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{
"cells": [
{
"cell_type": "markdown",
"id": "8ea54fcd-ef4a-42cb-ae26-cbdc6f6ffc64",
"metadata": {
"tags": []
},
"source": [
"# Duct Tape Pipeline\n",
"To explore how users may interact with interactive visualizations of counterfactuals for evolving the Interactive Model Card, we will need to first find a way to generate counterfactuals based on a given input. We want the user to be able to provide their input and direct the system to generate counterfactuals based on a part of speech that is significant to the model. The system should then provide a data frame of counterfactuals to be used in an interactive visualization. Below is an example wireframe of the experience based on previous research.\n",
"\n",
"\n",
"\n",
"## Goals of this notebook\n",
"* Clean up the flow in the \"duct tape pipeline\".\n",
"* See if I can extract the LIME list for visualization"
]
},
{
"cell_type": "markdown",
"id": "736e6375-dd6d-4188-b8b1-92bded2bcd02",
"metadata": {},
"source": [
"## Loading the libraries and models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7f581785-e642-4f74-9f67-06a63820eaf2",
"metadata": {},
"outputs": [],
"source": [
"#Import the libraries we know we'll need for the Generator.\n",
"import pandas as pd, spacy, nltk, numpy as np\n",
"from spacy import displacy\n",
"from spacy.matcher import Matcher\n",
"#!python -m spacy download en_core_web_sm\n",
"nlp = spacy.load(\"en_core_web_md\")\n",
"lemmatizer = nlp.get_pipe(\"lemmatizer\")\n",
"\n",
"#Import the libraries to support the model, predictions, and LIME.\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline\n",
"import lime\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from lime.lime_text import LimeTextExplainer\n",
"\n",
"#Import the libraries for generating interactive visualizations.\n",
"import altair as alt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cbe2b292-e33e-4915-8e61-bba5327fb643",
"metadata": {},
"outputs": [],
"source": [
"#Defining all necessary variables and instances.\n",
"tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
"model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
"class_names = ['negative', 'positive']\n",
"explainer = LimeTextExplainer(class_names=class_names)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "197c3e26-0fdf-49c6-9135-57f1fd55d3e3",
"metadata": {},
"outputs": [],
"source": [
"#Defining a Predictor required for LIME to function.\n",
"def predictor(texts):\n",
" outputs = model(**tokenizer(texts, return_tensors=\"pt\", padding=True))\n",
" probas = F.softmax(outputs.logits, dim=1).detach().numpy()\n",
" return probas"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "013af6ac-f7d1-41d2-a601-b0f9a4870815",
"metadata": {},
"outputs": [],
"source": [
"#Instantiate a matcher and use it to test some patterns.\n",
"matcher = Matcher(nlp.vocab)\n",
"pattern = [{\"ENT_TYPE\": {\"IN\":[\"NORP\",\"GPE\"]}}]\n",
"matcher.add(\"proper_noun\", [pattern])\n",
"pattern_test = [{\"DEP\": \"amod\"},{\"DEP\":\"attr\"},{\"TEXT\":\"-\"},{\"DEP\":\"attr\",\"OP\":\"+\"}]\n",
"matcher.add(\"amod_attr\",[pattern_test])\n",
"pattern_an = [{\"DEP\": \"amod\"},{\"POS\":{\"IN\":[\"NOUN\",\"PROPN\"]}},{\"DEP\":{\"NOT_IN\":[\"attr\"]}}]\n",
"matcher.add(\"amod_noun\", [pattern_an])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f6ac821d-7b56-446e-b9ca-42a5f5afd198",
"metadata": {},
"outputs": [],
"source": [
"def match_this(matcher, doc):\n",
" matches = matcher(doc)\n",
" for match_id, start, end in matches:\n",
" matched_span = doc[start:end]\n",
" print(f\"Mached {matched_span.text} by the rule {nlp.vocab.strings[match_id]}.\")\n",
" return matches"
]
},
{
"cell_type": "markdown",
"id": "c23d48c4-f5ab-4428-9244-0786e9903a8e",
"metadata": {
"tags": []
},
"source": [
"## Building the Duct-Tape Pipeline cell-by-cell"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a373fc00-401a-4def-9f09-de73d485ac13",
"metadata": {},
"outputs": [],
"source": [
"gender = [\"man\", \"woman\",\"girl\",\"boy\",\"male\",\"female\",\"husband\",\"wife\",\"girlfriend\",\"boyfriend\",\"brother\",\"sister\",\"aunt\",\"uncle\",\"grandma\",\"grandpa\",\"granny\",\"granps\",\"grandmother\",\"grandfather\",\"mama\",\"dada\",\"Ma\",\"Pa\",\"lady\",\"gentleman\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8b02a5d4-8a6b-4e5e-8f15-4f9182fe341f",
"metadata": {},
"outputs": [],
"source": [
"def select_crit(document, options=False, limelist=False):\n",
" '''This function is meant to select the critical part of a sentence. Critical, in this context means\n",
" the part of the sentence that is either: A) a PROPN from the correct entity group; B) an ADJ associated with a NOUN;\n",
" C) a NOUN that represents gender. It also checks this against what the model thinks is important if the user defines \"options\" as \"LIME\" or True.'''\n",
" chunks = list(document.noun_chunks)\n",
" pos_options = []\n",
" lime_options = []\n",
" \n",
" #Identify what the model cares about.\n",
" if options:\n",
" exp = explainer.explain_instance(document.text, predictor, num_features=15, num_samples=2000)\n",
" lime_results = exp.as_list()\n",
" #prints the results from lime for QA.\n",
" if limelist == True:\n",
" print(lime_results)\n",
" for feature in lime_results:\n",
" lime_options.append(feature[0])\n",
" lime_results = pd.DataFrame(lime_results, columns=[\"Word\",\"Weight\"])\n",
" \n",
" #Identify what we care about \"parts of speech\"\n",
" for chunk in chunks:\n",
" #The use of chunk[-1] is due to testing that it appears to always match the root\n",
" root = chunk[-1]\n",
" #This currently matches to a list I've created. I don't know the best way to deal with this so I'm leaving it as is for the moment.\n",
" if root.text.lower() in gender:\n",
" cur_values = [token.text for token in chunk if token.pos_ in [\"NOUN\",\"ADJ\"]]\n",
" if (all(elem in lime_options for elem in cur_values) and ((options == \"LIME\") or (options == True))) or ((options != \"LIME\") and (options != True)):\n",
" pos_options.extend(cur_values)\n",
" #print(f\"From {chunk.text}, {cur_values} added to pos_options due to gender.\") #for QA\n",
" #This is currently set to pick up entities in a particular set of groups (which I recently expanded). Should it just pick up all named entities?\n",
" elif root.ent_type_ in [\"GPE\",\"NORP\",\"DATE\",\"EVENT\"]:\n",
" cur_values = []\n",
" if (len(chunk) > 1) and (chunk[-2].dep_ == \"compound\"):\n",
" #creates the compound element of the noun\n",
" compound = [x.text for x in chunk if x.dep_ == \"compound\"]\n",
" print(f\"This is the contents of {compound} and it is {all(elem in lime_options for elem in compound)} that all elements are present in {lime_options}.\") #for QA\n",
" #checks to see all elements in the compound are important to the model or use the compound if not checking importance.\n",
" if (all(elem in lime_options for elem in compound) and ((options == \"LIME\") or (options == True))) or ((options != \"LIME\") and (options != True)):\n",
" #creates a span for the entirety of the compound noun and adds it to the list.\n",
" span = -1 * (1 + len(compound))\n",
" pos_options.append(chunk[span:].text)\n",
" cur_values + [token.text for token in chunk if token.pos_ == \"ADJ\"]\n",
" else: \n",
" cur_values = [token.text for token in chunk if (token.ent_type_ in [\"GPE\",\"NORP\",\"DATE\",\"EVENT\"]) or (token.pos_ == \"ADJ\")]\n",
" if (all(elem in lime_options for elem in cur_values) and ((options == \"LIME\") or (options == True))) or ((options != \"LIME\") and (options != True)):\n",
" pos_options.extend(cur_values)\n",
" print(f\"From {chunk.text}, {cur_values} and {pos_options} added to pos_options due to entity recognition.\") #for QA\n",
" elif len(chunk) > 1:\n",
" cur_values = [token.text for token in chunk if token.pos_ in [\"NOUN\",\"ADJ\"]]\n",
" if (all(elem in lime_options for elem in cur_values) and ((options == \"LIME\") or (options == True))) or ((options != \"LIME\") and (options != True)):\n",
" pos_options.extend(cur_values)\n",
" print(f\"From {chunk.text}, {cur_values} added to pos_options due to wildcard.\") #for QA\n",
" else:\n",
" print(f\"No options added for \\'{chunk.text}\\' \")\n",
" \n",
" \n",
" #Return the correct set of options based on user input, defaults to POS for simplicity.\n",
" if options == \"LIME\":\n",
" return pos_options, lime_results\n",
" else:\n",
" return pos_options"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d43e202e-64b9-4cea-b117-82492c9ee5f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From This film, ['film'] added to pos_options due to wildcard.\n",
"From Iraq, ['Iraq'] and ['film', 'Iraq'] added to pos_options due to entity recognition.\n"
]
}
],
"source": [
"#Test to make sure all three options work\n",
"text4 = \"This film was filmed in Iraq.\"\n",
"doc4 = nlp(text4)\n",
"lime4, limedf = select_crit(doc4,options=\"LIME\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "a0e55a24-65df-429e-a0cd-8daf91a5d242",
"metadata": {},
"outputs": [
{
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"source": [
"single_nearest = alt.selection_single(on='mouseover', nearest=True)\n",
"viz = alt.Chart(limedf).encode(\n",
" alt.X('Weight:Q', scale=alt.Scale(domain=(-1, 1))),\n",
" alt.Y('Word:N', sort='x', axis=None),\n",
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{
"cell_type": "markdown",
"id": "bf0512b6-336e-4842-9bde-34e03a1ca7c6",
"metadata": {},
"source": [
"### Testing predictions and visualization\n",
"Here I will attempt to import the model from huggingface, generate predictions for each of the sentences, and then visualize those predictions into a dot plot. If I can get this to work then I will move on to testing a full pipeline for letting the user pick which part of the sentence they wish to generate counterfactuals for."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "74c639bb-e74a-4a46-8047-3552265ae6a4",
"metadata": {},
"outputs": [],
"source": [
"#Discovering that there's a pipeline specifically to provide scores. \n",
"#I used it to get a list of lists of dictionaries that I can then manipulate to calculate the proper prediction score.\n",
"pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8726a284-99bd-47f1-9756-1c3ae603db10",
"metadata": {},
"outputs": [],
"source": [
"def eval_pred(text):\n",
" '''A basic function for evaluating the prediction from the model and turning it into a visualization friendly number.'''\n",
" preds = pipe(text)\n",
" neg_score = preds[0][0]['score']\n",
" pos_score = preds[0][1]['score']\n",
" if pos_score >= neg_score:\n",
" return pos_score\n",
" if neg_score >= pos_score:\n",
" return -1 * neg_score"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f38f5061-f30a-4c81-9465-37951c3ad9f4",
"metadata": {},
"outputs": [],
"source": [
"def eval_pred_test(text, return_all = False):\n",
" '''A basic function for evaluating the prediction from the model and turning it into a visualization friendly number.'''\n",
" preds = pipe(text)\n",
" neg_score = -1 * preds[0][0]['score']\n",
" sent_neg = preds[0][0]['label']\n",
" pos_score = preds[0][1]['score']\n",
" sent_pos = preds[0][1]['label']\n",
" prediction = 0\n",
" sentiment = ''\n",
" if pos_score > abs(neg_score):\n",
" prediction = pos_score\n",
" sentiment = sent_pos\n",
" elif abs(neg_score) > pos_score:\n",
" prediction = neg_score\n",
" sentiment = sent_neg\n",
" \n",
" if return_all:\n",
" return prediction, sentiment\n",
" else:\n",
" return prediction"
]
},
{
"cell_type": "markdown",
"id": "8b349a87-fe83-4045-a63a-d054489bb461",
"metadata": {},
"source": [
"## Load the dummy countries I created to test generating counterfactuals\n",
"I decided to test the pipeline with a known problem space. Taking the text from Aurélien Géron's observations in twitter, I built a built a small scale test using the learnings I had to prove that we can identify a particular part of speech, use it to generate counterfactuals, and then build a visualization off it."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "46ab3332-964c-449f-8cef-a9ff7df397a4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Algeria</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Angola</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Benin</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Botswana</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Burkina</td>\n",
" <td>Africa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Continent\n",
"0 Algeria Africa\n",
"1 Angola Africa\n",
"2 Benin Africa\n",
"3 Botswana Africa\n",
"4 Burkina Africa"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#load my test data from https://github.com/dbouquin/IS_608/blob/master/NanosatDB_munging/Countries-Continents.csv\n",
"df = pd.read_csv(\"Assets/Countries/countries.csv\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "51c75894-80af-4625-8ce8-660e500b496b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From This film, ['film'] added to pos_options due to wildcard.\n",
"From Iraq, ['Iraq'] and ['film', 'Iraq'] added to pos_options due to entity recognition.\n",
"['film', 'Iraq']\n"
]
},
{
"data": {
"text/plain": [
"'Iraq'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Note: we will need to build the function that lets the user choose from the options available. For now I have hard coded it as \"selection\", from \"user_options\".\n",
"user_options = select_crit(doc4)\n",
"print(user_options)\n",
"selection = user_options[1]\n",
"selection"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3d6419f1-bf7d-44bc-afb8-ac26ef9002df",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Continent</th>\n",
" <th>text</th>\n",
" <th>prediction</th>\n",
" <th>seed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Algeria</td>\n",
" <td>Africa</td>\n",
" <td>This film was filmed in Algeria.</td>\n",
" <td>0.806454</td>\n",
" <td>alternative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Angola</td>\n",
" <td>Africa</td>\n",
" <td>This film was filmed in Angola.</td>\n",
" <td>-0.775854</td>\n",
" <td>alternative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Benin</td>\n",
" <td>Africa</td>\n",
" <td>This film was filmed in Benin.</td>\n",
" <td>0.962272</td>\n",
" <td>alternative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Botswana</td>\n",
" <td>Africa</td>\n",
" <td>This film was filmed in Botswana.</td>\n",
" <td>0.785837</td>\n",
" <td>alternative</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Burkina</td>\n",
" <td>Africa</td>\n",
" <td>This film was filmed in Burkina.</td>\n",
" <td>0.872980</td>\n",
" <td>alternative</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Continent text prediction \\\n",
"0 Algeria Africa This film was filmed in Algeria. 0.806454 \n",
"1 Angola Africa This film was filmed in Angola. -0.775854 \n",
"2 Benin Africa This film was filmed in Benin. 0.962272 \n",
"3 Botswana Africa This film was filmed in Botswana. 0.785837 \n",
"4 Burkina Africa This film was filmed in Burkina. 0.872980 \n",
"\n",
" seed \n",
"0 alternative \n",
"1 alternative \n",
"2 alternative \n",
"3 alternative \n",
"4 alternative "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Create a function that generates the counterfactuals within a data frame.\n",
"def gen_cf_country(df,document,selection):\n",
" df['text'] = df.Country.apply(lambda x: document.text.replace(selection,x))\n",
" df['prediction'] = df.text.apply(eval_pred_test)\n",
" #added this because I think it will make the end results better if we ensure the seed is in the data we generate counterfactuals from.\n",
" df['seed'] = df.Country.apply(lambda x: 'seed' if x == selection else 'alternative')\n",
" return df\n",
"\n",
"df = gen_cf_country(df,doc4,selection)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ecb9dd41-2fab-49bd-bae5-30300ce39e41",
"metadata": {},
"outputs": [
{
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0.8064541816711426, \"seed\": \"alternative\"}, {\"Country\": \"Angola\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Angola.\", \"prediction\": -0.7758541703224182, \"seed\": \"alternative\"}, {\"Country\": \"Benin\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Benin.\", \"prediction\": 0.9622722268104553, \"seed\": \"alternative\"}, {\"Country\": \"Botswana\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Botswana.\", \"prediction\": 0.7858365774154663, \"seed\": \"alternative\"}, {\"Country\": \"Burkina\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Burkina.\", \"prediction\": 0.8729804754257202, \"seed\": \"alternative\"}, {\"Country\": \"Burundi\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Burundi.\", \"prediction\": -0.6306232810020447, \"seed\": \"alternative\"}, {\"Country\": \"Cameroon\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Cameroon.\", \"prediction\": 0.5283073782920837, \"seed\": \"alternative\"}, {\"Country\": \"Cape Verde\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Cape Verde.\", \"prediction\": 0.8932027220726013, \"seed\": \"alternative\"}, {\"Country\": \"Central African Republic\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Central African Republic.\", \"prediction\": 0.9326885342597961, \"seed\": \"alternative\"}, {\"Country\": \"Chad\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Chad.\", \"prediction\": 0.788737952709198, \"seed\": \"alternative\"}, {\"Country\": \"Comoros\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Comoros.\", \"prediction\": 0.9623100757598877, \"seed\": \"alternative\"}, {\"Country\": \"Congo\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Congo.\", \"prediction\": 0.6309685707092285, \"seed\": \"alternative\"}, {\"Country\": \"Congo, Democratic Republic of\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Congo, Democratic Republic of.\", \"prediction\": -0.54060298204422, \"seed\": \"alternative\"}, {\"Country\": \"Djibouti\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Djibouti.\", \"prediction\": 0.8894529938697815, \"seed\": \"alternative\"}, {\"Country\": \"Egypt\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Egypt.\", \"prediction\": 0.9648140072822571, \"seed\": \"alternative\"}, {\"Country\": \"Equatorial Guinea\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Equatorial Guinea.\", \"prediction\": 0.6021467447280884, \"seed\": \"alternative\"}, {\"Country\": \"Eritrea\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Eritrea.\", \"prediction\": 0.5404142141342163, \"seed\": \"alternative\"}, {\"Country\": \"Ethiopia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Ethiopia.\", \"prediction\": 0.7997546195983887, \"seed\": \"alternative\"}, {\"Country\": \"Gabon\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Gabon.\", \"prediction\": -0.8517823219299316, \"seed\": \"alternative\"}, {\"Country\": \"Gambia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Gambia.\", \"prediction\": -0.5401656031608582, \"seed\": \"alternative\"}, {\"Country\": \"Ghana\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Ghana.\", \"prediction\": 0.9684805870056152, \"seed\": \"alternative\"}, {\"Country\": \"Guinea\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Guinea.\", \"prediction\": 0.6188081502914429, \"seed\": \"alternative\"}, {\"Country\": \"Guinea-Bissau\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Guinea-Bissau.\", \"prediction\": -0.500963032245636, \"seed\": \"alternative\"}, {\"Country\": \"Ivory Coast\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Ivory Coast.\", \"prediction\": 0.9872506856918335, \"seed\": \"alternative\"}, {\"Country\": \"Kenya\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Kenya.\", \"prediction\": 0.9789031744003296, \"seed\": \"alternative\"}, {\"Country\": \"Lesotho\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Lesotho.\", \"prediction\": 0.6674107313156128, \"seed\": \"alternative\"}, {\"Country\": \"Liberia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Liberia.\", \"prediction\": -0.6720185279846191, \"seed\": \"alternative\"}, {\"Country\": \"Libya\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Libya.\", \"prediction\": 0.53217613697052, \"seed\": \"alternative\"}, {\"Country\": \"Madagascar\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Madagascar.\", \"prediction\": 0.9730344414710999, \"seed\": \"alternative\"}, {\"Country\": \"Malawi\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Malawi.\", \"prediction\": -0.7816339135169983, \"seed\": \"alternative\"}, {\"Country\": \"Mali\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Mali.\", \"prediction\": -0.6651991009712219, \"seed\": \"alternative\"}, {\"Country\": \"Mauritania\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Mauritania.\", \"prediction\": 0.6149344444274902, \"seed\": \"alternative\"}, {\"Country\": \"Mauritius\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Mauritius.\", \"prediction\": 0.9310740828514099, \"seed\": \"alternative\"}, {\"Country\": \"Morocco\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Morocco.\", \"prediction\": 0.9121577143669128, \"seed\": \"alternative\"}, {\"Country\": \"Mozambique\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Mozambique.\", \"prediction\": -0.7047757506370544, \"seed\": \"alternative\"}, {\"Country\": \"Namibia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Namibia.\", \"prediction\": -0.5836523175239563, \"seed\": \"alternative\"}, {\"Country\": \"Niger\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Niger.\", \"prediction\": -0.6313472390174866, \"seed\": \"alternative\"}, {\"Country\": \"Nigeria\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Nigeria.\", \"prediction\": 0.7361583113670349, \"seed\": \"alternative\"}, {\"Country\": \"Rwanda\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Rwanda.\", \"prediction\": -0.7642565965652466, \"seed\": \"alternative\"}, {\"Country\": \"Sao Tome and Principe\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Sao Tome and Principe.\", \"prediction\": 0.6587044596672058, \"seed\": \"alternative\"}, {\"Country\": \"Senegal\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Senegal.\", \"prediction\": 0.8155898451805115, \"seed\": \"alternative\"}, {\"Country\": \"Seychelles\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Seychelles.\", \"prediction\": 0.8802894949913025, \"seed\": \"alternative\"}, {\"Country\": \"Sierra Leone\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Sierra Leone.\", \"prediction\": 0.9483919143676758, \"seed\": \"alternative\"}, {\"Country\": \"Somalia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Somalia.\", \"prediction\": -0.6477505564689636, \"seed\": \"alternative\"}, {\"Country\": \"South Africa\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in South Africa.\", \"prediction\": 0.5048943161964417, \"seed\": \"alternative\"}, {\"Country\": \"South Sudan\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in South Sudan.\", \"prediction\": -0.8506219983100891, \"seed\": \"alternative\"}, {\"Country\": \"Sudan\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Sudan.\", \"prediction\": -0.8910807967185974, \"seed\": \"alternative\"}, {\"Country\": \"Swaziland\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Swaziland.\", \"prediction\": 0.7761040925979614, \"seed\": \"alternative\"}, {\"Country\": \"Tanzania\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Tanzania.\", \"prediction\": 0.669053316116333, \"seed\": \"alternative\"}, {\"Country\": \"Togo\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Togo.\", \"prediction\": 0.9404287934303284, \"seed\": \"alternative\"}, {\"Country\": \"Tunisia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Tunisia.\", \"prediction\": 0.8345948457717896, \"seed\": \"alternative\"}, {\"Country\": \"Uganda\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Uganda.\", \"prediction\": 0.7823328971862793, \"seed\": \"alternative\"}, {\"Country\": \"Zambia\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Zambia.\", \"prediction\": -0.6479448080062866, \"seed\": \"alternative\"}, {\"Country\": \"Zimbabwe\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Zimbabwe.\", \"prediction\": 0.7163158059120178, \"seed\": \"alternative\"}, {\"Country\": \"Afghanistan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Afghanistan.\", \"prediction\": -0.8350331783294678, \"seed\": \"alternative\"}, {\"Country\": \"Bahrain\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Bahrain.\", \"prediction\": 0.9627965092658997, \"seed\": \"alternative\"}, {\"Country\": \"Bangladesh\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Bangladesh.\", \"prediction\": 0.6659616231918335, \"seed\": \"alternative\"}, {\"Country\": \"Bhutan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Bhutan.\", \"prediction\": 0.9108285307884216, \"seed\": \"alternative\"}, {\"Country\": \"Brunei\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Brunei.\", \"prediction\": 0.7673805952072144, \"seed\": \"alternative\"}, {\"Country\": \"Burma (Myanmar)\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Burma (Myanmar).\", \"prediction\": 0.5261574387550354, \"seed\": \"alternative\"}, {\"Country\": \"Cambodia\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Cambodia.\", \"prediction\": 0.9706045389175415, \"seed\": \"alternative\"}, {\"Country\": \"China\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in China.\", \"prediction\": 0.6985915303230286, \"seed\": \"alternative\"}, {\"Country\": \"East Timor\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in East Timor.\", \"prediction\": -0.7553014159202576, \"seed\": \"alternative\"}, {\"Country\": \"India\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in India.\", \"prediction\": 0.9856906533241272, \"seed\": \"alternative\"}, {\"Country\": \"Indonesia\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Indonesia.\", \"prediction\": 0.9617947936058044, \"seed\": \"alternative\"}, {\"Country\": \"Iran\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Iran.\", \"prediction\": 0.935718834400177, \"seed\": \"alternative\"}, {\"Country\": \"Iraq\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Iraq.\", \"prediction\": -0.9768388867378235, \"seed\": \"seed\"}, {\"Country\": \"Israel\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Israel.\", \"prediction\": 0.8940765261650085, \"seed\": \"alternative\"}, {\"Country\": \"Japan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Japan.\", \"prediction\": 0.8561221957206726, \"seed\": \"alternative\"}, {\"Country\": \"Jordan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Jordan.\", \"prediction\": 0.5632433891296387, \"seed\": \"alternative\"}, {\"Country\": \"Kazakhstan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Kazakhstan.\", \"prediction\": 0.8813521862030029, \"seed\": \"alternative\"}, {\"Country\": \"Korea, North\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Korea, North.\", \"prediction\": -0.692742645740509, \"seed\": \"alternative\"}, {\"Country\": \"Korea, South\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Korea, South.\", \"prediction\": 0.7591306567192078, \"seed\": \"alternative\"}, {\"Country\": \"Kuwait\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Kuwait.\", \"prediction\": 0.9136238098144531, \"seed\": \"alternative\"}, {\"Country\": \"Kyrgyzstan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Kyrgyzstan.\", \"prediction\": 0.9416173100471497, \"seed\": \"alternative\"}, {\"Country\": \"Laos\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Laos.\", \"prediction\": 0.7455804347991943, \"seed\": \"alternative\"}, {\"Country\": \"Lebanon\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Lebanon.\", \"prediction\": 0.9018603563308716, \"seed\": \"alternative\"}, {\"Country\": \"Malaysia\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Malaysia.\", \"prediction\": 0.9053533673286438, \"seed\": \"alternative\"}, {\"Country\": \"Maldives\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Maldives.\", \"prediction\": 0.8150556087493896, \"seed\": \"alternative\"}, {\"Country\": \"Mongolia\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Mongolia.\", \"prediction\": 0.9706059098243713, \"seed\": \"alternative\"}, {\"Country\": \"Nepal\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Nepal.\", \"prediction\": 0.9837730526924133, \"seed\": \"alternative\"}, {\"Country\": \"Oman\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Oman.\", \"prediction\": 0.8641175627708435, \"seed\": \"alternative\"}, {\"Country\": \"Pakistan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Pakistan.\", \"prediction\": 0.8881147503852844, \"seed\": \"alternative\"}, {\"Country\": \"Philippines\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Philippines.\", \"prediction\": 0.9892238974571228, \"seed\": \"alternative\"}, {\"Country\": \"Qatar\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Qatar.\", \"prediction\": 0.9696690440177917, \"seed\": \"alternative\"}, {\"Country\": \"Russian Federation\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Russian Federation.\", \"prediction\": 0.9777944087982178, \"seed\": \"alternative\"}, {\"Country\": \"Saudi Arabia\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Saudi Arabia.\", \"prediction\": -0.7760475873947144, \"seed\": \"alternative\"}, {\"Country\": \"Singapore\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Singapore.\", \"prediction\": 0.9684174060821533, \"seed\": \"alternative\"}, {\"Country\": \"Sri Lanka\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Sri Lanka.\", \"prediction\": 0.9552921056747437, \"seed\": \"alternative\"}, {\"Country\": \"Syria\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Syria.\", \"prediction\": -0.8887014985084534, \"seed\": \"alternative\"}, {\"Country\": \"Tajikistan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Tajikistan.\", \"prediction\": 0.8012317419052124, \"seed\": \"alternative\"}, {\"Country\": \"Thailand\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Thailand.\", \"prediction\": 0.8334980607032776, \"seed\": \"alternative\"}, {\"Country\": \"Turkey\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Turkey.\", \"prediction\": 0.5693907141685486, \"seed\": \"alternative\"}, {\"Country\": \"Turkmenistan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Turkmenistan.\", \"prediction\": 0.8194981813430786, \"seed\": \"alternative\"}, {\"Country\": \"United Arab Emirates\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in United Arab Emirates.\", \"prediction\": 0.921615719795227, \"seed\": \"alternative\"}, {\"Country\": \"Uzbekistan\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Uzbekistan.\", \"prediction\": 0.8483680486679077, \"seed\": \"alternative\"}, {\"Country\": \"Vietnam\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Vietnam.\", \"prediction\": -0.9427406191825867, \"seed\": \"alternative\"}, {\"Country\": \"Yemen\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Yemen.\", \"prediction\": -0.8567103743553162, \"seed\": \"alternative\"}, {\"Country\": \"Albania\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Albania.\", \"prediction\": 0.9874222278594971, \"seed\": \"alternative\"}, {\"Country\": \"Andorra\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Andorra.\", \"prediction\": 0.9597309231758118, \"seed\": \"alternative\"}, {\"Country\": \"Armenia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Armenia.\", \"prediction\": 0.986950695514679, \"seed\": \"alternative\"}, {\"Country\": \"Austria\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Austria.\", \"prediction\": 0.8858200907707214, \"seed\": \"alternative\"}, {\"Country\": \"Azerbaijan\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Azerbaijan.\", \"prediction\": 0.9770861268043518, \"seed\": \"alternative\"}, {\"Country\": \"Belarus\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Belarus.\", \"prediction\": 0.5220555663108826, \"seed\": \"alternative\"}, {\"Country\": \"Belgium\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Belgium.\", \"prediction\": 0.9663146138191223, \"seed\": \"alternative\"}, {\"Country\": \"Bosnia and Herzegovina\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Bosnia and Herzegovina.\", \"prediction\": 0.9699962139129639, \"seed\": \"alternative\"}, {\"Country\": \"Bulgaria\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Bulgaria.\", \"prediction\": 0.8968954086303711, \"seed\": \"alternative\"}, {\"Country\": \"Croatia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Croatia.\", \"prediction\": 0.8545156717300415, \"seed\": \"alternative\"}, {\"Country\": \"Cyprus\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Cyprus.\", \"prediction\": 0.9457007646560669, \"seed\": \"alternative\"}, {\"Country\": \"CZ\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in CZ.\", \"prediction\": -0.9620359539985657, \"seed\": \"alternative\"}, {\"Country\": \"Denmark\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Denmark.\", \"prediction\": 0.9433714747428894, \"seed\": \"alternative\"}, {\"Country\": \"Estonia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Estonia.\", \"prediction\": 0.9754448533058167, \"seed\": \"alternative\"}, {\"Country\": \"Finland\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Finland.\", \"prediction\": 0.9832987189292908, \"seed\": \"alternative\"}, {\"Country\": \"France\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in France.\", \"prediction\": 0.9652075171470642, \"seed\": \"alternative\"}, {\"Country\": \"Georgia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Georgia.\", \"prediction\": 0.9579687714576721, \"seed\": \"alternative\"}, {\"Country\": \"Germany\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Germany.\", \"prediction\": -0.7719752192497253, \"seed\": \"alternative\"}, {\"Country\": \"Greece\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Greece.\", \"prediction\": 0.974821925163269, \"seed\": \"alternative\"}, {\"Country\": \"Hungary\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Hungary.\", \"prediction\": 0.9794204831123352, \"seed\": \"alternative\"}, {\"Country\": \"Iceland\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Iceland.\", \"prediction\": 0.9596456289291382, \"seed\": \"alternative\"}, {\"Country\": \"Ireland\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Ireland.\", \"prediction\": 0.9691770076751709, \"seed\": \"alternative\"}, {\"Country\": \"Italy\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Italy.\", \"prediction\": 0.973678469657898, \"seed\": \"alternative\"}, {\"Country\": \"Latvia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Latvia.\", \"prediction\": 0.9340384006500244, \"seed\": \"alternative\"}, {\"Country\": \"Liechtenstein\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Liechtenstein.\", \"prediction\": 0.9714267253875732, \"seed\": \"alternative\"}, {\"Country\": \"Lithuania\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Lithuania.\", \"prediction\": 0.9562608599662781, \"seed\": \"alternative\"}, {\"Country\": \"Luxembourg\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Luxembourg.\", \"prediction\": 0.9322720170021057, \"seed\": \"alternative\"}, {\"Country\": \"Macedonia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Macedonia.\", \"prediction\": 0.8895869255065918, \"seed\": \"alternative\"}, {\"Country\": \"Malta\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Malta.\", \"prediction\": 0.979903519153595, \"seed\": \"alternative\"}, {\"Country\": \"Moldova\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Moldova.\", \"prediction\": 0.8919235467910767, \"seed\": \"alternative\"}, {\"Country\": \"Monaco\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Monaco.\", \"prediction\": 0.9971835017204285, \"seed\": \"alternative\"}, {\"Country\": \"Montenegro\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Montenegro.\", \"prediction\": 0.9382426738739014, \"seed\": \"alternative\"}, {\"Country\": \"Netherlands\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Netherlands.\", \"prediction\": 0.9562605023384094, \"seed\": \"alternative\"}, {\"Country\": \"Norway\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Norway.\", \"prediction\": 0.9528943300247192, \"seed\": \"alternative\"}, {\"Country\": \"Poland\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Poland.\", \"prediction\": 0.9124379754066467, \"seed\": \"alternative\"}, {\"Country\": \"Portugal\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Portugal.\", \"prediction\": 0.9363807439804077, \"seed\": \"alternative\"}, {\"Country\": \"Romania\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Romania.\", \"prediction\": 0.982775866985321, \"seed\": \"alternative\"}, {\"Country\": \"San Marino\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in San Marino.\", \"prediction\": 0.924018144607544, \"seed\": \"alternative\"}, {\"Country\": \"Serbia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Serbia.\", \"prediction\": 0.740748405456543, \"seed\": \"alternative\"}, {\"Country\": \"Slovakia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Slovakia.\", \"prediction\": 0.5953425168991089, \"seed\": \"alternative\"}, {\"Country\": \"Slovenia\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Slovenia.\", \"prediction\": 0.8840153217315674, \"seed\": \"alternative\"}, {\"Country\": \"Spain\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Spain.\", \"prediction\": 0.9535741209983826, \"seed\": \"alternative\"}, {\"Country\": \"Sweden\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Sweden.\", \"prediction\": 0.9694980382919312, \"seed\": \"alternative\"}, {\"Country\": \"Switzerland\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Switzerland.\", \"prediction\": 0.7584144473075867, \"seed\": \"alternative\"}, {\"Country\": \"Ukraine\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Ukraine.\", \"prediction\": 0.7340573668479919, \"seed\": \"alternative\"}, {\"Country\": \"United Kingdom\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in United Kingdom.\", \"prediction\": 0.8982904553413391, \"seed\": \"alternative\"}, {\"Country\": \"Vatican City\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Vatican City.\", \"prediction\": 0.7796335816383362, \"seed\": \"alternative\"}, {\"Country\": \"Antigua and Barbuda\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Antigua and Barbuda.\", \"prediction\": 0.9056354761123657, \"seed\": \"alternative\"}, {\"Country\": \"Bahamas\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Bahamas.\", \"prediction\": 0.9206929802894592, \"seed\": \"alternative\"}, {\"Country\": \"Barbados\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Barbados.\", \"prediction\": 0.9170283079147339, \"seed\": \"alternative\"}, {\"Country\": \"Belize\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Belize.\", \"prediction\": 0.9203323125839233, \"seed\": \"alternative\"}, {\"Country\": \"Canada\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Canada.\", \"prediction\": 0.9400970339775085, \"seed\": \"alternative\"}, {\"Country\": \"Costa Rica\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Costa Rica.\", \"prediction\": 0.9815211892127991, \"seed\": \"alternative\"}, {\"Country\": \"Cuba\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Cuba.\", \"prediction\": 0.7347409725189209, \"seed\": \"alternative\"}, {\"Country\": \"Dominica\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Dominica.\", \"prediction\": 0.5335615277290344, \"seed\": \"alternative\"}, {\"Country\": \"Dominican Republic\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Dominican Republic.\", \"prediction\": 0.9594704508781433, \"seed\": \"alternative\"}, {\"Country\": \"El Salvador\", \"Continent\": \"North America\", \"text\": \"This film was filmed in El Salvador.\", \"prediction\": 0.9804539084434509, \"seed\": \"alternative\"}, {\"Country\": \"Grenada\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Grenada.\", \"prediction\": 0.6266372799873352, \"seed\": \"alternative\"}, {\"Country\": \"Guatemala\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Guatemala.\", \"prediction\": 0.7368012070655823, \"seed\": \"alternative\"}, {\"Country\": \"Haiti\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Haiti.\", \"prediction\": 0.9208669662475586, \"seed\": \"alternative\"}, {\"Country\": \"Honduras\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Honduras.\", \"prediction\": 0.7440645098686218, \"seed\": \"alternative\"}, {\"Country\": \"Jamaica\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Jamaica.\", \"prediction\": 0.8702073097229004, \"seed\": \"alternative\"}, {\"Country\": \"Mexico\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Mexico.\", \"prediction\": 0.9770798683166504, \"seed\": \"alternative\"}, {\"Country\": \"Nicaragua\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Nicaragua.\", \"prediction\": -0.6681438684463501, \"seed\": \"alternative\"}, {\"Country\": \"Panama\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Panama.\", \"prediction\": 0.737115740776062, \"seed\": \"alternative\"}, {\"Country\": \"Saint Kitts and Nevis\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Saint Kitts and Nevis.\", \"prediction\": 0.9829047918319702, \"seed\": \"alternative\"}, {\"Country\": \"Saint Lucia\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Saint Lucia.\", \"prediction\": 0.7933508157730103, \"seed\": \"alternative\"}, {\"Country\": \"Saint Vincent and the Grenadines\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Saint Vincent and the Grenadines.\", \"prediction\": 0.8782792091369629, \"seed\": \"alternative\"}, {\"Country\": \"Trinidad and Tobago\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Trinidad and Tobago.\", \"prediction\": 0.9884806871414185, \"seed\": \"alternative\"}, {\"Country\": \"US\", \"Continent\": \"North America\", \"text\": \"This film was filmed in US.\", \"prediction\": 0.926520586013794, \"seed\": \"alternative\"}, {\"Country\": \"Australia\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Australia.\", \"prediction\": 0.9371141195297241, \"seed\": \"alternative\"}, {\"Country\": \"Fiji\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Fiji.\", \"prediction\": 0.9061108827590942, \"seed\": \"alternative\"}, {\"Country\": \"Kiribati\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Kiribati.\", \"prediction\": 0.9559115767478943, \"seed\": \"alternative\"}, {\"Country\": \"Marshall Islands\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Marshall Islands.\", \"prediction\": 0.96001136302948, \"seed\": \"alternative\"}, {\"Country\": \"Micronesia\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Micronesia.\", \"prediction\": -0.57024085521698, \"seed\": \"alternative\"}, {\"Country\": \"Nauru\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Nauru.\", \"prediction\": 0.9323841333389282, \"seed\": \"alternative\"}, {\"Country\": \"New Zealand\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in New Zealand.\", \"prediction\": 0.9654895663261414, \"seed\": \"alternative\"}, {\"Country\": \"Palau\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Palau.\", \"prediction\": 0.7104437351226807, \"seed\": \"alternative\"}, {\"Country\": \"Papua New Guinea\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Papua New Guinea.\", \"prediction\": 0.5819137692451477, \"seed\": \"alternative\"}, {\"Country\": \"Samoa\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Samoa.\", \"prediction\": 0.9161322712898254, \"seed\": \"alternative\"}, {\"Country\": \"Solomon Islands\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Solomon Islands.\", \"prediction\": 0.9441730976104736, \"seed\": \"alternative\"}, {\"Country\": \"Tonga\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Tonga.\", \"prediction\": 0.550994873046875, \"seed\": \"alternative\"}, {\"Country\": \"Tuvalu\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Tuvalu.\", \"prediction\": 0.9912257790565491, \"seed\": \"alternative\"}, {\"Country\": \"Vanuatu\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Vanuatu.\", \"prediction\": 0.9395317435264587, \"seed\": \"alternative\"}, {\"Country\": \"Argentina\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Argentina.\", \"prediction\": 0.9719653129577637, \"seed\": \"alternative\"}, {\"Country\": \"Bolivia\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Bolivia.\", \"prediction\": 0.8009489178657532, \"seed\": \"alternative\"}, {\"Country\": \"Brazil\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Brazil.\", \"prediction\": 0.968963086605072, \"seed\": \"alternative\"}, {\"Country\": \"Chile\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Chile.\", \"prediction\": 0.8917940258979797, \"seed\": \"alternative\"}, {\"Country\": \"Colombia\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Colombia.\", \"prediction\": 0.731931746006012, \"seed\": \"alternative\"}, {\"Country\": \"Ecuador\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Ecuador.\", \"prediction\": 0.845059335231781, \"seed\": \"alternative\"}, {\"Country\": \"Guyana\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Guyana.\", \"prediction\": 0.6705957055091858, \"seed\": \"alternative\"}, {\"Country\": \"Paraguay\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Paraguay.\", \"prediction\": 0.6165609359741211, \"seed\": \"alternative\"}, {\"Country\": \"Peru\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Peru.\", \"prediction\": 0.7860054969787598, \"seed\": \"alternative\"}, {\"Country\": \"Suriname\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Suriname.\", \"prediction\": 0.9488070607185364, \"seed\": \"alternative\"}, {\"Country\": \"Uruguay\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Uruguay.\", \"prediction\": 0.744226336479187, \"seed\": \"alternative\"}, {\"Country\": \"Venezuela\", \"Continent\": \"South America\", \"text\": \"This film was filmed in Venezuela.\", \"prediction\": 0.8343830108642578, \"seed\": \"alternative\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_nearest = alt.selection_single(on='mouseover', nearest=True)\n",
"full = alt.Chart(df).encode(\n",
" alt.X('Continent:N'), # specify nominal data\n",
" alt.Y('prediction:Q'), # specify quantitative data\n",
" color=alt.Color('seed:N', legend=alt.Legend(title=\"Seed or Alternative\")),\n",
" size='seed:N',\n",
" tooltip=('Country','prediction')\n",
").mark_circle(opacity=.5).properties(width=300).add_selection(single_nearest)\n",
"\n",
"full"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "56bc30d7-03a5-43ff-9dfe-878197628305",
"metadata": {},
"outputs": [],
"source": [
"df2 = df.nlargest(5, 'prediction')\n",
"df3 = df.nsmallest(5, 'prediction')\n",
"frames = [df2,df3]\n",
"results = pd.concat(frames)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1610bb48-c9b9-4bee-bcb5-999886acb9e3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" outputDiv = document.getElementById(\"altair-viz-948f4471f5ee4ed8bb2720ca7dd085a7\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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" function maybeLoadScript(lib, version) {\n",
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" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-09f850c452d77d8e274c73526803ae5c\"}, \"mark\": \"circle\", \"encoding\": {\"color\": {\"field\": \"seed\", \"legend\": {\"title\": \"Seed or Alternative\"}, \"type\": \"nominal\"}, \"size\": {\"field\": \"seed\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"Country\", \"type\": \"nominal\"}, {\"field\": \"prediction\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"prediction\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Country\", \"sort\": \"-x\", \"type\": \"nominal\"}}, \"selection\": {\"selector002\": {\"type\": \"single\", \"on\": \"mouseover\", \"nearest\": true}}, \"width\": 300, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-09f850c452d77d8e274c73526803ae5c\": [{\"Country\": \"Monaco\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Monaco.\", \"prediction\": 0.9971835017204285, \"seed\": \"alternative\"}, {\"Country\": \"Tuvalu\", \"Continent\": \"Oceania\", \"text\": \"This film was filmed in Tuvalu.\", \"prediction\": 0.9912257790565491, \"seed\": \"alternative\"}, {\"Country\": \"Philippines\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Philippines.\", \"prediction\": 0.9892238974571228, \"seed\": \"alternative\"}, {\"Country\": \"Trinidad and Tobago\", \"Continent\": \"North America\", \"text\": \"This film was filmed in Trinidad and Tobago.\", \"prediction\": 0.9884806871414185, \"seed\": \"alternative\"}, {\"Country\": \"Albania\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in Albania.\", \"prediction\": 0.9874222278594971, \"seed\": \"alternative\"}, {\"Country\": \"Iraq\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Iraq.\", \"prediction\": -0.9768388867378235, \"seed\": \"seed\"}, {\"Country\": \"CZ\", \"Continent\": \"Europe\", \"text\": \"This film was filmed in CZ.\", \"prediction\": -0.9620359539985657, \"seed\": \"alternative\"}, {\"Country\": \"Vietnam\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Vietnam.\", \"prediction\": -0.9427406191825867, \"seed\": \"alternative\"}, {\"Country\": \"Sudan\", \"Continent\": \"Africa\", \"text\": \"This film was filmed in Sudan.\", \"prediction\": -0.8910807967185974, \"seed\": \"alternative\"}, {\"Country\": \"Syria\", \"Continent\": \"Asia\", \"text\": \"This film was filmed in Syria.\", \"prediction\": -0.8887014985084534, \"seed\": \"alternative\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bar = alt.Chart(results).encode( \n",
" alt.X('prediction:Q'), \n",
" alt.Y('Country:N', sort=\"-x\"),\n",
" color=alt.Color('seed:N', legend=alt.Legend(title=\"Seed or Alternative\")),\n",
" size='seed:N',\n",
" tooltip=('Country','prediction')\n",
").mark_circle().properties(width=300).add_selection(single_nearest)\n",
"\n",
"bar"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "96cd0798-5ac5-4ede-8373-e8ed71ab07b3",
"metadata": {},
"outputs": [],
"source": [
"def critical_words(document, options=False):\n",
" '''This function is meant to select the critical part of a sentence. Critical, in this context means\n",
" the part of the sentence that is either: A) a PROPN from the correct entity group; B) an ADJ associated with a NOUN;\n",
" C) a NOUN that represents gender. It also checks this against what the model thinks is important if the user defines \"options\" as \"LIME\" or True.'''\n",
" if type(document) is not spacy.tokens.doc.Doc:\n",
" document = nlp(document)\n",
" chunks = list(document.noun_chunks)\n",
" pos_options = []\n",
" lime_options = []\n",
" \n",
" #Identify what the model cares about.\n",
" if options:\n",
" exp = explainer.explain_instance(document.text, predictor, num_features=15, num_samples=2000)\n",
" lime_results = exp.as_list()\n",
" for feature in lime_results:\n",
" lime_options.append(feature[0])\n",
" lime_results = pd.DataFrame(lime_results, columns=[\"Word\",\"Weight\"])\n",
" \n",
" #Identify what we care about \"parts of speech\". The first section focuses on NOUNs and related ADJ.\n",
" for chunk in chunks:\n",
" #The use of chunk[-1] is due to testing that it appears to always match the root\n",
" root = chunk[-1]\n",
" #This currently matches to a list I've created. I don't know the best way to deal with this so I'm leaving it as is for the moment.\n",
" if root.ent_type_:\n",
" cur_values = []\n",
" if (len(chunk) > 1) and (chunk[-2].dep_ == \"compound\"):\n",
" #creates the compound element of the noun\n",
" compound = [x.text for x in chunk if x.dep_ == \"compound\"]\n",
" print(f\"This is the contents of {compound} and it is {all(elem in lime_options for elem in compound)} that all elements are present in {lime_options}.\") #for QA\n",
" #checks to see all elements in the compound are important to the model or use the compound if not checking importance.\n",
" if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):\n",
" #creates a span for the entirety of the compound noun and adds it to the list.\n",
" span = -1 * (1 + len(compound))\n",
" pos_options.append(chunk[span:].text)\n",
" cur_values + [token.text for token in chunk if token.pos_ == \"ADJ\"]\n",
" else:\n",
" print(f\"The elmenents in {compound} could not be added to the final list because they are not all relevant to the model.\")\n",
" else: \n",
" cur_values = [token.text for token in chunk if (token.ent_type_) or (token.pos_ == \"ADJ\")]\n",
" if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):\n",
" pos_options.extend(cur_values)\n",
" print(f\"From {chunk.text}, {cur_values} added to pos_options due to entity recognition.\") #for QA\n",
" elif len(chunk) >= 1:\n",
" cur_values = [token.text for token in chunk if token.pos_ in [\"NOUN\",\"ADJ\"]]\n",
" if (all(elem in lime_options for elem in cur_values) and (options is True)) or ((options is False)):\n",
" pos_options.extend(cur_values)\n",
" print(f\"From {chunk.text}, {cur_values} added to pos_options due to wildcard.\") #for QA\n",
" else:\n",
" print(f\"No options added for \\'{chunk.text}\\' \")\n",
" # Here I am going to try to pick up pronouns, which are people, and Adjectival Compliments.\n",
" for token in document:\n",
" if (token.text not in pos_options) and ((token.text in lime_options) or (options == False)):\n",
" #print(f\"executed {token.text} with {token.pos_} and {token.dep_}\") #QA\n",
" if (token.pos_ == \"ADJ\") and (token.dep_ in [\"acomp\",\"conj\"]):\n",
" pos_options.append(token.text) \n",
" elif (token.pos_ == \"PRON\") and (token.morph.get(\"PronType\")[0] == \"Prs\"):\n",
" pos_options.append(token.text)\n",
" \n",
" #Return the correct set of options based on user input, defaults to POS for simplicity.\n",
" if options:\n",
" return pos_options, lime_results\n",
" else:\n",
" return pos_options"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "b04e7783-e51b-49b0-8165-afe1d5a1c576",
"metadata": {},
"outputs": [],
"source": [
"#Testing new code\n",
"a = \"People are fat and lazy.\"\n",
"b = \"I think she is beautiful.\"\n",
"doca = nlp(a)\n",
"docb = nlp(b)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "0a6bc521-9282-41ad-82c9-29e447d77635",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No options added for 'People' \n"
]
},
{
"data": {
"text/plain": [
"['fat', 'lazy']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optsa, limea = critical_words(doca, True)\n",
"optsa"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "042e94d3-65a5-4a20-b69a-96ec3296d7d4",
"metadata": {},
"outputs": [],
"source": [
"def lime_viz(df):\n",
" single_nearest = alt.selection_single(on='mouseover', nearest=True)\n",
" viz = alt.Chart(df).encode(\n",
" alt.X('Weight:Q', scale=alt.Scale(domain=(-1, 1))),\n",
" alt.Y('Word:N', sort='x', axis=None),\n",
" color=alt.Color(\"Weight\", scale=alt.Scale(scheme='blueorange', domain=[0], type=\"threshold\", range='diverging'), legend=None),\n",
" tooltip = (\"Word\",\"Weight\")\n",
" ).mark_bar().properties(title =\"Importance of individual words\")\n",
"\n",
" text = viz.mark_text(\n",
" fill=\"black\",\n",
" align='right',\n",
" baseline='middle'\n",
" ).encode(\n",
" text='Word:N'\n",
" )\n",
" limeplot = alt.LayerChart(layer=[viz,text], width = 300).configure_axis(grid=False).configure_view(strokeWidth=0)\n",
" return limeplot"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "924eeea8-1d5d-4fe7-8308-164521919269",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No options added for 'I' \n",
"From a white woman, ['white', 'woman'] added to pos_options due to wildcard.\n",
"From the street, ['street'] added to pos_options due to wildcard.\n",
"From an asian man, ['asian', 'man'] added to pos_options due to wildcard.\n"
]
},
{
"data": {
"text/plain": [
"['white', 'woman', 'street', 'asian', 'man', 'I']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test8 = \"I saw a white woman walking down the street with an asian man.\"\n",
"opts8, lime8 = critical_words(test8,True)\n",
"opts8"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "734366df-ad99-4d80-87e1-51793e150681",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-adaa380d0d924bb594dd3aaee854acfd\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-adaa380d0d924bb594dd3aaee854acfd\") {\n",
" outputDiv = document.getElementById(\"altair-viz-adaa380d0d924bb594dd3aaee854acfd\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"strokeWidth\": 0}, \"axis\": {\"grid\": false}}, \"layer\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"Weight\", \"legend\": null, \"scale\": {\"domain\": [0], \"range\": \"diverging\", \"scheme\": \"blueorange\", \"type\": \"threshold\"}, \"type\": \"quantitative\"}, \"tooltip\": [{\"field\": \"Word\", \"type\": \"nominal\"}, {\"field\": \"Weight\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Weight\", \"scale\": {\"domain\": [-1, 1]}, \"type\": \"quantitative\"}, \"y\": {\"axis\": null, \"field\": \"Word\", \"sort\": \"x\", \"type\": \"nominal\"}}, \"title\": \"Importance of individual words\"}, {\"mark\": {\"type\": \"text\", \"align\": \"right\", \"baseline\": \"middle\", \"fill\": \"black\"}, \"encoding\": {\"color\": {\"field\": \"Weight\", \"legend\": null, \"scale\": {\"domain\": [0], \"range\": \"diverging\", \"scheme\": \"blueorange\", \"type\": \"threshold\"}, \"type\": \"quantitative\"}, \"text\": {\"field\": \"Word\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"Word\", \"type\": \"nominal\"}, {\"field\": \"Weight\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"Weight\", \"scale\": {\"domain\": [-1, 1]}, \"type\": \"quantitative\"}, \"y\": {\"axis\": null, \"field\": \"Word\", \"sort\": \"x\", \"type\": \"nominal\"}}, \"title\": \"Importance of individual words\"}], \"data\": {\"name\": \"data-d686d7fc533c26b0bdc6066e4351f840\"}, \"width\": 300, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-d686d7fc533c26b0bdc6066e4351f840\": [{\"Word\": \"with\", \"Weight\": 0.3289028288853927}, {\"Word\": \"woman\", \"Weight\": -0.26094440033196564}, {\"Word\": \"asian\", \"Weight\": 0.24561077002890458}, {\"Word\": \"walking\", \"Weight\": 0.19194218998931795}, {\"Word\": \"white\", \"Weight\": -0.14942503537339621}, {\"Word\": \"down\", \"Weight\": -0.14547403123420313}, {\"Word\": \"the\", \"Weight\": 0.14096934306553166}, {\"Word\": \"I\", \"Weight\": -0.08672932329874143}, {\"Word\": \"street\", \"Weight\": 0.06704680513000527}, {\"Word\": \"a\", \"Weight\": -0.03171807940472653}, {\"Word\": \"an\", \"Weight\": -0.006746730007490843}, {\"Word\": \"saw\", \"Weight\": 0.0019276122088497296}, {\"Word\": \"man\", \"Weight\": -0.0005652423244728638}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lime_viz(lime8)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "816e1c4b-7f02-41b1-b430-2f3750ae6c4a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No options added for 'I' \n",
"From a white woman, ['white', 'woman'] added to pos_options due to wildcard.\n",
"From the street, ['street'] added to pos_options due to wildcard.\n",
"From an asian man, ['asian', 'man'] added to pos_options due to wildcard.\n"
]
}
],
"source": [
"probability, sentiment = eval_pred_test(test8, return_all=True)\n",
"options, lime = critical_words(test8,options=True)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "a437a4eb-73b3-4b3c-a719-8dde2ad6dd3c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From I, [] added to pos_options due to wildcard.\n",
"From men, ['men'] added to pos_options due to wildcard.\n",
"From women, ['women'] added to pos_options due to wildcard.\n",
"From the same respect, ['same', 'respect'] added to pos_options due to wildcard.\n"
]
}
],
"source": [
"bug = \"I find men and women deserve the same respect.\"\n",
"options = critical_words(bug)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "8676defd-0908-4218-a1d6-218de3fb7119",
"metadata": {},
"outputs": [],
"source": [
"bug_doc = nlp(bug)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "21b9e39b-2fcd-4c6f-8fe6-0d571cd79cca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I\n",
"PRON\n",
"a man\n",
"NOUN\n",
"woman\n",
"NOUN\n",
"the same respect\n",
"NOUN\n"
]
}
],
"source": [
"for chunk in bug_doc.noun_chunks:\n",
" print(chunk.text)\n",
" print(chunk[-1].pos_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38279d2d-e763-4329-a65e-1a67d6f5ebb8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|