diff --git a/src/examples/colors/data/lang.txt b/src/examples/colors/data/lang.txt index cf4f500..0b50934 100644 --- a/src/examples/colors/data/lang.txt +++ b/src/examples/colors/data/lang.txt @@ -19,15 +19,15 @@ LNUM LNAME LGEO LFW 18 Ucayali Campa Peru Allene Heitzman Jason D. Patent * Campa_DAT_new.txt new 19 Camsa * * * * Camsa_DAT_new.txt new 20 Candoshi * * * * Candoshi_DAT_new.txt new -21 Cavine{\x96}a * * * * Cavinena_DAT_new.txt new +21 Cavinena * * * * Cavinena_DAT_new.txt new 22 Cayapa Ecuador Neil Wiebe Scott Merrifield William R. Merrifield Cayapa_DAT_new.txt new -23 Ch{\x87}cobo * * * * Chacobo_DAT_new.txt new +23 Chacobo * * * * Chacobo_DAT_new.txt new 24 Chavacano * * * * Chavacano_DAT_new.txt new 25 Chayahuita * * * * Chayahuita_DAT_new.txt new 26 Chinanteco Mexico Al & Jeff Anderson Jason D. Patent * Chinantec_DAT_new.txt new 27 Chiquitano Bolivia M. Kr{\x9F}si, L. Rodriguez, E. Lyn (?) Jason Patent * Chiquitano_DAT_new.txt new 28 Chumburu * Hansford Scott Merrifield William R. Merrifield Chumburu_DAT_new.txt new -29 Cof{\x87}n * * * * Cofan_DAT_new.txt new +29 Cofan * * * * Cofan_DAT_new.txt new 30 Colorado * * * * Colorado_DAT_new.txt new 31 Eastern Cree Canada Lieselotte Bartlett Scott Merrifield William R. Merrifield Cree_DAT_new.txt new 32 Culina Peru, Brazil P. Adams and T. Fern{\x87}ndez Jason Patent * Culina_DAT_new.txt new @@ -40,7 +40,7 @@ LNUM LNAME LGEO LFW 39 Guahibo Colombia Riena Kondo Kenneth J. Merrifield William R. Merrifield Guahibo_DAT_new.txt new 40 Guambiano * * * * Guambiano_DAT_new.txt new 41 Guarijio Mexico Ron and Sharon Stoltzfus Kenneth J. Merrifield William R. Merrifield Guarijio_DAT_new.txt new -42 Ng{\x8A}bere Panama Arosemena Patent, Jason * Guaymi_DAT_new.txt new +42 Ngbere Panama Arosemena Patent, Jason * Guaymi_DAT_new.txt new 43 Gunu Cameroon D. Heath Ken Merrifield Ken Merrifield Gunu_DAT_new.txt new 44 Halbi India F. Woods and P. Hopple Jason Patent * Halbi_DAT_new.txt new 45 Huasteco * * * * Huastec_DAT_new.txt new @@ -72,11 +72,11 @@ LNUM LNAME LGEO LFW 71 Mikasuki U S A David West Scott Merrifield Scott Merrifield Mikasuki_DAT_new.txt new 72 Mixteco * * * * Mixtec_DAT_new.txt new 73 Mundu * * * * Mundu_DAT_new.txt new -74 M{\x9C}ra Pirah{\x8B} * * * * Mura-Piraha_DAT_new.txt new +74 Mura Piraha * * * * Mura-Piraha_DAT_new.txt new 75 Murle * * * * Murle_DAT_new.txt new 76 Murinbata * * * * Murrinh-Patha_DAT_new.txt new 77 Nafaanra * * * * Nafaanra_DAT_new.txt new -78 N{\x87}huatl * * * * Nahuatl_DAT_new.txt new +78 Nahuatl * * * * Nahuatl_DAT_new.txt new 79 Ocaina * * * * Ocaina_DAT_new.txt new 80 Papago * * * * Oodham_DAT_new.txt new 81 Patep * * * * Patep_DAT_new.txt new @@ -85,7 +85,7 @@ LNUM LNAME LGEO LFW 84 Saramaccan * * * * Saramaccan_DAT_new.txt new 85 Seri * * * * Seri_DAT_new.txt new 86 Shipibo Peru Guillermo Ramirez Ken Merrifield Ken Merrifield Shipibo_DAT_new.txt new -87 Sirion{\x97} * * * * Siriono_DAT_new.txt new +87 Siriono * * * * Siriono_DAT_new.txt new 88 Slave Canada Monus Jason D. Patent * Slave_DAT_new.txt new 89 Sursurunga * * * * Sursurunga_DAT_new.txt new 90 Tabla * * * * Tabla_DAT_new.txt new diff --git a/src/examples/colors/meaning.py b/src/examples/colors/meaning.py index 731e5e8..529de7c 100644 --- a/src/examples/colors/meaning.py +++ b/src/examples/colors/meaning.py @@ -5,7 +5,8 @@ from ultk.language.language import Language, Expression import ultk.effcomm.informativity as informativity import ultk.effcomm.rate_distortion as rd -import ultk.effcomm.sampling as sampling +#import ultk.effcomm.sampling as sampling +import ultk.language.sampling as sampling from collections import Counter import os import numpy as np @@ -26,36 +27,42 @@ language_codes = dict() language_terms = dict() +GENERATE_IB_BOUND=False +USE_RKK = False +USE_NOGA_ARRAYS = False +GENERATE_LANG_COLOR_INFO=False - - -#Generate all WCS color codes +#Get current dir for relative paths current_dir = os.path.dirname(os.path.realpath(__file__)) -#####----TESTING - Grab the Noga model -- remove later -DEFAULT_MODEL_URL = 'https://www.dropbox.com/s/70w953orv27kz1o/IB_color_naming_model.zip?dl=1' -def load_model(filename=None, model_dir='./model/'): - if not os.path.isdir(model_dir): - os.makedirs(model_dir) - if filename is None: - filename = model_dir + 'IB_color_naming_model/model.pkl' - if not os.path.isfile(filename): - print('downloading default model from %s ...' % DEFAULT_MODEL_URL) - urlretrieve(DEFAULT_MODEL_URL, model_dir + 'temp.zip') - print('extracting model files ...') - with ZipFile(model_dir + 'temp.zip', 'r') as zf: - zf.extractall(model_dir) - #os.remove(model_dir + 'temp.zip') - os.rename(model_dir + 'IB_color_naming_model/IB_color_naming.pkl', filename) - with open(filename, 'rb') as f: - print('loading model from file: %s' % filename) - model_data = pickle.load(f) - return model_data - -model_data = load_model(filename=f"{current_dir}/model/model.pkl", model_dir=f"{current_dir}/model/") +if(USE_NOGA_ARRAYS): + + #####----TESTING - Grab the Noga model -- remove later + DEFAULT_MODEL_URL = 'https://www.dropbox.com/s/70w953orv27kz1o/IB_color_naming_model.zip?dl=1' + def load_model(filename=None, model_dir='./model/'): + if not os.path.isdir(model_dir): + os.makedirs(model_dir) + if filename is None: + filename = model_dir + 'IB_color_naming_model/model.pkl' + if not os.path.isfile(filename): + print('downloading default model from %s ...' % DEFAULT_MODEL_URL) + urlretrieve(DEFAULT_MODEL_URL, model_dir + 'temp.zip') + print('extracting model files ...') + with ZipFile(model_dir + 'temp.zip', 'r') as zf: + zf.extractall(model_dir) + #os.remove(model_dir + 'temp.zip') + os.rename(model_dir + 'IB_color_naming_model/IB_color_naming.pkl', filename) + with open(filename, 'rb') as f: + print('loading model from file: %s' % filename) + model_data = pickle.load(f) + return model_data + + model_data = load_model(filename=f"{current_dir}/model/model.pkl", model_dir=f"{current_dir}/model/") +#Generate all WCS color codes #Convert the Munsell hues of the WCS data to CIELab data munsell_to_cielab = {} +referents_by_color_code = {} with open( f"{current_dir}/data/cnum-vhcm-lab-new.txt", newline="", encoding="utf-8" ) as csvfile: @@ -66,11 +73,15 @@ def load_model(filename=None, model_dir='./model/'): float(row["a*"]), float(row["b*"]), ] -color_codes = [{"name": key} for key in munsell_to_cielab.keys()] + referents_by_color_code[row["V"] + row["H"]] = Referent(name=row["V"] + row["H"], properties={"L": float(row["L*"]), "a": float(row["a*"]), "b": float(row["b*"])}) + +#Generate a list of Meanings +#color_codes = [{"name": key, "L":munsell_to_cielab[key][0], "a":munsell_to_cielab[key][1], "b":munsell_to_cielab[key][2]} for key in munsell_to_cielab.keys()] + +print(f"Color codes:{referents_by_color_code}") # Generate referents for all color codes -referents = pd.DataFrame(color_codes) -color_universe = Universe.from_dataframe(referents) +color_universe = Universe(referents=referents_by_color_code.values()) with open(f"{current_dir}/data/lang.txt", newline="", encoding="utf-8") as csvfile: lang_reader = csv.DictReader(csvfile, delimiter="\t") @@ -104,11 +115,11 @@ def meaning(center, point): return math.exp((-1/(2*SIGMA_SQUARED_SCALAR) * np.linalg.norm(center-point))) #Generate the meaning space -meaning_space_indices = np.zeros(shape=(len(munsell_to_cielab), len(munsell_to_cielab))) +meaning_space_indices = np.zeros(shape=(len(color_universe.referents), len(color_universe.referents))) print(meaning_space_indices) -for center_index, center in enumerate(munsell_to_cielab): - for point_index, point in enumerate(munsell_to_cielab): - meaning_space_indices[center_index][point_index] = meaning(center, point) +for c1_index, c1 in enumerate(color_universe.referents): + for c2_index, c2 in enumerate(color_universe.referents): + meaning_space_indices[c1_index][c2_index] = meaning(np.array((c1.L, c1.a, c1.b)), np.array((c2.L, c2.a, c2.b))) @@ -162,20 +173,25 @@ def meaning(center, point): languages = {} +#Create a Set of Expressions to pull from later when rrandomly sampling for artificial languages +expression_set = set() + #Average out the language, in case of disagreements. For each color, find the most common term associated with that color for language_code in average_language_by_meaning: - expressions = {} + color_names = {} for color in average_language_by_meaning[language_code]: - #Find the most common term associated with each color - most_frequent_expression = average_language_by_meaning[language_code][color].most_common(1)[0][0] - #print(f"Most frequent expr:{most_frequent_expression}") - if most_frequent_expression not in expressions: - expressions[most_frequent_expression] = [] - expressions[most_frequent_expression].append(color) + #Find the most common term associated with each color chip + most_frequent_color_term = average_language_by_meaning[language_code][color].most_common(1)[0][0] + if most_frequent_color_term not in color_names: + color_names[most_frequent_color_term] = [] + color_names[most_frequent_color_term].append(color) + #Create list of expressions to add to the Language language_expressions = [] - for expression_form in expressions: + for color_name in color_names: #language_expressions.append(Expression(form=expression_form, meaning=Meaning(tuple([Referent(name=color) for color in expressions[expression_form]]), universe=color_universe))) - language_expressions.append(Expression(form=expression_form, meaning=Meaning(tuple([Referent(name=color) for color in expressions[expression_form]]), universe=color_universe))) + expression = Expression(form=color_name, meaning=Meaning(tuple([referents_by_color_code[color] for color in color_names[color_name]]), universe=color_universe)) + expression_set.add(expression) + language_expressions.append(expression) languages[language_code] = Language(language_expressions, lang_code=language_code) @@ -184,13 +200,15 @@ def meaning(center, point): plt.savefig(f"{current_dir}/outputs/old_meaning_dists.jpg") #Temporarily use Zaslavsky data to verify information -meaning_dists = model_data['pU_M'] -noga_prior = np.array([row[0] for row in model_data['pM']]) -noga_bound = model_data['IB_curve'] +if(USE_NOGA_ARRAYS): + noga_meaning_dists = model_data['pU_M'] + noga_prior = np.array([row[0] for row in model_data['pM']]) + noga_bound = model_data['IB_curve'] #Generate the heatmap for the Zaslavasky meaning function -plt.imshow(meaning_dists, cmap="hot") -plt.savefig(f"{current_dir}/outputs/noga_meaning_dists.jpg") +if(USE_NOGA_ARRAYS): + plt.imshow(noga_meaning_dists, cmap="hot") + plt.savefig(f"{current_dir}/outputs/noga_meaning_dists.jpg") #result = meaning(munsell_to_cielab[meaning_space_indices[0]], munsell_to_cielab[meaning_space_indices[1]]) #Generate the meaning/accuracy/complexity for all languages based on the prior, meaning and Language @@ -204,16 +222,34 @@ def meaning(center, point): #These langauges have fewer than 5 definitions for major terms excluded_language_codes = [7, 19, 20, 25, 27, 31, 38, 48, 70, 78, 80, 88, 91, 92, 93] - if language_code not in excluded_language_codes: - language_data.append((language_name, "natural") + rd.language_to_ib_point(language=language, prior=noga_prior, meaning_dists=(meaning_dists))) + if int(language_code) not in excluded_language_codes: + #RKK - complexity is the number of color terms in the language + if USE_NOGA_ARRAYS: + ib_point = rd.language_to_ib_point(language=language, prior=noga_prior, meaning_dists=(noga_meaning_dists)) + else: + ib_point = rd.language_to_ib_point(language=language, prior=uniform_prior, meaning_dists=(meaning_dists)) + + if(USE_RKK): + language_data.append((language_name, "natural", math.log(len(language.expressions)), ib_point[1], ib_point[2])) + else: + #Use just the information bound metric + language_data.append((language_name, "natural", ib_point[0], ib_point[1], ib_point[2])) #Generate some fake languages using the real languages as a baseline via permutation -artificial_languages = sampling.get_hypothetical_variants(languages=list(languages.values()), total=400) +#artificial_languages = sampling.get_hypothetical_variants(languages=list(languages.values()), total=400) +artificial_languages = sampling.random_languages(expressions=expression_set, sampling_strategy="stratified", sample_size=20) #Analyze each of the artificial languages artificial_lang_count = 0 for artificial_language in artificial_languages: artificial_lang_count +=1 - language_data.append((f"artificial lang {artificial_lang_count}", "artificial") + rd.language_to_ib_point(language=artificial_language, prior=noga_prior, meaning_dists=(meaning_dists))) + if USE_NOGA_ARRAYS: + artificial_ib_point = rd.language_to_ib_point(language=artificial_language, prior=noga_prior, meaning_dists=(noga_meaning_dists)) + else: + artificial_ib_point = rd.language_to_ib_point(language=artificial_language, prior=uniform_prior, meaning_dists=(meaning_dists)) + if(USE_RKK): + language_data.append((f"artificial lang {artificial_lang_count}", "artificial", math.log(len(artificial_language.expressions)), artificial_ib_point[1], artificial_ib_point[2])) + else: + language_data.append((f"artificial lang {artificial_lang_count}", "artificial", artificial_ib_point[0], artificial_ib_point[1], artificial_ib_point[2])) print(f"Artificial languages{artificial_languages}") @@ -247,14 +283,32 @@ def meaning(center, point): #Get the IB bound for the specified parameters #ib_boundary = rd.get_ib_bound(prior=uniform_prior, meaning_dists=meaning_dists, betas=np.logspace(-2, 2, 10)) -""" -ib_boundary = rd.get_ib_bound(prior=uniform_prior, meaning_dists=meaning_dists, betas=np.arange(0, 2, .2)) - -ib_boundary_points = pd.DataFrame([("ib_bound", "ib_bound", ib_point.rate, ib_point.accuracy, ib_point.distortion ) - for ib_point in ib_boundary if ib_point is not None], columns =['name','type','complexity', 'informativity', 'comm_cost']) - -combined_data = pd.concat([ib_boundary_points, combined_data]) -""" +if(GENERATE_IB_BOUND): + ib_boundary = rd.get_ib_bound(prior=uniform_prior, meaning_dists=meaning_dists, betas=np.arange(0, 2, .2)) + + ib_boundary_points = pd.DataFrame([("ib_bound", "ib_bound", ib_point.rate, ib_point.accuracy, ib_point.distortion ) + for ib_point in ib_boundary if ib_point is not None], columns =['name','type','complexity', 'informativity', 'comm_cost']) + + combined_data = pd.concat([ib_boundary_points, combined_data]) + + +#Generate plot for color data across languages +if GENERATE_LANG_COLOR_INFO: + for language_code in languages: + language:Language = languages[language_code] + language_name = language_codes[language_code] + language_color_data = [] + for expression in language.expressions: + form = expression.form + for referent in expression.meaning.referents: + language_color_data.append((form, ord(referent.name[0])-96, int(referent.name[1:]))) + language_color_data = pd.DataFrame(language_color_data, columns =['form','V','H']) + plot = ( + pn.ggplot(pn.aes(x="H", y="V")) + + pn.geom_point(language_color_data, pn.aes(color="form")) + ) + 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