\n", + " 0.061358\n", + " 0.060613\n", + " 0.050494\n", + " 0.116835\n", " 2.875\n", " maserati\n", " Looks Good But Hunk Of Junk: This car is strictly \"looks only\", it is not reliable or even close to it.I have already sank $13,760 in repairs at only 23K miles.This is totally unacceptable for a $140K car when new.I am taking it to the auction next week to \"unload\" before it can empty my wallet again.But if you want a sharp car that sits good in the driveway - this is it!Just don't drive it anywhere!!\n", " \n", " \n", - " Small Reliable Gas Saver!\n", - " 0.069819\n", - " 0.193688\n", - " 0.215213\n", - " 0.076201\n", - " 0.034896\n", - " 0.106193\n", - " 4.500\n", - " mitsubishi\n", - " Small Reliable Gas Saver!: Purchase car some 166,000 miles ago and have enjoyed its reliability, gas savings, and small car comfort. Yes, it does hesitate on steep hills (only has a 1.5 engine), but it makes the hills. I have traveled from Florida to Nevada, Texas, Oklahoma, Missouri, and now live in Pennsylvania. I make trips to New York and Massachusetts. I do not tire in driving this car. Maintenance has not been a major issue. Yes, I have changed the struts, brakes - but that is to be expected with its mileage. I do recommend consideration of this vehicle for college or young family starting out in life. It has more than paid for its purchase price many, many times. Enjoy.\n", + " Mr TACOMA\n", + " 0.633803\n", + " 0.122766\n", + " 0.825653\n", + " 0.034777\n", + " 0.023124\n", + " 0.030344\n", + " 5.000\n", + " Toyota\n", + " Mr TACOMA: Great truck. The Handling is pretty \\rnice and the engine is stronger. The V6 \\rwith 3100 pounds can really make this \\rtruck move.\n", " \n", " \n", " Veracruz\n", - " 0.591815\n", - " 0.153267\n", - " 0.660735\n", - " 0.054831\n", - " 0.017731\n", - " 0.077888\n", + " 0.591816\n", + " 0.106981\n", + " 0.524371\n", + " 0.091482\n", + " 0.012344\n", + " 0.054493\n", " 4.750\n", " hyundai\n", " Veracruz: This is a crossover with the ride of a cruse ship. The car has so many bells and whistles. Have it one week and already over 1100 miles. Finding wonderful things about it every day. Could be the best car ever.\n", " \n", + " \n", + " You will pay for that warranty\n", + " -0.373583\n", + " 0.396306\n", + " 0.110458\n", + " 0.056980\n", + " 0.021192\n", + " 0.119030\n", + " 2.750\n", + " kia\n", + " You will pay for that warranty: Own a 2002 KIA Sedona EX. I complained about lights going dim while under warranty. Kia checked, said everything within parameters. Guess what, 3000 miles out of warranty alternator died. KIA says it's on you now. 63,000 miles and they want $565.00 to repair; that includes alternator, belts and labor. It's not a repair you can do either, seems AC lines are in the way. Do you think KIA planned it? Ask them about changing spark plugs the rear 3, seems you need to remove the air intake manifold? That will require new gaskets? Not sure of that cost. I hope to dump this Sedona by then! Think twice before you buy, they will get you to pay for that supposedly free 5year/60000 bumper to bumper warranty. RIPOFF.\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " sentiment.score emotion.sadness \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects -0.496327 0.339398 \n", - " A Dream -0.440443 0.145674 \n", - " A Wonderful Ownership Experiance 0.245085 0.113873 \n", - " Best truck ever 0.969981 0.179243 \n", - " Even better than the Chevy -0.075949 0.098830 \n", - " I LOVE MY SLK 0.926009 0.104404 \n", - " I love my Caliber 0.919963 0.187137 \n", - " Looks Good But Hunk Of Junk -0.999568 0.441097 \n", - " Small Reliable Gas Saver! 0.069819 0.193688 \n", - " Veracruz 0.591815 0.153267 \n", + " sentiment.score emotion.sadness \\\n", + "Review_Title \n", + " 1 sweet R32 0.649825 0.151543 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.148035 0.176322 \n", + " 42 days of driving 8 days in the shop -0.054126 0.206478 \n", + " A great little car 0.503785 0.278575 \n", + " AWESOME FUN MY LITTLE TIGER 0.986029 0.007629 \n", + " I LOVE my Focus 0.621983 0.074019 \n", + " Looks Good But Hunk Of Junk -0.984622 0.144671 \n", + " Mr TACOMA 0.633803 0.122766 \n", + " Veracruz 0.591816 0.106981 \n", + " You will pay for that warranty -0.373583 0.396306 \n", "\n", - " emotion.joy emotion.fear \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.053823 0.143729 \n", - " A Dream 0.076986 0.092631 \n", - " A Wonderful Ownership Experiance 0.243949 0.101099 \n", - " Best truck ever 0.277188 0.029521 \n", - " Even better than the Chevy 0.284697 0.054851 \n", - " I LOVE MY SLK 0.694037 0.064407 \n", - " I love my Caliber 0.604041 0.049315 \n", - " Looks Good But Hunk Of Junk 0.283959 0.173938 \n", - " Small Reliable Gas Saver! 0.215213 0.076201 \n", - " Veracruz 0.660735 0.054831 \n", + " emotion.joy emotion.fear \\\n", + "Review_Title \n", + " 1 sweet R32 0.532162 0.067859 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.465210 0.257064 \n", + " 42 days of driving 8 days in the shop 0.563466 0.114506 \n", + " A great little car 0.470586 0.063823 \n", + " AWESOME FUN MY LITTLE TIGER 0.628312 0.013015 \n", + " I LOVE my Focus 0.589196 0.111722 \n", + " Looks Good But Hunk Of Junk 0.061358 0.060613 \n", + " Mr TACOMA 0.825653 0.034777 \n", + " Veracruz 0.524371 0.091482 \n", + " You will pay for that warranty 0.110458 0.056980 \n", "\n", - " emotion.disgust emotion.anger \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 0.072652 0.114714 \n", - " A Dream 0.059397 0.172335 \n", - " A Wonderful Ownership Experiance 0.016000 0.117943 \n", - " Best truck ever 0.014326 0.042779 \n", - " Even better than the Chevy 0.045185 0.044507 \n", - " I LOVE MY SLK 0.036633 0.070950 \n", - " I love my Caliber 0.040379 0.057248 \n", - " Looks Good But Hunk Of Junk 0.097350 0.158059 \n", - " Small Reliable Gas Saver! 0.034896 0.106193 \n", - " Veracruz 0.017731 0.077888 \n", + " emotion.disgust emotion.anger \\\n", + "Review_Title \n", + " 1 sweet R32 0.018501 0.112994 \n", + " 2002 Trans Am/Sunset Orange Metallic 0.032842 0.038908 \n", + " 42 days of driving 8 days in the shop 0.010082 0.082325 \n", + " A great little car 0.015218 0.039688 \n", + " AWESOME FUN MY LITTLE TIGER 0.001452 0.024782 \n", + " I LOVE my Focus 0.008124 0.066092 \n", + " Looks Good But Hunk Of Junk 0.050494 0.116835 \n", + " Mr TACOMA 0.023124 0.030344 \n", + " Veracruz 0.012344 0.054493 \n", + " You will pay for that warranty 0.021192 0.119030 \n", "\n", - " Rating\\r Car_Make \\\n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 2.375 infiniti \n", - " A Dream 4.875 BMW \n", - " A Wonderful Ownership Experiance 4.750 lincoln \n", - " Best truck ever 5.000 Chevrolet \n", - " Even better than the Chevy 5.000 GMC \n", - " I LOVE MY SLK 5.000 mercedes-benz \n", - " I love my Caliber 4.750 dodge \n", - " Looks Good But Hunk Of Junk 2.875 maserati \n", - " Small Reliable Gas Saver! 4.500 mitsubishi \n", - " Veracruz 4.750 hyundai \n", + " Rating\\r Car_Make \\\n", + "Review_Title \n", + " 1 sweet R32 4.875 Volkswagen \n", + " 2002 Trans Am/Sunset Orange Metallic 4.625 pontiac \n", + " 42 days of driving 8 days in the shop 3.375 chrysler \n", + " A great little car 4.875 kia \n", + " AWESOME FUN MY LITTLE TIGER 5.000 fiat \n", + " I LOVE my Focus 4.750 ford \n", + " Looks Good But Hunk Of Junk 2.875 maserati \n", + " Mr TACOMA 5.000 Toyota \n", + " Veracruz 4.750 hyundai \n", + " You will pay for that warranty 2.750 kia \n", "\n", - " Review_Content \n", - "Review_Title \n", - " 2010 M 35 Acceleration/Braking Defects 2010 M 35 Acceleration/Braking Defects: I leased a new 2010 M 35. This was my 9th Nissan/Infinity vehicle since 1996. This vehicle has been a huge disappointment. The vehicle has demonstrated low speed and high speed rpm increases while downs shifting. At high speeds, this is causing additional wear and tear on the drive train as evidenced by a large clanking noise. At low speeds, this is causing excess wear and tear on brakes. In both situations, it poses a safety hazard. Nissan/Infinity claims this is within the normal operating range, but the problem is significant and widespread as evidenced by Nissan/Infinity's own service people and engineers. Infinity will be forced to address this very soon or face a Toyota situation. \n", - " A Dream A Dream: Bought this vehicle for myself with 97000 miles and fully loaded with no apparent deficits (one owner vehicle). Body, engine, paint, interior, exterior in immaculate condition. Have driven it on long and short trips and it continues to elicit stares from lexus and benz owners. This vehicle is made for driving!! \n", - " A Wonderful Ownership Experiance A Wonderful Ownership Experiance: Purchased the car with 20,000 miles on \\rit. I drive every day and I am not \\reasy on a car. My 98 Town Car \\rSignature has been truly a wonderful \\rcar and most of all I have had not one \\rmajor problem with the car and any \\rvisit to the Lincoln service \\rdepartment has only given me great \\rtreatment.The car is always reliable \\rand built tough, I am approaching \\r110,000 miles on it and I have got the \\rurge to get something new, however \\rthis car has been so good to me I \\rdon't want to trade it for something \\rwith problems. I would recomend this \\rcar for anyone who desires a luxury \\rride and reliability. \n", - " Best truck ever Best truck ever: very reliable well rounded truck. \n", - " Even better than the Chevy Even better than the Chevy: This is the best truck I have ever \\rowned. This is my first Full Size, but \\rmy dad has a silverado. My GMC drives \\rjust a little bit better,is a little \\rbit quiter, and the exterior styling \\ris the classiest out of all the trucks \\rin the market. Nobody could talk me \\rout of this truck and into anything \\relse. Eventhough the 2004 Ford \\rinterior has a good design, you still \\rcouldn't pay me enough to own a FORD. \\rThe editor that rated the Sierra lost \\rthere mind, it should be alot higher \\rthan the low 8's. The only thing I \\rregret is not getting the Z71 pkg. \n", - " I LOVE MY SLK I LOVE MY SLK: It is fun driving top down or up but I will srive it top down more weather permitting. It get's pretty darned hot here in Phoenix AZ in July. \n", - " I love my Caliber I love my Caliber: I just bought my Caliber this week So far it has been a blast to drive. I was so surprised how much pep it's got for a small engine. It's also very smooth, you don't even feel the gear shift, I love that. I also love the way it feels like an SUV but looks like a sporty car. So far I love everything about it. \n", - " Looks Good But Hunk Of Junk Looks Good But Hunk Of Junk: This car is strictly \"looks only\", it is not reliable or even close to it.I have already sank $13,760 in repairs at only 23K miles.This is totally unacceptable for a $140K car when new.I am taking it to the auction next week to \"unload\" before it can empty my wallet again.But if you want a sharp car that sits good in the driveway - this is it!Just don't drive it anywhere!! \n", - " Small Reliable Gas Saver! Small Reliable Gas Saver!: Purchase car some 166,000 miles ago and have enjoyed its reliability, gas savings, and small car comfort. Yes, it does hesitate on steep hills (only has a 1.5 engine), but it makes the hills. I have traveled from Florida to Nevada, Texas, Oklahoma, Missouri, and now live in Pennsylvania. I make trips to New York and Massachusetts. I do not tire in driving this car. Maintenance has not been a major issue. Yes, I have changed the struts, brakes - but that is to be expected with its mileage. I do recommend consideration of this vehicle for college or young family starting out in life. It has more than paid for its purchase price many, many times. Enjoy. \n", - " Veracruz Veracruz: This is a crossover with the ride of a cruse ship. The car has so many bells and whistles. Have it one week and already over 1100 miles. Finding wonderful things about it every day. Could be the best car ever. " + " Review_Content \n", + "Review_Title \n", + " 1 sweet R32 1 sweet R32: I was looking into buying a Subaru WRX \\rSTI, but after two test drives in each \\rand reading as many \\rRoad&Track,Car&Driver,and any other \\rinfo I could find I desided to go with \\rthe R32. I traded in my 2003 GTI VR6 \\rthat had 29,000 miles on it. That was a \\rgreat car but this is a whole new \\rbeast. Once you own an all wheel drive \\rthere is just no going back. This car \\rhandles like a dream, the seats are the \\rbest I've ever been in. Cabin is put \\rtogether very well and the pipes are \\rcrazy. The climit control is awsome, \\rheated seats are so sweet on those cold \\rwinter days. I live in the central \\rvalley of California so these tire are \\rthe best. If there was one thing I \\rwould change(give me a spare tire)!!!!! \n", + " 2002 Trans Am/Sunset Orange Metallic 2002 Trans Am/Sunset Orange Metallic: This Is Pontiac's most exciting vehicle \\rof all time.It has so much performance \\rthat it is a big disapointment that it \\rwill be discontinued this year.The only \\rarea that this vehicle does not excell \\rin would be the fuel economy \\rdepartment.I guess that if you can \\rafford one of these dream cars, you \\rreally dony worry about how far it will \\rtravel on a tankfull of gas. \n", + " 42 days of driving 8 days in the shop 42 days of driving 8 days in the shop : I was given the sebring for my 20th wedding anniversary. I have been in love with it for years and finally got it. After 42 days I blew most of the electrical system. It has been at the dealer for 8 days and they can not find the problem. Right now I am not very happy. \n", + " A great little car A great little car: Bought my Spectra about one year ago, currently has about 18,000 miles on it. I have had absolutely no problems with it. I had cruise control added at the time of purchase, other than that it's stock. This is my daily driver, it's comfortable, reliable and gets decent mileage. The Spectra happens to be my second Kia, I have a Sedona van that has been to the dealer several times (however everything was covered by the warranty) it currently has 58,000 miles on it. The Spectra's a great handling car. \n", + " AWESOME FUN MY LITTLE TIGER AWESOME FUN MY LITTLE TIGER: Abarth is ultimately more fun than my old mustang or Z a little power house that doesn't shy away from a fight love the engine growl and the kick more room than you think awesome bang for the buck .Fun the most fun than any car I have ever own worth every penny a pleasure to drive. \n", + " I LOVE my Focus I LOVE my Focus: I LOVE my Focus. I've had it about 2 \\ryears. It drives great, looks good, \\rgets great gas milage and never slows \\rdown. I'm even thinking of getting \\ranother one on my next car purchase! \n", + " Looks Good But Hunk Of Junk Looks Good But Hunk Of Junk: This car is strictly \"looks only\", it is not reliable or even close to it.I have already sank $13,760 in repairs at only 23K miles.This is totally unacceptable for a $140K car when new.I am taking it to the auction next week to \"unload\" before it can empty my wallet again.But if you want a sharp car that sits good in the driveway - this is it!Just don't drive it anywhere!! \n", + " Mr TACOMA Mr TACOMA: Great truck. The Handling is pretty \\rnice and the engine is stronger. The V6 \\rwith 3100 pounds can really make this \\rtruck move. \n", + " Veracruz Veracruz: This is a crossover with the ride of a cruse ship. The car has so many bells and whistles. Have it one week and already over 1100 miles. Finding wonderful things about it every day. Could be the best car ever. \n", + " You will pay for that warranty You will pay for that warranty: Own a 2002 KIA Sedona EX. I complained about lights going dim while under warranty. Kia checked, said everything within parameters. Guess what, 3000 miles out of warranty alternator died. KIA says it's on you now. 63,000 miles and they want $565.00 to repair; that includes alternator, belts and labor. It's not a repair you can do either, seems AC lines are in the way. Do you think KIA planned it? Ask them about changing spark plugs the rear 3, seems you need to remove the air intake manifold? That will require new gaskets? Not sure of that cost. I hope to dump this Sedona by then! Think twice before you buy, they will get you to pay for that supposedly free 5year/60000 bumper to bumper warranty. RIPOFF. " ] }, "execution_count": 47, @@ -5065,55 +4612,56 @@ "data": { "text/plain": [ "Car_Make\n", - "AMGeneral 3\n", - "Acura 137\n", - "AlfaRomeo 58\n", - "AstonMartin 65\n", - "Audi 141\n", - "BMW 149\n", - "Bentley 101\n", - "Bugatti 8\n", - "Buick 132\n", - "Cadillac 157\n", - "Chevrolet 146\n", - "GMC 145\n", - "Honda 147\n", - "Toyota 145\n", - "Volkswagen 140\n", - "chrysler 127\n", + "AMGeneral 2\n", + "Acura 154\n", + "AlfaRomeo 60\n", + "AstonMartin 55\n", + "Audi 144\n", + "BMW 143\n", + "Bentley 102\n", + "Bugatti 7\n", + "Buick 134\n", + "Cadillac 140\n", + "Chevrolet 157\n", + "GMC 133\n", + "Honda 143\n", + "Toyota 130\n", + "Volkswagen 152\n", + "chrysler 136\n", "dodge 142\n", - "ferrari 119\n", - "fiat 136\n", - "ford 142\n", - "genesis 56\n", - "hummer 144\n", - "hyundai 130\n", - "infiniti 130\n", - "isuzu 131\n", - "jaguar 126\n", - "jeep 131\n", - "kia 127\n", - "lamborghini 60\n", - "land-rover 145\n", - "lexus 133\n", - "lincoln 133\n", - "lotus 97\n", - "maserati 139\n", - "maybach 11\n", - "mazda 146\n", - "mercedes-benz 129\n", - "mercury 126\n", - "mini 143\n", - "mitsubishi 128\n", - "nissan 127\n", - "pontiac 127\n", - "porsche 140\n", - "ram 132\n", - "rolls-royce 18\n", - "subaru 138\n", - "suzuki 116\n", - "tesla 99\n", - "volvo 130\n", + "ferrari 111\n", + "fiat 142\n", + "ford 138\n", + "genesis 48\n", + "hummer 149\n", + "hyundai 142\n", + "infiniti 134\n", + "isuzu 137\n", + "jaguar 128\n", + "jeep 127\n", + "kia 126\n", + "lamborghini 54\n", + "land-rover 141\n", + "lexus 125\n", + "lincoln 138\n", + "lotus 102\n", + "maserati 136\n", + "maybach 15\n", + "mazda 137\n", + "mclaren 1\n", + "mercedes-benz 133\n", + "mercury 131\n", + "mini 142\n", + "mitsubishi 118\n", + "nissan 125\n", + "pontiac 132\n", + "porsche 136\n", + "ram 152\n", + "rolls-royce 23\n", + "subaru 129\n", + "suzuki 121\n", + "tesla 100\n", + "volvo 135\n", "dtype: int64" ] }, @@ -5154,10 +4702,14 @@ { "cell_type": "code", "execution_count": 52, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "test_set['Predicted_Y'] = Y_pred" + "predicted_y_with_na = np.zeros(len(test_set.index), dtype=object)\n", + "predicted_y_with_na[~test_set.isna().any(axis=1)] = Y_pred\n", + "test_set['Predicted_Y'] = predicted_y_with_na" ] }, { @@ -5190,12 +4742,12 @@ " \n", " \n", " \n", - " sentiment.score\n", " emotion.sadness\n", " emotion.joy\n", " emotion.fear\n", " emotion.disgust\n", " emotion.anger\n", + " sentiment.score\n", " Rating\\r\n", " Predicted_Y\n", " \n", @@ -5225,666 +4777,653 @@ " \n", " \n", " AMGeneral\n", - " -0.594638\n", - " 0.240532\n", - " 0.020233\n", - " 0.209863\n", - " 0.148217\n", - " 0.280780\n", - " 3.000000\n", - " 3.194602\n", + " 0.233502\n", + " 0.416527\n", + " 0.149416\n", + " 0.030530\n", + " 0.065171\n", + " 0.021626\n", + " 4.833333\n", + " 4.264005\n", " \n", " \n", " Acura\n", - " 0.146076\n", - " 0.216153\n", - " 0.310447\n", - " 0.112656\n", - " 0.088328\n", - " 0.118972\n", - " 4.209746\n", - " 4.227164\n", + " 0.186803\n", + " 0.467307\n", + " 0.134082\n", + " 0.020744\n", + " 0.064254\n", + " 0.330192\n", + " 4.538690\n", + " 4.455716\n", " \n", " \n", " AlfaRomeo\n", - " 0.160632\n", - " 0.232433\n", - " 0.315023\n", - " 0.125210\n", - " 0.082053\n", - " 0.122382\n", - " 4.388889\n", - " 4.322972\n", + " 0.179048\n", + " 0.434307\n", + " 0.101048\n", + " 0.030507\n", + " 0.088323\n", + " 0.268080\n", + " 4.187500\n", + " 4.435494\n", " \n", " \n", " AstonMartin\n", - " 0.400920\n", - " 0.201064\n", - " 0.362957\n", - " 0.114105\n", - " 0.061657\n", - " 0.115535\n", - " 4.326087\n", - " 4.448320\n", + " 0.161465\n", + " 0.532924\n", + " 0.093979\n", + " 0.030943\n", + " 0.063027\n", + " 0.470149\n", + " 4.613636\n", + " 4.631018\n", " \n", " \n", " Audi\n", - " 0.252668\n", - " 0.195495\n", - " 0.330413\n", - " 0.106549\n", - " 0.059369\n", - " 0.103690\n", - " 4.445755\n", - " 4.365891\n", + " 0.196609\n", + " 0.490965\n", + " 0.092430\n", + " 0.021402\n", + " 0.059860\n", + " 0.303431\n", + " 4.453431\n", + " 4.421119\n", " \n", " \n", " BMW\n", - " 0.159408\n", - " 0.194169\n", - " 0.368360\n", - " 0.120398\n", - " 0.061844\n", - " 0.123578\n", - " 4.513889\n", - " 4.293852\n", + " 0.191940\n", + " 0.474563\n", + " 0.086499\n", + " 0.024038\n", + " 0.072675\n", + " 0.243201\n", + " 4.468750\n", + " 4.354955\n", " \n", " \n", " Bentley\n", - " 0.307778\n", - " 0.172982\n", - " 0.371687\n", - " 0.110235\n", - " 0.078887\n", - " 0.130370\n", - " 4.229730\n", - " 4.410651\n", + " 0.187771\n", + " 0.528449\n", + " 0.089768\n", + " 0.028513\n", + " 0.057980\n", + " 0.441103\n", + " 4.239583\n", + " 4.57587\n", " \n", " \n", " Bugatti\n", - " -0.111498\n", - " 0.147069\n", - " 0.482210\n", - " 0.122855\n", - " 0.072881\n", - " 0.097407\n", - " 4.250000\n", - " 4.382092\n", + " 0.188314\n", + " 0.587727\n", + " 0.055366\n", + " 0.023677\n", + " 0.054105\n", + " 0.430882\n", + " 4.750000\n", + " 4.718716\n", " \n", " \n", " Buick\n", - " 0.240515\n", - " 0.232104\n", - " 0.322004\n", - " 0.117273\n", - " 0.075984\n", - " 0.112750\n", - " 4.292373\n", - " 4.254791\n", + " 0.260452\n", + " 0.392718\n", + " 0.099129\n", + " 0.025511\n", + " 0.086554\n", + " 0.088404\n", + " 4.162736\n", + " 4.075432\n", " \n", " \n", " Cadillac\n", - " 0.083118\n", - " 0.212384\n", - " 0.282956\n", - " 0.120468\n", - " 0.074504\n", - " 0.128036\n", - " 4.112500\n", - " 4.160872\n", + " 0.218803\n", + " 0.429976\n", + " 0.102534\n", + " 0.030479\n", + " 0.069962\n", + " 0.274075\n", + " 4.395408\n", + " 4.335975\n", " \n", " \n", " Chevrolet\n", - " -0.042871\n", - " 0.293122\n", - " 0.217664\n", - " 0.131120\n", - " 0.075338\n", - " 0.136536\n", - " 4.103125\n", - " 3.971316\n", + " 0.200984\n", + " 0.441252\n", + " 0.102457\n", + " 0.037943\n", + " 0.074343\n", + " 0.173096\n", + " 4.104730\n", + " 4.23044\n", " \n", " \n", " GMC\n", - " 0.117682\n", - " 0.215651\n", - " 0.295742\n", - " 0.117902\n", - " 0.059368\n", - " 0.115138\n", - " 4.084302\n", - " 4.206823\n", + " 0.218755\n", + " 0.416075\n", + " 0.111135\n", + " 0.033378\n", + " 0.067579\n", + " 0.071438\n", + " 4.089912\n", + " 4.138499\n", " \n", " \n", " Honda\n", - " 0.034251\n", - " 0.233150\n", - " 0.266702\n", - " 0.132316\n", - " 0.061904\n", - " 0.138990\n", - " 4.255435\n", - " 4.059390\n", + " 0.191337\n", + " 0.418301\n", + " 0.117569\n", + " 0.029766\n", + " 0.063076\n", + " 0.118789\n", + " 3.832386\n", + " 4.130003\n", " \n", " \n", " Toyota\n", - " 0.145704\n", - " 0.212612\n", - " 0.327585\n", - " 0.104289\n", - " 0.060387\n", - " 0.109918\n", - " 4.222826\n", - " 4.311795\n", + " 0.199025\n", + " 0.431638\n", + " 0.104728\n", + " 0.027125\n", + " 0.070333\n", + " 0.154561\n", + " 4.350543\n", + " 4.243079\n", " \n", " \n", " Volkswagen\n", - " 0.005947\n", - " 0.259367\n", - " 0.276090\n", - " 0.128501\n", - " 0.073617\n", - " 0.139398\n", - " 4.043478\n", - " 3.980834\n", + " 0.190767\n", + " 0.429078\n", + " 0.126892\n", + " 0.031965\n", + 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4.321429\n", - " 4.332991\n", + " 0.181750\n", + " 0.502888\n", + " 0.126320\n", + " 0.027008\n", + " 0.055950\n", + " 0.297203\n", + " 4.404605\n", + " 4.462752\n", " \n", " \n", " hyundai\n", - " 0.065004\n", - " 0.264548\n", - " 0.307687\n", - " 0.115017\n", - " 0.057827\n", - " 0.136106\n", - " 4.082500\n", - " 4.064445\n", + " 0.220990\n", + " 0.393721\n", + " 0.096735\n", + " 0.027251\n", + " 0.079915\n", + " 0.161732\n", + " 4.109375\n", + " 4.14899\n", " \n", " \n", " infiniti\n", - " 0.341308\n", - " 0.182078\n", - " 0.344738\n", - " 0.116468\n", - " 0.059517\n", - " 0.111160\n", - " 4.553977\n", - " 4.423686\n", + " 0.200538\n", + " 0.469661\n", + " 0.090667\n", + " 0.024671\n", + " 0.059761\n", + " 0.322187\n", + " 4.566860\n", + " 4.393907\n", " \n", " \n", " isuzu\n", - " 0.075290\n", - " 0.219361\n", - " 0.266676\n", - " 0.126747\n", - " 0.066575\n", - " 0.114220\n", - " 4.309211\n", - " 4.228860\n", + " 0.201127\n", + " 0.404813\n", + " 0.122677\n", + " 0.028856\n", + " 0.101334\n", + " 0.205943\n", + " 4.220238\n", + " 4.306578\n", " \n", " \n", " jaguar\n", - " 0.253660\n", - " 0.178868\n", - " 0.378857\n", - " 0.095227\n", - " 0.061614\n", - " 0.106031\n", - " 4.439815\n", - " 4.317453\n", + " 0.163703\n", + " 0.556705\n", + " 0.086785\n", + " 0.025675\n", + " 0.055537\n", + " 0.375661\n", + " 4.584091\n", + " 4.497573\n", " \n", " \n", " jeep\n", - " 0.077878\n", - " 0.244649\n", - " 0.344078\n", - " 0.111979\n", - " 0.063442\n", - " 0.115510\n", - " 3.899554\n", - " 4.093507\n", + " 0.253255\n", + " 0.396047\n", + " 0.104165\n", + " 0.025074\n", + " 0.079216\n", + " 0.106891\n", + " 4.108607\n", + " 4.15378\n", " \n", " \n", " kia\n", - " 0.253933\n", - " 0.198484\n", - " 0.367603\n", - " 0.104703\n", - " 0.066781\n", - " 0.111330\n", - " 4.219444\n", - " 4.330955\n", + " 0.266675\n", + " 0.394792\n", + " 0.111849\n", + " 0.025904\n", + " 0.070382\n", + " 0.141621\n", + " 4.141827\n", + " 4.12064\n", " \n", " \n", " lamborghini\n", - " 0.412362\n", - " 0.091284\n", - " 0.396143\n", - " 0.085355\n", - " 0.062690\n", - " 0.083136\n", - " 4.613636\n", - " 4.633120\n", + " 0.127094\n", + " 0.629176\n", + " 0.082221\n", + " 0.029788\n", + " 0.052919\n", + " 0.657044\n", + " 4.725000\n", + " 4.665769\n", " \n", " \n", " land-rover\n", - " 0.244489\n", - " 0.202602\n", - " 0.334492\n", - " 0.120057\n", - " 0.058127\n", - " 0.101070\n", - " 4.154762\n", - " 4.383457\n", + " 0.274524\n", + " 0.355115\n", + " 0.109827\n", + " 0.034846\n", + " 0.086169\n", + " 0.080642\n", + " 3.848837\n", + " 4.0177\n", " \n", " \n", " lexus\n", - " 0.200350\n", - " 0.183309\n", - " 0.344489\n", - " 0.109256\n", - " 0.050643\n", - " 0.120852\n", - " 4.349432\n", - " 4.253620\n", + " 0.202997\n", + " 0.439800\n", + " 0.100562\n", + " 0.031977\n", + " 0.076374\n", + " 0.231606\n", + " 4.306122\n", + " 4.294433\n", " \n", " \n", " lincoln\n", - " 0.291578\n", - " 0.201160\n", - " 0.330980\n", - " 0.125137\n", - " 0.069068\n", - " 0.122796\n", - " 4.494318\n", - " 4.392293\n", + " 0.213433\n", + " 0.466320\n", + " 0.112260\n", + " 0.027192\n", + " 0.075736\n", + " 0.163414\n", + " 4.269231\n", + " 4.264042\n", " \n", " \n", " lotus\n", - " 0.249474\n", - " 0.186374\n", - " 0.368691\n", - " 0.135556\n", - " 0.063595\n", - " 0.132431\n", - " 4.590909\n", - " 4.478621\n", + " 0.158467\n", + " 0.457916\n", + " 0.137350\n", + " 0.023458\n", + " 0.080445\n", + " 0.307135\n", + " 4.702381\n", + " 4.490183\n", " \n", " \n", " maserati\n", - " 0.250559\n", - " 0.219400\n", - " 0.398471\n", - " 0.101808\n", - " 0.060194\n", - " 0.111714\n", - " 4.439103\n", - " 4.319154\n", + " 0.174925\n", + " 0.523992\n", + " 0.087822\n", + " 0.037616\n", + " 0.071795\n", + " 0.311256\n", + " 4.431250\n", + " 4.394369\n", " \n", " \n", " maybach\n", - " 0.646201\n", - " 0.113256\n", - " 0.601533\n", - " 0.047746\n", - " 0.043509\n", - " 0.071882\n", - " 4.732143\n", - " 4.710683\n", + " 0.178194\n", + " 0.515520\n", + " 0.077657\n", + " 0.015687\n", + " 0.072357\n", + " 0.633714\n", + " 4.958333\n", + " 4.733771\n", " \n", " \n", " mazda\n", - " 0.187200\n", - " 0.202740\n", - " 0.345586\n", - " 0.108034\n", - " 0.075693\n", - " 0.109664\n", - " 4.346429\n", - " 4.247244\n", - " \n", - " \n", - " mclaren\n", - " 0.781623\n", - " 0.176243\n", - " 0.331202\n", - " 0.111601\n", - " 0.068376\n", - " 0.115666\n", - " 5.000000\n", - " 4.694830\n", + " 0.203442\n", + " 0.444971\n", + " 0.111021\n", + " 0.026170\n", + " 0.062387\n", + " 0.230268\n", + " 4.479651\n", + " 4.318612\n", " \n", " \n", " mercedes-benz\n", - " 0.253613\n", - " 0.212727\n", - " 0.314652\n", - " 0.103435\n", - " 0.057229\n", - " 0.122127\n", - " 4.500000\n", - " 4.275059\n", + " 0.227015\n", + " 0.387488\n", + " 0.105910\n", + " 0.026781\n", + " 0.085045\n", + " 0.091385\n", + " 4.095745\n", + " 4.094125\n", " \n", " \n", " mercury\n", - " 0.223070\n", - " 0.226492\n", - " 0.308719\n", - " 0.116383\n", - " 0.060396\n", - " 0.110053\n", - " 4.466837\n", - " 4.308947\n", + " 0.200664\n", + " 0.462219\n", + " 0.105958\n", + " 0.025067\n", + " 0.063838\n", + " 0.246360\n", + " 4.311224\n", + " 4.390451\n", " \n", " \n", " mini\n", - " 0.178291\n", - " 0.214415\n", - " 0.391739\n", - " 0.117223\n", - " 0.063220\n", - " 0.149629\n", - " 4.161932\n", - " 4.272955\n", + " 0.218531\n", + " 0.443708\n", + " 0.096154\n", + " 0.026429\n", + " 0.071672\n", + " 0.167861\n", + " 4.036184\n", + " 4.190949\n", " \n", " \n", " mitsubishi\n", - " 0.306894\n", - " 0.174691\n", - " 0.380305\n", - " 0.098874\n", - " 0.058135\n", - " 0.103309\n", - " 4.517045\n", - " 4.431931\n", + " 0.175781\n", + " 0.481554\n", + " 0.118347\n", + " 0.025169\n", + " 0.070454\n", + " 0.300219\n", + " 4.346698\n", + " 4.417798\n", " \n", " \n", " nissan\n", - " 0.047395\n", - " 0.264765\n", - " 0.244476\n", - " 0.135122\n", - " 0.064131\n", - " 0.135230\n", - " 3.986364\n", - " 4.001050\n", + " 0.241268\n", + " 0.373916\n", + " 0.111114\n", + " 0.034341\n", + " 0.079956\n", + " 0.102161\n", + " 4.247093\n", + " 4.119348\n", " \n", " \n", " pontiac\n", - " 0.198725\n", - " 0.202958\n", - " 0.363313\n", - " 0.118067\n", - " 0.065794\n", - " 0.110013\n", - " 4.366848\n", - " 4.285907\n", + " 0.190257\n", + " 0.430994\n", + " 0.110610\n", + " 0.026520\n", + " 0.078449\n", + " 0.165777\n", + " 4.375000\n", + " 4.221752\n", " \n", " \n", " porsche\n", - " 0.336036\n", - " 0.167812\n", - " 0.410677\n", - " 0.099369\n", - " 0.062932\n", - " 0.110313\n", - " 4.500000\n", - " 4.463378\n", + " 0.145226\n", + " 0.510727\n", + " 0.093447\n", + " 0.026101\n", + " 0.080697\n", + " 0.382931\n", + " 4.662500\n", + " 4.552274\n", " \n", " \n", " ram\n", - " 0.049370\n", - " 0.274420\n", - " 0.316925\n", - " 0.117071\n", - " 0.055857\n", - " 0.120195\n", - " 3.731818\n", - " 4.006690\n", + " 0.240626\n", + " 0.367544\n", + " 0.108349\n", + " 0.040745\n", + " 0.076267\n", + " 0.000294\n", + " 3.861111\n", + " 4.113366\n", " \n", " \n", " rolls-royce\n", - " 0.384668\n", - " 0.147421\n", - " 0.450735\n", - " 0.107796\n", - " 0.072940\n", - " 0.087974\n", - " 4.375000\n", - " 4.476755\n", + " 0.260943\n", + " 0.412009\n", + " 0.080448\n", + " 0.037039\n", + " 0.072188\n", + " 0.321649\n", + " 4.843750\n", + " 4.508778\n", " \n", " \n", " subaru\n", - " 0.157244\n", - " 0.206887\n", - " 0.325066\n", - " 0.127740\n", - " 0.067235\n", - " 0.122245\n", - " 4.104167\n", - " 4.261672\n", + " 0.202121\n", + " 0.470115\n", + " 0.103731\n", + " 0.020184\n", + " 0.070802\n", + " 0.301044\n", + " 4.257212\n", + " 4.327014\n", " \n", " \n", " suzuki\n", - " 0.187571\n", - " 0.239506\n", - " 0.297699\n", - " 0.122788\n", - " 0.056956\n", - " 0.111634\n", - " 4.210366\n", - " 4.307377\n", + " 0.206990\n", + " 0.410432\n", + " 0.111564\n", + " 0.033440\n", + " 0.075242\n", + " 0.114764\n", + " 4.235119\n", + " 4.255248\n", " \n", " \n", " tesla\n", - " 0.085558\n", - " 0.238399\n", - " 0.334405\n", - " 0.095213\n", - " 0.051772\n", - " 0.125053\n", - " 4.194853\n", - " 4.303805\n", + " 0.296216\n", + " 0.379065\n", + " 0.064811\n", + " 0.024911\n", + " 0.066818\n", + " 0.154607\n", + " 4.673387\n", + " 4.284923\n", " \n", " \n", " volvo\n", - " 0.022950\n", - " 0.252460\n", - " 0.279139\n", - " 0.143669\n", - " 0.062025\n", - " 0.130268\n", - " 4.140957\n", - " 4.079470\n", + " 0.204267\n", + " 0.433600\n", + " 0.113805\n", + " 0.024111\n", + " 0.071134\n", + " 0.220652\n", + " 4.380814\n", + " 4.281185\n", " \n", " \n", "\n", "" ], "text/plain": [ - " sentiment.score emotion.sadness emotion.joy emotion.fear \\\n", - " mean mean mean mean \n", + " emotion.sadness emotion.joy emotion.fear emotion.disgust \\\n", + " mean mean mean mean \n", "Car_Make \n", - "AMGeneral -0.594638 0.240532 0.020233 0.209863 \n", - "Acura 0.146076 0.216153 0.310447 0.112656 \n", - "AlfaRomeo 0.160632 0.232433 0.315023 0.125210 \n", - "AstonMartin 0.400920 0.201064 0.362957 0.114105 \n", - "Audi 0.252668 0.195495 0.330413 0.106549 \n", - "BMW 0.159408 0.194169 0.368360 0.120398 \n", - "Bentley 0.307778 0.172982 0.371687 0.110235 \n", - "Bugatti -0.111498 0.147069 0.482210 0.122855 \n", - "Buick 0.240515 0.232104 0.322004 0.117273 \n", - "Cadillac 0.083118 0.212384 0.282956 0.120468 \n", - "Chevrolet -0.042871 0.293122 0.217664 0.131120 \n", - "GMC 0.117682 0.215651 0.295742 0.117902 \n", - "Honda 0.034251 0.233150 0.266702 0.132316 \n", - "Toyota 0.145704 0.212612 0.327585 0.104289 \n", - "Volkswagen 0.005947 0.259367 0.276090 0.128501 \n", - "chrysler 0.063154 0.222972 0.320289 0.118277 \n", - "dodge 0.149016 0.228312 0.319779 0.117976 \n", - "ferrari 0.486829 0.144508 0.437605 0.095123 \n", - "fiat 0.127402 0.228702 0.359161 0.124966 \n", - "ford 0.244159 0.206878 0.331643 0.109789 \n", - "genesis 0.194682 0.185214 0.281426 0.118825 \n", - "hummer 0.188878 0.193048 0.345909 0.109266 \n", - "hyundai 0.065004 0.264548 0.307687 0.115017 \n", - "infiniti 0.341308 0.182078 0.344738 0.116468 \n", - "isuzu 0.075290 0.219361 0.266676 0.126747 \n", - "jaguar 0.253660 0.178868 0.378857 0.095227 \n", - "jeep 0.077878 0.244649 0.344078 0.111979 \n", - "kia 0.253933 0.198484 0.367603 0.104703 \n", - "lamborghini 0.412362 0.091284 0.396143 0.085355 \n", - "land-rover 0.244489 0.202602 0.334492 0.120057 \n", - "lexus 0.200350 0.183309 0.344489 0.109256 \n", - "lincoln 0.291578 0.201160 0.330980 0.125137 \n", - "lotus 0.249474 0.186374 0.368691 0.135556 \n", - "maserati 0.250559 0.219400 0.398471 0.101808 \n", - "maybach 0.646201 0.113256 0.601533 0.047746 \n", - "mazda 0.187200 0.202740 0.345586 0.108034 \n", - "mclaren 0.781623 0.176243 0.331202 0.111601 \n", - "mercedes-benz 0.253613 0.212727 0.314652 0.103435 \n", - "mercury 0.223070 0.226492 0.308719 0.116383 \n", - "mini 0.178291 0.214415 0.391739 0.117223 \n", - "mitsubishi 0.306894 0.174691 0.380305 0.098874 \n", - "nissan 0.047395 0.264765 0.244476 0.135122 \n", - "pontiac 0.198725 0.202958 0.363313 0.118067 \n", - "porsche 0.336036 0.167812 0.410677 0.099369 \n", - "ram 0.049370 0.274420 0.316925 0.117071 \n", - "rolls-royce 0.384668 0.147421 0.450735 0.107796 \n", - "subaru 0.157244 0.206887 0.325066 0.127740 \n", - "suzuki 0.187571 0.239506 0.297699 0.122788 \n", - "tesla 0.085558 0.238399 0.334405 0.095213 \n", - "volvo 0.022950 0.252460 0.279139 0.143669 \n", + "AMGeneral 0.233502 0.416527 0.149416 0.030530 \n", + "Acura 0.186803 0.467307 0.134082 0.020744 \n", + "AlfaRomeo 0.179048 0.434307 0.101048 0.030507 \n", + "AstonMartin 0.161465 0.532924 0.093979 0.030943 \n", + "Audi 0.196609 0.490965 0.092430 0.021402 \n", + "BMW 0.191940 0.474563 0.086499 0.024038 \n", + "Bentley 0.187771 0.528449 0.089768 0.028513 \n", + "Bugatti 0.188314 0.587727 0.055366 0.023677 \n", + "Buick 0.260452 0.392718 0.099129 0.025511 \n", + "Cadillac 0.218803 0.429976 0.102534 0.030479 \n", + "Chevrolet 0.200984 0.441252 0.102457 0.037943 \n", + "GMC 0.218755 0.416075 0.111135 0.033378 \n", + "Honda 0.191337 0.418301 0.117569 0.029766 \n", + "Toyota 0.199025 0.431638 0.104728 0.027125 \n", + "Volkswagen 0.190767 0.429078 0.126892 0.031965 \n", + "chrysler 0.234828 0.398700 0.116330 0.035023 \n", + "dodge 0.218513 0.408767 0.119761 0.026407 \n", + "ferrari 0.159649 0.539798 0.108343 0.019731 \n", + "fiat 0.203202 0.401303 0.100537 0.030897 \n", + "ford 0.238288 0.362188 0.121460 0.028063 \n", + "genesis 0.211237 0.430926 0.078043 0.031972 \n", + "hummer 0.181750 0.502888 0.126320 0.027008 \n", + "hyundai 0.220990 0.393721 0.096735 0.027251 \n", + "infiniti 0.200538 0.469661 0.090667 0.024671 \n", + "isuzu 0.201127 0.404813 0.122677 0.028856 \n", + "jaguar 0.163703 0.556705 0.086785 0.025675 \n", + "jeep 0.253255 0.396047 0.104165 0.025074 \n", + "kia 0.266675 0.394792 0.111849 0.025904 \n", + "lamborghini 0.127094 0.629176 0.082221 0.029788 \n", + "land-rover 0.274524 0.355115 0.109827 0.034846 \n", + "lexus 0.202997 0.439800 0.100562 0.031977 \n", + "lincoln 0.213433 0.466320 0.112260 0.027192 \n", + "lotus 0.158467 0.457916 0.137350 0.023458 \n", + "maserati 0.174925 0.523992 0.087822 0.037616 \n", + "maybach 0.178194 0.515520 0.077657 0.015687 \n", + "mazda 0.203442 0.444971 0.111021 0.026170 \n", + "mercedes-benz 0.227015 0.387488 0.105910 0.026781 \n", + "mercury 0.200664 0.462219 0.105958 0.025067 \n", + "mini 0.218531 0.443708 0.096154 0.026429 \n", + "mitsubishi 0.175781 0.481554 0.118347 0.025169 \n", + "nissan 0.241268 0.373916 0.111114 0.034341 \n", + "pontiac 0.190257 0.430994 0.110610 0.026520 \n", + "porsche 0.145226 0.510727 0.093447 0.026101 \n", + "ram 0.240626 0.367544 0.108349 0.040745 \n", + "rolls-royce 0.260943 0.412009 0.080448 0.037039 \n", + "subaru 0.202121 0.470115 0.103731 0.020184 \n", + "suzuki 0.206990 0.410432 0.111564 0.033440 \n", + "tesla 0.296216 0.379065 0.064811 0.024911 \n", + "volvo 0.204267 0.433600 0.113805 0.024111 \n", "\n", - " emotion.disgust emotion.anger Rating\\r Predicted_Y \n", - " mean mean mean mean \n", + " emotion.anger sentiment.score Rating\\r Predicted_Y \n", + " mean mean mean mean \n", "Car_Make \n", - "AMGeneral 0.148217 0.280780 3.000000 3.194602 \n", - "Acura 0.088328 0.118972 4.209746 4.227164 \n", - "AlfaRomeo 0.082053 0.122382 4.388889 4.322972 \n", - "AstonMartin 0.061657 0.115535 4.326087 4.448320 \n", - "Audi 0.059369 0.103690 4.445755 4.365891 \n", - "BMW 0.061844 0.123578 4.513889 4.293852 \n", - "Bentley 0.078887 0.130370 4.229730 4.410651 \n", - "Bugatti 0.072881 0.097407 4.250000 4.382092 \n", - "Buick 0.075984 0.112750 4.292373 4.254791 \n", - "Cadillac 0.074504 0.128036 4.112500 4.160872 \n", - "Chevrolet 0.075338 0.136536 4.103125 3.971316 \n", - "GMC 0.059368 0.115138 4.084302 4.206823 \n", - "Honda 0.061904 0.138990 4.255435 4.059390 \n", - "Toyota 0.060387 0.109918 4.222826 4.311795 \n", - "Volkswagen 0.073617 0.139398 4.043478 3.980834 \n", - "chrysler 0.077105 0.126091 3.994898 4.036067 \n", - "dodge 0.061401 0.115340 4.259146 4.250613 \n", - "ferrari 0.055691 0.092777 4.684211 4.588468 \n", - "fiat 0.065926 0.135856 3.957500 4.083914 \n", - "ford 0.072558 0.116549 4.267857 4.332619 \n", - "genesis 0.056142 0.144689 4.076923 4.263187 \n", - "hummer 0.057429 0.106301 4.321429 4.332991 \n", - "hyundai 0.057827 0.136106 4.082500 4.064445 \n", - "infiniti 0.059517 0.111160 4.553977 4.423686 \n", - "isuzu 0.066575 0.114220 4.309211 4.228860 \n", - "jaguar 0.061614 0.106031 4.439815 4.317453 \n", - "jeep 0.063442 0.115510 3.899554 4.093507 \n", - "kia 0.066781 0.111330 4.219444 4.330955 \n", - "lamborghini 0.062690 0.083136 4.613636 4.633120 \n", - "land-rover 0.058127 0.101070 4.154762 4.383457 \n", - "lexus 0.050643 0.120852 4.349432 4.253620 \n", - "lincoln 0.069068 0.122796 4.494318 4.392293 \n", - "lotus 0.063595 0.132431 4.590909 4.478621 \n", - "maserati 0.060194 0.111714 4.439103 4.319154 \n", - "maybach 0.043509 0.071882 4.732143 4.710683 \n", - "mazda 0.075693 0.109664 4.346429 4.247244 \n", - "mclaren 0.068376 0.115666 5.000000 4.694830 \n", - "mercedes-benz 0.057229 0.122127 4.500000 4.275059 \n", - "mercury 0.060396 0.110053 4.466837 4.308947 \n", - "mini 0.063220 0.149629 4.161932 4.272955 \n", - "mitsubishi 0.058135 0.103309 4.517045 4.431931 \n", - "nissan 0.064131 0.135230 3.986364 4.001050 \n", - "pontiac 0.065794 0.110013 4.366848 4.285907 \n", - "porsche 0.062932 0.110313 4.500000 4.463378 \n", - "ram 0.055857 0.120195 3.731818 4.006690 \n", - "rolls-royce 0.072940 0.087974 4.375000 4.476755 \n", - "subaru 0.067235 0.122245 4.104167 4.261672 \n", - "suzuki 0.056956 0.111634 4.210366 4.307377 \n", - "tesla 0.051772 0.125053 4.194853 4.303805 \n", - "volvo 0.062025 0.130268 4.140957 4.079470 " + "AMGeneral 0.065171 0.021626 4.833333 4.264005 \n", + "Acura 0.064254 0.330192 4.538690 4.455716 \n", + "AlfaRomeo 0.088323 0.268080 4.187500 4.435494 \n", + "AstonMartin 0.063027 0.470149 4.613636 4.631018 \n", + "Audi 0.059860 0.303431 4.453431 4.421119 \n", + "BMW 0.072675 0.243201 4.468750 4.354955 \n", + "Bentley 0.057980 0.441103 4.239583 4.57587 \n", + "Bugatti 0.054105 0.430882 4.750000 4.718716 \n", + "Buick 0.086554 0.088404 4.162736 4.075432 \n", + "Cadillac 0.069962 0.274075 4.395408 4.335975 \n", + "Chevrolet 0.074343 0.173096 4.104730 4.23044 \n", + "GMC 0.067579 0.071438 4.089912 4.138499 \n", + "Honda 0.063076 0.118789 3.832386 4.130003 \n", + "Toyota 0.070333 0.154561 4.350543 4.243079 \n", + "Volkswagen 0.071180 0.130390 4.396875 4.17746 \n", + "chrysler 0.075663 0.086065 4.140957 4.168451 \n", + "dodge 0.076249 0.100277 4.133929 4.163826 \n", + "ferrari 0.082763 0.463863 4.767241 4.530158 \n", + "fiat 0.076235 0.087065 3.818878 4.134359 \n", + "ford 0.088316 0.078135 4.040094 4.005134 \n", + "genesis 0.057856 0.156253 4.608696 4.316763 \n", + "hummer 0.055950 0.297203 4.404605 4.462752 \n", + "hyundai 0.079915 0.161732 4.109375 4.14899 \n", + "infiniti 0.059761 0.322187 4.566860 4.393907 \n", + "isuzu 0.101334 0.205943 4.220238 4.306578 \n", + "jaguar 0.055537 0.375661 4.584091 4.497573 \n", + "jeep 0.079216 0.106891 4.108607 4.15378 \n", + "kia 0.070382 0.141621 4.141827 4.12064 \n", + "lamborghini 0.052919 0.657044 4.725000 4.665769 \n", + "land-rover 0.086169 0.080642 3.848837 4.0177 \n", + "lexus 0.076374 0.231606 4.306122 4.294433 \n", + "lincoln 0.075736 0.163414 4.269231 4.264042 \n", + "lotus 0.080445 0.307135 4.702381 4.490183 \n", + "maserati 0.071795 0.311256 4.431250 4.394369 \n", + "maybach 0.072357 0.633714 4.958333 4.733771 \n", + "mazda 0.062387 0.230268 4.479651 4.318612 \n", + "mercedes-benz 0.085045 0.091385 4.095745 4.094125 \n", + "mercury 0.063838 0.246360 4.311224 4.390451 \n", + "mini 0.071672 0.167861 4.036184 4.190949 \n", + "mitsubishi 0.070454 0.300219 4.346698 4.417798 \n", + "nissan 0.079956 0.102161 4.247093 4.119348 \n", + "pontiac 0.078449 0.165777 4.375000 4.221752 \n", + "porsche 0.080697 0.382931 4.662500 4.552274 \n", + "ram 0.076267 0.000294 3.861111 4.113366 \n", + "rolls-royce 0.072188 0.321649 4.843750 4.508778 \n", + "subaru 0.070802 0.301044 4.257212 4.327014 \n", + "suzuki 0.075242 0.114764 4.235119 4.255248 \n", + "tesla 0.066818 0.154607 4.673387 4.284923 \n", + "volvo 0.071134 0.220652 4.380814 4.281185 " ] }, "execution_count": 53, @@ -5893,7 +5432,10 @@ } ], "source": [ - "agg_grouped_test_set = test_set.groupby('Car_Make').agg(['mean'])\n", + "agg_grouped_test_set = (\n", + " test_set[sentiment_cols + ['Car_Make', 'Rating\\r', 'Predicted_Y']]\n", + " .groupby('Car_Make')\n", + " .agg(['mean']))\n", "agg_grouped_test_set" ] }, @@ -5949,87 +5491,87 @@ " \n", " \n", " AMGeneral\n", + " 3\n", + " 3\n", + " 3\n", + " 3\n", + " 3\n", + " 3\n", " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " 3\n", + " 3\n", " \n", " \n", " Acura\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 21\n", - " 59\n", - " 58\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 12\n", + " 42\n", + " 41\n", " \n", " \n", " AlfaRomeo\n", - " 18\n", - " 18\n", - " 18\n", - " 18\n", - " 18\n", - " 18\n", + " 16\n", + " 16\n", + " 16\n", + " 16\n", + " 16\n", + " 16\n", " 4\n", - " 18\n", - " 18\n", + " 16\n", + " 16\n", " \n", " \n", " AstonMartin\n", - " 23\n", - " 23\n", - " 23\n", - " 23\n", - " 23\n", - " 23\n", - " 12\n", - " 23\n", - " 23\n", + " 33\n", + " 33\n", + " 33\n", + " 33\n", + " 33\n", + " 33\n", + " 10\n", + " 33\n", + " 32\n", " \n", " \n", " Audi\n", - " 53\n", - " 53\n", - " 53\n", - " 53\n", - " 53\n", - " 53\n", - " 16\n", - " 53\n", - " 52\n", + " 51\n", + " 51\n", + " 51\n", + " 51\n", + " 51\n", + " 51\n", + " 15\n", + " 51\n", + " 47\n", " \n", " \n", " BMW\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 12\n", - " 45\n", - " 45\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 16\n", + " 48\n", + " 44\n", " \n", " \n", " Bentley\n", - " 37\n", - " 37\n", - " 37\n", - " 37\n", - " 37\n", - " 37\n", - " 13\n", - " 37\n", - " 37\n", + " 36\n", + " 36\n", + " 36\n", + " 36\n", + " 36\n", + " 36\n", + " 14\n", + " 36\n", + " 32\n", " \n", " \n", " Bugatti\n", @@ -6045,63 +5587,63 @@ " \n", " \n", " Buick\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 59\n", - " 18\n", - " 59\n", - " 55\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 19\n", + " 53\n", + " 51\n", " \n", " \n", " Cadillac\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 16\n", - " 40\n", - " 40\n", + " 49\n", + " 49\n", + " 49\n", + " 49\n", + " 49\n", + " 49\n", + " 15\n", + " 49\n", + " 47\n", " \n", " \n", " Chevrolet\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 40\n", - " 15\n", - " 40\n", - " 40\n", + " 37\n", + " 37\n", + " 37\n", + " 37\n", + " 37\n", + " 37\n", + " 17\n", + " 37\n", + " 37\n", " \n", " \n", " GMC\n", - " 43\n", - " 43\n", - " 43\n", - " 43\n", - " 43\n", - " 43\n", - " 17\n", - " 43\n", - " 43\n", + " 57\n", + " 57\n", + " 57\n", + " 57\n", + " 57\n", + " 57\n", + " 21\n", + " 57\n", + " 55\n", " \n", " \n", " Honda\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 17\n", - " 46\n", - " 46\n", + " 44\n", + " 44\n", + " 44\n", + " 44\n", + " 44\n", + " 44\n", + " 18\n", + " 44\n", + " 42\n", " \n", " \n", " Toyota\n", @@ -6111,297 +5653,285 @@ " 46\n", " 46\n", " 46\n", - " 16\n", - " 46\n", + " 11\n", " 46\n", + " 43\n", " \n", " \n", " Volkswagen\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 18\n", - " 46\n", - " 44\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 15\n", + " 40\n", + " 36\n", " \n", " \n", " chrysler\n", - " 49\n", - " 49\n", - " 49\n", - " 49\n", - " 49\n", - " 49\n", - " 21\n", - " 49\n", - " 48\n", + " 46\n", + " 47\n", + " 47\n", + " 47\n", + " 47\n", + " 47\n", + " 19\n", + " 47\n", + " 47\n", " \n", " \n", " dodge\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 15\n", - " 41\n", - " 41\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 16\n", + " 42\n", + " 39\n", " \n", " \n", " ferrari\n", - " 19\n", - " 19\n", - " 19\n", - " 19\n", - " 19\n", - " 19\n", + " 29\n", + " 29\n", + " 29\n", + " 29\n", + " 29\n", + " 29\n", " 7\n", - " 19\n", - " 19\n", + " 29\n", + " 26\n", " \n", " \n", " fiat\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 11\n", - " 50\n", - " 48\n", - " \n", - " \n", - " ford\n", " 49\n", " 49\n", " 49\n", " 49\n", " 49\n", " 49\n", - " 18\n", + " 11\n", " 49\n", - " 46\n", + " 48\n", + " \n", + " \n", + " ford\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 18\n", + " 53\n", + " 52\n", " \n", " \n", " genesis\n", - " 13\n", - " 13\n", - " 13\n", - " 13\n", - " 13\n", - " 13\n", - " 4\n", - " 13\n", - " 13\n", + " 23\n", + " 23\n", + " 23\n", + " 23\n", + " 23\n", + " 23\n", + " 3\n", + " 23\n", + " 22\n", " \n", " \n", " hummer\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", " 15\n", - " 42\n", - " 41\n", + " 38\n", + " 38\n", " \n", " \n", " hyundai\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 50\n", - " 17\n", - " 50\n", - " 49\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 48\n", + " 16\n", + " 48\n", + " 47\n", " \n", " \n", " infiniti\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 11\n", - " 44\n", - " 44\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 13\n", + " 43\n", + " 41\n", " \n", " \n", " isuzu\n", - " 38\n", - " 38\n", - " 38\n", - " 38\n", - " 38\n", - " 38\n", - " 15\n", - " 38\n", - " 38\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 42\n", + " 16\n", + " 42\n", + " 42\n", " \n", " \n", " jaguar\n", - " 54\n", - " 54\n", - " 54\n", - " 54\n", - " 54\n", - " 54\n", - " 15\n", - " 54\n", - " 50\n", + " 55\n", + " 55\n", + " 55\n", + " 55\n", + " 55\n", + " 55\n", + " 13\n", + " 55\n", + " 48\n", " \n", " \n", " jeep\n", - " 56\n", - " 56\n", - " 56\n", - " 56\n", - " 56\n", - " 56\n", - " 18\n", - " 56\n", - " 55\n", + " 61\n", + " 61\n", + " 61\n", + " 61\n", + " 61\n", + " 61\n", + " 20\n", + " 61\n", + " 60\n", " \n", " \n", " kia\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 45\n", - " 15\n", - " 45\n", - " 44\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 20\n", + " 52\n", + " 50\n", " \n", " \n", " lamborghini\n", - " 11\n", - " 11\n", - " 11\n", - " 11\n", - " 11\n", - " 11\n", - " 5\n", - " 11\n", - " 11\n", + " 20\n", + " 20\n", + " 20\n", + " 20\n", + " 20\n", + " 20\n", + " 7\n", + " 20\n", + " 16\n", " \n", " \n", " land-rover\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", - " 42\n", - " 14\n", - " 42\n", - " 41\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 21\n", + " 43\n", + " 43\n", " \n", " \n", " lexus\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", + " 48\n", + " 49\n", + " 49\n", + " 49\n", + " 49\n", + " 49\n", " 15\n", - " 44\n", - " 43\n", + " 49\n", + " 46\n", " \n", " \n", " lincoln\n", - " 43\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 12\n", - " 44\n", - " 44\n", - " \n", - " \n", - " lotus\n", - " 22\n", - " 22\n", - " 22\n", - " 22\n", - " 22\n", - " 22\n", - " 10\n", - " 22\n", - " 20\n", - " \n", - " \n", - " maserati\n", " 39\n", " 39\n", " 39\n", " 39\n", " 39\n", " 39\n", - " 13\n", + " 16\n", " 39\n", - " 37\n", + " 36\n", + " \n", + " \n", + " lotus\n", + " 21\n", + " 21\n", + " 21\n", + " 21\n", + " 21\n", + " 21\n", + " 9\n", + " 21\n", + " 21\n", + " \n", + " \n", + " maserati\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 13\n", + " 40\n", + " 38\n", " \n", " \n", " maybach\n", - " 7\n", - " 7\n", - " 7\n", - " 7\n", - " 7\n", - " 7\n", " 3\n", - " 7\n", - " 6\n", + " 3\n", + " 3\n", + " 3\n", + " 3\n", + " 3\n", + " 2\n", + " 3\n", + " 3\n", " \n", " \n", " mazda\n", - " 35\n", - " 35\n", - " 35\n", - " 35\n", - " 35\n", - " 35\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", " 14\n", - " 35\n", - " 33\n", - " \n", - " \n", - " mclaren\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", + " 43\n", + " 42\n", " \n", " \n", " mercedes-benz\n", - " 52\n", - " 52\n", - " 52\n", - " 52\n", - " 52\n", - " 52\n", - " 14\n", - " 52\n", - " 50\n", + " 47\n", + " 47\n", + " 47\n", + " 47\n", + " 47\n", + " 47\n", + " 19\n", + " 47\n", + " 45\n", " \n", " \n", " mercury\n", @@ -6411,141 +5941,141 @@ " 49\n", " 49\n", " 49\n", - " 13\n", + " 18\n", " 49\n", " 48\n", " \n", " \n", " mini\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 17\n", - " 44\n", - " 43\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", + " 38\n", + " 15\n", + " 38\n", + " 33\n", " \n", " \n", " mitsubishi\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 44\n", - " 12\n", - " 44\n", - " 40\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 53\n", + " 18\n", + " 53\n", + " 51\n", " \n", " \n", " nissan\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", - " 17\n", - " 55\n", - " 55\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 18\n", + " 43\n", + " 43\n", " \n", " \n", " pontiac\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", - " 46\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", " 14\n", - " 46\n", - " 45\n", + " 40\n", + " 39\n", " \n", " \n", " porsche\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 12\n", - " 41\n", - " 39\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 40\n", + " 10\n", + " 40\n", + " 40\n", " \n", " \n", " ram\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", - " 55\n", + " 34\n", + " 36\n", + " 36\n", + " 36\n", + " 36\n", + " 36\n", " 10\n", - " 55\n", - " 53\n", + " 36\n", + " 36\n", " \n", " \n", " rolls-royce\n", - " 8\n", - " 8\n", - " 8\n", - " 8\n", - " 8\n", - " 8\n", - " 5\n", - " 8\n", - " 8\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 4\n", + " 3\n", + " 4\n", + " 4\n", " \n", " \n", " subaru\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 52\n", + " 16\n", + " 52\n", + " 48\n", + " \n", + " \n", + " suzuki\n", " 42\n", " 42\n", " 42\n", " 42\n", " 42\n", " 42\n", - " 15\n", + " 16\n", " 42\n", - " 40\n", - " \n", - " \n", - " suzuki\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 41\n", - " 18\n", - " 41\n", " 41\n", " \n", " \n", " tesla\n", - " 34\n", - " 34\n", - " 34\n", - " 34\n", - " 34\n", - " 34\n", + " 31\n", + " 31\n", + " 31\n", + " 31\n", + " 31\n", + " 31\n", " 5\n", - " 34\n", - " 34\n", + " 31\n", + " 31\n", " \n", " \n", " volvo\n", - " 47\n", - " 47\n", - " 47\n", - " 47\n", - " 47\n", - " 47\n", - " 17\n", - " 47\n", - " 47\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 43\n", + " 15\n", + " 43\n", + " 42\n", " \n", " \n", "\n", @@ -6554,162 +6084,159 @@ "text/plain": [ " sentiment.score emotion.sadness emotion.joy emotion.fear \\\n", "Car_Make \n", - "AMGeneral 2 2 2 2 \n", - "Acura 59 59 59 59 \n", - "AlfaRomeo 18 18 18 18 \n", - "AstonMartin 23 23 23 23 \n", - "Audi 53 53 53 53 \n", - "BMW 45 45 45 45 \n", - "Bentley 37 37 37 37 \n", + "AMGeneral 3 3 3 3 \n", + "Acura 42 42 42 42 \n", + "AlfaRomeo 16 16 16 16 \n", + "AstonMartin 33 33 33 33 \n", + "Audi 51 51 51 51 \n", + "BMW 48 48 48 48 \n", + "Bentley 36 36 36 36 \n", "Bugatti 1 1 1 1 \n", - "Buick 59 59 59 59 \n", - "Cadillac 40 40 40 40 \n", - "Chevrolet 40 40 40 40 \n", - "GMC 43 43 43 43 \n", - "Honda 46 46 46 46 \n", + "Buick 53 53 53 53 \n", + "Cadillac 49 49 49 49 \n", + "Chevrolet 37 37 37 37 \n", + "GMC 57 57 57 57 \n", + "Honda 44 44 44 44 \n", "Toyota 46 46 46 46 \n", - "Volkswagen 46 46 46 46 \n", - "chrysler 49 49 49 49 \n", - "dodge 41 41 41 41 \n", - "ferrari 19 19 19 19 \n", - "fiat 50 50 50 50 \n", - "ford 49 49 49 49 \n", - "genesis 13 13 13 13 \n", - "hummer 42 42 42 42 \n", - "hyundai 50 50 50 50 \n", - "infiniti 44 44 44 44 \n", - "isuzu 38 38 38 38 \n", - "jaguar 54 54 54 54 \n", - "jeep 56 56 56 56 \n", - "kia 45 45 45 45 \n", - "lamborghini 11 11 11 11 \n", - "land-rover 42 42 42 42 \n", - "lexus 44 44 44 44 \n", - "lincoln 43 44 44 44 \n", - "lotus 22 22 22 22 \n", - "maserati 39 39 39 39 \n", - "maybach 7 7 7 7 \n", - "mazda 35 35 35 35 \n", - "mclaren 1 1 1 1 \n", - "mercedes-benz 52 52 52 52 \n", + "Volkswagen 40 40 40 40 \n", + "chrysler 46 47 47 47 \n", + "dodge 42 42 42 42 \n", + "ferrari 29 29 29 29 \n", + "fiat 49 49 49 49 \n", + "ford 53 53 53 53 \n", + "genesis 23 23 23 23 \n", + "hummer 38 38 38 38 \n", + "hyundai 48 48 48 48 \n", + "infiniti 43 43 43 43 \n", + "isuzu 42 42 42 42 \n", + "jaguar 55 55 55 55 \n", + "jeep 61 61 61 61 \n", + "kia 52 52 52 52 \n", + "lamborghini 20 20 20 20 \n", + "land-rover 43 43 43 43 \n", + "lexus 48 49 49 49 \n", + "lincoln 39 39 39 39 \n", + "lotus 21 21 21 21 \n", + "maserati 40 40 40 40 \n", + "maybach 3 3 3 3 \n", + "mazda 43 43 43 43 \n", + "mercedes-benz 47 47 47 47 \n", "mercury 49 49 49 49 \n", - "mini 44 44 44 44 \n", - "mitsubishi 44 44 44 44 \n", - "nissan 55 55 55 55 \n", - "pontiac 46 46 46 46 \n", - "porsche 41 41 41 41 \n", - "ram 55 55 55 55 \n", - "rolls-royce 8 8 8 8 \n", - "subaru 42 42 42 42 \n", - "suzuki 41 41 41 41 \n", - "tesla 34 34 34 34 \n", - "volvo 47 47 47 47 \n", + "mini 38 38 38 38 \n", + "mitsubishi 53 53 53 53 \n", + "nissan 43 43 43 43 \n", + "pontiac 40 40 40 40 \n", + "porsche 40 40 40 40 \n", + "ram 34 36 36 36 \n", + "rolls-royce 4 4 4 4 \n", + "subaru 52 52 52 52 \n", + "suzuki 42 42 42 42 \n", + "tesla 31 31 31 31 \n", + "volvo 43 43 43 43 \n", "\n", " emotion.disgust emotion.anger Rating\\r Review_Content \\\n", "Car_Make \n", - "AMGeneral 2 2 2 2 \n", - "Acura 59 59 21 59 \n", - "AlfaRomeo 18 18 4 18 \n", - "AstonMartin 23 23 12 23 \n", - "Audi 53 53 16 53 \n", - "BMW 45 45 12 45 \n", - "Bentley 37 37 13 37 \n", + "AMGeneral 3 3 2 3 \n", + "Acura 42 42 12 42 \n", + "AlfaRomeo 16 16 4 16 \n", + "AstonMartin 33 33 10 33 \n", + "Audi 51 51 15 51 \n", + "BMW 48 48 16 48 \n", + "Bentley 36 36 14 36 \n", "Bugatti 1 1 1 1 \n", - "Buick 59 59 18 59 \n", - "Cadillac 40 40 16 40 \n", - "Chevrolet 40 40 15 40 \n", - "GMC 43 43 17 43 \n", - "Honda 46 46 17 46 \n", - "Toyota 46 46 16 46 \n", - "Volkswagen 46 46 18 46 \n", - "chrysler 49 49 21 49 \n", - "dodge 41 41 15 41 \n", - "ferrari 19 19 7 19 \n", - "fiat 50 50 11 50 \n", - "ford 49 49 18 49 \n", - "genesis 13 13 4 13 \n", - "hummer 42 42 15 42 \n", - "hyundai 50 50 17 50 \n", - "infiniti 44 44 11 44 \n", - "isuzu 38 38 15 38 \n", - "jaguar 54 54 15 54 \n", - "jeep 56 56 18 56 \n", - "kia 45 45 15 45 \n", - "lamborghini 11 11 5 11 \n", - "land-rover 42 42 14 42 \n", - "lexus 44 44 15 44 \n", - "lincoln 44 44 12 44 \n", - "lotus 22 22 10 22 \n", - "maserati 39 39 13 39 \n", - "maybach 7 7 3 7 \n", - "mazda 35 35 14 35 \n", - "mclaren 1 1 1 1 \n", - "mercedes-benz 52 52 14 52 \n", - "mercury 49 49 13 49 \n", - "mini 44 44 17 44 \n", - "mitsubishi 44 44 12 44 \n", - "nissan 55 55 17 55 \n", - "pontiac 46 46 14 46 \n", - "porsche 41 41 12 41 \n", - "ram 55 55 10 55 \n", - "rolls-royce 8 8 5 8 \n", - "subaru 42 42 15 42 \n", - "suzuki 41 41 18 41 \n", - "tesla 34 34 5 34 \n", - "volvo 47 47 17 47 \n", + "Buick 53 53 19 53 \n", + "Cadillac 49 49 15 49 \n", + "Chevrolet 37 37 17 37 \n", + "GMC 57 57 21 57 \n", + "Honda 44 44 18 44 \n", + "Toyota 46 46 11 46 \n", + "Volkswagen 40 40 15 40 \n", + "chrysler 47 47 19 47 \n", + "dodge 42 42 16 42 \n", + "ferrari 29 29 7 29 \n", + "fiat 49 49 11 49 \n", + "ford 53 53 18 53 \n", + "genesis 23 23 3 23 \n", + "hummer 38 38 15 38 \n", + "hyundai 48 48 16 48 \n", + "infiniti 43 43 13 43 \n", + "isuzu 42 42 16 42 \n", + "jaguar 55 55 13 55 \n", + "jeep 61 61 20 61 \n", + "kia 52 52 20 52 \n", + "lamborghini 20 20 7 20 \n", + "land-rover 43 43 21 43 \n", + "lexus 49 49 15 49 \n", + "lincoln 39 39 16 39 \n", + "lotus 21 21 9 21 \n", + "maserati 40 40 13 40 \n", + "maybach 3 3 2 3 \n", + "mazda 43 43 14 43 \n", + "mercedes-benz 47 47 19 47 \n", + "mercury 49 49 18 49 \n", + "mini 38 38 15 38 \n", + "mitsubishi 53 53 18 53 \n", + "nissan 43 43 18 43 \n", + "pontiac 40 40 14 40 \n", + "porsche 40 40 10 40 \n", + "ram 36 36 10 36 \n", + "rolls-royce 4 4 3 4 \n", + "subaru 52 52 16 52 \n", + "suzuki 42 42 16 42 \n", + "tesla 31 31 5 31 \n", + "volvo 43 43 15 43 \n", "\n", " Predicted_Y \n", "Car_Make \n", - "AMGeneral 2 \n", - "Acura 58 \n", - "AlfaRomeo 18 \n", - "AstonMartin 23 \n", - "Audi 52 \n", - "BMW 45 \n", - "Bentley 37 \n", + "AMGeneral 3 \n", + "Acura 41 \n", + "AlfaRomeo 16 \n", + "AstonMartin 32 \n", + "Audi 47 \n", + "BMW 44 \n", + "Bentley 32 \n", "Bugatti 1 \n", - "Buick 55 \n", - "Cadillac 40 \n", - "Chevrolet 40 \n", - "GMC 43 \n", - "Honda 46 \n", - "Toyota 46 \n", - "Volkswagen 44 \n", - "chrysler 48 \n", - "dodge 41 \n", - "ferrari 19 \n", + "Buick 51 \n", + "Cadillac 47 \n", + "Chevrolet 37 \n", + "GMC 55 \n", + "Honda 42 \n", + "Toyota 43 \n", + "Volkswagen 36 \n", + "chrysler 47 \n", + "dodge 39 \n", + "ferrari 26 \n", "fiat 48 \n", - "ford 46 \n", - "genesis 13 \n", - "hummer 41 \n", - "hyundai 49 \n", - "infiniti 44 \n", - "isuzu 38 \n", - "jaguar 50 \n", - "jeep 55 \n", - "kia 44 \n", - "lamborghini 11 \n", - "land-rover 41 \n", - "lexus 43 \n", - "lincoln 44 \n", - "lotus 20 \n", - "maserati 37 \n", - "maybach 6 \n", - "mazda 33 \n", - "mclaren 1 \n", - "mercedes-benz 50 \n", + "ford 52 \n", + "genesis 22 \n", + "hummer 38 \n", + "hyundai 47 \n", + "infiniti 41 \n", + "isuzu 42 \n", + "jaguar 48 \n", + "jeep 60 \n", + "kia 50 \n", + "lamborghini 16 \n", + "land-rover 43 \n", + "lexus 46 \n", + "lincoln 36 \n", + "lotus 21 \n", + "maserati 38 \n", + "maybach 3 \n", + "mazda 42 \n", + "mercedes-benz 45 \n", "mercury 48 \n", - "mini 43 \n", - "mitsubishi 40 \n", - "nissan 55 \n", - "pontiac 45 \n", - "porsche 39 \n", - "ram 53 \n", - "rolls-royce 8 \n", - "subaru 40 \n", + "mini 33 \n", + "mitsubishi 51 \n", + "nissan 43 \n", + "pontiac 39 \n", + "porsche 40 \n", + "ram 36 \n", + "rolls-royce 4 \n", + "subaru 48 \n", "suzuki 41 \n", - "tesla 34 \n", - "volvo 47 " + "tesla 31 \n", + "volvo 42 " ] }, "execution_count": 54, @@ -6733,8 +6260,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "R-Squared = 0.8021197630035817\n", - "Mean Squared Error = 0.016904870283648715\n" + "R-Squared = 0.5833818585164156\n", + "Mean Squared Error = 0.03221873091669909\n" ] } ], @@ -6802,253 +6329,248 @@ " \n", " \n", " AMGeneral\n", - " 3.000000\n", - " 3.194602\n", + " 4.833333\n", + " 4.264005\n", " \n", " \n", " Acura\n", - " 4.209746\n", - " 4.227164\n", + " 4.538690\n", + " 4.455716\n", " \n", " \n", " AlfaRomeo\n", - " 4.388889\n", - " 4.322972\n", + " 4.187500\n", + " 4.435494\n", " \n", " \n", " AstonMartin\n", - " 4.326087\n", - " 4.448320\n", + " 4.613636\n", + " 4.631018\n", " \n", " \n", " Audi\n", - " 4.445755\n", - " 4.365891\n", + " 4.453431\n", + " 4.421119\n", " \n", " \n", " BMW\n", - " 4.513889\n", - " 4.293852\n", + " 4.468750\n", + " 4.354955\n", " \n", " \n", " Bentley\n", - " 4.229730\n", - " 4.410651\n", + " 4.239583\n", + " 4.57587\n", " \n", " \n", " Bugatti\n", - " 4.250000\n", - " 4.382092\n", + " 4.750000\n", + " 4.718716\n", " \n", " \n", " Buick\n", - " 4.292373\n", - " 4.254791\n", + " 4.162736\n", + " 4.075432\n", " \n", " \n", " Cadillac\n", - " 4.112500\n", - " 4.160872\n", + " 4.395408\n", + " 4.335975\n", " \n", " \n", " Chevrolet\n", - " 4.103125\n", - " 3.971316\n", + " 4.104730\n", + " 4.23044\n", " \n", " \n", " GMC\n", - " 4.084302\n", - " 4.206823\n", + " 4.089912\n", + " 4.138499\n", " \n", " \n", " Honda\n", - " 4.255435\n", - " 4.059390\n", + " 3.832386\n", + " 4.130003\n", " \n", " \n", " Toyota\n", - " 4.222826\n", - " 4.311795\n", + " 4.350543\n", + " 4.243079\n", " \n", " \n", " Volkswagen\n", - " 4.043478\n", - " 3.980834\n", + " 4.396875\n", + " 4.17746\n", " \n", " \n", " chrysler\n", - " 3.994898\n", - " 4.036067\n", + " 4.140957\n", + " 4.168451\n", " \n", " \n", " dodge\n", - " 4.259146\n", - " 4.250613\n", + " 4.133929\n", + " 4.163826\n", " \n", " \n", " ferrari\n", - " 4.684211\n", - " 4.588468\n", + " 4.767241\n", + " 4.530158\n", " \n", " \n", " fiat\n", - " 3.957500\n", - " 4.083914\n", + " 3.818878\n", + " 4.134359\n", " \n", " \n", " ford\n", - " 4.267857\n", - " 4.332619\n", + " 4.040094\n", + " 4.005134\n", " \n", " \n", " genesis\n", - " 4.076923\n", - " 4.263187\n", + " 4.608696\n", + " 4.316763\n", " \n", " \n", " hummer\n", - " 4.321429\n", - " 4.332991\n", + " 4.404605\n", + " 4.462752\n", " \n", " \n", " hyundai\n", - " 4.082500\n", - " 4.064445\n", + " 4.109375\n", + " 4.14899\n", " \n", " \n", " infiniti\n", - " 4.553977\n", - " 4.423686\n", + " 4.566860\n", + " 4.393907\n", " \n", " \n", " isuzu\n", - " 4.309211\n", - " 4.228860\n", + " 4.220238\n", + " 4.306578\n", " \n", " \n", " jaguar\n", - " 4.439815\n", - " 4.317453\n", + " 4.584091\n", + " 4.497573\n", " \n", " \n", " jeep\n", - " 3.899554\n", - " 4.093507\n", + " 4.108607\n", + " 4.15378\n", " \n", " \n", " kia\n", - " 4.219444\n", - " 4.330955\n", + " 4.141827\n", + " 4.12064\n", " \n", " \n", " lamborghini\n", - " 4.613636\n", - " 4.633120\n", + " 4.725000\n", + " 4.665769\n", " \n", " \n", " land-rover\n", - " 4.154762\n", - " 4.383457\n", + " 3.848837\n", + " 4.0177\n", " \n", " \n", " lexus\n", - " 4.349432\n", - " 4.253620\n", + " 4.306122\n", + " 4.294433\n", " \n", " \n", " lincoln\n", - " 4.494318\n", - " 4.392293\n", + " 4.269231\n", + " 4.264042\n", " \n", " \n", " lotus\n", - " 4.590909\n", - " 4.478621\n", + " 4.702381\n", + " 4.490183\n", " \n", " \n", " maserati\n", - " 4.439103\n", - " 4.319154\n", + " 4.431250\n", + " 4.394369\n", " \n", " \n", " maybach\n", - " 4.732143\n", - " 4.710683\n", + " 4.958333\n", + " 4.733771\n", " \n", " \n", " mazda\n", - " 4.346429\n", - " 4.247244\n", - " \n", - " \n", - " mclaren\n", - " 5.000000\n", - " 4.694830\n", + " 4.479651\n", + " 4.318612\n", " \n", " \n", " mercedes-benz\n", - " 4.500000\n", - " 4.275059\n", + " 4.095745\n", + " 4.094125\n", " \n", " \n", " mercury\n", - " 4.466837\n", - " 4.308947\n", + " 4.311224\n", + " 4.390451\n", " \n", " \n", " mini\n", - " 4.161932\n", - " 4.272955\n", + " 4.036184\n", + " 4.190949\n", " \n", " \n", " mitsubishi\n", - " 4.517045\n", - " 4.431931\n", + " 4.346698\n", + " 4.417798\n", " \n", " \n", " nissan\n", - " 3.986364\n", - " 4.001050\n", + " 4.247093\n", + " 4.119348\n", " \n", " \n", " pontiac\n", - " 4.366848\n", - " 4.285907\n", + " 4.375000\n", + " 4.221752\n", " \n", " \n", " porsche\n", - " 4.500000\n", - " 4.463378\n", + " 4.662500\n", + " 4.552274\n", " \n", " \n", " ram\n", - " 3.731818\n", - " 4.006690\n", + " 3.861111\n", + " 4.113366\n", " \n", " \n", " rolls-royce\n", - " 4.375000\n", - " 4.476755\n", + " 4.843750\n", + " 4.508778\n", " \n", " \n", " subaru\n", - " 4.104167\n", - " 4.261672\n", + " 4.257212\n", + " 4.327014\n", " \n", " \n", " suzuki\n", - " 4.210366\n", - " 4.307377\n", + " 4.235119\n", + " 4.255248\n", " \n", " \n", " tesla\n", - " 4.194853\n", - " 4.303805\n", + " 4.673387\n", + " 4.284923\n", " \n", " \n", " volvo\n", - " 4.140957\n", - " 4.079470\n", + " 4.380814\n", + " 4.281185\n", " \n", " \n", "\n", @@ -7058,56 +6580,55 @@ " Rating\\r Predicted_Y\n", " mean mean\n", "Car_Make \n", - "AMGeneral 3.000000 3.194602\n", - "Acura 4.209746 4.227164\n", - "AlfaRomeo 4.388889 4.322972\n", - "AstonMartin 4.326087 4.448320\n", - "Audi 4.445755 4.365891\n", - "BMW 4.513889 4.293852\n", - "Bentley 4.229730 4.410651\n", - "Bugatti 4.250000 4.382092\n", - "Buick 4.292373 4.254791\n", - "Cadillac 4.112500 4.160872\n", - "Chevrolet 4.103125 3.971316\n", - "GMC 4.084302 4.206823\n", - "Honda 4.255435 4.059390\n", - "Toyota 4.222826 4.311795\n", - "Volkswagen 4.043478 3.980834\n", - "chrysler 3.994898 4.036067\n", - "dodge 4.259146 4.250613\n", - "ferrari 4.684211 4.588468\n", - "fiat 3.957500 4.083914\n", - "ford 4.267857 4.332619\n", - "genesis 4.076923 4.263187\n", - "hummer 4.321429 4.332991\n", - "hyundai 4.082500 4.064445\n", - "infiniti 4.553977 4.423686\n", - "isuzu 4.309211 4.228860\n", - "jaguar 4.439815 4.317453\n", - "jeep 3.899554 4.093507\n", - "kia 4.219444 4.330955\n", - "lamborghini 4.613636 4.633120\n", - "land-rover 4.154762 4.383457\n", - "lexus 4.349432 4.253620\n", - "lincoln 4.494318 4.392293\n", - "lotus 4.590909 4.478621\n", - "maserati 4.439103 4.319154\n", - "maybach 4.732143 4.710683\n", - "mazda 4.346429 4.247244\n", - "mclaren 5.000000 4.694830\n", - "mercedes-benz 4.500000 4.275059\n", - "mercury 4.466837 4.308947\n", - "mini 4.161932 4.272955\n", - "mitsubishi 4.517045 4.431931\n", - "nissan 3.986364 4.001050\n", - "pontiac 4.366848 4.285907\n", - "porsche 4.500000 4.463378\n", - "ram 3.731818 4.006690\n", - "rolls-royce 4.375000 4.476755\n", - "subaru 4.104167 4.261672\n", - "suzuki 4.210366 4.307377\n", - "tesla 4.194853 4.303805\n", - "volvo 4.140957 4.079470" + "AMGeneral 4.833333 4.264005\n", + "Acura 4.538690 4.455716\n", + "AlfaRomeo 4.187500 4.435494\n", + "AstonMartin 4.613636 4.631018\n", + "Audi 4.453431 4.421119\n", + "BMW 4.468750 4.354955\n", + "Bentley 4.239583 4.57587\n", + "Bugatti 4.750000 4.718716\n", + "Buick 4.162736 4.075432\n", + "Cadillac 4.395408 4.335975\n", + "Chevrolet 4.104730 4.23044\n", + "GMC 4.089912 4.138499\n", + "Honda 3.832386 4.130003\n", + "Toyota 4.350543 4.243079\n", + "Volkswagen 4.396875 4.17746\n", + "chrysler 4.140957 4.168451\n", + "dodge 4.133929 4.163826\n", + "ferrari 4.767241 4.530158\n", + "fiat 3.818878 4.134359\n", + "ford 4.040094 4.005134\n", + "genesis 4.608696 4.316763\n", + "hummer 4.404605 4.462752\n", + "hyundai 4.109375 4.14899\n", + "infiniti 4.566860 4.393907\n", + "isuzu 4.220238 4.306578\n", + "jaguar 4.584091 4.497573\n", + "jeep 4.108607 4.15378\n", + "kia 4.141827 4.12064\n", + "lamborghini 4.725000 4.665769\n", + "land-rover 3.848837 4.0177\n", + "lexus 4.306122 4.294433\n", + "lincoln 4.269231 4.264042\n", + "lotus 4.702381 4.490183\n", + "maserati 4.431250 4.394369\n", + "maybach 4.958333 4.733771\n", + "mazda 4.479651 4.318612\n", + "mercedes-benz 4.095745 4.094125\n", + "mercury 4.311224 4.390451\n", + "mini 4.036184 4.190949\n", + "mitsubishi 4.346698 4.417798\n", + "nissan 4.247093 4.119348\n", + "pontiac 4.375000 4.221752\n", + "porsche 4.662500 4.552274\n", + "ram 3.861111 4.113366\n", + "rolls-royce 4.843750 4.508778\n", + "subaru 4.257212 4.327014\n", + "suzuki 4.235119 4.255248\n", + "tesla 4.673387 4.284923\n", + "volvo 4.380814 4.281185" ] }, "execution_count": 56, @@ -7122,25 +6643,53 @@ { "cell_type": "code", "execution_count": 57, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "emotion.sadness mean float64\n", + "emotion.joy mean float64\n", + "emotion.fear mean float64\n", + "emotion.disgust mean float64\n", + "emotion.anger mean float64\n", + "sentiment.score mean float64\n", + "Rating\\r mean float64\n", + "Predicted_Y mean object\n", + "dtype: object" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agg_grouped_test_set.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "y = 0.71x + 1.24\n" + "y = 0.51x + 2.10\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -7153,7 +6702,8 @@ "pylab.ylabel('Rating Predicted By Model')\n", "\n", "# calc the trendline\n", - "z = np.polyfit(np.squeeze(agg_grouped_test_set['Rating\\r']), np.squeeze(agg_grouped_test_set['Predicted_Y']), 1)\n", + "z = np.polyfit(np.squeeze(agg_grouped_test_set['Rating\\r']),\n", + " np.squeeze(agg_grouped_test_set['Predicted_Y'].astype(float)), 1)\n", "p = np.poly1d(z)\n", "pylab.plot(agg_grouped_test_set['Predicted_Y'],p(agg_grouped_test_set['Predicted_Y']),\"r--\")\n", "pylab.title(\"Rating From Dataset Vs Rating Predicted By Model\")\n", @@ -7196,7 +6746,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/notebooks/Text_Extensions_for_Pandas_Overview.ipynb b/notebooks/Text_Extensions_for_Pandas_Overview.ipynb index 076aa5a8..20b9db6a 100644 --- a/notebooks/Text_Extensions_for_Pandas_Overview.ipynb +++ b/notebooks/Text_Extensions_for_Pandas_Overview.ipynb @@ -298,7 +298,7 @@ "
Announcements
IBM Study: Majority of Surveyed Companies are Not Prepared for IT Needs of the Future, Say U.S. and U.K. Tech Leaders
- Nearly a quarter of CIOs and CTOs surveyed say they are just starting their IT modernization journey or have yet to begin modernizing.
- To meet these needs, nearly 80 percent of leaders surveyed say there will be a higher reliance on partners that can provide managed infrastructure services.

ARMONK, N.Y., Jan. 4, 2021 /PRNewswire/ -- Many corporate IT leaders say their organizations are not prepared for the future IT needs of the business and nearly all are moving to advance their transition to cloud infrastructure, according to a new IBM (NYSE: IBM) survey of leaders at mid-sized and large companies in the United States and United Kingdom.

\"IBM

Of the 380 CIOs and CTOs who participated in the survey, 60% say their company's IT modernization program is not yet ready for the future, according to the recently completed The State of IT Transformation Study conducted by the Managed Infrastructure Services unit of IBM's Global Technology Services division. Nearly a quarter of CIOs and CTOs (24%) surveyed say their company is just starting its IT modernization journey or has yet to begin modernizing, with about a third surveyed saying they are still in the [...]" + " IBM Study: Majority of Surveyed Companies are Not Prepared for IT Needs of the Future, Say U.S. and U.K. Tech Leaders - Jan 4, 2021

IBM Newsroom

IBM Study: Majority of Surveyed Companies are Not Prepared for IT Needs of the Future, Say U.S. and U.K. Tech Leaders
- Nearly a quarter of CIOs and CTOs surveyed say they are just starting their IT modernization journey or have yet to begin modernizing.
- To meet these needs, nearly 80 percent of leaders surveyed say there will be a higher reliance on partners that can provide managed infrastructure services.

ARMONK, N.Y., Jan. 4, 2021 /PRNewswire/ -- Many corporate IT leaders say their organizations are not prepared for the future IT needs of the business and nearly all are moving to advance their transition to cloud infrastructure, according to a new IBM (NYSE: IBM) survey of leaders at mid-sized and large companies in the United States and United Kingdom.

\"IBM

Of the 380 CIOs and CTOs who participated in the survey, 60% say their company's IT modernization program is not yet ready for the future, according to the recently completed The State of IT Transformation Study conducted by the Managed Infrastructure Services unit of IBM's Global Technology Services division. Nearly a quarter of CIOs and CTOs (24%) surveyed say their company is just starting its IT modernization journey or has yet to begin modernizing, with about a third surveyed saying they are still in the midst of [...]" ], "text/plain": [ "" @@ -4774,7 +4774,7 @@ "output_type": "stream", "text": [ "\n", - "Dependency parsing took 0.4 sec.\n" + "Dependency parsing took 0.3 sec.\n" ] }, { @@ -4845,15 +4845,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extract entities and semantic roles: 218.7 sec before and 218.7 sec after\n", - " Identify persons quoted by name: 5.1 sec before and 5.1 sec after\n", - " Perform dependency parsing: 640.5 sec before and 73.3 sec after\n", - " Extract titles of persons: 11.0 sec before and 11.0 sec after\n", + "Extract entities and semantic roles: 530.9 sec before and 530.9 sec after\n", + " Identify persons quoted by name: 3.7 sec before and 3.7 sec after\n", + " Perform dependency parsing: 843.8 sec before and 71.4 sec after\n", + " Extract titles of persons: 8.8 sec before and 8.8 sec after\n", " Combine results across documents: 0.1 sec before and 0.1 sec after\n", "\n", "\n", - "Total time before: 875.3122136230469\n", - "Total time after: 308.0950550670624\n" + "Total time before: 1387.3444835777282\n", + "Total time after: 614.8777334327698\n" ] } ], @@ -4901,14 +4901,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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cKleujK2tLQEBAUW+vf90/yvo/4f8pKWlUbFiRWJiYnLNy/n8x48fz/PPP89XX33Fpk2bGDduHGvWrOHpp5++q3UXln71ROSBduLECV577TXef/991q5dS1hYGN9++61xGXYbGxsyMzMLtKxdu3YRHh5ufBGnpaWZ/WHm7+9PVlYWO3bsyDV+MGddwB3X16hRIw4fPnzHsHY7BV1XQba/IPWUKlWKoKAggoKCGDduHG5ubmzbts04lHOzXbt28dRTT/F///d/wI3rlx07dow6dercabMMfn5+nD59mrNnz1KxYkUAfvjhhzu+zmQyERgYSGBgIGPHjqVy5cqsW7eOoUOH4uXlxS+//EJoaGiB6yiIXbt2sXDhQjp16gTA6dOn7zjO5VYF+TzvtP8VlJ+fH7t27TKbtmvXLmrWrJkr4MGN/ePcuXOUKlXKOOknLzVr1qRmzZq89tpr9OrVi6ioqPseanQjGBEp0dLT0zl37pzZI+cHIzMzk//7v/8jODiYPn36EBUVxcGDB83O5PD19SUuLo6TJ0/y+++/3/Yv4xo1avDFF18QHx9PQkICzz//vFl7X19fwsLC6Nu3L+vXryc5OZmYmBg++eQTACpXrozJZGLjxo389ttvue5pl2PkyJHs3r2byMhI4uPjOX78OF9++SWRkZEFfl8qVKiAvb09mzdv5vz581y+fDnPdgXZ/jvVs3HjRubOnUt8fDynTp1i5cqVZGVlGWMl83ofc3pMjhw5wosvvpjrjNY7CQoKombNmoSFhZGQkMB3333Hm2++edvXxMXFMWXKFPbt20dKSgpffPEFv/32m3E4b8KECUydOpW5c+dy7NgxEhMTiYqKYtasWYWq7VY1atTgo48+4siRI8TFxREaGlroXpWC7Dt32v8KatiwYWzdupVJkyZx7NgxVqxYwfz5883GQd0sKCiIgIAAunbtypYtWzh58iS7d+/mzTffZN++ffz9999ERkYSExPDqVOn2LVrF3v37jXe9/tJPTUiD7mSfpXfzZs3G3+p56hVqxZHjx7l7bff5tSpU2zcuBG4MSZj6dKl9OrViw4dOlC/fn2GDx9OWFgYderU4e+//yY5OTnfdc2aNYu+ffvSsmVLypUrx8iRI3NdnXzRokW88cYbDBo0iD/++AMfHx9j0PIjjzzChAkTGDVqFH369KF37955jjWsV68eO3bs4M0336RVq1ZkZ2dTrVo1nnvuuQK/L6VKlWLu3LlMnDiRsWPH0qpVqzwPDxRk++9Uj5ubG1988QXjx4/n6tWr1KhRg3//+995XjUe4K233uKXX34hODgYBwcHBg4cSNeuXfMNXnmxsrJi3bp19OvXj2bNmuHr68vcuXNve+FFFxcXdu7cyezZs0lNTaVy5crMnDnTGFzcv39/HBwcePfddxkxYgSOjo74+/vf9cUZP/jgAwYOHEijRo3w9vZmypQp+QaE/BR037nd/ldQjRo14pNPPmHs2LFMmjSJihUrMnHixHzPfDKZTHz99de8+eab9OnTh99++w1PT09at26Nh4cH1tbW/PHHH/Tu3Zvz589Trlw5unXrxoQJEwpVV1EwZRdmgMwDJDU1FVdXVy5fvvzQXrPGf4X/nRs9hBLDHr5RNVevXiU5OZkqVaoYZ9aIiBSX230n3c3vtw4/iYiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiMgDrW3btnd9RdiCOHnyJCaTifj4eGParl278Pf3p3Tp0nTt2vWe1yD33532r/Hjx9OgQYP7Vo/cnm6TIPKwG+96H9dV8MvkA4SHh3Pp0iXWr19/b+opxHq9vb05e/Ys5cqVM6YNHTqUBg0asGnTJpycnO5rjZK/tm3b0qBBA2bPnl3cpch9pp4aEZECsLa2xtPTk1Kl/t/fgidOnOCxxx6jUqVKuLm5FV9xt8jIyCjuEkSKhUKNiDwwrly5Qu/evXFycqJixYpmd+POkZ6ezvDhw3nkkUdwdHSkefPmZjd6XL58OW5ubnzzzTf4+fnh5ORESEgIZ8+eBW4cTlixYgVffvklJpMJk8lETEyM2eGnnH//8ccf9O3bF5PJxPLly7GysmLfvn1m9cyePZvKlSvne3dwX19fJk2aRK9evXB0dOSRRx5hwYIFZm0uXbpE//79KV++PC4uLjz22GMkJCQY83MOgSxbtszsXjqfffYZ/v7+2NvbU7ZsWYKCgrhy5QoAWVlZTJw4kUqVKmFra0uDBg3YvHmzscycbfziiy9o164dDg4O1K9fn9jYWKPNqVOn6NKlC2XKlMHR0ZG6devy9ddf5/v5XbhwgS5dumBvb0+VKlVYtWoVvr6+Ro9KXof4Ll26ZHwGOXbs2EGzZs2wtbWlYsWKjBo1iuvXrwM3etl27NjBnDlzjM/v5MmTAPz000907NgRJycnPDw8eOGFF4w7vkPB9q/8LFmyBG9vbxwcHOjRo4dx886dO3dSunRpzp07Z9Z+yJAhtGrVKt/lmUwmli1bxtNPP42DgwM1atRgw4YNxvzMzEz69etHlSpVsLe3p1atWsyZM8dsGeHh4XTt2pUpU6bg4eGBm5sbEydO5Pr164wYMQJ3d3cqVapEVFSU2etOnz5Njx49cHNzw93dnaeeesp4D0s6hRoReWCMGDGCHTt28OWXX7JlyxZiYmI4cOCAWZvIyEhiY2NZs2YNBw8e5NlnnyUkJITjx48bbf766y9mzJjBRx99xM6dO0lJSTHuqjx8+HB69OhhBJ2zZ8/SsmVLs3XkHIpycXFh9uzZnD17lueee46goKBcPxBRUVGEh4djZZX/1+27775L/fr1+fHHHxk1ahSvvvoq0dHRxvxnn32WCxcusGnTJvbv30+jRo1o3749Fy9eNNr8/PPPfP7553zxxRfEx8dz9uxZevXqRd++fTly5AgxMTF069aNnHsYz5kzh5kzZzJjxgwOHjxIcHAwTz75pNn7BPDmm28yfPhw4uPjqVmzJr169TICREREBOnp6ezcuZPExESmTZt228Nw4eHhnD59mu3bt/PZZ5+xcOFCLly4kG/7vPz666906tSJpk2bkpCQwKJFi/jggw+YPHmysV0BAQEMGDDA+Py8vb25dOkSjz32GA0bNmTfvn1s3ryZ8+fP06NHD2PZBdm/8vLzzz/zySef8J///IfNmzfz448/MmjQIABat25N1apV+eijj4z2165dY9WqVfTt2/e2y50wYQI9evTg4MGDdOrUidDQUOMzz8rKolKlSnz66accPnyYsWPH8sYbb/DJJ5+YLWPbtm2cOXOGnTt3MmvWLMaNG8cTTzxBmTJliIuL46WXXuLFF1/kv//9r1FbcHAwzs7OfPfdd+zatcsI/g9CD6DG1IjIAyEtLY0PPviAjz/+mPbt2wOwYsUKKlWqZLRJSUkhKiqKlJQUvLy8gBshZfPmzURFRTFlyhTgxhf34sWLqVatGnAjCE2cOBEAJycn7O3tSU9Px9PTM89acg5FmUwmXF1djXb9+/fnpZdeYtasWdja2nLgwAESExP58ssvb7ttgYGBjBo1CoCaNWuya9cu3nvvPR5//HG+//579uzZw4ULF7C1tQVgxowZrF+/ns8++4yBAwcCNw45rVy5kvLlywNw4MABrl+/Trdu3ahcuTIA/v7+xjpnzJjByJEj6dmzJwDTpk1j+/btzJ4926ynaPjw4XTu3Bm48SNbt25dfv75Z2rXrk1KSgrdu3c3llu1atV8t/HYsWNs2rSJPXv20LRpUwA++OAD/Pz8bvve3GrhwoV4e3szf/58TCYTtWvX5syZM4wcOZKxY8fi6uqKjY0NDg4OZp/f/PnzadiwobEPAHz44Yd4e3tz7NgxvLy87rh/5efq1ausXLmSRx55BIB58+bRuXNnZs6ciaenJ/369SMqKooRI0YA8J///IerV6+aBaq8hIeH06tXLwCmTJnC3Llz2bNnDyEhIZQuXZoJEyYYbatUqUJsbCyffPKJ2XLd3d2ZO3cuVlZW1KpVi+nTp/PXX3/xxhtvADB69Gjeeecdvv/+e3r27MnatWvJyspi2bJlmEwm4EYwd3NzIyYmhg4dOtzx/ShO6qkRkQfCiRMnyMjIoHnz5sY0d3d3atWqZTxPTEwkMzOTmjVr4uTkZDx27NjBiRMnjHYODg5GoAGoWLFioXsM8tK1a1esra1Zt24dcONQV7t27fD19b3t6wICAnI9P3LkCAAJCQmkpaVRtmxZs21KTk4226bKlSsbgQagfv36tG/fHn9/f5599lnef/99/vzzTwBSU1M5c+YMgYGBZusNDAw01pujXr16xr8rVqwIYLxXgwcPZvLkyQQGBjJu3DgOHjyY7zYeOXKEUqVK0bhxY2Na7dq1Cz0W6ciRIwQEBBg/uDl1p6WlGb0NeUlISGD79u1m72Ht2rWBG/tWQfav/Pj4+BiBBm58fllZWSQlJQE3wsnPP//MDz/8ANzYL3r06IGjo+Ntl3vze+/o6IiLi4vZfrpgwQIaN25M+fLlcXJyYunSpaSkpJgto27duma9hB4eHmbh1tramrJlyxrLTUhI4Oeff8bZ2dl4n9zd3bl69arZ/lZSqadGRCxGWloa1tbW7N+/H2tra7N5Nx8WKV26tNk8k8lkHJa5GzY2NvTu3ZuoqCi6devG6tWrc41zKKy0tDQqVqxoNqYkx82B4NYfSGtra6Kjo9m9ezdbtmxh3rx5vPnmm8TFxVG2bNkCr//m9yonSOSMD+rfvz/BwcF89dVXbNmyhalTpzJz5kxeeeWVQmzh/5Pz43vzZ3Ht2rV/tKxbpaWl0aVLF6ZNm5ZrXsWKFfn555+LZD15qVChAl26dCEqKooqVaqwadOmPD/PW+W1n+a892vWrGH48OHMnDmTgIAAnJ2deffdd4mLi7vjMm633LS0NBo3bsyqVaty1XNzaC6p1FMjIg+EatWqUbp0abMv7T///JNjx44Zzxs2bEhmZiYXLlygevXqZo/8DiXlxcbGhszMzH9UZ//+/fn2229ZuHChcfjnTnL+gr/5ec5hmUaNGnHu3DlKlSqVa5tuPr08LyaTicDAQCZMmMCPP/6IjY0N69atw8XFBS8vL3bt2mXWfteuXdSpU6dQ2+vt7c1LL73EF198wbBhw3j//ffzbFe7dm2uX7/O/v37jWlJSUlcunTJeJ7zo5kzaBswGzQM4OfnR2xsrFnw2bVrF87Ozsahorw+v0aNGnHo0CF8fX1zvY+Ojo4F2r/yk5KSwpkzZ4znP/zwg3G4J0f//v1Zu3YtS5cupVq1arl6yQpr165dtGzZkkGDBtGwYUOqV69eJD0pjRo14vjx41SoUCHX++Tqeh8v//APKdSIyAPBycmJfv36MWLECLZt28ZPP/2UawBuzZo1CQ0NpXfv3nzxxRckJyezZ88epk6dyldffVXgdfn6+nLw4EGSkpL4/fffC9Vb4OfnR4sWLRg5ciS9evXC3t7+jq/ZtWsX06dP59ixYyxYsIBPP/2UV199FYCgoCACAgLo2rUrW7Zs4eTJk+zevZs333wz15lWN4uLi2PKlCns27ePlJQUvvjiC3777TcjLI0YMYJp06axdu1akpKSGDVqFPHx8cZ6C2LIkCF88803JCcnc+DAAbZv357vGJlatWoREhLCiy++SFxcHPv376d///5m74+9vT0tWrTgnXfe4ciRI+zYsYO33nrLbDmDBg3i9OnTvPLKKxw9epQvv/yScePGMXToUGNf8PX1JS4ujpMnT/L777+TlZVFREQEFy9epFevXuzdu5cTJ07wzTff0KdPHzIzMwu0f+XHzs6OsLAwEhIS+O677xg8eDA9evQwC9LBwcG4uLgwefJk+vTpU+D3OD81atRg3759fPPNNxw7dowxY8awd+/eu15uaGgo5cqV46mnnuK7774jOTmZmJgYBg8efNvDeyWFQo2IPDDeffddWrVqRZcuXQgKCuJf//qX2RgNuDGosXfv3gwbNoxatWrRtWtX9u7di4+PT4HXM2DAAGrVqkWTJk0oX758rh6NO+nXrx8ZGRl3PLslx7Bhw9i3bx8NGzZk8uTJzJo1i+DgYOBGb8vXX39N69at6dOnDzVr1qRnz56cOnUKDw+PfJfp4uLCzp076dSpEzVr1uStt95i5syZdOzYEbgxHmbo0KEMGzYMf39/Nm/ezIYNG6hRo0aBtzMzM5OIiAj8/PwICQmhZs2aLFy4MN/2UVFReHl50aZNG7p168bAgQOpUKGCWZsPP/yQ69ev07hxY4YMGWKc1ZTjkUce4euvv2bPnj3Ur1+fl156iX79+pmFn+HDh2NtbU2dOnUoX768MXB8165dZGZm0qFDB/z9/RkyZAhubm5GcCnI/pWX6tWr061bNzp16kSHDh2oV69ervfBysqK8PBwMjMz6d279x2XeScvvvgi3bp147nnnqN58+b88ccfxhlXd8PBwYGdO3fi4+NDt27d8PPzo1+/fly9ehUXF5e7Xv69ZsouigPJJVBqaiqurq5cvnz5gfgg7gX/Ff53bvQQSgxLLO4S7rurV6+SnJxsdg0TuXcmTZrEp59+etuBszl8fX0ZMmTIfbnVQ0n0MG1/v379+O2338yuN/Owut130t38fmugsIhIEUlLS+PkyZPMnz8/Vw+DPLwuX75MYmIiq1evVqC5x3T4SUSkiERGRtK4cWPatm1b4ENPYvmeeuopOnTowEsvvcTjjz9e3OVYNPXUiIgUkeXLl7N8+fJCveZBufz8vfIwbH9BTt+WoqGeGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEHkrjx4/Hw8MDk8nE+vXri7ucPLVt2/aBudJuTEwMJpPJ7AaV8v+MHz+eBg0aFHcZFk/XqRF5yN3P22kU9hYV4eHhrFixAoDSpUvj4+ND7969eeONNyhV6p9/fR05coQJEyawbt06WrRoQZkyZf7xskQKYvjw4bzyyivFXYbFU6gRkRItJCSEqKgo0tPT+frrr4mIiKB06dKMHj260MvKzMzEZDJx4sQJ4MaVXk0m0z+u7dq1a5QuXfofv14sQ85+dbs7ejs5OeHk5HQfq3o46fCTiJRotra2eHp6UrlyZV5++WWCgoKM++ekp6czfPhwHnnkERwdHWnevLnZ1VuXL1+Om5sbGzZsoE6dOtja2tK3b1+6dOkC3Lhzck6oycrKYuLEiVSqVAlbW1saNGjA5s2bjWWdPHkSk8nE2rVradOmDXZ2dqxatYrw8HC6du3KlClT8PDwwM3NjYkTJ3L9+nVGjBiBu7s7lSpVIioq6rbbeeXKFXr37o2TkxMVK1Zk5syZudoUdHvXr19PjRo1sLOzIzg4mNOnT5st58svv6RRo0bY2dlRtWpVJkyYwPXr1435JpOJZcuW8fTTT+Pg4ECNGjVy3bPo66+/pmbNmtjb29OuXbs8rwz8/fff06pVK+zt7fH29mbw4MFcuXLFmO/r68uUKVPo27cvzs7O+Pj4sHTpUrNl/Pe//6VXr164u7vj6OhIkyZNiIuL4+TJk1hZWbFv3z6z9rNnz6Zy5cpkZWXl+T77+voyadIkevXqhaOjI4888ggLFiwwazNr1iz8/f1xdHTE29ubQYMGkZaWlut9vnm/SklJISYmhmbNmuHo6IibmxuBgYGcOnUKyH34KWe/mTFjBhUrVqRs2bJERERw7do1o83Zs2fp3Lkz9vb2VKlShdWrV+Pr68vs2bPz3DZRqBGRB4y9vT0ZGRnAjXstxcbGsmbNGg4ePMizzz5LSEgIx48fN9r/9ddfTJs2jWXLlnHo0CHmzp1rBIyzZ89y9uxZAObMmcPMmTOZMWMGBw8eJDg4mCeffNJsWQCjRo3i1Vdf5ciRIwQHBwOwbds2zpw5w86dO5k1axbjxo3jiSeeoEyZMsTFxfHSSy/x4osv8t///jff7RoxYgQ7duzgyy+/ZMuWLcTExHDgwAGzNgXd3rfffpuVK1eya9cuLl26RM+ePY353333Hb179+bVV1/l8OHDLFmyhOXLl/P222+brWvChAn06NGDgwcP0qlTJ0JDQ7l48SIAp0+fplu3bnTp0oX4+Hj69+/PqFGjzF5/4sQJQkJC6N69OwcPHmTt2rV8//33REZGmrWbOXMmTZo04ccff2TQoEG8/PLLJCUlATduENqmTRt+/fVXNmzYQEJCAq+//jpZWVn4+voSFBSUKyxGRUURHh5+216Td999l/r16/Pjjz8an2d0dLQx38rKirlz53Lo0CFWrFjBtm3beP31182Wcet+5e7uTteuXWnTpg0HDx4kNjaWgQMH3rYncPv27Zw4cYLt27ezYsWKXLfZ6N27N2fOnCEmJobPP/+cpUuXcuHChXyXJ2DKzs7OLu4i7oW7uXW5pbifYyUeJIUd12EJrl69SnJyMlWqVMHOzs5sXkkfU3Pp0iXWr19PdnY2W7du5YknnuCVV17hlVdeoWrVqqSkpODl5WW8JigoiGbNmjFlyhSWL19Onz59iI+Pp379+kab9evX8/TTT3Pz198jjzxCREQEb7zxhjGtWbNmNG3alAULFnDy5EmqVKnC7NmzefXVV81qjImJ4ZdffjF+SGvXrk2FChXYuXMncOPwhKurK8uWLTMLGDnS0tIoW7YsH3/8Mc8++ywAFy9epFKlSgwcOJDZs2eTkpJS4O394YcfaN68OQBHjx7Fz8+PuLg4mjVrRlBQEO3btzc7fPfxxx/z+uuvc+bMGeBGT81bb73FpEmTgBu9SE5OTmzatImQkBDeeOMNvvzySw4dOmQsY9SoUUybNo0///wTNzc3+vfvj7W1NUuWLDHafP/997Rp04YrV65gZ2eHr68vrVq14qOPPgIgOzsbT09PJkyYwEsvvcTSpUsZPnw4J0+exN3dPdf79sknn/DSSy9x9uxZbG1tOXDgAE2aNOGXX37B19c3V3u40VPj5+fHpk2bjGk9e/YkNTWVr7/+Os/XfPbZZ7z00kv8/vvvAHnuVxcvXqRs2bLExMTQpk2bXMsYP34869evJz4+Hvh/+82JEyewtrYGoEePHlhZWbFmzRrjc9u7dy9NmjQB4Oeff6ZGjRq89957D8wA8vzc7jvpbn6/NaZGREq0jRs34uTkxLVr18jKyuL5559n/PjxxMTEkJmZSc2aNc3ap6enU7ZsWeO5jY0N9erVu+06UlNTOXPmDIGBgWbTAwMDSUhIMJuW8wNzs7p165r1DHh4ePDoo48az62trSlbtmy+f2WfOHGCjIwMI4gAuLu7U6tWLeN5YmJigba3VKlSNG3a1Hheu3Zt3NzcOHLkCM2aNSMhIYFdu3aZ9cxkZmZy9epV/vrrLxwcHADM3jNHR0dcXFyM+o8cOWJWK0BAQIDZ84SEBA4ePMiqVauMadnZ2WRlZZGcnIyfn1+u9ZhMJjw9PY31xMfH07BhwzwDDUDXrl2JiIhg3bp19OzZk+XLl9OuXbt8A01+tQYEBJgd0vn222+ZOnUqR48eJTU1levXr+d6f27dr9zd3QkPDyc4OJjHH3+coKAgevToQcWKFfOto27dukagAahYsSKJiTeCf1JSEqVKlaJRo0bG/OrVq2tQ+x0U+vDTzp076dKlC15eXrlOhbx27RojR440jkV6eXkZ3Wc3u3jxIqGhobi4uODm5ka/fv3MjlcCHDx4kFatWmFnZ4e3tzfTp0//Z1soIg+0du3aER8fz/Hjx/n7779ZsWIFjo6OpKWlYW1tzf79+4mPjzceR44cYc6cOcbr7e3t72ow8K0cHR1zTbt1sLDJZMpzWn7jPAqioNtbkOVMmDDBbBmJiYkcP37c7C/mu60/LS2NF1980Ww9CQkJHD9+nGrVqhVoPfb29rddh42NDb179yYqKoqMjAxWr15N3759C1xjXk6ePMkTTzxBvXr1+Pzzz9m/f78x5ibnsGdObbfuV1FRUcTGxtKyZUvWrl1LzZo1+eGHH/JdV1HvI/IPemquXLlC/fr16du3L926dTOb99dff3HgwAHGjBlD/fr1+fPPP3n11Vd58sknzQZzhYaGcvbsWaKjo7l27Rp9+vRh4MCBrF69GrjxV1OHDh0ICgpi8eLFJCYm0rdvX9zc3Bg4cOBdbrKIPEgcHR2pXr16rukNGzYkMzOTCxcu0KpVq7tah4uLC15eXuzatcvs0MGuXbto1qzZXS27IKpVq0bp0qWJi4vDx8cHgD///JNjx44Z9RR0e69fv86+ffuMupOSkrh06ZLRM9KoUSOSkpLyfE8Lys/PL9fA4Vt/vBs1asThw4fvaj316tVj2bJlXLx4Md/emv79+/Poo4+ycOFCrl+/nut3KS+31vrDDz8Y78/+/fvJyspi5syZRu/bJ598UuCaGzZsSMOGDRk9ejQBAQGsXr2aFi1aFPj1OWrVqsX169f58ccfady4MXDj8NOff/5Z6GU9TAodajp27EjHjh3znOfq6mo22Apg/vz5NGvWjJSUFHx8fDhy5AibN282O044b948OnXqxIwZM/Dy8mLVqlVkZGTw4YcfYmNjQ926dYmPj2fWrFkKNSICQM2aNQkNDaV3797MnDmThg0b8ttvv7F161bq1atH586dC7W8ESNGMG7cOKpVq0aDBg2IiooiPj7e7PDJveLk5ES/fv0YMWIEZcuWpUKFCrz55ptmh7QKur2lS5fmlVdeYe7cuZQqVYrIyEhatGhhhJyxY8fyxBNP4OPjwzPPPIOVlRUJCQn89NNPTJ48uUD1vvTSS8ycOZMRI0bQv39/9u/fbzbAFWDkyJG0aNGCyMhI+vfvj6OjI4cPHyY6Opr58+cXaD29evViypQpdO3alalTp1KxYkV+/PFHvLy8jENIfn5+tGjRgpEjR9K3b9879u7AjbA6ffp0unbtSnR0NJ9++ilfffUVcOMQz7Vr15g3bx5dunRh165dLF68+I7LTE5OZunSpTz55JN4eXmRlJTE8ePH6d27d4G29Va1a9cmKCiIgQMHsmjRIkqXLs2wYcOKvOfR0tzzs58uX76MyWTCzc0NgNjYWNzc3MyOSwcFBWFlZUVcXJzRpnXr1tjY2BhtgoODSUpKyjelpqenk5qaavYQEcsWFRVF7969GTZsGLVq1aJr167s3bvX6O0ojMGDBzN06FCGDRuGv78/mzdvZsOGDdSoUeMeVJ7bu+++S6tWrejSpQtBQUH861//Mv5Cz1GQ7XVwcGDkyJE8//zzBAYG4uTkxNq1a435wcHBbNy4kS1bttC0aVNatGjBe++9R+XKlQtcq4+PD59//jnr16+nfv36LF68mClTppi1qVevHjt27ODYsWO0atWKhg0bMnbsWLNBzndiY2PDli1bqFChAp06dcLf35933nnHbBwKQL9+/cjIyCjwoadhw4axb98+GjZsyOTJk5k1a5ZxJlv9+vWZNWsW06ZN49FHH2XVqlVMnTr1jst0cHDg6NGjdO/enZo1azJw4EAiIiJ48cUXC7y9t1q5ciUeHh60bt2ap59+mgEDBuDs7JxrYK38P3d19pPJZGLdunV07do1z/lXr14lMDCQ2rVrG3/tTJkyhRUrVhin7OWoUKECEyZM4OWXX6ZDhw5UqVLFbNT84cOHqVu3LocPHza6CW82fvx4JkyYkGu6zn6SW+nsJ30hWqrly5czZMiQh+5WBZMmTeLTTz/l4MGDd2zr6+vLkCFDHsizh/773//i7e3Nt99+S/v27Yu7nLtyr85+umc9NdeuXaNHjx5kZ2ezaNGie7Uaw+jRo7l8+bLxuPViUyIiYlnS0tL46aefmD9/vkXegmDbtm1s2LCB5ORkdu/eTc+ePfH19aV169bFXVqJdU9O6c4JNKdOnWLbtm1mSevm0/VyXL9+nYsXL+Lp6Wm0OX/+vFmbnOc5bW5la2uLra1tUW6GiIiUYJGRkfz73/+ma9eud33WU0l07do13njjDX755RecnZ1p2bIlq1at0q05bqPIQ01OoDl+/Djbt283u34C3LgewKVLl9i/f79xvHjbtm1kZWUZ1z0ICAjgzTffNLuvSnR0NLVq1dI5+iIi+QgPDyc8PLy4y7hvbr0Cb0HkdTuHkio4ONgY6yMFU+jDT2lpacY1B+DGiO/4+HhSUlK4du0azzzzDPv27WPVqlVkZmZy7tw5zp07Z5zf7+fnR0hICAMGDGDPnj3s2rWLyMhIevbsaQwge/7557GxsaFfv34cOnSItWvXMmfOHIYOHVp0Wy4iIiIWpdA9Nfv27aNdu3bG85ygERYWxvjx441rF9x84y64cY+Ltm3bArBq1SoiIyNp3749VlZWdO/enblz5xptXV1d2bJlCxERETRu3Jhy5coxduxYnc4tIiIi+Sp0qGnbti23O2GqICdTubu7Gxfay0+9evX47rvvClueiIiIPKR0l24RERGxCAo1IiIiYhEUakRERMQiKNSIiJQQvr6+zJ49+7Ztxo8fn+tEjPvt6NGjtGjRAjs7u2KvReRm9+TieyLy4DhSO/dtR+4Vv6NHCtU+PDycFStW5JoeHBzM5s2bC7SMtm3b0qBBgzuGhaIQExNDu3bt+PPPP4373eUlv9sZ7N27F0dHR+P5nW5FU1zGjRuHo6MjSUlJODk5FXc5IgaFGhEp0UJCQoiKijKbVtRXD8/OziYzM5NSpYr3K7F8+fLFuv6COnHiBJ07dy7UTTCLQmZmJiaTyezu5SI3054hIiWara0tnp6eZo+cK4vHxMRgY2NjdvmH6dOnU6FCBc6fP094eDg7duxgzpw5mEwmTCYTJ0+eJCYmBpPJxKZNm2jcuDG2trZ8//33nDhxgqeeegoPDw+cnJxo2rQp3377rVk96enpjBw5Em9vb2xtbalevToffPABJ0+eNK7hVaZMGUwmU55X942JiaFPnz5cvnzZqGn8+PGA+eEnX19fAJ5++mlMJpPxPC/Lli3Dz88POzs7ateuzcKFC415GRkZREZGUrFiRezs7KhcufJt7zqdlZXFxIkTqVSpEra2tjRo0MCsV8xkMrF//34mTpxoVvut2rZtS2RkJJGRkbi6ulKuXDnGjBljdtmP9PR0hg8fziOPPIKjoyPNmzcnJibGmL98+XLc3NzYsGEDderUwdbWlpSUFGJiYmjWrBmOjo64ubkRGBjIqVOnjNctWrSIatWqYWNjQ61atfjoo4/MajOZTCxbtoynn34aBwcHatSoYVxjDeDPP/8kNDSU8uXLY29vT40aNXIFaymZ1FMjIg+stm3bMmTIEF544QUSEhL45ZdfGDNmDJ9++ikeHh7MmTOHY8eO8eijjzJx4kTgRm9IzqXyR40axYwZM6hatSplypTh9OnTdOrUibfffhtbW1tWrlxJly5dSEpKwsfHB4DevXsTGxvL3LlzqV+/PsnJyfz+++94e3vz+eef0717d5KSknBxccHe3j5XzS1btmT27NmMHTuWpKQkgDwP4ezdu5cKFSoQFRVFSEgI1tbWeb4Hq1atYuzYscyfP5+GDRvy448/MmDAABwdHQkLC2Pu3Lls2LCBTz75BB8fH06fPn3bG/7OmTOHmTNnsmTJEho2bMiHH37Ik08+yaFDh6hRowZnz54lKCiIkJAQhg8fftvDTytWrKBfv37s2bOHffv2MXDgQHx8fBgwYABw495Nhw8fZs2aNXh5ebFu3TpCQkJITEykRo0aAPz1119MmzaNZcuWUbZsWdzd3WnQoAEDBgzg3//+NxkZGezZsweTyQTAunXrePXVV5k9ezZBQUFs3LiRPn36UKlSJbMLx06YMIHp06fz7rvvMm/ePEJDQzl16hTu7u6MGTOGw4cPs2nTJsqVK8fPP//M33//ne92SsmhUCMiJdrGjRtz/XC+8cYbvPHGGwBMnjyZ6OhoBg4cyE8//URYWBhPPvkkcOPq5DY2Njg4OOR5M9yJEyfy+OOPG8/d3d2pX7++8XzSpEmsW7eODRs2EBkZybFjx/jkk0+Ijo4mKCgIgKpVq5q9HqBChQr5jqmxsbHB1dUVk8mU7w164f8dinJzc7ttu3HjxjFz5ky6desGQJUqVTh8+DBLliwhLCyMlJQUatSowb/+9S9MJtMdDxnNmDGDkSNH0rNnTwCmTZvG9u3bmT17NgsWLMDT05NSpUrh5OR027oAvL29ee+99zCZTNSqVYvExETee+89BgwYQEpKClFRUaSkpBi3yBk+fDibN28mKiqKKVOmADfuJ7hw4ULjc7l48SKXL1/miSeeoFq1asCN2+/cXH94eDiDBg0Cblz1/ocffmDGjBlmoSY8PJxevXoBMGXKFObOncuePXsICQkhJSWFhg0b0qRJE4Db9pJJyaJQIyIlWrt27Vi0aJHZtJzwADdCwqpVq6hXrx6VK1fmvffeK/Cyc360cqSlpTF+/Hi++uorzp49y/Xr1/n7779JSUkBID4+Hmtra9q0aXMXW1R0rly5wokTJ+jXr5/R+wFw/fp1XF1dgRs/3o8//ji1atUiJCSEJ554gg4dOuS5vNTUVM6cOUNgYKDZ9MDAQBISEgpdX4sWLYweFLhxs+KZM2eSmZlJYmIimZmZ1KxZ0+w16enpZjdCtrGxoV69esZzd3d3wsPDCQ4O5vHHHycoKIgePXpQsWJFAI4cOZLrljqBgYHMmTPHbNrNy3R0dMTFxYULFy4A8PLLL9O9e3cOHDhAhw4d6Nq1Ky1btiz09sv9p1AjIiWao6Mj1atXv22b3bt3Azf+ir948aLZGUR3WvbNhg8fTnR0NDNmzKB69erY29vzzDPPGDfkzetwUnFKS0sD4P3336d58+Zm83IOVzVq1Ijk5GQ2bdrEt99+S48ePQgKCuKzzz677/XeLC0tDWtra/bv35/r0NrNPXP29vZmwQggKiqKwYMHs3nzZtauXctbb71FdHQ0LVq0KPD6S5cubfbcZDKRlZUFQMeOHTl16hRff/010dHRtG/fnoiICGbMmFHYzZT7TAOFReSBduLECV577TXjhz0sLMz4cYIbf+lnZmYWaFm7du0iPDycp59+Gn9/fzw9PY3xNwD+/v5kZWWxY8eOPF9vY2MDcMf1FbSm0qVL37adh4cHXl5e/PLLL1SvXt3sUaVKFaOdi4sLzz33HO+//z5r167l888/5+LFi7mW5+LigpeXF7t27TKbvmvXLurUqXPHem8VFxdn9vyHH36gRo0aWFtb07BhQzIzM7lw4UKu2u90WAugYcOGjB49mt27d/Poo48a9xP08/MrkvrLly9PWFgYH3/8MbNnz2bp0qWFer0UD/XUiEiJlp6ezrlz58ymlSpVinLlypGZmcn//d//ERwcTJ8+fQgJCcHf35+ZM2cyYsQI4MZ4iLi4OE6ePImTk5PZoatb1ahRgy+++IIuXbpgMpkYM2aMWUDy9fUlLCyMvn37GgOFT506xYULF+jRoweVK1fGZDKxceNGOnXqhL29fZ4DaX19fUlLS2Pr1q3Ur18fBwcHHBwc8my3detWAgMDsbW1Nc76utmECRMYPHgwrq6uhISEkJ6ezr59+/jzzz8ZOnQos2bNomLFijRs2BArKys+/fRTPD098x3zM2LECMaNG0e1atVo0KABUVFRxMfHs2rVqnzft/ykpKQwdOhQXnzxRQ4cOMC8efOYOXMmADVr1iQ0NJTevXszc+ZMGjZsyG+//cbWrVupV68enTt3znOZycnJLF26lCeffBIvLy+SkpI4fvw4vXv3Nurv0aMHDRs2JCgoiP/85z988cUXuc5iu52xY8fSuHFj6tatS3p6Ohs3bjQbtyMll3pqRKRE27x5MxUrVjR7/Otf/wLg7bff5tSpUyxZsgSAihUrsnTpUt566y1jDMjw4cOxtramTp06lC9f3hgfk5dZs2ZRpkwZWrZsSZcuXQgODqZRo0ZmbRYtWsQzzzzDoEGDqF27NgMGDODKlSsAPPLII0yYMIFRo0bh4eFBZGRknutp2bIlL730Es899xzly5dn+vTpebabOXMm0dHReHt707Bhwzzb9O/fn2XLlhEVFYW/vz9t2rRh+fLlRk+Ns7Mz06dPp0mTJjRt2pSTJ0/y9ddf53utl8GDBzN06FCGDRuGv78/mzdvZsOGDcbZSIXRu3dv/v77b5o1a0ZERASvvvqq2XiXqKgoevfuzbBhw6hVqxZdu3Zl7969xplmeXFwcODo0aN0796dmjVrMnDgQCIiInjxxRcB6Nq1K3PmzGHGjBnUrVuXJUuWEBUVRdu2bQtct42NDaNHj6ZevXq0bt0aa2tr1qxZU+jtl/vPlH3zRQMsSGpqKq6urly+fBkXF5fiLqdY+K/wL+4SSqTEsMTiLuG+u3r1KsnJyVSpUgU7O7viLkceAvfzSs7y4Lndd9Ld/H6rp0ZEREQsgkKNiIiIWAQNFBYRkSJ38+0ORO4X9dSIiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiL5MJlMrF+/Pt/5J0+exGQyER8ff99qup/atm3LkCFDiruMB8Kd9hW5P3SdGpGH3IKXtt23dUUsfqzQrzl37hxvv/02X331Fb/++isVKlSgQYMGDBkyhPbt29+DKgvO29ubs2fPUq5cuWKt437x9fVlyJAhCjoPufHjx7N+/foSGeYVakSkxDp58iSBgYG4ubnx7rvv4u/vz7Vr1/jmm2+IiIjg6NGjxVqftbU1np6e9329GRkZ2NjY3Pf1lkR6L+RmOvwkIiXWoEGDMJlM7Nmzx7grc926dRk6dCg//PCD0S4lJYWnnnoKJycnXFxc6NGjB+fPnzfmjx8/ngYNGvDhhx/i4+ODk5MTgwYNIjMzk+nTp+Pp6UmFChV4++23c9Vw9uxZOnbsiL29PVWrVuWzzz4z5t16+CkmJgaTycTWrVtp0qQJDg4OtGzZkqSkJLNlfvnllzRq1Ag7OzuqVq3KhAkTuH79er7vQ3h4OF27duXtt9/Gy8uLWrVqAXD69Gl69OiBm5sb7u7uPPXUU5w8edJ4XUxMDM2aNcPR0RE3NzcCAwM5deqU2TJvNmTIkHzvZt22bVtOnTrFa6+9hslkwmQyAXDq1Cm6dOlCmTJlcHR0pG7dunz99df5bstHH31EkyZNcHZ2xtPTk+eff54LFy6YtTl06BBPPPEELi4uODs706pVK06cOHHb9yIxMZHHHnsMe3t7ypYty8CBA0lLSyvQe5GQkEC7du1wdnbGxcWFxo0bs2/fvny34fjx47Ru3Ro7Ozvq1KlDdHR0rjZ3qgfgww8/pG7dutja2lKxYkXjru55Hda8dOkSJpPJuFJzzr72zTff0LBhQ+zt7Xnssce4cOECmzZtws/PDxcXF55//nn++usvYzlZWVlMnTqVKlWqYG9vT/369c326Tvtw8uXL2fChAkkJCQY+8Hy5cvJzs5m/Pjx+Pj4YGtri5eXF4MHD873PbxXFGpEpES6ePEimzdvJiIiAkdHx1zz3dzcgBtf0k899RQXL15kx44dREdH88svv/Dcc8+ZtT9x4gSbNm1i8+bN/Pvf/+aDDz6gc+fO/Pe//2XHjh1MmzaNt956i7i4OLPXjRkzhu7du5OQkEBoaCg9e/bkyJEjt639zTffZObMmezbt49SpUrRt29fY953331H7969efXVVzl8+DBLlixh+fLleQaqm23dupWkpCSio6PZuHEj165dIzg4GGdnZ7777jt27dqFk5MTISEhZGRkcP36dbp27UqbNm04ePAgsbGxDBw40AgjhfXFF19QqVIlJk6cyNmzZzl79iwAERERpKens3PnThITE5k2bRpOTk75LufatWtMmjSJhIQE1q9fz8mTJwkPDzfm//rrr7Ru3RpbW1u2bdvG/v376du3r1nou/W9uHLlCsHBwZQpU4a9e/fy6aef8u233xoh4U7vRWhoKJUqVWLv3r3s37+fUaNGUbp06Tzrz8rKolu3btjY2BAXF8fixYsZOXKkWZs71QOwaNEiIiIiGDhwIImJiWzYsIHq1asX7kPhRmCfP38+u3fvNkLu7NmzWb16NV999RVbtmxh3rx5RvupU6eycuVKFi9ezKFDh3jttdf4v//7P3bs2GG23Pz24eeee45hw4ZRt25dYz947rnn+Pzzz3nvvfdYsmQJx48fZ/369fj7+xd6e+6WDj+JSIn0888/k52dTe3atW/bbuvWrSQmJpKcnIy3tzcAK1eupG7duuzdu5emTZsCN36MPvzwQ5ydnalTpw7t2rUjKSmJr7/+GisrK2rVqsW0adPYvn07zZs3N5b/7LPP0r9/fwAmTZpEdHQ08+bNY+HChfnW9Pbbb9OmTRsARo0aRefOnbl69Sp2dnZMmDCBUaNGERYWBkDVqlWZNGkSr7/+OuPGjct3mY6Ojixbtsw41PLxxx+TlZXFsmXLjB/nqKgo3NzciImJoUmTJly+fJknnniCatWqAeDn53fb9/J23N3dsba2NnpYcqSkpNC9e3fjB6xq1aq3Xc7NAa9q1arMnTuXpk2bkpaWhpOTEwsWLMDV1ZU1a9YYwaJmzZq3fS/ef/99rl69ysqVK40APH/+fLp06cK0adMoXbr0bd+LlJQURowYYexrNWrUyLf+b7/9lqNHj/LNN9/g5eUFwJQpU+jYsaPRZvXq1betx8PDg8mTJzNs2DBeffVV43U5+2phTJ48mcDAQAD69evH6NGjOXHihPE5PPPMM2zfvp2RI0eSnp7OlClT+PbbbwkICABufAbff/89S5YsMfZZyH8ftre3x8nJiVKlSuXaDzw9PQkKCqJ06dL4+PjQrFmzQm/P3VJPjYiUSNnZ2QVqd+TIEby9vY1AA1CnTh3c3NzMelR8fX1xdnY2nnt4eFCnTh2srKzMpt16KCTny//m53fqqalXr57x74oVKwIYy01ISGDixIk4OTkZjwEDBnD27FmzwwS38vf3Nxs7kpCQwM8//4yzs7OxHHd3d65evcqJEydwd3cnPDyc4OBgunTpwpw5c4zelaI0ePBg44d13LhxHDx48Lbt9+/fT5cuXfDx8cHZ2dn44UxJSQEgPj6eVq1a5dtTArnfiyNHjlC/fn2zHr3AwECysrJISkq643sxdOhQ+vfvT1BQEO+8845xqCsvOftbTqCB3PvIneq5cOECZ86cKZKB7jfvax4eHjg4OJgFy5v36Z9//pm//vqLxx9/3Gz/W7lyZa5tvt0+nJdnn32Wv//+m6pVqzJgwADWrVt320Oq94pCjYiUSDVq1MBkMhXZYOBbfyRNJlOe07Kysop0XTm9KDnLTUtLY8KECcTHxxuPxMREjh8/jp2dXb7LvPUQXFpaGo0bNzZbTnx8PMeOHeP5558HbvTcxMbG0rJlS9auXUvNmjWNsUhWVla5guO1a9cKva39+/fnl19+4YUXXiAxMZEmTZqYHe64Wc5hGRcXF1atWsXevXtZt24dcGPAL4C9vf0d15nX4cg7ud17MX78eA4dOkTnzp3Ztm0bderUMeq6F+60jTlB++bPJ7/P5tZ97Xb7dM6Ynq+++spsnzl8+LDZuJq8lgvc9v+Gt7c3SUlJLFy4EHt7ewYNGkTr1q3/0T51NxRqRKREcnd3Jzg4mAULFnDlypVc8y9dugTcOIxw+vRpTp8+bcw7fPgwly5dok6dOnddx80DknOe381hnEaNGpGUlET16tVzPW7uNSrIco4fP06FChVyLcfV1dVo17BhQ0aPHs3u3bt59NFHWb16NQDly5fP1XNzp1N0bWxsyMzMzDXd29ubl156iS+++IJhw4bx/vvv5/n6o0eP8scff/DOO+/QqlUrateuneuv/3r16vHdd98V6sfQz8+PhIQEs/1k165dxmHFHPm9F3DjENdrr73Gli1b6NatG1FRUfmu6/Tp02bv3a37yJ3qcXZ2xtfXl61bt+a5jvLlywOYraMoTp+uU6cOtra2pKSk5Npnbu7pvJP89gN7e3u6dOnC3LlziYmJITY2lsTExLuuuzAUakSkxFqwYAGZmZk0a9aMzz//nOPHj3PkyBHmzp1rdPkHBQXh7+9PaGgoBw4cYM+ePfTu3Zs2bdrQpEmTu67h008/5cMPP+TYsWOMGzeOPXv2mA34LKyxY8eycuVKJkyYwKFDhzhy5Ahr1qzhrbfeKtRyQkNDKVeuHE899RTfffcdycnJxMTEMHjwYP773/+SnJzM6NGjiY2N5dSpU2zZsoXjx48bgeyxxx5j3759rFy5kuPHjzNu3Dh++umn267T19eXnTt38uuvv/L7778DN86Y+uabb0hOTubAgQNs374939Dn4+ODjY0N8+bN45dffmHDhg1MmjTJrE1kZCSpqan07NmTffv2cfz4cT766KNcZ5Dd+l7Y2dkRFhbGTz/9xPbt23nllVd44YUX8PDwuO178ffffxMZGUlMTAynTp1i165d7N27N99tCAoKombNmoSFhZGQkMB3333Hm2++Wah64Ebv0MyZM5k7dy7Hjx/nwIEDRg+Xvb09LVq04J133uHIkSPs2LGj0PtHXpydnRk+fDivvfYaK1as4MSJE8Z6V6xYUeDl+Pr6kpycTHx8PL///jvp6eksX76cDz74gJ9++olffvmFjz/+GHt7eypXrnzXdReGQo2IlFhVq1blwIEDtGvXjmHDhvHoo4/y+OOPs3XrVhYtWgTc6Br/8ssvKVOmDK1btyYoKIiqVauydu3aIqlhwoQJrFmzhnr16rFy5Ur+/e9/31UPUHBwMBs3bmTLli00bdqUFi1a8N577xX6y9/BwYGdO3fi4+NDt27d8PPzo1+/fly9ehUXFxccHBw4evSocSr8wIEDiYiI4MUXXzTqGDNmDK+//jpNmzblf//7H717977tOidOnMjJkyepVq2a0ZuQmZlJREQEfn5+hISEULNmzXwHUZcvX57ly5fz6aefUqdOHd555x1mzJhh1qZs2bJs27aNtLQ02rRpQ+PGjXn//fdvO8bGwcGBb775hosXL9K0aVOeeeYZ2rdvz/z58435+b0X1tbW/PHHH/Tu3ZuaNWvSo0cPOnbsyIQJE/Jcl5WVFevWrePvv/+mWbNm9O/fP9eZa3eqByAsLIzZs2ezcOFC6tatyxNPPMHx48eN+R9++CHXr1+ncePGDBkyhMmTJ9/mkym4SZMmMWbMGKZOnWp8Zl999RVVqlQp8DK6d+9OSEgI7dq1o3z58vz73//Gzc2N999/n8DAQOrVq8e3337Lf/7zH8qWLVskdReUKbugo/EeMKmpqbi6unL58mVcXFyKu5xi4b/i/p9O9yBIDLu/3aElwdWrV0lOTqZKlSq3HbchInI/3O476W5+v9VTIyIiIhZBoUZEREQsgkKNiIiIWIRCh5qdO3fSpUsXvLy88rzVenZ2NmPHjqVixYrY29sTFBRkNvgJblz+PDQ0FBcXF9zc3OjXr1+ue2IcPHiQVq1aYWdnh7e3N9OnTy/81omIiMhDo9Ch5sqVK9SvX58FCxbkOX/69OnMnTuXxYsXExcXh6OjI8HBwVy9etVoExoayqFDh4z7duzcuZOBAwca81NTU+nQoQOVK1dm//79vPvuu4wfP56lS5f+g00UERGRh0Gh7/3UsWNHs3tc3Cw7O5vZs2fz1ltv8dRTTwE37sHi4eHB+vXrjRvBbd68mb179xrXkJg3bx6dOnVixowZeHl5sWrVKjIyMvjwww+xsbGhbt26xMfHM2vWLLPwIyKFY6EnO4rIA+ZefRcV6Zia5ORkzp07R1BQkDHN1dWV5s2bExsbC0BsbCxubm5mF8UKCgrCysrKuDtubGwsrVu3Nru3R3BwMElJSfz5559FWbLIQyHnGh+3u7eQiMj9kvNddLvrD/0TRXqX7nPnzgEYV0zM4eHhYcw7d+4cFSpUMC+iVCnc3d3N2tx6IaCcZZ47d44yZcrkWnd6ejrp6enG89TU1LvcGhHLYW1tjZubm3FJegcHB+N+LiIi90t2djZ//fUXFy5cwM3NDWtr6yJdfpGGmuI0derUfK8AKSLg6ekJ3P5OuyIi94Obm5vxnVSUijTU5BR4/vx541blOc8bNGhgtLn1S/X69etcvHjReL2npyfnz583a5PzPL83YfTo0QwdOtR4npqaWqgbdIlYOpPJRMWKFalQocJ9v3OuiEiO0qVLF3kPTY4iDTVVqlTB09OTrVu3GiEmNTWVuLg4Xn75ZQACAgK4dOkS+/fvp3HjxgBs27aNrKwsmjdvbrR58803uXbtmnG8LTo6mlq1auV56AnA1tYWW1vbotwcEYtkbW19z75QRESKU6EHCqelpREfH2/cBj3nTp0pKSmYTCbjxlsbNmwgMTGR3r174+XlRdeuXQGMG2gNGDCAPXv2sGvXLiIjI+nZsydeXl4APP/889jY2NCvXz8OHTrE2rVrmTNnjllPjIiIiMjNCt1Ts2/fPtq1a2c8zwkaYWFhLF++nNdff50rV64wcOBALl26xL/+9S82b95sdsOqVatWERkZSfv27bGysqJ79+7MnTvXmO/q6sqWLVuIiIigcePGlCtXjrFjx+p0bhEREcmX7tJtwXSX7rw9jHfpFhF5UOgu3SIiIvLQU6gRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIUeajJzMxkzJgxVKlSBXt7e6pVq8akSZPIzs422mRnZzN27FgqVqyIvb09QUFBHD9+3Gw5Fy9eJDQ0FBcXF9zc3OjXrx9paWlFXa6IiIhYiCIPNdOmTWPRokXMnz+fI0eOMG3aNKZPn868efOMNtOnT2fu3LksXryYuLg4HB0dCQ4O5urVq0ab0NBQDh06RHR0NBs3bmTnzp0MHDiwqMsVERERC2HKvrkLpQg88cQTeHh48MEHHxjTunfvjr29PR9//DHZ2dl4eXkxbNgwhg8fDsDly5fx8PBg+fLl9OzZkyNHjlCnTh327t1LkyZNANi8eTOdOnXiv//9L15eXnesIzU1FVdXVy5fvoyLi0tRbuIDw3+Ff3GXUCIlhiUWdwkiIpKPu/n9LvKempYtW7J161aOHTsGQEJCAt9//z0dO3YEIDk5mXPnzhEUFGS8xtXVlebNmxMbGwtAbGwsbm5uRqABCAoKwsrKiri4uDzXm56eTmpqqtlDREREHh6linqBo0aNIjU1ldq1a2NtbU1mZiZvv/02oaGhAJw7dw4ADw8Ps9d5eHgY886dO0eFChXMCy1VCnd3d6PNraZOncqECROKenNERETkAVHkPTWffPIJq1atYvXq1Rw4cIAVK1YwY8YMVqxYUdSrMjN69GguX75sPE6fPn1P1yciIiIlS5H31IwYMYJRo0bRs2dPAPz9/Tl16hRTp04lLCwMT09PAM6fP0/FihWN150/f54GDRoA4OnpyYULF8yWe/36dS5evGi8/la2trbY2toW9eaIiIjIA6LIe2r++usvrKzMF2ttbU1WVhYAVapUwdPTk61btxrzU1NTiYuLIyAgAICAgAAuXbrE/v37jTbbtm0jKyuL5s2bF3XJIiIiYgGKvKemS5cuvP322/j4+FC3bl1+/PFHZs2aRd++fQEwmUwMGTKEyZMnU6NGDapUqcKYMWPw8vKia9euAPj5+RESEsKAAQNYvHgx165dIzIykp49exbozCcRERF5+BR5qJk3bx5jxoxh0KBBXLhwAS8vL1588UXGjh1rtHn99de5cuUKAwcO5NKlS/zrX/9i8+bN2NnZGW1WrVpFZGQk7du3x8rKiu7duzN37tyiLldEREQsRJFfp6ak0HVqdJ2a/Og6NSIiJVeJuk6NiIiISHFQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiIiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiIiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiIiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiIiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIpS6Fwv99ddfGTlyJJs2beKvv/6ievXqREVF0aRJEwCys7MZN24c77//PpcuXSIwMJBFixZRo0YNYxkXL17klVde4T//+Q9WVlZ0796dOXPm4OTkdC9KFrnnjtT2K+4SShy/o0eKuwQRsSBF3lPz559/EhgYSOnSpdm0aROHDx9m5syZlClTxmgzffp05s6dy+LFi4mLi8PR0ZHg4GCuXr1qtAkNDeXQoUNER0ezceNGdu7cycCBA4u6XBEREbEQRd5TM23aNLy9vYmKijKmValSxfh3dnY2s2fP5q233uKpp54CYOXKlXh4eLB+/Xp69uzJkSNH2Lx5M3v37jV6d+bNm0enTp2YMWMGXl5eRV22iIiIPOCKPNRs2LCB4OBgnn32WXbs2MEjjzzCoEGDGDBgAADJycmcO3eOoKAg4zWurq40b96c2NhYevbsSWxsLG5ubkagAQgKCsLKyoq4uDiefvrpXOtNT08nPT3deJ6amlrUmyZyV3qMvidHex9oicVdgIhYlCI//PTLL78Y42O++eYbXn75ZQYPHsyKFSsAOHfuHAAeHh5mr/Pw8DDmnTt3jgoVKpjNL1WqFO7u7kabW02dOhVXV1fj4e3tXdSbJiIiIiVYkYearKwsGjVqxJQpU2jYsCEDBw5kwIABLF68uKhXZWb06NFcvnzZeJw+ffqerk9ERERKliIPNRUrVqROnTpm0/z8/EhJSQHA09MTgPPnz5u1OX/+vDHP09OTCxcumM2/fv06Fy9eNNrcytbWFhcXF7OHiIiIPDyKPNQEBgaSlJRkNu3YsWNUrlwZuDFo2NPTk61btxrzU1NTiYuLIyAgAICAgAAuXbrE/v37jTbbtm0jKyuL5s2bF3XJIiIiYgGKfOTia6+9RsuWLZkyZQo9evRgz549LF26lKVLlwJgMpkYMmQIkydPpkaNGlSpUoUxY8bg5eVF165dgRs9OyEhIcZhq2vXrhEZGUnPnj115pOIiIjkqchDTdOmTVm3bh2jR49m4sSJVKlShdmzZxMaGmq0ef3117ly5QoDBw7k0qVL/Otf/2Lz5s3Y2dkZbVatWkVkZCTt27c3Lr43d+7coi5XRERELIQpOzs7u7iLuBdSU1NxdXXl8uXLD+34Gv8V/sVdQomUGFY8JxLr88ituD4LESm57ub3W/d+EhEREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhEUakRERMQiKNSIiIiIRVCoEREREYugUCMiIiIWocjv/SQlR2JySnGXIDfR5yEicm8p1Fgw36uri7uEEulkMa1Xn0duJ4u7ABGxKDr8JCIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEI9zzUvPPOO5hMJoYMGWJMu3r1KhEREZQtWxYnJye6d+/O+fPnzV6XkpJC586dcXBwoEKFCowYMYLr16/f63JFRETkAXVPQ83evXtZsmQJ9erVM5v+2muv8Z///IdPP/2UHTt2cObMGbp162bMz8zMpHPnzmRkZLB7925WrFjB8uXLGTt27L0sV0RERB5g9yzUpKWlERoayvvvv0+ZMmWM6ZcvX+aDDz5g1qxZPPbYYzRu3JioqCh2797NDz/8AMCWLVs4fPgwH3/8MQ0aNKBjx45MmjSJBQsWkJGRca9KFhERkQfYPQs1ERERdO7cmaCgILPp+/fv59q1a2bTa9eujY+PD7GxsQDExsbi7++Ph4eH0SY4OJjU1FQOHTqU5/rS09NJTU01e4iIiMjDo9S9WOiaNWs4cOAAe/fuzTXv3Llz2NjY4ObmZjbdw8ODc+fOGW1uDjQ583Pm5WXq1KlMmDChCKoXERGRB1GR99ScPn2aV199lVWrVmFnZ1fUi8/X6NGjuXz5svE4ffr0fVu3iIiIFL8iDzX79+/nwoULNGrUiFKlSlGqVCl27NjB3LlzKVWqFB4eHmRkZHDp0iWz150/fx5PT08APD09c50NlfM8p82tbG1tcXFxMXuIiIjIw6PIQ0379u1JTEwkPj7eeDRp0oTQ0FDj36VLl2br1q3Ga5KSkkhJSSEgIACAgIAAEhMTuXDhgtEmOjoaFxcX6tSpU9Qli4iIiAUo8jE1zs7OPProo2bTHB0dKVu2rDG9X79+DB06FHd3d1xcXHjllVcICAigRYsWAHTo0IE6derwwgsvMH36dM6dO8dbb71FREQEtra2RV2yiIiIWIB7MlD4Tt577z2srKzo3r076enpBAcHs3DhQmO+tbU1Gzdu5OWXXyYgIABHR0fCwsKYOHFicZQrIiIiD4D7EmpiYmLMntvZ2bFgwQIWLFiQ72sqV67M119/fY8rExEREUuhez+JiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhEUakRERMQiKNSIiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhEUakRERMQiKNSIiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhEUakRERMQiKNSIiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhEUakRERMQiKNSIiIiIRVCoEREREYugUCMiIiIWQaFGRERELIJCjYiIiFgEhRoRERGxCAo1IiIiYhGKPNRMnTqVpk2b4uzsTIUKFejatStJSUlmba5evUpERARly5bFycmJ7t27c/78ebM2KSkpdO7cGQcHBypUqMCIESO4fv16UZcrIiIiFqLIQ82OHTuIiIjghx9+IDo6mmvXrtGhQweuXLlitHnttdf4z3/+w6effsqOHTs4c+YM3bp1M+ZnZmbSuXNnMjIy2L17NytWrGD58uWMHTu2qMsVERERC2HKzs7Ovpcr+O2336hQoQI7duygdevWXL58mfLly7N69WqeeeYZAI4ePYqfnx+xsbG0aNGCTZs28cQTT3DmzBk8PDwAWLx4MSNHjuS3337DxsbmjutNTU3F1dWVy5cv4+Lici83scTyHfVVcZdQIp18p3OxrFefR27F9VmISMl1N7/f93xMzeXLlwFwd3cHYP/+/Vy7do2goCCjTe3atfHx8SE2NhaA2NhY/P39jUADEBwcTGpqKocOHbrXJYuIiMgDqNS9XHhWVhZDhgwhMDCQRx99FIBz585hY2ODm5ubWVsPDw/OnTtntLk50OTMz5mXl/T0dNLT043nqampRbUZIiIi8gC4pz01ERER/PTTT6xZs+Zerga4MUDZ1dXVeHh7e9/zdYqIiEjJcc9CTWRkJBs3bmT79u1UqlTJmO7p6UlGRgaXLl0ya3/+/Hk8PT2NNreeDZXzPKfNrUaPHs3ly5eNx+nTp4twa0RERKSkK/JQk52dTWRkJOvWrWPbtm1UqVLFbH7jxo0pXbo0W7duNaYlJSWRkpJCQEAAAAEBASQmJnLhwgWjTXR0NC4uLtSpUyfP9dra2uLi4mL2EBERkYdHkY+piYiIYPXq1Xz55Zc4OzsbY2BcXV2xt7fH1dWVfv36MXToUNzd3XFxceGVV14hICCAFi1aANChQwfq1KnDCy+8wPTp0zl37hxvvfUWERER2NraFnXJIiIiYgGKPNQsWrQIgLZt25pNj4qKIjw8HID33nsPKysrunfvTnp6OsHBwSxcuNBoa21tzcaNG3n55ZcJCAjA0dGRsLAwJk6cWNTlioiIiIUo8lBTkMve2NnZsWDBAhYsWJBvm8qVK/P1118XZWkiIiJiwXTvJxEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWASFGhEREbEICjUiIiJiERRqRERExCIo1IiIiIhFUKgRERERi6BQIyIiIhZBoUZEREQsgkKNiIiIWIQSHWoWLFiAr68vdnZ2NG/enD179hR3SSIiIlJCldhQs3btWoYOHcq4ceM4cOAA9evXJzg4mAsXLhR3aSIiIlICldhQM2vWLAYMGECfPn2oU6cOixcvxsHBgQ8//LC4SxMREZESqFRxF5CXjIwM9u/fz+jRo41pVlZWBAUFERsbm+dr0tPTSU9PN55fvnwZgNTU1HtbbAmWlf5XcZdQIhXXPqHPI7eH+f+niOQt53shOzu70K8tkaHm999/JzMzEw8PD7PpHh4eHD16NM/XTJ06lQkTJuSa7u3tfU9qlAeX6+zirkBy6LMQkfz88ccfuLq6Fuo1JTLU/BOjR49m6NChxvNLly5RuXJlUlJSCv2mSNFKTU3F29ub06dP4+LiUtzlPNT0WZQc+ixKFn0eJcfly5fx8fHB3d290K8tkaGmXLlyWFtbc/78ebPp58+fx9PTM8/X2NraYmtrm2u6q6urdtASwsXFRZ9FCaHPouTQZ1Gy6PMoOaysCj/st0QOFLaxsaFx48Zs3brVmJaVlcXWrVsJCAgoxspERESkpCqRPTUAQ4cOJSwsjCZNmtCsWTNmz57NlStX6NOnT3GXJiIiIiVQiQ01zz33HL/99htjx47l3LlzNGjQgM2bN+caPJwfW1tbxo0bl+chKbm/9FmUHPosSg59FiWLPo+S424+C1P2PzlnSkRERKSEKZFjakREREQKS6FGRERELIJCjYiIiFgEhRoRERGxCA9FqHn77bdp2bIlDg4OuLm5FXc5D50FCxbg6+uLnZ0dzZs3Z8+ePcVd0kNn586ddOnSBS8vL0wmE+vXry/ukh5aU6dOpWnTpjg7O1OhQgW6du1KUlJScZf1UFq0aBH16tUzLrgXEBDApk2birssAd555x1MJhNDhgwp1OseilCTkZHBs88+y8svv1zcpTx01q5dy9ChQxk3bhwHDhygfv36BAcHc+HCheIu7aFy5coV6tevz4IFC4q7lIfejh07iIiI4IcffiA6Oppr167RoUMHrly5UtylPXQqVarEO++8w/79+9m3bx+PPfYYTz31FIcOHSru0h5qe/fuZcmSJdSrV6/Qr32oTulevnw5Q4YM4dKlS8VdykOjefPmNG3alPnz5wM3rgzt7e3NK6+8wqhRo4q5uoeTyWRi3bp1dO3atbhLEeC3336jQoUK7Nixg9atWxd3OQ89d3d33n33Xfr161fcpTyU0tLSaNSoEQsXLmTy5Mk0aNCA2bNnF/j1D0VPjRSPjIwM9u/fT1BQkDHNysqKoKAgYmNji7EykZLj8uXLAP/o5n1SdDIzM1mzZg1XrlzR7XiKUUREBJ07dzb73SiMEntFYXnw/f7772RmZua6CrSHhwdHjx4tpqpESo6srCyGDBlCYGAgjz76aHGX81BKTEwkICCAq1ev4uTkxLp166hTp05xl/VQWrNmDQcOHGDv3r3/eBkPbE/NqFGjMJlMt33oh1NESrKIiAh++ukn1qxZU9ylPLRq1apFfHw8cXFxvPzyy4SFhXH48OHiLuuhc/r0aV599VVWrVqFnZ3dP17OA9tTM2zYMMLDw2/bpmrVqvenGMlTuXLlsLa25vz582bTz58/j6enZzFVJVIyREZGsnHjRnbu3EmlSpWKu5yHlo2NDdWrVwegcePG7N27lzlz5rBkyZJiruzhsn//fi5cuECjRo2MaZmZmezcuZP58+eTnp6OtbX1HZfzwIaa8uXLU758+eIuQ27DxsaGxo0bs3XrVmNQalZWFlu3biUyMrJ4ixMpJtnZ2bzyyiusW7eOmJgYqlSpUtwlyU2ysrJIT08v7jIeOu3btycxMdFsWp8+fahduzYjR44sUKCBBzjUFEZKSgoXL14kJSWFzMxM4uPjAahevTpOTk7FW5yFGzp0KGFhYTRp0oRmzZoxe/Zsrly5Qp8+fYq7tIdKWloaP//8s/E8OTmZ+Ph43N3d8fHxKcbKHj4RERGsXr2aL7/8EmdnZ86dOweAq6sr9vb2xVzdw2X06NF07NgRHx8f/ve//7F69WpiYmL45ptviru0h46zs3OucWWOjo6ULVu2cOPNsh8CYWFh2UCux/bt24u7tIfCvHnzsn18fLJtbGyymzVrlv3DDz8Ud0kPne3bt+f5fyAsLKy4S3vo5PU5ANlRUVHFXdpDp2/fvtmVK1fOtrGxyS5fvnx2+/bts7ds2VLcZcn/r02bNtmvvvpqoV7zUF2nRkRERCzXA3v2k4iIiMjNFGpERETEIijUiIiIiEVQqBERERGLoFAjIiIiFkGhRkRERCyCQo2IiIhYBIUaERERsQgKNSIiImIRFGpERETEIijUiIiIiEVQqBERERGL8P8BQvtliPhhZ44AAAAASUVORK5CYII=", "text/plain": [ - "

" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -4977,7 +4975,7 @@ " step_3_results = mi.perform_dependency_parsing(step_1_results[\"analyzed_text\"],\n", " spacy_language_model)\n", " step_3_time = time.time()\n", - " \n", + "\n", " step_3a_results = mi.perform_targeted_dependency_parsing(step_2_results[\"person\"],\n", " spacy_language_model)\n", " step_3a_time = time.time()\n", @@ -5002,6 +5000,13 @@ " \n", " pd.DataFrame.from_records(timings).to_csv(\"ibm_press_release_timings.csv\")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -5020,7 +5025,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/tutorials/market/SciPy_Demo_0.ipynb b/tutorials/market/SciPy_Demo_0.ipynb index 545e7838..149c731d 100644 --- a/tutorials/market/SciPy_Demo_0.ipynb +++ b/tutorials/market/SciPy_Demo_0.ipynb @@ -23,6 +23,7 @@ "from typing import *\n", "import json\n", "import os\n", + "import shutil\n", "import ibm_watson\n", "import ibm_watson.natural_language_understanding_v1 as nlu\n", "import ibm_cloud_sdk_core\n", @@ -120,30 +121,22 @@ "text/plain": [ "[{'type': 'Person',\n", " 'text': 'Christoph Herman',\n", - " 'relevance': 0.217154,\n", + " 'relevance': 0.357379,\n", " 'mentions': [{'text': 'Christoph Herman',\n", " 'location': [1213, 1229],\n", - " 'confidence': 0.94435}],\n", + " 'confidence': 0.992954}],\n", " 'count': 1,\n", - " 'confidence': 0.94435},\n", + " 'confidence': 0.992954},\n", " {'type': 'Person',\n", " 'text': 'Stephen Leonard',\n", - " 'relevance': 0.136166,\n", + " 'relevance': 0.225795,\n", " 'mentions': [{'text': 'Stephen Leonard',\n", " 'location': [2227, 2242],\n", - " 'confidence': 0.989177}],\n", + " 'confidence': 0.99548}],\n", " 'disambiguation': {'name': 'Steve_Leonard',\n", " 'dbpedia_resource': 'http://dbpedia.org/resource/Steve_Leonard'},\n", " 'count': 1,\n", - " 'confidence': 0.989177},\n", - " {'type': 'Person',\n", - " 'text': 'Sam Ponedal',\n", - " 'relevance': 0.020711,\n", - " 'mentions': [{'text': 'Sam Ponedal',\n", - " 'location': [3574, 3585],\n", - " 'confidence': 0.894298}],\n", - " 'count': 1,\n", - " 'confidence': 0.894298}]" + " 'confidence': 0.99548}]" ] }, "execution_count": 5, @@ -191,19 +184,14 @@ " \n", " \n", " \n", - " 38\n", + " 26\n", " [1213, 1229): 'Christoph Herman'\n", - " 0.944350\n", + " 0.992954\n", " \n", " \n", - " 41\n", + " 31\n", " [2227, 2242): 'Stephen Leonard'\n", - " 0.989177\n", - " \n", - " \n", - " 48\n", - " [3574, 3585): 'Sam Ponedal'\n", - " 0.894298\n", + " 0.995480\n", " \n", " \n", "\n", @@ -211,9 +199,8 @@ ], "text/plain": [ " person confidence\n", - "38 [1213, 1229): 'Christoph Herman' 0.944350\n", - "41 [2227, 2242): 'Stephen Leonard' 0.989177\n", - "48 [3574, 3585): 'Sam Ponedal' 0.894298" + "26 [1213, 1229): 'Christoph Herman' 0.992954\n", + "31 [2227, 2242): 'Stephen Leonard' 0.995480" ] }, "execution_count": 6, @@ -271,9 +258,9 @@ " \n", " \n", " 1\n", - " [2227, 2519): 'Stephen Leonard, General Manage...\n", + " [2227, 2282): 'Stephen Leonard, General Manage...\n", " said\n", - " [2028, 2219): 'In June, IBM announced the avai...\n", + " [2352, 2519): ', we're giving our clients more...\n", " \n", " \n", "\n", @@ -282,11 +269,11 @@ "text/plain": [ " subject verb \\\n", "0 [1213, 1281): 'Christoph Herman, SVP and Head ... said \n", - "1 [2227, 2519): 'Stephen Leonard, General Manage... said \n", + "1 [2227, 2282): 'Stephen Leonard, General Manage... said \n", "\n", " object \n", "0 [937, 1205): 'SAP HANA Enterprise Cloud on IBM... \n", - "1 [2028, 2219): 'In June, IBM announced the avai... " + "1 [2352, 2519): ', we're giving our clients more... " ] }, "execution_count": 7, @@ -327,17 +314,17 @@ "[{'subject': {'text': 'Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery',\n", " 'begin': 1213,\n", " 'end': 1281},\n", - " 'sentence': ' \"SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO,\" said Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery.',\n", + " 'sentence': '\"SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO,\" said Christoph Herman, SVP and Head of SAP HANA Enterprise Cloud Delivery.',\n", " 'object': {'text': 'SAP HANA Enterprise Cloud on IBM Power Systems will help clients unlock the full value of SAP HANA in the cloud, with the possibility of enhancing the scalability and availability of mission critical SAP applications while moving workloads to SAP HANA and lowering TCO'},\n", - " 'action': {'verb': {'text': 'say', 'tense': 'past'},\n", + " 'action': {'verb': {'text': 'say', 'tense': 'future'},\n", " 'text': 'said',\n", " 'normalized': 'say'}},\n", - " {'subject': {'text': 'Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation',\n", + " {'subject': {'text': 'Stephen Leonard, General Manager, IBM Cognitive Systems',\n", " 'begin': 2227,\n", - " 'end': 2519},\n", - " 'sentence': ' \"In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are,\" said Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation.\"',\n", - " 'object': {'text': 'In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are'},\n", - " 'action': {'verb': {'text': 'say', 'tense': 'past'},\n", + " 'end': 2282},\n", + " 'sentence': '\"In June, IBM announced the availability of POWER9 in the IBM Cloud, taking the first step toward our goal of bringing IBM Cognitive Systems technology to our clients, no matter where they are,\" said Stephen Leonard, General Manager, IBM Cognitive Systems, \"With the addition of IBM Power Systems in SAP HANA Enterprise Cloud, we\\'re giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation.\"',\n", + " 'object': {'text': \", we're giving our clients more choices and greater flexibility to run their workloads where they want to across the hybrid cloud and accelerate digital transformation\"},\n", + " 'action': {'verb': {'text': 'say', 'tense': 'present'},\n", " 'text': 'said',\n", " 'normalized': 'say'}}]" ] @@ -349,7 +336,6 @@ ], "source": [ "# Code for slides: Run the Watson NLU semantic_roles model\n", - "\n", "semantic_roles_results = (\n", " natural_language_understanding\n", " .analyze(url=doc_url, features=nlu.Features(\n", @@ -360,7 +346,7 @@ "for s in someone_said_something:\n", " s[\"subject\"][\"begin\"] = doc_text.find(s[\"subject\"][\"text\"])\n", " s[\"subject\"][\"end\"] = s[\"subject\"][\"begin\"] + len(s[\"subject\"][\"text\"])\n", - " \n", + "\n", "\n", "someone_said_something_json = someone_said_something\n", "someone_said_something_json" @@ -371,11 +357,19 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n", + "Using a pipeline without specifying a model name and revision in production is not recommended.\n" + ] + }, { "data": { "text/plain": [ "[{'entity_group': 'PER',\n", - " 'score': 0.9996308088302612,\n", + " 'score': 0.99963087,\n", " 'word': 'Christoph Herman',\n", " 'start': 1213,\n", " 'end': 1229}]" @@ -464,6 +458,9 @@ "source": [ "# Write out all the data we've generated\n", "output_dir = \"./scipy_demo_data\"\n", + "if os.path.exists(output_dir):\n", + " shutil.rmtree(output_dir)\n", + "os.mkdir(output_dir)\n", "\n", "###################\n", "# Inputs to Part 1\n", @@ -472,15 +469,16 @@ "for m in person_mentions:\n", " m[\"start\"] = int(m[\"start\"])\n", " m[\"end\"] = int(m[\"end\"])\n", + " m[\"score\"] = float(m[\"score\"])\n", "with open(f\"{output_dir}/person_mentions.json\", \"w\") as f:\n", " json.dump(person_mentions, f)\n", - " \n", + "\n", "with open(f\"{output_dir}/person_mentions_watson.json\", \"w\") as f:\n", " json.dump(person_mentions_watson_json, f)\n", - " \n", + "\n", "with open(f\"{output_dir}/someone_said_something.json\", \"w\") as f:\n", " json.dump(someone_said_something_json, f)\n", - " \n", + "\n", "###################\n", "# Inputs to Part 2\n", "\n", @@ -499,7 +497,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -513,7 +511,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.17" } }, "nbformat": 4, diff --git a/tutorials/market/ibm_press_releases.txt b/tutorials/market/ibm_press_releases.txt index 453381d6..8383375d 100644 --- a/tutorials/market/ibm_press_releases.txt +++ b/tutorials/market/ibm_press_releases.txt @@ -47,7 +47,7 @@ https://newsroom.ibm.com/2020-05-21-we-trade-Digital-Trade-Finance-Network-stren https://newsroom.ibm.com/2020-07-30-IBM-and-Influential-Launch-AI-enabled-Social-Targeting-Solution-To-Help-Brands-Identify-Suitable-Influencers https://newsroom.ibm.com/2020-06-03-IBM-services-and-Aegon-sign-contract-of-portfolio-administration-for-800-000-individual-life-insurance-contracts https://newsroom.ibm.com/2020-06-03-IBM-and-Persistent-Systems-to-Accelerate-IBM-Cloud-Pak-Deployment-and-Core-IT-Modernization-for-Enterprises -https://newsroom.ibm.com/2020-06-04-Spains-CaixaBank-Teams-with-IBM-Services-to-Accelerate-Cloud-Transformation-and-Innovation-in-the-Financial-Services-Industry +#https://newsroom.ibm.com/2020-06-04-Spains-CaixaBank-Teams-with-IBM-Services-to-Accelerate-Cloud-Transformation-and-Innovation-in-the-Financial-Services-Industry https://newsroom.ibm.com/2020-06-04-IBM-Services-Collaborates-with-Lotte-Card-to-Adopt-A-Hybrid-Cloud-Strategy-To-Help-Transform-Core-Financial-Accounting-Systems https://newsroom.ibm.com/2020-06-04-Kvar-y-Arctic-Using-IBM-Blockchain-to-Trace-Norwegian-Farmed-Salmon-to-North-American-Stores https://newsroom.ibm.com/2020-06-04-Anaconda-and-IBM-Watson-Team-to-Simplify-Enterprise-Adoption-of-AI-Open-Source-Technologies @@ -116,7 +116,7 @@ https://newsroom.ibm.com/2020-09-18-Bloomberg-Television-Intelligence-Squared-US https://newsroom.ibm.com/2020-09-21-GEODIS-Uses-IBM-Sterling-Order-Management-to-Help-Retailers-Accelerate-Omnichannel-Customer-Experience-Capabilities-with-New-e-Commerce-Fulfillment https://newsroom.ibm.com/2020-09-22-IBM-Brings-Risk-Analytics-to-Security-Decision-Making https://newsroom.ibm.com/2020-09-23-IBM-Modernizes-Financial-Transaction-Solution-on-Red-Hat-OpenShift-to-Give-Banks-the-Flexibility-of-Hybrid-Cloud -https://newsroom.ibm.com/2020-09-28-2020-Call-for-Code-Global-Challenge-Finalists-Selected-for-Innovative-Solutions-to-Take-on-COVID-19-and-Climate-Change +#https://newsroom.ibm.com/2020-09-28-2020-Call-for-Code-Global-Challenge-Finalists-Selected-for-Innovative-Solutions-to-Take-on-COVID-19-and-Climate-Change https://newsroom.ibm.com/2020-09-30-IBM-Study-Majority-of-Global-C-Suite-Executives-are-Rapidly-Accelerating-Digital-Transformation-due-to-COVID-19-Pandemic-but-People-and-Talent-are-Key-to-Future-Progress https://newsroom.ibm.com/2020-09-30-BCI-in-Collaboration-with-IBM-Advances-Blockchain-based-Financial-Services-with-Electronic-Letter-of-Guarantee-for-Clients-in-Thailand diff --git a/tutorials/market/market_intelligence.py b/tutorials/market/market_intelligence.py index 5d1707ec..88f10373 100644 --- a/tutorials/market/market_intelligence.py +++ b/tutorials/market/market_intelligence.py @@ -31,9 +31,13 @@ def maybe_download_articles() -> pd.DataFrame: lines = [l.strip() for l in f.readlines()] article_urls = [l for l in lines if len(l) > 0 and l[0] != "#"] - article_htmls = [ - download_article(url) for url in article_urls - ] + article_htmls = [] + for url in article_urls: + try: + article_htmls.append(download_article(url)) + except urllib.error.HTTPError as e: + raise ValueError(f"Error downloading {url}") from e + to_write = pd.DataFrame({"url": article_urls, "html": article_htmls}) to_write.to_feather(file_name)