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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| import desbordante | ||
| import pandas | ||
| from tabulate import tabulate | ||
|
|
||
| TABLE = 'examples/datasets/dog_breeds.csv' | ||
| CLR = { | ||
| 'bold_default': '\033[1m', | ||
| 'bold_red': '\u001b[1;31m', | ||
| 'bold_green': '\033[1;32m', | ||
| 'bold_yellow': '\033[1;33m', | ||
| 'bold_blue': '\033[1;34m', | ||
| 'gray': '\033[90m', | ||
| 'default': '\033[0m', | ||
| 'blue_bg': '\033[1;46m', | ||
| 'default_bg': '\033[49m', | ||
| 'red_bg': '\033[41m' | ||
| } | ||
|
|
||
| def print_table(table, title=None): | ||
| if title is not None: | ||
| print(title) | ||
|
|
||
| print(tabulate(table, tablefmt='pipe', stralign='center')) | ||
|
|
||
|
|
||
| def print_map(map, table): | ||
| column_names = list(table.columns) | ||
| for key, value in map.items(): | ||
| print(column_names[key], end='') | ||
| print(value) | ||
|
|
||
|
|
||
| def print_10_discovered_nars(algo, table): | ||
| counter = 1 | ||
| for nar in algo.get_nars()[:10]: | ||
| print("NAR " + str(counter) + f":{CLR['bold_default']}") | ||
| counter += 1 | ||
| print_map(nar.ante, pdtable) | ||
| print(" |\n |\n V") | ||
| print_map(nar.cons, pdtable) | ||
| print(" support = ", nar.support) | ||
| print(" confidence = ", nar.confidence) | ||
| print(f"{CLR['default']}") | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| algo = desbordante.nar.algorithms.des() | ||
| algo.load_data(table=(TABLE, ',', True)) | ||
| print("Numerical Association Rules (NAR) are an extension of traditional " | ||
| "Association Rules (AR), which search for patterns in data. Unlike ARs, " | ||
| "which work with binary attributes (e.g., whether an item was purchased " | ||
| "or not), NARs can handle numerical data (e.g., how many units of an item " | ||
| "were purchased). This makes NARs more flexible for discovering relationships " | ||
| "in datasets with numerical data.") | ||
| print("Suppose we have a table containing students' exam grades and how many " | ||
| "hours they studied for the exam. Such a table might hold the following " | ||
| "numerical association rule:\n") | ||
| print(f"{CLR['bold_default']}Study_Hours[15.5 - 30.2] {CLR['gray']}⎤-Antecedent") | ||
| print(f"{CLR['default']}{CLR['bold_default']}Subject[Topology] {CLR['gray']}⎦") | ||
| print(f"{CLR['default']}{CLR['bold_default']} |\n |\n V") | ||
| print(f"Grade[3 - 5]] {CLR['gray']}]-Consequent") | ||
| print(f"{CLR['default']}{CLR['bold_default']} support = ", 0.21) | ||
| print(" confidence = ", 0.93) | ||
| print() | ||
| print(f"{CLR['default']}This rule states that students who study Topology for " | ||
| "between 15.5 and 30.2 hours will receive a grade between 3 and 5. This " | ||
| "rule has a support measure of 0.21, which means that 21% of rows in the dataset " | ||
| "satisfy both the antecedent's and consequent's requirements. This rule also " | ||
| "has a confidence measure of 0.93, meaning that 93% of rows that satisfy the " | ||
| "antecedent also satisfy the consequent. Note that attributes can be integers, " | ||
| "floating points, or strings.\n") | ||
| print("Desbordante implements an algorithm called \"Differential Evolution Solver\" " | ||
| "(DES), described by Iztok Fister et al. in \"uARMSolver: A framework for " | ||
| "Association Rule Mining\". It is a nature-inspired stochastic optimization " | ||
| "algorithm.\n") | ||
| print("As a demonstration of working with some of DES' parameters, let's inspect " | ||
| "a dataset containing information about 159 dog breeds.\n") | ||
| pdtable = pandas.read_csv(TABLE) | ||
|
|
||
| print("Fragment of the dog_breeds.csv table:") | ||
| print(pdtable) | ||
| print("\nA fragment of the table is presented above. In total, each dog breed has" | ||
| " 13 attributes.") | ||
| print("Now, let's mine some NARs. We will use a minimum support of 0.1 and a minimum " | ||
| "confidence of 0.7. Note that the minimum support and confidence only affect " | ||
| "which of the discovered NARs are returned, but the mining process itself " | ||
| "remains unchanged. We will also use a population size of 500 and " | ||
| "max_fitness_evaluations of 700. Larger values for max_fitness_evaluations " | ||
| "tend to return broader rules encompassing more attributes. The population size " | ||
| "parameter affects the number of NARs being generated and mutated. Larger values " | ||
| "are slower but output more NARs.\n") | ||
| algo.execute(minconf=0.7, minsup=0.1, population_size=500, | ||
| max_fitness_evaluations=700) | ||
| print_10_discovered_nars(algo, pdtable) | ||
| print("\nThe above NAR is the only one discovered with these settings. The NAR " | ||
| "states that about 92% of all dog breeds of type " | ||
| "'Hound' have an intelligence rating between 6 and 8 out of 10 and are between " | ||
| "sizes 0 and 4 out of 5 (0 being 'Toy' and 5 being 'Giant'). This suggests " | ||
| "that, in general, hounds are intelligent dogs and no more than " | ||
| "8% of all hounds are of 'Giant' size. Let's see if that is true.\n") | ||
|
|
||
|
|
||
|
|
||
|
|
||
| hound_rows = pdtable[pdtable['Type'] == 'Hound'] | ||
|
|
||
| line_number_counter = 0 | ||
| red_line_indices = [] | ||
| for index, row in hound_rows.iterrows(): | ||
| if row['Intelligence'] < 6 or row['Intelligence'] > 8: | ||
| red_line_indices.append(line_number_counter) | ||
| line_number_counter += 1 | ||
|
|
||
| hound_row_strings = hound_rows[['Name', 'Intelligence', 'Size']].to_string().splitlines() | ||
| for i in range(0, len(hound_row_strings)): | ||
| if i-1 in red_line_indices: | ||
| print(CLR['red_bg'], hound_row_strings[i], CLR['default_bg']) | ||
| else: | ||
| print(hound_row_strings[i]) | ||
|
|
||
| print("\nAs can be seen, only 2 rows that have 'Type' equal to 'Hound' don't " | ||
| "have an intelligence rating between 6 and 8.") | ||
| print("Let's try again, but this time with different settings. This time, minsup " | ||
| "will have a more lenient value of 0.05 and the population size will be 700. " | ||
| "This will help discover more NARs. The value of max_fitness_evaluations " | ||
| "will also need to be increased to 1500 in accordance with the population " | ||
| "size to produce a non-empty result.\n") | ||
| algo.execute(minconf=0.7, minsup=0.05, population_size=700, | ||
| max_fitness_evaluations=1500) | ||
| print_10_discovered_nars(algo, pdtable) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| Name,Origin,Type,Friendliness,Life Span,Size,Grooming Needs,Exercise Requirements,Good with Children,Intelligence,Shedding,Health Issues Risk,Weight,Training Difficulty | ||
| Affenpinscher,Germany,Toy,7,14,1,High,1.5,Yes,8,Moderate,Low,4,6 | ||
| Afghan Hound,Afghanistan,Hound,5,13,3,Very High,2,No,4,High,Moderate,25,8 | ||
| Airedale Terrier,England,Terrier,8,12,2,High,2,Yes,7,Moderate,Low,21,6 | ||
| Akita,Japan,Working,6,11,3,Moderate,2,With Training,7,High,High,45,9 | ||
| Alaskan Malamute,Alaska USA,Working,7,11,3,High,3,Yes,6,Very High,Moderate,36,8 | ||
| American Bulldog,USA,Working,8,11,3,Low,2,Yes,6,Moderate,Moderate,42,7 | ||
| American Eskimo,USA,Non-Sporting,9,14,2,High,2,Yes,9,High,Low,12,5 | ||
| American Foxhound,USA,Hound,8,12,2,Low,2.5,Yes,6,Moderate,Low,31,6 | ||
| Australian Cattle Dog,Australia,Herding,7,14,2,Moderate,2.5,Yes,9,Moderate,Low,15,7 | ||
| Australian Shepherd,USA,Herding,8,14,2,High,2.5,Yes,9,High,Moderate,25,7 | ||
| Australian Terrier,Australia,Terrier,7,13,1,Moderate,1.5,Yes,7,Low,Low,6.5,6 | ||
| Basenji,Central Africa,Hound,6,13,2,Low,2,No,6,Low,Low,10,9 | ||
| Basset Hound,France,Hound,8,11,2,Low,1,Yes,5,Moderate,High,24,7 | ||
| Beagle,England,Hound,9,13,2,Low,2,Yes,7,Moderate,Moderate,10,7 | ||
| Bearded Collie,Scotland,Herding,8,13,2,Very High,2,Yes,8,High,Low,22,6 | ||
| Bernese Mountain Dog,Switzerland,Working,9,8,3,High,1.5,Yes,7,High,High,43,5 | ||
| Bichon Frise,Mediterranean,Non-Sporting,9,13,1,Very High,1,Yes,8,Low,Low,4,5 | ||
| Black Russian Terrier,Russia,Working,7,11,3,High,2,Yes,8,Moderate,Moderate,50,8 | ||
| Bloodhound,Belgium,Hound,7,11,3,Moderate,2,Yes,6,Moderate,High,43,8 | ||
| Border Collie,UK,Herding,8,13,2,High,3,Yes,10,Moderate,Low,17,6 | ||
| Border Terrier,UK,Terrier,8,13,1,Moderate,1.5,Yes,7,Moderate,Low,6,6 | ||
| Borzoi,Russia,Hound,6,11,3,High,2,No,6,Moderate,Low,37,7 | ||
| Boston Terrier,USA,Non-Sporting,9,12,1,Low,1.5,Yes,8,Low,Moderate,9,5 | ||
| Bouvier des Flandres,Belgium,Herding,7,11,3,Very High,2,Yes,8,High,Moderate,34,7 | ||
| Boxer,Germany,Working,9,11,3,Low,2,Yes,7,Moderate,Moderate,28,6 | ||
| Briard,France,Herding,7,12,3,High,2,Yes,8,Moderate,Low,35,7 | ||
| Brittany,France,Sporting,8,13,2,Moderate,2.5,Yes,8,Moderate,Low,17,5 | ||
| Brussels Griffon,Belgium,Toy,7,13,1,High,1.5,Yes,8,Moderate,Moderate,4,7 | ||
| Bull Terrier,England,Terrier,7,12,2,Low,2,With Training,7,Low,Moderate,27,7 | ||
| Bullmastiff,England,Working,6,9,3,Low,1.5,With Training,6,Moderate,High,52,7 | ||
| Cairn Terrier,Scotland,Terrier,8,14,1,Moderate,1.5,Yes,7,Moderate,Low,7,6 | ||
| Canaan Dog,Israel,Herding,6,13,2,Low,2,With Training,8,Moderate,Low,21,8 | ||
| Cane Corso,Italy,Working,6,11,3,Low,2,With Training,7,Low,Moderate,45,9 | ||
| Cardigan Welsh Corgi,Wales,Herding,8,13,1,Moderate,2,Yes,8,High,Moderate,13,6 | ||
| Cavalier King Charles Spaniel,UK,Toy,10,11,1,Moderate,1,Yes,6,Moderate,High,6,4 | ||
| Chesapeake Bay Retriever,USA,Sporting,7,11,3,Moderate,2.5,Yes,7,Moderate,Low,30,7 | ||
| Chihuahua,Mexico,Toy,6,16,1,Low,1,No,7,Low,Moderate,2,7 | ||
| Chinese Crested,China,Toy,7,14,1,High,1,Yes,8,Low,Low,3,6 | ||
| Chinese Shar-Pei,China,Non-Sporting,6,10,2,Low,1.5,With Training,6,Moderate,High,22,8 | ||
| Chow Chow,China,Non-Sporting,5,10,2,High,1.5,No,5,High,High,26,9 | ||
| Clumber Spaniel,England,Sporting,7,11,2,Moderate,1.5,Yes,6,High,Moderate,31,6 | ||
| Cocker Spaniel,England,Sporting,9,13,2,High,1.5,Yes,7,High,Moderate,12,5 | ||
| Collie,Scotland,Herding,9,13,2,High,2,Yes,8,High,Moderate,24,5 | ||
| Curly-Coated Retriever,England,Sporting,7,12,3,Moderate,2,Yes,7,Low,Low,32,6 | ||
| Dachshund,Germany,Hound,7,14,1,Low,1.5,With Training,7,Moderate,High,10,7 | ||
| Dalmatian,Croatia,Non-Sporting,8,12,2,Low,2.5,Yes,7,Moderate,High,24,7 | ||
| Dandie Dinmont Terrier,Scotland,Terrier,7,13,1,Moderate,1.5,Yes,7,Low,Low,8,7 | ||
| Doberman Pinscher,Germany,Working,8,11,3,Low,2.5,With Training,9,Low,Moderate,35,7 | ||
| English Bulldog,England,Non-Sporting,7,9,2,Low,1,Yes,6,Moderate,High,20,7 | ||
| English Cocker Spaniel,England,Sporting,8,12,2,High,1.5,Yes,7,Moderate,Moderate,14,5 | ||
| English Foxhound,England,Hound,7,12,2,Low,2.5,Yes,6,Moderate,Low,29,7 | ||
| English Setter,England,Sporting,8,12,2,High,2,Yes,7,Moderate,Moderate,26,6 | ||
| English Springer Spaniel,England,Sporting,9,13,2,High,2,Yes,7,Moderate,Moderate,20,6 | ||
| English Toy Spaniel,England,Toy,8,11,1,High,1,Yes,7,Moderate,Moderate,5,6 | ||
| Entlebucher Mountain Dog,Switzerland,Herding,7,12,2,Low,2.5,Yes,8,Moderate,Low,27,7 | ||
| Field Spaniel,England,Sporting,7,12,2,High,2,Yes,7,Moderate,Low,18,6 | ||
| Finnish Lapphund,Finland,Herding,8,12,2,High,2,Yes,7,High,Low,20,6 | ||
| Finnish Spitz,Finland,Non-Sporting,7,13,2,Moderate,2,Yes,8,Moderate,Low,15,7 | ||
| Flat-Coated Retriever,England,Sporting,9,11,3,Moderate,2.5,Yes,8,Moderate,Moderate,32,5 | ||
| French Bulldog,France,Non-Sporting,8,11,1,Low,1,Yes,7,Low,High,11,6 | ||
| German Pinscher,Germany,Working,7,13,2,Low,2,With Training,8,Moderate,Low,13,7 | ||
| German Shepherd,Germany,Herding,8,11,3,Moderate,2.5,With Training,9,High,Moderate,31,6 | ||
| German Shorthaired Pointer,Germany,Sporting,8,13,3,Low,2.5,Yes,8,Moderate,Low,26,6 | ||
| German Wirehaired Pointer,Germany,Sporting,7,13,3,Moderate,2.5,Yes,8,Moderate,Low,27,7 | ||
| Giant Schnauzer,Germany,Working,7,13,3,High,2,With Training,8,Low,Moderate,38,8 | ||
| Golden Retriever,Scotland,Sporting,10,11,3,High,2,Yes,8,High,Moderate,29,4 | ||
| Gordon Setter,Scotland,Sporting,8,11,3,High,2,Yes,7,Moderate,Moderate,26,6 | ||
| Great Dane,Germany,Working,8,8,4,Low,2,Yes,6,Moderate,High,67,6 | ||
| Great Pyrenees,France,Working,7,11,3,High,1.5,Yes,6,High,Moderate,46,8 | ||
| Greater Swiss Mountain Dog,Switzerland,Working,8,10,3,Low,2,Yes,7,Moderate,Moderate,61,7 | ||
| Greyhound,Egypt,Hound,7,11,3,Low,2,Yes,7,Low,Low,33,6 | ||
| Harrier,England,Hound,8,12,2,Low,2,Yes,6,Moderate,Low,25,6 | ||
| Havanese,Cuba,Toy,9,14,1,High,1.5,Yes,8,Low,Low,5,5 | ||
| Ibizan Hound,Spain,Hound,7,13,2,Low,2,Yes,7,Low,Low,22,7 | ||
| Icelandic Sheepdog,Iceland,Herding,8,12,2,Moderate,2,Yes,7,Moderate,Low,14,6 | ||
| Irish Red and White Setter,Ireland,Sporting,8,12,3,High,2.5,Yes,7,Moderate,Moderate,27,6 | ||
| Irish Setter,Ireland,Sporting,8,13,3,High,2.5,Yes,7,High,Moderate,29,6 | ||
| Irish Terrier,Ireland,Terrier,7,13,2,Moderate,1.5,Yes,7,Low,Low,11,7 | ||
| Irish Water Spaniel,Ireland,Sporting,8,11,3,High,2,Yes,7,Low,Low,24,6 | ||
| Irish Wolfhound,Ireland,Hound,7,7,4,Moderate,2,Yes,6,Moderate,High,54,6 | ||
| Italian Greyhound,Italy,Toy,7,13,1,Low,1.5,No,7,Low,Moderate,4,7 | ||
| Japanese Chin,Japan,Toy,7,11,1,Moderate,1,Yes,7,Moderate,Low,3,6 | ||
| Japanese Spitz,Japan,Non-Sporting,8,13,1,High,1.5,Yes,8,High,Low,6,6 | ||
| Keeshond,Netherlands,Non-Sporting,8,13,2,High,1.5,Yes,8,High,Low,16,6 | ||
| Kerry Blue Terrier,Ireland,Terrier,7,13,2,High,1.5,Yes,7,Low,Low,16,7 | ||
| Komondor,Hungary,Working,6,11,3,Very High,2,With Training,6,Low,Low,40,8 | ||
| Kuvasz,Hungary,Working,6,11,3,Moderate,2,With Training,7,Moderate,Moderate,42,8 | ||
| Labrador Retriever,Canada,Sporting,10,11,3,Moderate,2,Yes,8,High,Moderate,30,4 | ||
| Lakeland Terrier,England,Terrier,7,13,1,Moderate,1.5,Yes,7,Low,Low,7,7 | ||
| Leonberger,Germany,Working,8,9,4,High,2,Yes,7,High,High,61,8 | ||
| Lhasa Apso,Tibet,Non-Sporting,6,13,1,Very High,1,No,7,Low,Low,6,8 | ||
| Lowchen,France,Non-Sporting,8,14,1,High,1.5,Yes,8,Low,Low,6,6 | ||
| Maltese,Malta,Toy,8,13,1,Very High,1,No,7,Low,Low,2.5,6 | ||
| Manchester Terrier,England,Terrier,7,15,2,Low,1.5,With Training,8,Moderate,Low,7,7 | ||
| Mastiff,England,Working,6,9,4,Low,1.5,Yes,6,Moderate,High,78,7 | ||
| Miniature Pinscher,Germany,Toy,6,13,1,Low,1.5,No,7,Low,Low,4,8 | ||
| Miniature Schnauzer,Germany,Standard,8,12,1,High,1.5,Yes,8,Moderate,Low,8,6 | ||
| Neapolitan Mastiff,Italy,Working,6,8,3,Moderate,1,Yes,5,Moderate,High,70,8 | ||
| Newfoundland,Canada,Working,9,9,4,High,2,Yes,7,Moderate,Moderate,61,7 | ||
| Norfolk Terrier,England,Terrier,8,14,1,Low,1.5,Yes,7,Low,Low,5,6 | ||
| Norwegian Buhund,Norway,Herding,8,12,2,Moderate,2,Yes,7,Moderate,Low,12,6 | ||
| Norwegian Elkhound,Norway,Hound,7,12,2,Moderate,2,Yes,7,Moderate,Moderate,23,7 | ||
| Norwegian Lundehund,Norway,Non-Sporting,7,12,1,High,2,Yes,7,Moderate,High,8,7 | ||
| Old English Sheepdog,England,Herding,8,10,3,Very High,2,Yes,6,High,Moderate,32,6 | ||
| Otterhound,England,Hound,7,10,3,Very High,2.5,Yes,6,High,Moderate,37,7 | ||
| Papillon,France,Toy,9,13,1,Moderate,1,Yes,8,Low,Low,3,5 | ||
| Pekingese,China,Toy,6,12,1,Very High,1,With Training,5,Moderate,High,5,7 | ||
| Pembroke Welsh Corgi,Wales,Herding,9,12,1,Moderate,2,Yes,8,Moderate,Moderate,12,5 | ||
| Petit Basset Griffon Vendeen,France,Hound,7,12,1,High,2,Yes,6,High,Moderate,15,7 | ||
| Pharaoh Hound,Malta,Hound,7,12,2,Moderate,2,Yes,8,Moderate,Low,20,7 | ||
| Pointer,England,Sporting,8,12,2,Moderate,2.5,Yes,7,Moderate,Moderate,25,6 | ||
| Pomeranian,Germany,Toy,8,12,1,High,1,Yes,7,High,Moderate,3,7 | ||
| Poodle (Miniature),France,Non-Sporting,8,15,1,Very High,1.5,Yes,8,Low,Low,7,6 | ||
| Poodle (Standard),France,Non-Sporting,9,12,3,Very High,2,Yes,8,Moderate,Moderate,22,5 | ||
| Poodle (Toy),France,Toy,9,15,0,Very High,1,Yes,8,Low,Low,4,5 | ||
| Portuguese Podengo,Portugal,Hound,7,12,2,Moderate,2,Yes,8,Moderate,Low,15,8 | ||
| Portuguese Water Dog,Portugal,Sporting,8,11,2,High,2,Yes,8,Moderate,Moderate,18,6 | ||
| Pudelpointer,Germany,Sporting,7,12,2,Moderate,2.5,Yes,8,Moderate,Low,23,7 | ||
| Pug,China,Toy,8,12,1,Moderate,1,Yes,7,Moderate,High,7,6 | ||
| Pyrenean Mountain Dog,France,Working,7,10,4,High,2,Yes,6,High,High,54,7 | ||
| Rat Terrier,USA,Terrier,7,15,2,Low,2,Yes,7,Moderate,Moderate,5,7 | ||
| Redbone Coonhound,USA,Hound,8,12,2,Moderate,2,Yes,6,Moderate,Low,25,7 | ||
| Rhodesian Ridgeback,Africa,Hound,6,11,3,Moderate,2.5,With Training,8,Moderate,Moderate,36,7 | ||
| Rottweiler,Germany,Working,7,9,3,Moderate,2,With Training,8,High,High,40,8 | ||
| Saint Bernard,Switzerland,Working,9,8,4,High,2,Yes,6,High,Moderate,68,7 | ||
| Saluki,Middle East,Hound,6,12,2,Moderate,2.5,No,7,Moderate,Moderate,22,7 | ||
| Samoyed,Siberia,Working,9,12,2,High,2,Yes,7,High,Moderate,21,7 | ||
| Schipperke,Belgium,Non-Sporting,7,13,1,Moderate,2,No,7,Moderate,Moderate,7,7 | ||
| Scottish Deerhound,Scotland,Hound,6,8,3,Moderate,2,Yes,6,Moderate,Moderate,41,7 | ||
| Scottish Terrier,Scotland,Terrier,7,12,1,Moderate,1.5,Yes,6,Moderate,High,9,7 | ||
| Shetland Sheepdog,Scotland,Herding,9,12,2,High,2,Yes,8,High,Moderate,11,5 | ||
| Shiba Inu,Japan,Non-Sporting,7,13,1,Moderate,2,No,7,Moderate,Moderate,9,7 | ||
| Shih Tzu,China,Toy,8,14,1,Very High,1,Yes,6,Moderate,Moderate,4,7 | ||
| Siberian Husky,Siberia,Working,8,12,2,Moderate,2.5,Yes,7,Moderate,Moderate,21,7 | ||
| Silky Terrier,Australia,Terrier,7,13,1,High,1.5,Yes,7,Moderate,Low,4,7 | ||
| Skye Terrier,Scotland,Terrier,7,13,1,Moderate,1.5,No,6,Moderate,Moderate,8,7 | ||
| Sloughi,North Africa,Hound,6,12,2,Low,2.5,With Training,7,Low,Moderate,18,7 | ||
| Smooth Fox Terrier,England,Terrier,7,13,1,Low,2,Yes,7,Moderate,Moderate,7,7 | ||
| Soft-Coated Wheaten Terrier,Ireland,Terrier,8,12,2,High,2,Yes,7,Moderate,Moderate,12,6 | ||
| Spanish Water Dog,Spain,Sporting,7,12,2,Very High,2,Yes,8,Moderate,Moderate,15,6 | ||
| Spinone Italiano,Italy,Sporting,7,12,3,High,2,Yes,7,Moderate,Moderate,32,7 | ||
| Staffordshire Bull Terrier,England,Terrier,7,12,2,Low,2,Yes,7,Moderate,High,16,7 | ||
| Standard Poodle,France,Non-Sporting,9,12,3,Very High,2,Yes,8,Moderate,Moderate,26,5 | ||
| Standard Schnauzer,Germany,Standard,7,15,2,High,2,Yes,7,Moderate,Moderate,14,6 | ||
| Sussex Spaniel,England,Sporting,7,11,2,High,1.5,Yes,6,Moderate,Moderate,18,6 | ||
| Swedish Vallhund,Sweden,Herding,8,12,2,Moderate,2,Yes,8,Moderate,Low,12,7 | ||
| Thai Ridgeback,Thailand,Hound,6,12,2,Moderate,2,With Training,7,Moderate,Moderate,15,7 | ||
| Tibetan Mastiff,Tibet,Working,7,10,3,High,2,With Training,6,Moderate,Moderate,45,8 | ||
| Toy Fox Terrier,USA,Toy,8,15,0,Moderate,1.5,Yes,8,Low,Low,2,7 | ||
| Treeing Walker Coonhound,USA,Hound,8,12,2,Moderate,2,Yes,7,Moderate,Moderate,20,7 | ||
| Welsh Springer Spaniel,Wales,Sporting,8,13,2,High,2,Yes,7,Moderate,Moderate,16,6 | ||
| Welsh Terrier,Wales,Terrier,7,14,2,Moderate,1.5,Yes,7,Moderate,Moderate,9,7 | ||
| West Highland White Terrier,Scotland,Terrier,8,13,1,Moderate,1.5,Yes,7,Moderate,Moderate,7,7 | ||
| Whippet,England,Hound,7,13,2,Low,2,Yes,8,Low,Low,8,6 | ||
| Wire Fox Terrier,England,Terrier,7,14,1,Moderate,2,Yes,7,Moderate,Moderate,8,7 | ||
| Wirehaired Dachshund,Germany,Hound,7,13,1,Moderate,1.5,With Training,7,Moderate,High,8,7 | ||
| Wirehaired Pointing Griffon,Netherlands,Sporting,7,13,2,High,2,Yes,7,Moderate,Moderate,20,6 | ||
| Xoloitzcuintli,Mexico,Non-Sporting,7,15,3,Low,2,With Training,8,Low,Moderate,25,6 | ||
| Yorkshire Terrier,England,Toy,8,13,0,High,1,Yes,7,Moderate,Moderate,2.5,6 |