diff --git a/examples/advanced/verifying_pac/verifying_domain_pac_custom_domain.py b/examples/advanced/verifying_pac/verifying_domain_pac_custom_domain.py new file mode 100644 index 0000000000..0e7912139f --- /dev/null +++ b/examples/advanced/verifying_pac/verifying_domain_pac_custom_domain.py @@ -0,0 +1,129 @@ +'''Example 1 (advanced): Custom domain''' + +from tabulate import tabulate +from csv import reader +from math import sqrt + +import desbordante + +RED = '\033[31m' +GREEN = '\033[32m' +BLUE = '\033[34m' +CYAN = '\033[36m' +BOLD = '\033[1;37m' +ENDC = '\033[0m' + +USER_PREFERENCES = 'examples/datasets/verifying_pac/user_preferences.csv' + + +def csv_to_str(filename: str) -> str: + with open(filename, newline='') as table: + rows = list(reader(table, delimiter=',')) + headers = rows[0] + rows = rows[1:] + return tabulate(rows, headers=headers) + + +print( + f'''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +A Domain PAC on a column set X and domain D, with given ε and δ means that {BOLD}Pr(x ∈ D±ε) ≥ δ{ENDC}. +For more information, see "Checks and Balances: Monitoring Data Quality Problems in Network +Traffic Databases" by Filp Korn et al (Proceedings of the 29th VLDB Conference, Berlin, 2003). +If you have not read the basic Domain PAC examples yet (see the {CYAN}examples/basic/verifying_pac/{ENDC} +directory), it is recommended to start there. + +Assume we have a dataset of user preferences, where each user\'s interest in several topics is +encoded as values in [0, 1], where 0 is "not interested at all" and 1 is "very interested": +{BOLD}{csv_to_str(USER_PREFERENCES)}{ENDC} + +We need to estimate whether this group of users will be interested in the original Domain PAC paper +("Checks and Balances: ..."). +To do this, we represent each user profile as a vector in a multi-dimensional topic space: + ^ Topic 2 + | + | user + | x + | / + |/ Topic 1 + -+-------> + | + +A "perfect" target reader might have the profile: {BLUE}(0.9, 0.4, 0.05){ENDC}. +This corresponds to: + * high interest in Databases; + * moderate interest in Networks; + * low interest in Machine Learning. +Our goal is to measure how close real users are to this ideal profile. + +We use cosine distance, which measures the angle between two vectors rather then their absolute +length. This is useful because we care about interest proportions, not total magnitude. + {BOLD}dist(x, y) = 1 - cos(angle between x and y) = 1 - (x, y)/(|x| * |y|){ENDC}, +where (x, y) is a dot product between x and y. +''') + + +def cosine_dist(x: list[float], y: list[float]) -> float: + dot_product = 0 + x_length = 0 + y_length = 0 + for i in range(len(x)): + dot_product += x[i] * y[i] + x_length += x[i] * x[i] + y_length += y[i] * y[i] + x_length = sqrt(x_length) + y_length = sqrt(y_length) + return 1 - dot_product / (x_length * y_length) + + +print(f'''A custom domain is defined by two parameters: + 1. Distance function -- takes a value tuple and returns the distance to the domain. + 2. Domain name (optional) -- used for readable output. +In this example: + * distance function: {BLUE}dist(x, (0.9, 0.4, 0.05)){ENDC}; + * domain name: {BLUE}"(0.9, 0.4, 0.05)"{ENDC}. +This effectively defines the domain as "users close to the ideal profile". +''') + +PERFECT_USER = [0.9, 0.4, 0.05] + + +# Argument is always a list of strings +def dist_from_domain(x: list[str]) -> float: + x_f = [float(x_i) for x_i in x] + return cosine_dist(x_f, PERFECT_USER) + + +domain = desbordante.pac.domains.CustomDomain(dist_from_domain, + "(0.9, 0.4, 0.05)") + +print(f'We run the Domain PAC verifier with domain={BLUE}{domain}{ENDC}.') +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(USER_PREFERENCES, ',', True), + domain=domain, + column_indices=[0, 1, 2]) +algo.execute() +pac_1 = algo.get_pac() +print(f'''Algorithm result: + {GREEN}{pac_1}{ENDC} +Now we lower the required probability threshold: min_delta={BLUE}0.6{ENDC}.''') + +algo.execute(min_delta=0.6) +pac_2 = algo.get_pac() + +print(f'''Algorithm result: + {GREEN}{pac_2}{ENDC} +Interpretation: + * With a larger ε ({BLUE}{pac_1.epsilon:.3f}{ENDC}), nearly all users show some level of interest + * With a very small ε ({BLUE}{pac_2.epsilon:.3f}{ENDC}), only {BLUE}{pac_2.delta * 100.0:.0f}%{ENDC} of users closely match the ideal reader. + +You can check outliers to identify which users are closer to or farther from the ideal profile. +For an introduction to outliers, see {CYAN}examples/basic/verifying_pac/verifying_domain_pac1.py{ENDC}. + +You now know how to use Domain PACs with built-in domains as well as custom ones. +Try applying them to your own data and see what insights you can uncover.''' + ) + +# C++ note: Custom domain is called "Untyped domain" in C++ code, because it erases type +# information, converting all values to strings. If you use C++ library, it's recommended to +# implement IDomain interface or derive from MetricBasedDomain (if your domain is based on +# coordinate-wise metrics). See Parallelepiped and Ball implementations as examples. diff --git a/examples/basic/verifying_pac/verifying_domain_pac1.py b/examples/basic/verifying_pac/verifying_domain_pac1.py new file mode 100644 index 0000000000..eb55f46069 --- /dev/null +++ b/examples/basic/verifying_pac/verifying_domain_pac1.py @@ -0,0 +1,169 @@ +'''Example 1: 1D segment, highlights''' + +from tabulate import tabulate +from csv import reader + +import desbordante + +RED = '\033[31m' +YELLOW = '\033[33m' +BOLD_YELLOW = '\033[1;33m' +GREEN = '\033[32m' +BLUE = '\033[34m' +CYAN = '\033[36m' +BOLD = '\033[1;37m' +ENDC = '\033[0m' + +ENGINE_TEMPS_BAD = 'examples/datasets/verifying_pac/engine_temps_bad.csv' +ENGINE_TEMPS_GOOD = 'examples/datasets/verifying_pac/engine_temps_good.csv' + + +def read_column(filename: str, col_num: int) -> (str, list[str]): + with open(filename, newline='') as table: + rows = list(reader(table, delimiter=',')) + header = rows[0][col_num] + values = [row[col_num] for row in rows[1:]] + return header, values + + +def column_to_str(filename: str, col_num: int) -> str: + header, values = read_column(filename, col_num) + values_str = ', '.join(values) + return f'{BOLD}{header}: [{values_str}]{ENDC}' + + +def display_columns_diff(filename_old: str, col_num_old: int, + filename_new: str, col_num_new: int) -> str: + _, values_old = read_column(filename_old, col_num_old) + header, values_new = read_column(filename_new, col_num_new) + values = [] + for i in range(len(values_new)): + value = values_new[i] + if values_old[i] != value: + value = f'{BOLD_YELLOW}' + value + f'{BOLD}' + values.append(value) + values_str = ', '.join(values) + return f'{BOLD}{header}: [{values_str}]{ENDC}' + + +print( + f'''This example illustrates the usage of Domain Probabilistic Approximate Constraints (PACs). +A Domain PAC on column set X and domain D, with given ε and δ means that Pr(x ∈ D±ε) ≥ δ. +For more information consult "Checks and Balances: Monitoring Data Quality Problems in Network +Traffic Databases" by Flip Korn et al (Proceedings of the 29th VLDB Conference, Berlin, 2003). + +This is the first example in the "Basic Domain PAC verification" series. Others can be found in +{CYAN}examples/basic/verifying_pac/{ENDC} directory. +''') + +print( + f'''Suppose we are working on a new model of engine. Its operating temperature range is {BLUE}[85, 95]{ENDC}°C. +The engine is made of high-strength metal, so short-term temperature deviations are acceptable and +will not cause immediate damage. In other words, engine operates properly when Pr(t ∈ [85, 95]±ε) ≥ δ. +Based on engineering analysis, the acceptable limits are: ε = {BLUE}5{ENDC}, δ = {BLUE}0.9{ENDC}. + +In terms of Domain PACs, the following constraint should hold: {BLUE}Pr(x ∈ [85, 95]±5) ≥ 0.9{ENDC}. +''') + +print( + 'The following table contains readings from the engine temperature sensor:' +) +# Values are printed in one line for brevity, original table is single-column +print(f'{column_to_str(ENGINE_TEMPS_BAD, 0)}') +print() + +print( + 'We now use the Domain PAC verifier to determine whether the engine is operating safely.' +) + +print( + f'''First, we need to define the domain. Available options are: + * {BLUE}Parallelepiped{ENDC} -- a closed n-ary parallelepiped + * {BLUE}Ball{ENDC} -- a closed n-ary ball + * {BLUE}CustomDomain{ENDC} -- a domain with user-defined metric +A segment is simply a one-dimensional parallelepiped, so we use the {BLUE}Parallelepiped{ENDC} domain here.''') +# Parallelepiped has a special constructor for segment. +# Notice the usage of quotes: these strings will be converted to values once the table is loaded. +segment = desbordante.pac.domains.Parallelepiped('85', '95') + +print( + f'''We run algorithm with the following options: domain={BLUE}{segment}{ENDC}. All other parameters use default +values: min_epsilon={BLUE}0{ENDC}, max_epsilon={BLUE}∞{ENDC}, min_delta={BLUE}0.9{ENDC}, delta_steps={BLUE}100{ENDC}. +''') + +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +# Note that domain should be set in `load_data`, not `execute` +algo.load_data(table=(ENGINE_TEMPS_BAD, ',', True), + column_indices=[0], + domain=segment) +algo.execute() + +print(f'Algorithm result: {YELLOW}{algo.get_pac()}{ENDC}.\n') +print( + f'''This result is not directly informative for our goal. Since both ε and δ exceed the required values, +we cannot determine whether the constraint holds for ε={BLUE}5{ENDC} and δ={BLUE}0.9{ENDC}. + +Let\'s run algorithm with min_epsilon={BLUE}5{ENDC} and max_epsilon={BLUE}5{ENDC}. This will give us the exact δ, +for which PAC with ε={BLUE}5{ENDC} holds. +''') + +# Note that, when min_epsilon or max_epsilon is specified, default min_delta becomes 0 +algo.execute(min_epsilon=5, max_epsilon=5) + +print(f'Algorithm result: {RED}{algo.get_pac()}{ENDC}.\n') +print( + f'''Also, let\'s run algorithm with max_epsilon={BLUE}0{ENDC} and min_delta={BLUE}0.9{ENDC} to check which ε +is needed to satisfy δ={BLUE}0.9{ENDC}. With these parameters algorithm enters special mode and returns +pair (ε, min_delta), so that we can validate PAC with the given δ. +''') + +# Actually, algorithm enters this mode whenever max_epsilon is less than epsilon needed to satisfy +# min_delta. +algo.execute(max_epsilon=0, min_delta=0.9) + +pac = algo.get_pac() +print(f'Algorithm result: {RED}{pac}{ENDC}.\n') +print( + f'''Here algorithm gives δ={BLUE}{pac.delta}{ENDC}, which is greater than {BLUE}0.9{ENDC}, because achieving δ={BLUE}0.9{ENDC} requires +ε={BLUE}{pac.epsilon}{ENDC} and PAC ({BLUE}{pac.epsilon}{ENDC}, {BLUE}{pac.delta}{ENDC}) holds. So, this means that δ={BLUE}0.9{ENDC} would also require ε={BLUE}{pac.epsilon}{ENDC}. +''') + +print( + 'We can see that desired PAC doesn\'t hold, so the engine can blow up!\n') + +print( + f'''Let\'s look at values violating PAC. Domain PAC verifier can detect values between eps_1 +and eps_2, i. e. values that lie in D±eps_2 \\ D±eps_1. Such values are called outliers (or highlights). +Let\'s find outliers for different eps_1, eps_2 values:''') + +value_ranges = [(0, 1), (1, 2), (2, 3), (3, 5), (5, 7), (7, 10)] +highlights_table = [(f'{BLUE}{v_range[0]}{ENDC}', f'{BLUE}{v_range[1]}{ENDC}', + str(algo.get_highlights(*v_range))) + for v_range in value_ranges] +print(tabulate(highlights_table, headers=('eps_1', 'eps_2', 'outliers'))) +print() + +print('''We can see two problems: + 1. The engine operated at low temperatures for an extended period, slightly below 80°C. + 2. The peak temperature was too high, but this occurred only once.\n''') + +print('''The second version of engine has: + 1. A pre-heating system to prevent operation at low temperatures. + 2. An emergency cooling system to limit peak temperatures. +The updated sensor readings (modified values highlighted) are:''') +print(f'{display_columns_diff(ENGINE_TEMPS_BAD, 0, ENGINE_TEMPS_GOOD, 0)}') +print() + +print(f'''We run the Domain PAC verifier again.''') +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(ENGINE_TEMPS_GOOD, ',', True), + column_indices=[0], + domain=segment) +algo.execute() + +print(f'''Algorithm result: {GREEN}{algo.get_pac()}{ENDC}. + +The desired PAC now holds, which means the improved engine operates within acceptable limits. + +It is recommended to continue with the second example ({CYAN}examples/basic/verifying_pac/verifying_domain_pac2.py{ENDC}), +which demonstrates more advanced usage of the Parallelepiped domain.''') diff --git a/examples/basic/verifying_pac/verifying_domain_pac2.py b/examples/basic/verifying_pac/verifying_domain_pac2.py new file mode 100644 index 0000000000..dcecb2e592 --- /dev/null +++ b/examples/basic/verifying_pac/verifying_domain_pac2.py @@ -0,0 +1,123 @@ +'''Example 2: 2D parallelepiped, leveling coefficients''' + +from tabulate import tabulate +from csv import reader + +import desbordante + +RED = '\033[31m' +GREEN = '\033[32m' +BLUE = '\033[34m' +CYAN = '\033[36m' +BOLD = '\033[1;37m' +ENDC = '\033[0m' + +ENGINE_TEMPS = 'examples/datasets/verifying_pac/engine_temps_bad.csv' + + +def csv_to_str(filename: str) -> str: + with open(filename, newline='') as table: + rows = list(reader(table, delimiter=',')) + headers = rows[0] + rows = rows[1:] + return tabulate(rows, headers=headers) + + +print( + f'''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs) +on multiple columns. It continues the first Domain PAC verification example +{CYAN}(examples/basic/verifying_pac/verifying_domain_pac1.py){ENDC}. If you have not read the first part yet, +it is recommended to start there. + +In the first example we verified {BLUE}Domain PAC Pr(x ∈ [85, 95]±5) ≥ 0.9{ENDC} on engine temperature sensor readings. +Now, in addition to temperature readings, we also have tachometer data:''') +print(f'{BOLD}{csv_to_str(ENGINE_TEMPS)}{ENDC}') +print() + +print( + f'''The normal operating RPM for this engine is {BLUE}[1500, 3500]{ENDC}. Values outside this range are +not harmful by themselves (as long as they are within {BLUE}[0, 5000]{ENDC}), but: + * A cold engine may stall at low RPM and can be damaged at high RPM. + * An overheated engine is especially vulnerable at RPM values outside {BLUE}[1500, 3500]{ENDC}, because + cooling efficiency depends on RPM. +As in the first example, we use the Domain PAC verifier to check whether the engine operates properly. +''') + +print( + f'''Firstly, we need to create domain. We have a Cartesian product of two segments: {BLUE}[85, 95] x [1500, 3500]{ENDC}, +so it would be natural to use parallelepiped. + +We now work with two columns: temperature and RPM. The acceptable operating region is a Cartesian product +of two segments: + * temperature: [85, 95]; + * RPM: [1500, 3500]. +This forms a parallelepiped domain: {BLUE}[85, 95] x [1500, 3500]{ENDC}. +''') +# Arguments of generic version are A = (a1, a2, ..., an) and B = (b1, b2, ..., bn). +# Domain is [A, B] = [a1, b1] x [a2, b2] x ... x [an, bn]. +parallelepiped = desbordante.pac.domains.Parallelepiped(['85', '1500'], + ['95', '3500']) + +print( + f'''We run the Domain PAC verifier with the following parameters: domain={BLUE}{parallelepiped}{ENDC}, +max_epsilon={BLUE}15{ENDC}. +''') +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(ENGINE_TEMPS, ',', True), + domain=parallelepiped, + column_indices=[0, 1]) +algo.execute(max_epsilon=10) +pac = algo.get_pac() +print(f'Algorithm result: {RED}{pac}{ENDC}.') +print( + f'A result with δ = {BLUE}{pac.delta}{ENDC} is unexpected. To understand what is happening, we examine the outliers.' +) + +print( + f'''Outliers between {BLUE}+{0:.1f}{ENDC} and {BLUE}+{pac.epsilon:.1f}{ENDC} are: {BOLD}{algo.get_highlights(0, pac.epsilon)}{ENDC}. +''') +print( + f'''There are very few outliers, which suggests that the parameters may not be chosen correctly. + +The question is: what does ε = {BLUE}{pac.epsilon}{ENDC} mean in two-dimensional domain? Should ε correspond to: + * 10 degrees of temperature difference, or + * 1500 RPM difference? +To answer this, we need to understand how distance is computed. + +The parallelepiped uses the Chebyshev metric to calculate distance between value tuples: + {BOLD}d(x, y) = max{{|x[1] - y[1]|, ..., |x[n] - y[n]|}}{ENDC} + +In our case: + * temperature differences are on the order of tens; + * RPM differences are on the order of thousands. +As a result, RPM differences dominate the distance computation, making temperature differences +almost irrelevant. This issue affects all coordinate-wise metric-based domains (currently +Parallelepiped and Ball, though custom domains can be implemented in C++). + +To address this, such domains support {CYAN}leveling coefficients{ENDC}, which rescale individual +dimensions. With leveling coefficients, the distance becomes: + {BOLD}d(x, y) = max{{|x[1] - y[1]| * lc[1], ..., |x[n] - y[n]| * lc[n]}}{ENDC} +To normalize temperatures and RPM scales, we use leveling_coefficients={BLUE}[1, 0.01]{ENDC} parameter. +This treats a 100 RPM difference as comparable to a 1°C difference. + +With leveling coefficients applied, we rerun the algorithm. +''') + +parallelepiped = desbordante.pac.domains.Parallelepiped(['85', '1500'], + ['95', '3500'], + [1, 0.01]) +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(ENGINE_TEMPS, ',', True), + domain=parallelepiped, + column_indices=[0, 1]) +algo.execute(max_epsilon=10, min_delta=0.9) +pac = algo.get_pac() +print(f'''Algorithm result: {GREEN}{pac}{ENDC}. +This result is now meaningful and consistent with the findings from the first example. + +To better understand the effect of levelling coefficients, let's inspect the outliers. +Outliers between {BLUE}+0{ENDC} and {BLUE}+5{ENDC}: {BOLD}{algo.get_highlights(0, 5)}{ENDC}. +You can observe that the outliers reflect the normalized distance. + +It is recommended to continue with the third example ({CYAN}examples/basic/verifying_pac/verifying_domain_pac3.py{ENDC}), +which introduces another basic domain type: Ball.''') diff --git a/examples/basic/verifying_pac/verifying_domain_pac3.py b/examples/basic/verifying_pac/verifying_domain_pac3.py new file mode 100644 index 0000000000..79a7509010 --- /dev/null +++ b/examples/basic/verifying_pac/verifying_domain_pac3.py @@ -0,0 +1,112 @@ +'''Example 3: 2D ball''' + +from tabulate import tabulate +from csv import reader +from math import inf + +import desbordante + +RED = '\033[31m' +GREEN = '\033[32m' +BLUE = '\033[34m' +CYAN = '\033[36m' +BOLD = '\033[1;37m' +ENDC = '\033[0m' + +ENGINE_TEMPS = 'examples/datasets/verifying_pac/engine_temps_bad.csv' + + +def csv_to_str(filename: str) -> str: + with open(filename, newline='') as table: + rows = list(reader(table, delimiter=',')) + headers = rows[0] + rows = rows[1:] + return tabulate(rows, headers=headers) + + +print( + f'''This example demonstrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +It is the third example of "Basic Domain PAC verification" series (see the {CYAN}examples/basic/verifying_pac/{ENDC} directory). +If you haven\'t read first and second parts yet, it is recommended to start there. + +In the first example we verified the following Domain PAC on temperature sensor readings: + {BLUE}Domain PAC Pr(x ∈ [85, 95]±5) ≥ 0.9{ENDC} + +In the second example, we added tachometer readings and validated a Domain PAC on two columns using +the Parallelepiped domain. + +The sensor readings are the same as before: +{BOLD}{csv_to_str(ENGINE_TEMPS)}{ENDC} +''') + +print( + f'''The parallelepiped {BLUE}[{{85, 1500}}, {{95, 3500}}]{ENDC} is a rectangle in the (temperature, RPM) space: + (95, 1500) (95, 3500) + +--------+ + | | + +--------+ + (85, 1500) (85, 3500) + +Our task from the second example was: + The normal operating RPM for this engine is {BLUE}[1500, 3500]{ENDC}. Values outside this range are + not harmful by themselves (as long as they are within {BLUE}[0, 5000]{ENDC}), but: + * A cold engine may stall at low RPM and can be damaged at high RPM. + * An overheated engine is especially vulnerable at RPM values outside {BLUE}[1500, 3500]{ENDC}, because + cooling efficiency depends on RPM. + +A rectangle does not perfectly describe these conditions. For example, + * (80, 3900) is very harmful, + * (80, 1600) is mostly acceptable. +However, both points have the same distance from the rectangle boundary. This shows that a shape +with sharp corners does not model the risk accurately. +What we really want is a smooth shape without corners -- an ellipse. + +In this approach, ellipses (and their higher-dimensional equivalents) are represented by the Ball domain. +You might wonder: a ball has the same radius in all dimensions, while an ellipse has different ones. +The answer is leveling coefficients. + +In metric-space terms, a ball is defined as {BLUE}B = {{x : dist(x, center) < r}}. +The Ball domain uses the Euclidean metric: + {BOLD}dist(x, y) = sqrt((x[1] - y[1])^2 * lc[1] + ... + (x[n] - y[n])^2 * lc[n]){ENDC} +Here, lc is the list of leveling coefficients, introduced in the second example. They allow us to +scale dimensions differently -- effectively turning a circle into an ellipse. + +To balance temperature and RPM scales, we use levelling_coefficients={BLUE}[1, 0.005]{ENDC}. +This treats a 200 RPM difference as roughly equivalent to a 1°C difference. +''') + +# Formally, it's a disk: D = {x : dist(x, center) ≤ r}, but name "ball" is more clear +ellipse = desbordante.pac.domains.Ball(["90", "2500"], 5, [1, 0.005]) + +print(f'We now run the Domain PAC verifier with domain={BLUE}{ellipse}{ENDC}.') +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(ENGINE_TEMPS, ',', True), + domain=ellipse, + column_indices=[0, 1]) +algo.execute() +pac = algo.get_pac() +print(f'''Algorithm result: + {GREEN}{pac}{ENDC} +Outliers between {BLUE}+{pac.epsilon:.1f}{ENDC} and {BLUE}+∞{ENDC}: {BOLD}{algo.get_highlights(pac.epsilon, inf)}{ENDC}. +''') + +parallelepiped = desbordante.pac.domains.Parallelepiped(['85', '1500'], + ['95', '3500'], + [1, 0.005]) +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(ENGINE_TEMPS, ',', True), + domain=parallelepiped, + column_indices=[0, 1]) +algo.execute() +pac = algo.get_pac() + +print(f'''For comparison, the Parallelepiped domain previously produced: + {RED}{pac}{ENDC} +Outliers between {BLUE}+{pac.epsilon:.1f}{ENDC} and {BLUE}+∞{ENDC}: {BOLD}{algo.get_highlights(pac.epsilon, inf)}{ENDC}. +''') +print( + f'''Although the numerical values differ slightly, the Ball domain better reflects the actual operating +conditions, because it models gradual risk changes instead of sharp rectangular boundaries. + +It is recommended to continue with the fourth example ({CYAN}examples/basic/verifying_pac/verifying_domain_pac3.py{ENDC}), +which demonstrates another practical usage of the Ball domain.''') diff --git a/examples/basic/verifying_pac/verifying_domain_pac4.py b/examples/basic/verifying_pac/verifying_domain_pac4.py new file mode 100644 index 0000000000..b8ac1381a2 --- /dev/null +++ b/examples/basic/verifying_pac/verifying_domain_pac4.py @@ -0,0 +1,70 @@ +'''Example 4: string values''' + +from tabulate import tabulate +from csv import reader + +import desbordante + +RED = '\033[31m' +GREEN = '\033[32m' +BLUE = '\033[34m' +CYAN = '\033[36m' +BOLD = '\033[1;37m' +ENDC = '\033[0m' + +LEVENSHTEIN_TYPOS = 'examples/datasets/verifying_pac/levenshtein_typos.csv' + + +def csv_to_str(filename: str) -> str: + with open(filename, newline='') as table: + rows = list(reader(table, delimiter=',')) + headers = rows[0] + rows = rows[1:] + return tabulate(rows, headers=headers) + + +print( + f'''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +It is the final example in the "Basic Domain PAC verification" series (see the {CYAN}examples/basic/verifying_pac/{ENDC} directory). +If you haven\'t read the first three parts yet, it is recommended to start there. + +Consider the following dataset of users\' attempts to type a difficult Spanish word: +{BOLD}{csv_to_str(LEVENSHTEIN_TYPOS)}{ENDC} + +We want to show that most users can remember difficult words almost exactly. +In probabilistic terms, we want to verify the following Domain PAC: + {BLUE}Pr(dist(x, "Desbordante") ≤ 3) ≥ 0.9{ENDC} + +To measure the similarity between words, we use the Levenshtein distance, which counts how many +character insertions, deletions, or substitutions are required to transform one string into another. +In Desbordante, Levenshtein distance is the default metric for strings, so no additional +configuration is needed. +Based on the previous examples, the most suitable domain here is the Ball domain, because we are +measuring distance from a single center value. +''') + +ball = desbordante.pac.domains.Ball(["Desbordante"], 0) + +print( + f'We run the Domain PAC verifier with the following parameter: domain={BLUE}{ball}{ENDC}.' +) +algo = desbordante.pac_verification.algorithms.DomainPACVerifier() +algo.load_data(table=(LEVENSHTEIN_TYPOS, ',', True), + domain=ball, + column_indices=[0]) +algo.execute() +pac = algo.get_pac() +print(f'Result: {GREEN}{pac}{ENDC}.') + +print( + f'''This means that {GREEN}{pac.delta * 100}%{ENDC} of our users make no more than {GREEN}{pac.epsilon}{ENDC} typos in the word +"Desbordante", which satisfies our requirement. + +If you would like to see which specific words contain a given number of typos, you can inspect the +outliers (see previous examples). +''') + +print( + f'''Now that you have completed all basic examples, you can continue with the advanced example: +{CYAN}examples/advanced/verifying_pac/verifying_domain_pac_custom_domain.py{ENDC}. +This example demonstrates how to define and use a Custom domain.''') diff --git a/examples/datasets/verifying_pac/engine_temps_bad.csv b/examples/datasets/verifying_pac/engine_temps_bad.csv new file mode 100644 index 0000000000..b75bd32164 --- /dev/null +++ b/examples/datasets/verifying_pac/engine_temps_bad.csv @@ -0,0 +1,23 @@ +t,rpm +79,900 +78,2000 +78,3000 +78,2500 +79,3600 +80,3000 +79,2500 +85,1500 +90,1000 +89,1400 +94,1700 +96,1800 +104,3500 +93,2700 +90,2100 +88,1900 +86,1500 +84,2000 +87,3100 +90,3400 +95,4000 +92,2800 diff --git a/examples/datasets/verifying_pac/engine_temps_good.csv b/examples/datasets/verifying_pac/engine_temps_good.csv new file mode 100644 index 0000000000..f3b4d1ad44 --- /dev/null +++ b/examples/datasets/verifying_pac/engine_temps_good.csv @@ -0,0 +1,23 @@ +t +79 +80 +81 +82 +81 +81 +83 +85 +90 +89 +94 +96 +100 +93 +90 +88 +86 +84 +87 +90 +95 +92 diff --git a/examples/datasets/verifying_pac/levenshtein_typos.csv b/examples/datasets/verifying_pac/levenshtein_typos.csv new file mode 100644 index 0000000000..9d25f974d0 --- /dev/null +++ b/examples/datasets/verifying_pac/levenshtein_typos.csv @@ -0,0 +1,11 @@ +Query +Desbordante +Dezbordante +Desbordanto +Deezbardanta +Desbordant +Desbordante +Disbordantah +Desbbdante +Desbordante +desbordante diff --git a/examples/datasets/verifying_pac/user_preferences.csv b/examples/datasets/verifying_pac/user_preferences.csv new file mode 100644 index 0000000000..d060c3707c --- /dev/null +++ b/examples/datasets/verifying_pac/user_preferences.csv @@ -0,0 +1,11 @@ +Databases,Networks,Machine learning +0.7,0.2,1 +0.4,0.9,0.2 +0.85,0.4,0 +0.91,0.38,0.05 +0.75,0.41,0.1 +0.3,0.2,0.7 +0.77,0.2,0.01 +1,0.5,0.1 +0.885,0.41,0.02 +0.9009,0.4,0.037 diff --git a/examples/test_examples/snapshots/snap_test_examples_pytest.py b/examples/test_examples/snapshots/snap_test_examples_pytest.py index b2403583e2..588b5124ea 100644 --- a/examples/test_examples/snapshots/snap_test_examples_pytest.py +++ b/examples/test_examples/snapshots/snap_test_examples_pytest.py @@ -7,224 +7,328 @@ snapshots = Snapshot() -snapshots['test_example[advanced/afd_multiple_error_thresholds.py-None-afd_multiple_error_thresholds_output] afd_multiple_error_thresholds_output'] = '''[[1 2 3] -> 4, [0 2 3] -> 4, [0 1 3] -> 4, [0 1 2] -> 4] -[[3] -> 0, [3] -> 1, [3] -> 2, [3] -> 4, [2] -> 0, [2] -> 1, [2] -> 3, [2] -> 4, [1] -> 0, [1] -> 2, [1] -> 3, [1] -> 4, [0] -> 1, [0] -> 2, [0] -> 3, [0] -> 4] -[[3] -> 0, [3] -> 1, [3] -> 2, [3] -> 4, [2] -> 0, [2] -> 1, [2] -> 3, [2] -> 4, [1] -> 0, [1] -> 2, [1] -> 3, [1] -> 4, [0] -> 1, [0] -> 2, [0] -> 3, [0] -> 4] -[[4] -> 1, [4] -> 2, [4] -> 3, [3] -> 0, [3] -> 1, [3] -> 2, [3] -> 4, [2] -> 0, [2] -> 1, [2] -> 3, [2] -> 4, [1] -> 0, [1] -> 2, [1] -> 3, [1] -> 4, [0] -> 1, [0] -> 2, [0] -> 3, [0] -> 4] +snapshots['test_example[advanced/verifying_pac/verifying_domain_pac_custom_domain.py-None-verifying_domain_pac_custom_domain_output] verifying_domain_pac_custom_domain_output'] = '''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +A Domain PAC on a column set X and domain D, with given ε and δ means that \x1b[1;37mPr(x ∈ D±ε) ≥ δ\x1b[0m. +For more information, see "Checks and Balances: Monitoring Data Quality Problems in Network +Traffic Databases" by Filp Korn et al (Proceedings of the 29th VLDB Conference, Berlin, 2003). +If you have not read the basic Domain PAC examples yet (see the \x1b[36mexamples/basic/verifying_pac/\x1b[0m +directory), it is recommended to start there. + +Assume we have a dataset of user preferences, where each user's interest in several topics is +encoded as values in [0, 1], where 0 is "not interested at all" and 1 is "very interested": +\x1b[1;37m Databases Networks Machine learning +----------- ---------- ------------------ + 0.7 0.2 1 + 0.4 0.9 0.2 + 0.85 0.4 0 + 0.91 0.38 0.05 + 0.75 0.41 0.1 + 0.3 0.2 0.7 + 0.77 0.2 0.01 + 1 0.5 0.1 + 0.885 0.41 0.02 + 0.9009 0.4 0.037\x1b[0m + +We need to estimate whether this group of users will be interested in the original Domain PAC paper +("Checks and Balances: ..."). +To do this, we represent each user profile as a vector in a multi-dimensional topic space: + ^ Topic 2 + | + | user + | x + | / + |/ Topic 1 + -+-------> + | + +A "perfect" target reader might have the profile: \x1b[34m(0.9, 0.4, 0.05)\x1b[0m. +This corresponds to: + * high interest in Databases; + * moderate interest in Networks; + * low interest in Machine Learning. +Our goal is to measure how close real users are to this ideal profile. + +We use cosine distance, which measures the angle between two vectors rather then their absolute +length. This is useful because we care about interest proportions, not total magnitude. + \x1b[1;37mdist(x, y) = 1 - cos(angle between x and y) = 1 - (x, y)/(|x| * |y|)\x1b[0m, +where (x, y) is a dot product between x and y. + +A custom domain is defined by two parameters: + 1. Distance function -- takes a value tuple and returns the distance to the domain. + 2. Domain name (optional) -- used for readable output. +In this example: + * distance function: \x1b[34mdist(x, (0.9, 0.4, 0.05))\x1b[0m; + * domain name: \x1b[34m"(0.9, 0.4, 0.05)"\x1b[0m. +This effectively defines the domain as "users close to the ideal profile". + +We run the Domain PAC verifier with domain=\x1b[34m(0.9, 0.4, 0.05)\x1b[0m. +Algorithm result: + \x1b[32mDomain PAC Pr(x ∈ (0.9, 0.4, 0.05)±0.37695) ≥ 0.9 on columns [Databases Networks Machine learning]\x1b[0m +Now we lower the required probability threshold: min_delta=\x1b[34m0.6\x1b[0m. +Algorithm result: + \x1b[32mDomain PAC Pr(x ∈ (0.9, 0.4, 0.05)±0.0141436) ≥ 0.7 on columns [Databases Networks Machine learning]\x1b[0m +Interpretation: + * With a larger ε (\x1b[34m0.377\x1b[0m), nearly all users show some level of interest + * With a very small ε (\x1b[34m0.014\x1b[0m), only \x1b[34m70%\x1b[0m of users closely match the ideal reader. + +You can user highlights to identify which users are closer to or farther from the ideal profile. +For an introduction to highlights, see \x1b[36mexamples/basic/verifying_pac/verifying_domain_pac1.py\x1b[0m. ''' -snapshots['test_example[advanced/aind_typos.py-None-aind_typos_output] aind_typos_output'] = '''This pipeline demonstrates the process of mining and verifying AINDs -(approximate inclusion dependencies). This pipeline can be used for data -cleaning by identifying typos in the datasets based on the identified AINDs. - -It consists of the following steps: -1. Mine all possible AINDs from a set of tables. -2. Filter out exact INDs (which have zero error). -3. Verify the AINDs to identify clusters of data that violate the dependencies. -4. Display detailed information about the dependencies. - -Let's find all AINDs with an error threshold less than 0.4. -The datasets under consideration for this scenario are 'orders', 'customers' -and 'products'. - -Dataset 'orders': -+----+------+---------------+-----------+ -| | id | customer_id | product | -|----+------+---------------+-----------| -| 0 | 1 | 101 | Laptop | -| 1 | 2 | 102 | Phone | -| 2 | 3 | 103 | Tablet | -| 3 | 4 | 104 | Monitor | -| 4 | 5 | 108 | Keyboard | -| 5 | 6 | 201 | Mouse | -| 6 | 7 | 102 | Charger | -+----+------+---------------+-----------+ - -Dataset 'customers': -+----+------+---------+-----------+ -| | id | name | country | -|----+------+---------+-----------| -| 0 | 101 | Alice | USA | -| 1 | 102 | Bob | UK | -| 2 | 103 | Charlie | Canada | -| 3 | 104 | David | Germany | -| 4 | 105 | Eve | France | -+----+------+---------+-----------+ - -Dataset 'products': -+----+------+----------+-------------+ -| | id | name | category | -|----+------+----------+-------------| -| 0 | 1 | Laptop | Electronics | -| 1 | 2 | Phone | Electronics | -| 2 | 3 | Tablet | Electronics | -| 3 | 4 | Monitor | Electronics | -| 4 | 5 | Keyboard | Accessories | -| 5 | 6 | Mouse | Accessories | -| 6 | 7 | Charger | Accessories | -+----+------+----------+-------------+ - -Here is the list of exact INDs: -(orders.csv, [id]) -> (products.csv, [id]) -(orders.csv, [product]) -> (products.csv, [name]) -(products.csv, [id]) -> (orders.csv, [id]) -(products.csv, [name]) -> (orders.csv, [product]) -(orders.csv, [id, product]) -> (products.csv, [id, name]) -(products.csv, [id, name]) -> (orders.csv, [id, product]) - -Here is the list of AINDs: -AIND: (orders.csv, [customer_id]) -> (customers.csv, [id]) with error threshold = 0.333333 -AIND: (customers.csv, [id]) -> (orders.csv, [customer_id]) with error threshold = 0.2 - -Let's see detailed information about AINDs: -AIND: (orders.csv, [customer_id]) -> (customers.csv, [id]) with error threshold = 0.333333 -\x1b[1;41m AIND holds with error = 0.33 \x1b[0m -Number of clusters violating IND: 2 -\x1b[1;46m #1 cluster: \x1b[1;49m\x1b[0m -5: 201 -\x1b[1;46m #2 cluster: \x1b[1;49m\x1b[0m -4: 108 - -AIND: (customers.csv, [id]) -> (orders.csv, [customer_id]) with error threshold = 0.2 -\x1b[1;41m AIND holds with error = 0.2 \x1b[0m -Number of clusters violating IND: 1 -\x1b[1;46m #1 cluster: \x1b[1;49m\x1b[0m -4: 105 - -Based on the analysis of the AINDs and their errors, we can make the following -conclusions: - - First AIND: The AIND between `orders.customer_id` and `customers.id` holds -with an error threshold of 0.33. The clusters violating the inclusion dependency -indicate possible data issues. -- The `orders.customer_id` value '201' does not match any entry in the -`customers.id` column. This suggests that there might have been a typo where -'201' should have been '101', indicating that the customer who bought the -'Mouse' might actually be Alice. -- Similarly, the `orders.customer_id` value '108' also violates the AIND. This -suggests a missing customer entry in the `customers` table. The customer -corresponding to '108' may not exist in the dataset, pointing to a data -inconsistency. - - Second AIND: The AIND between `customers.id` and `orders.customer_id` with an -error threshold of 0.2 does not indicate any real typo. The issue here is that -this AIND counts customers who have not placed any orders, which is expected -behavior. This dependency does not reflect typos but instead reveals missing -orders for certain customers. Since it's not a typo, this AIND is not useful for -cleaning data. - -It's important to take the error threshold into account when working with AINDs. -A higher threshold will reveal more potential errors, but it might also include -non-typo cases, such as customers who have not made any orders yet. +snapshots['test_example[basic/verifying_pac/verifying_domain_pac1.py-None-verifying_domain_pac1_output] verifying_domain_pac1_output'] = '''This example illustrates the usage of Domain Probabilistic Approximate Constraints (PACs). +A Domain PAC on column set X and domain D, with given ε and δ means that Pr(x ∈ D±ε) ≥ δ. +For more information consult "Checks and Balances: Monitoring Data Quality Problems in Network +Traffic Databases" by Flip Korn et al (Proceedings of the 29th VLDB Conference, Berlin, 2003). + +This is the first example in the "Basic Domain PAC verification" series. Others can be found in +\x1b[36mexamples/basic/verifying_pac/\x1b[0m directory. + +Suppose we are working on a new model of engine. Its operating temperature range is \x1b[34m[85, 95]\x1b[0m°C. +The engine is made of high-strength metal, so short-term temperature deviations are acceptable and +will not cause immediate damage. In other words, engine operates properly when Pr(t ∈ [85, 95]±ε) ≥ δ. +Based on engineering analysis, the acceptable limits are: ε = \x1b[34m5\x1b[0m, δ = \x1b[34m0.9\x1b[0m. +In terms of Domain PACs, the following constraint should hold: \x1b[34mPr(x ∈ [85, 95]±5) ≥ 0.9\x1b[0m. + +The following table contains readings from the engine temperature sensor: +\x1b[1;37mt: [79, 78, 78, 78, 79, 80, 79, 85, 90, 89, 94, 96, 104, 93, 90, 88, 86, 84, 87, 90, 95, 92]\x1b[0m + +We now use the Domain PAC verifier to determine whether the engine is operating safely. +First, we need to define the domain. A segment is a special case of a parallelepiped, so we use it here. +We run algorithm with the following options: domain=\x1b[34m[85, 95]\x1b[0m. +All other parameters use default values: min_epsilon=\x1b[34m0\x1b[0m, max_epsilon=\x1b[34m∞\x1b[0m, min_delta=\x1b[34m0.9\x1b[0m, delta_steps=\x1b[34m100\x1b[0m. + +Algorithm result: \x1b[33mDomain PAC Pr(x ∈ [85, 95]±7) ≥ 0.954545 on columns [t]\x1b[0m. +This PAC is not very informative. Let's run algorithm with min_epsilon=\x1b[34m5\x1b[0m and max_epsilon=\x1b[34m5\x1b[0m. +This will give us the exact δ, for which PAC with ε=\x1b[34m5\x1b[0m holds. + +Algorithm result: \x1b[31mDomain PAC Pr(x ∈ [85, 95]±5) ≥ 0.681818 on columns [t]\x1b[0m. +Also, let's run algorithm with max_epsilon=\x1b[34m0\x1b[0m and min_delta=\x1b[34m0.9\x1b[0m to check which ε +is needed to satisfy δ=\x1b[34m0.9\x1b[0m. With these parameters algorithm enters special mode and returns +pair (ε, min_delta), so that we can validate PAC with the given δ. + +Algorithm result: \x1b[31mDomain PAC Pr(x ∈ [85, 95]±7) ≥ 0.954545 on columns [t]\x1b[0m. +Here algorithm gives δ=\x1b[34m0.9545454545454546\x1b[0m, which is greater than \x1b[34m0.9\x1b[0m, because achieving δ=\x1b[34m0.9\x1b[0m requires +ε=\x1b[34m7.0\x1b[0m and PAC (\x1b[34m7.0\x1b[0m, \x1b[34m0.9545454545454546\x1b[0m) holds. So, this means that δ=\x1b[34m0.9\x1b[0m would also require ε=\x1b[34m7.0\x1b[0m. + +We can see that desired PAC doesn't hold, so the engine can blow up! + +Let's look at values violating PAC. Domain PAC verifier can detect values between eps_1 +and eps_2, i. e. values that lie in D±eps_2 \\ D±eps_1. Such values are called highlights or outliers. +Let's find outliers for different eps_1, eps_2 values: + eps_1 eps_2 highlights +------- ------- ------------------------ + \x1b[34m0\x1b[0m \x1b[34m1\x1b[0m [96, 84] + \x1b[34m1\x1b[0m \x1b[34m2\x1b[0m + \x1b[34m2\x1b[0m \x1b[34m3\x1b[0m + \x1b[34m3\x1b[0m \x1b[34m5\x1b[0m [80] + \x1b[34m5\x1b[0m \x1b[34m7\x1b[0m [79, 79, 79, 78, 78, 78] + \x1b[34m7\x1b[0m \x1b[34m10\x1b[0m [104] + +We can see two problems: + 1. The engine operated at low temperatures for an extended period, slightly below 80°C. + 2. The peak temperature was too high, but this occurred only once. + +The second version of engine has: + 1. A pre-heating system to prevent operation at low temperatures. + 2. An emergency cooling system to limit peak temperatures. +The updated sensor readings (modified values highlighted) are: +\x1b[1;37mt: [79, \x1b[1;33m80\x1b[1;37m, \x1b[1;33m81\x1b[1;37m, \x1b[1;33m82\x1b[1;37m, \x1b[1;33m81\x1b[1;37m, \x1b[1;33m81\x1b[1;37m, \x1b[1;33m83\x1b[1;37m, 85, 90, 89, 94, 96, \x1b[1;33m100\x1b[1;37m, 93, 90, 88, 86, 84, 87, 90, 95, 92]\x1b[0m + +We run the Domain PAC verifier again. +Algorithm result: \x1b[32mDomain PAC Pr(x ∈ [85, 95]±5) ≥ 0.954545 on columns [t]\x1b[0m. +The desired PAC now holds, which means the improved engine operates within acceptable limits. + +It is recommended to continue with the second example (\x1b[36mexamples/basic/verifying_pac/verifying_domain_pac2.py\x1b[0m), +which demonstrates more advanced usage of the Parallelepiped domain. ''' -snapshots['test_example[advanced/comparison_mining_fd_approximate.py-None-comparison_mining_fd_approximate_output] comparison_mining_fd_approximate_output'] = ''' -======================================================= -This example demonstrates key characteristics of the -approximate functional dependency (FD) discovery -algorithms, AID-FD and EulerFD. -======================================================= - - -EulerFD is a randomized algorithm, and its results vary based on the seed value. For instance: -With a seed of 2704, EulerFD found 76 FDs. -With a seed of 1321, EulerFD found 78 FDs. -With a seed of 9208, EulerFD found 80 FDs. -An exact FD discovery algorithm, in contrast, consistently identified 78 FDs. - -This highlights a key property of EulerFD: it may produce results with both -false positives (extra FDs) and false negatives (missing FDs) -compared to exact methods. - - ---------------------------------------------------------------------- -Let's examine the differences between the results of the exact algorithm and EulerFD. - -First, consider the results with a seed of 2704, where EulerFD identified 76 FDs. -Compared to the exact method, EulerFD failed to identify the following 4 FDs: -[1 2 3 5 7 9 11 13] -> 8 -[1 2 3 5 7 9 13 14] -> 8 -[1 2 4 5 7 9 11 13] -> 8 -[1 2 4 5 7 9 13 14] -> 8 -Additionally, it incorrectly identified these 2 false FDs: -[1 2 3 5 7 9 13] -> 8 -[1 2 4 5 7 9 13] -> 8 -Thus, a single run of EulerFD can both miss valid FDs and generate false FDs. - -Next, let's analyze the results with a seed of 1321, where EulerFD identified 78 FDs -EulerFD not found 0 FDs. -EulerFD found 0 false FDs. -Therefore, with the seed 1321, EulerFD obtained the exact result. +snapshots['test_example[basic/verifying_pac/verifying_domain_pac2.py-None-verifying_domain_pac2_output] verifying_domain_pac2_output'] = '''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs) +on multiple columns. It continues the first Domain PAC verification example +\x1b[36m(examples/basic/verifying_pac/verifying_domain_pac1.py)\x1b[0m. If you have not read the first part yet, +it is recommended to start there. + +In the first example we verified \x1b[34mDomain PAC Pr(x ∈ [85, 95]±5) ≥ 0.9\x1b[0m on engine temperature sensor readings. +Now, in addition to temperature readings, we also have tachometer data: +\x1b[1;37m t rpm +--- ----- + 79 900 + 78 2000 + 78 3000 + 78 2500 + 79 3600 + 80 3000 + 79 2500 + 85 1500 + 90 1000 + 89 1400 + 94 1700 + 96 1800 +104 3500 + 93 2700 + 90 2100 + 88 1900 + 86 1500 + 84 2000 + 87 3100 + 90 3400 + 95 4000 + 92 2800\x1b[0m + +The normal operating RPM for this engine is \x1b[34m[1500, 3500]\x1b[0m. Values outside this range are +not harmful by themselves (as long as they are within \x1b[34m[0, 5000]\x1b[0m), but: + * A cold engine may stall at low RPM and can be damaged at high RPM. + * An overheated engine is especially vulnerable at RPM values outside \x1b[34m[1500, 3500]\x1b[0m, because + cooling efficiency depends on RPM. +As in the first example, we use the Domain PAC verifier to check whether the engine operates properly. + +Firstly, we need to create domain. We have a Cartesian product of two segments: \x1b[34m[85, 95] x [1500, 3500]\x1b[0m, +so it would be natural to use parallelepiped. +We now work with two columns: temperature and RPM. The acceptable operating region is a Cartesian product +of two segments: + * temperature: [85, 95]; + * RPM: [1500, 3500]. +This forms a parallelepiped domain: \x1b[34m[85, 95] x [1500, 3500]\x1b[0m. + +We run the Domain PAC verifier with the following parameters: domain=\x1b[34m[{85, 1500}, {95, 3500}]\x1b[0m, +max_epsilon=\x1b[34m15\x1b[0m. + +Algorithm result: \x1b[31mDomain PAC Pr(x ∈ [{85, 1500}, {95, 3500}]±1) ≥ 0.5 on columns [t rpm]\x1b[0m. +A result with δ = \x1b[34m0.5\x1b[0m is unexpected. To understand what is happening, we examine the highlights. +Highlights between \x1b[34m0\x1b[0m and \x1b[34m1.0\x1b[0m are: \x1b[1;37m[{96, 1800}, {84, 2000}]\x1b[0m. + +There are very few highlights, which suggests that the parameters may not be chosen correctly. + +The question is: what does ε = \x1b[34m1.0\x1b[0m mean in two-dimensional domain? Should ε correspond to: + * 10 degrees of temperature difference, or + * 1500 RPM difference? +To answer this, we need to understand how distance is computed. + +The parallelepiped uses the Chebyshev metric to calculate distance between value tuples: + \x1b[1;37md(x, y) = max{|x[1] - y[1]|, ..., |x[n] - y[n]|}\x1b[0m +In our case: + * temperature differences are on the order of tens; + * RPM differences are on the order of thousands. +As a result, RPM differences dominate the distance computation, making temperature differences +almost irrelevant. This issue affects all coordinate-wise metric-based domains (currently +Parallelepiped and Ball, though custom domains can be implemented in C++). + +To address this, such domains support \x1b[36mleveling coefficients\x1b[0m, which rescale individual +dimensions. With leveling coefficients, the distance becomes: + \x1b[1;37md(x, y) = max{|x[1] - y[1]| * lc[1], ..., |x[n] - y[n]| * lc[n]}\x1b[0m +To normalize temperatures and RPM scales, we use leveling_coefficients=\x1b[34m[1, 0.01]\x1b[0m parameter. +This treats a 100 RPM difference as comparable to a 1°C difference. + +With leveling coefficients applied, we rerun the algorithm. + +Algorithm result: \x1b[32mDomain PAC Pr(x ∈ [{85, 1500}, {95, 3500}]±7) ≥ 0.954545 on columns [t rpm]\x1b[0m. +This result is now meaningful and consistent with the findings from the first example. + +It is recommended to continue with the third example (\x1b[36mexamples/basic/verifying_pac/verifying_domain_pac3.py\x1b[0m), +which introduces another basic domain type: Ball. ''' -snapshots['test_example[advanced/comparison_pfd_vs_afd.py-None-comparison_pfd_vs_afd_output] comparison_pfd_vs_afd_output'] = '''pFDs \\ AFDs = OrderedSet(['[DeviceId] -> Data']) -AFDs \\ pFDs = OrderedSet() -AFDs ∩ pFDs = OrderedSet(['[Data] -> Id', '[Data] -> DeviceId', '[Id] -> DeviceId', '[Id] -> Data']) -1 - PerValue([DeviceId] -> Data) = 0.1714285714 -e([DeviceId] -> Data) = 0.23076923076923078 -In case of PerValue error measure, violations on data from the single "glitchy" -sensor device among many do not prevent dependecy from being found +snapshots['test_example[basic/verifying_pac/verifying_domain_pac3.py-None-verifying_domain_pac3_output] verifying_domain_pac3_output'] = '''This example demonstrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +It is the third example of "Basic Domain PAC verification" series (see the \x1b[36mexamples/basic/verifying_pac/\x1b[0m directory). +If you haven't read first and second parts yet, it is recommended to start there. + +In the first example we verified the following Domain PAC on temperature sensor readings: + \x1b[34mDomain PAC Pr(x ∈ [85, 95]±5) ≥ 0.9\x1b[0m +In the second example, we added tachometer readings and validated a Domain PAC on two columns using +the Parallelepiped domain. + +The sensor readings are the same as before: +\x1b[1;37m t rpm +--- ----- + 79 900 + 78 2000 + 78 3000 + 78 2500 + 79 3600 + 80 3000 + 79 2500 + 85 1500 + 90 1000 + 89 1400 + 94 1700 + 96 1800 +104 3500 + 93 2700 + 90 2100 + 88 1900 + 86 1500 + 84 2000 + 87 3100 + 90 3400 + 95 4000 + 92 2800\x1b[0m + +The parallelepiped \x1b[34m[{85, 1500}, {95, 3500}]\x1b[0m is a rectangle in the (temperature, RPM) space: + (95, 1500) (95, 3500) + +--------+ + | | + +--------+ + (85, 1500) (85, 3500) +Our task from the second example was: + The normal operating RPM for this engine is \x1b[34m[1500, 3500]\x1b[0m. Values outside this range are + not harmful by themselves (as long as they are within \x1b[34m[0, 5000]\x1b[0m), but: + * A cold engine may stall at low RPM and can be damaged at high RPM. + * An overheated engine is especially vulnerable at RPM values outside \x1b[34m[1500, 3500]\x1b[0m, because + cooling efficiency depends on RPM. + +A rectangle does not perfectly describe these conditions. For example, + * (80, 3900) is very harmful, + * (80, 1600) is mostly acceptable. +However, both points have the same distance from the rectangle boundary. This shows that a shape +with sharp corners does not model the risk accurately. +What we rally want is a smooth shape without corners -- an ellipse. + +In this approach, ellipses (and their higher-dimensional equivalents) are represented by the Ball domain. +You might wonder: a ball has the same radius in all dimensions, while an ellipse has different ones. +The answer is leveling coefficients. + +In metric-space terms, a ball is defined as \x1b[34mB = {x : dist(x, center) < r}. +The Ball domain uses the Euclidean metric: + \x1b[1;37mdist(x, y) = sqrt((x[1] - y[1])^2 * lc[1] + ... + (x[n] - y[n])^2 * lc[n])\x1b[0m +Here, lc is the list of leveling coefficients, introduced in the second example. They allow us to +scale dimensions differently -- effectively turning a circle into an ellipse. + +To balance temperature and RPM scales, we use levelling_coefficients=\x1b[34m[1, 0.005]\x1b[0m. +This treats a 200 RPM difference as roughly equivalent to a 1°C difference. + +We now run the Domain PAC verifier with domain=\x1b[34mB({90, 2500}, 5)\x1b[0m. +Algorithm result: + \x1b[32mDomain PAC Pr(x ∈ B({90, 2500}, 5)±7.29837) ≥ 0.909091 on columns [t rpm]\x1b[0m +For comparison, the Parallelepiped domain previously produced: + \x1b[31mDomain PAC Pr(x ∈ [{85, 1500}, {95, 3500}]±7) ≥ 0.954545 on columns [t rpm]\x1b[0m + +Although the numerical values differ slightly, the Ball domain better reflects the actual operating +conditions, because it models gradual risk changes instead of sharp rectangular boundaries. + +It is recommended to continue with the fourth example (\x1b[36mexamples/basic/verifying_pac/verifying_domain_pac3.py\x1b[0m), +which demonstrates another practical usage of the Ball domain. ''' -snapshots['test_example[advanced/comparison_ucc_and_aucc_1.py-None-comparison_ucc_and_aucc_1_output] comparison_ucc_and_aucc_1_output'] = '''\x1b[1m\x1b[36mThis example illustrates the difference between exact and approximate Unique -Column Combinations (UCC and AUCC). Intuitively, a UCC declares that some columns uniquely -identify every tuple in a table. An AUCC allows a certain degree of violation. For more -information on UCC, consult "A Hybrid Approach for Efficient Unique Column Combination Discovery" -by T. Papenbrock and F. Naumann. For more information on AUCC, consult "Efficient Discovery of -Approximate Dependencies" by S. Kruse and F. Naumann. -\x1b[0m -The following table contains records about employees: -\x1b[1m\x1b[36mName Grade Salary Work_experience --------------------------------------------- -Mark 7 1150 10 -Joyce 5 1100 5 -Harry 3 1000 7 -Grace 4 900 12 -Harry 6 1200 1 -Samuel 1 950 9 -Nancy 2 800 3 -\x1b[0mWe'll try to find typos, using UCC mining and AUCC verifying algorithms. - -Let's run UCC mining algorithm: -Found UCCs: -\t\x1b[1m\x1b[36m[Grade]\x1b[0m -\t\x1b[1m\x1b[36m[Salary]\x1b[0m -\t\x1b[1m\x1b[36m[Work_experience]\x1b[0m - -There is no UCC for \x1b[1m\x1b[36mName\x1b[0m column. Maybe it's an error? -Let's run AUCC verification algorithm for column \x1b[1m\x1b[36mName\x1b[0m: -\x1b[31mUCC does not hold\x1b[0m, but AUCC holds with threshold = 0.048 -Threshold is small. It means that, possibly, there is an error in this column. -Let's look at the table again: -\x1b[1m\x1b[36mName Grade Salary Work_experience --------------------------------------------- -Mark 7 1150 10 -Joyce 5 1100 5 -Harry 3 1000 7 -Grace 4 900 12 -Harry 6 1200 1 -Samuel 1 950 9 -Nancy 2 800 3 -\x1b[0m -There are two \x1b[1m\x1b[36mHarrys\x1b[0m. They have different work experience, so they are -two different employees. If they had unique names, AUCC would hold with threshold = 0, and -\x1b[1m\x1b[36mName\x1b[0m could be used as a key: -\x1b[1m\x1b[36mName Grade Salary Work_experience -------------------------------------------------- -Mark 7 1150 10 -Joyce 5 1100 5 -Harry Brown 3 1000 7 -Grace 4 900 12 -Harry Adams 6 1200 1 -Samuel 1 950 9 -Nancy 2 800 3 -\x1b[0m -Let's run UCC mining algorithm: -Found UCCs: -\t\x1b[1m\x1b[36m[Name]\x1b[0m -\t\x1b[1m\x1b[36m[Grade]\x1b[0m -\t\x1b[1m\x1b[36m[Salary]\x1b[0m -\t\x1b[1m\x1b[36m[Work_experience]\x1b[0m +snapshots['test_example[basic/verifying_pac/verifying_domain_pac4.py-None-verifying_domain_pac4_output] verifying_domain_pac4_output'] = '''This example illustrates the usage of Domain Probabilistic Approximate Constraints (Domain PACs). +It is the final example in the "Basic Domain PAC verification" series (see the \x1b[36mexamples/basic/verifying_pac/\x1b[0m directory). +If you haven't read the first three parts yet, it is recommended to start there. + +Consider the following dataset of users' attempts to type a difficult Spanish word: +\x1b[1;37mQuery +------------ +Desbordante +Dezbordante +Desbordanto +Deezbardanta +Desbordant +Desbordante +Disbordantah +Desbbdante +Desbordante +desbordante\x1b[0m + +We want to show that most users can remember difficult words almost exactly. +In probabilistic terms, we want to verify the following Domain PAC: + \x1b[34mPr(dist(x, "Desbordante") ≤ 3) ≥ 0.9\x1b[0m Now we have cleaned the data and \x1b[1m\x1b[36mName\x1b[0m can now be used as a key. ''' diff --git a/src/core/algorithms/CMakeLists.txt b/src/core/algorithms/CMakeLists.txt index d0d1471449..646a9a072b 100644 --- a/src/core/algorithms/CMakeLists.txt +++ b/src/core/algorithms/CMakeLists.txt @@ -14,6 +14,7 @@ set(SUBDIRS od/fastod od/order od/set_based_verifier + pac statistics ucc ) @@ -67,6 +68,7 @@ target_link_libraries( ${DESBORDANTE_PREFIX}::nar::des ${DESBORDANTE_PREFIX}::od::fast ${DESBORDANTE_PREFIX}::od::order + ${DESBORDANTE_PREFIX}::pac::verifier::domain_pac_verifier ${DESBORDANTE_PREFIX}::ucc::hy ${DESBORDANTE_PREFIX}::ucc::pyro ${DESBORDANTE_PREFIX}::ucc::hpivalid diff --git a/src/core/algorithms/pac/CMakeLists.txt b/src/core/algorithms/pac/CMakeLists.txt new file mode 100644 index 0000000000..4404b07cf0 --- /dev/null +++ b/src/core/algorithms/pac/CMakeLists.txt @@ -0,0 +1,2 @@ +add_subdirectory(model) +add_subdirectory(pac_verifier) diff --git a/src/core/algorithms/pac/domain_pac.h b/src/core/algorithms/pac/domain_pac.h new file mode 100644 index 0000000000..56262697ec --- /dev/null +++ b/src/core/algorithms/pac/domain_pac.h @@ -0,0 +1,72 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/pac.h" +#include "core/model/table/relational_schema.h" +#include "core/model/table/vertical.h" + +namespace model { +/// @brief Given an ordered domain D on an attribute, a Domain PAC specifies that all attribute +/// values fall within @c epsilon of D with at least probability @c delta. +class DomainPAC : public PAC { +private: + std::shared_ptr domain_; + Vertical columns_; + + std::string StringStem(std::string const& arg) const { + std::ostringstream oss; + oss << "Pr(" << arg << " ∈ " << domain_->ToString() << "±" << GetEpsilon() << ") ≥ " + << GetDelta(); + return oss.str(); + } + +public: + DomainPAC(std::shared_ptr rel_schema, double epsilon, double delta, + std::shared_ptr domain, Vertical const& columns) + : PAC(rel_schema, epsilon, delta), domain_(std::move(domain)), columns_(columns) {} + + DomainPAC(DomainPAC const&) = default; + DomainPAC(DomainPAC&&) = default; + DomainPAC& operator=(DomainPAC const&) = default; + DomainPAC& operator=(DomainPAC&&) = default; + + Vertical const& GetColumns() const { + return columns_; + } + + std::vector GetColumnNames() const { + std::vector result; + result.reserve(columns_.GetArity()); + std::ranges::transform(columns_.GetColumns(), std::back_inserter(result), + std::mem_fn(&Column::GetName)); + return result; + } + + pac::model::IDomain const& GetDomain() const { + return *domain_; + } + + std::shared_ptr GetDomainPtr() { + return domain_; + } + + virtual std::string ToShortString() const override { + return StringStem(columns_.ToString()); + } + + virtual std::string ToLongString() const override { + std::ostringstream oss; + oss << "Domain PAC " << StringStem("x") << " on columns " << columns_.ToString(); + return oss.str(); + } +}; +} // namespace model diff --git a/src/core/algorithms/pac/model/CMakeLists.txt b/src/core/algorithms/pac/model/CMakeLists.txt new file mode 100644 index 0000000000..d704eeaf9b --- /dev/null +++ b/src/core/algorithms/pac/model/CMakeLists.txt @@ -0,0 +1,14 @@ +add_subdirectory(default_domains) + +set(NAME pac.model) +desbordante_add_lib(NAME OBJECT) +target_sources( + ${NAME} + PRIVATE tuple_type.cpp + PUBLIC FILE_SET HEADERS BASE_DIRS "${PROJECT_SOURCE_DIR}/src" +) +target_link_libraries( + ${NAME} + PUBLIC ${DESBORDANTE_PREFIX}::model::types + PRIVATE Boost::headers better-enums +) diff --git a/src/core/algorithms/pac/model/default_domains/CMakeLists.txt b/src/core/algorithms/pac/model/default_domains/CMakeLists.txt new file mode 100644 index 0000000000..2fbf2bdc55 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/CMakeLists.txt @@ -0,0 +1,13 @@ +set(NAME pac.model.default_domains) +desbordante_add_lib(NAME OBJECT) +target_sources( + ${NAME} + PRIVATE ball.cpp metric_based_domain.cpp parallelepiped.cpp + PUBLIC FILE_SET HEADERS BASE_DIRS "${PROJECT_SOURCE_DIR}/src" +) +target_link_libraries( + ${NAME} + PUBLIC ${DESBORDANTE_PREFIX}::pac::model ${DESBORDANTE_PREFIX}::model::types better-enums + ${DESBORDANTE_PREFIX}::config + PRIVATE Boost::headers +) diff --git a/src/core/algorithms/pac/model/default_domains/ball.cpp b/src/core/algorithms/pac/model/default_domains/ball.cpp new file mode 100644 index 0000000000..2721693eeb --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/ball.cpp @@ -0,0 +1,39 @@ +#include "core/algorithms/pac/model/default_domains/ball.h" + +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" + +namespace pac::model { +double Ball::EuclideanDist(Tuple const& x, Tuple const& y) const { + double dist = 0; + for (std::size_t i = 0; i < metrizable_types_.size(); ++i) { + auto coord_dist = DistBetweenBytes(i, x[i], y[i]); + dist += coord_dist * coord_dist; + } + return std::sqrt(dist); +} + +Ball::~Ball() { + for (std::size_t i = 0; i < center_.size(); ++i) { + auto const& destroy = destructors_[i]; + if (destroy) { + destroy(center_[i]); + } else { + delete[] center_[i]; + } + } +} + +std::string Ball::ToString() const { + // tuple_type_->ValueToString() would be clearer, but domain isn't guaranteed to be instantiated + // with types by here. + std::ostringstream oss; + oss << "B(" << StringValueToString(center_str_) << ", " << radius_ << ')'; + return oss.str(); +} +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/ball.h b/src/core/algorithms/pac/model/default_domains/ball.h new file mode 100644 index 0000000000..f0272ddd8f --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/ball.h @@ -0,0 +1,45 @@ +#pragma once + +#include +#include +#include +#include + +#include "core/algorithms/pac/model/default_domains/metric_based_domain.h" +#include "core/algorithms/pac/model/tuple.h" + +namespace pac::model { +/// @brief Closed ball (disk), defined by @c center and @c radius. +/// D = {x | d(x, @c center) <= @c radius} +class Ball final : public MetricBasedDomain { +private: + Tuple center_; + std::vector center_str_; + double radius_; + std::vector<::model::Type::Destructor> destructors_; + + /// @brief Euclidean distance. + /// d(X, Y) = (x[1] - y[1])^2 + ... + (x[n] - y[n])^2) + double EuclideanDist(Tuple const& x, Tuple const& y) const; + +protected: + virtual double DistFromDomainInternal(Tuple const& value) const override { + return std::max(0.0, EuclideanDist(value, center_) - radius_); + } + + virtual void ConvertValues() override { + center_ = AllocateValues(center_str_); + destructors_ = GetDestructors(); + } + +public: + Ball(std::vector&& center_str, double radius, + std::vector&& leveling_coefficients = {}) + : MetricBasedDomain(std::move(leveling_coefficients)), + center_str_(std::move(center_str)), + radius_(radius) {} + + virtual ~Ball(); + virtual std::string ToString() const override; +}; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/domain_type.h b/src/core/algorithms/pac/model/default_domains/domain_type.h new file mode 100644 index 0000000000..9cb618d646 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/domain_type.h @@ -0,0 +1,9 @@ +#pragma once + +#include "core/util/better_enum_with_visibility.h" + +namespace pac::model { +/// @brief Default domain names; used in CLI. +/// "custom" here is UntypedDomain. +BETTER_ENUM(DomainType, unsigned char, ball, parallelepiped, custom_domain); +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/metric_based_domain.cpp b/src/core/algorithms/pac/model/default_domains/metric_based_domain.cpp new file mode 100644 index 0000000000..dea62327d1 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/metric_based_domain.cpp @@ -0,0 +1,87 @@ +#include "core/algorithms/pac/model/default_domains/metric_based_domain.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/config/exceptions.h" +#include "core/model/types/imetrizable_type.h" + +namespace pac::model { +using namespace ::model; + +void MetricBasedDomain::ThrowIfEmpty() const { + if (metrizable_types_.empty()) { + throw std::runtime_error( + "Metric-based domain must be instantiated with types (SetTypes) before use"); + } +} + +double MetricBasedDomain::DistBetweenBytes(std::size_t type_num, std::byte const* x, + std::byte const* y) const { + if (x == nullptr || y == nullptr) { + return dist_from_null_is_infty_ ? std::numeric_limits::infinity() : 0; + } + return metrizable_types_[type_num]->Dist(x, y) * leveling_coeffs_[type_num]; +} + +std::vector MetricBasedDomain::AllocateValues( + std::vector const& str_values) const { + if (str_values.size() != metrizable_types_.size()) { + throw config::ConfigurationError( + "Metric-based domain must be initialized with the same number of values as the " + "number of columns used"); + } + + std::vector values(metrizable_types_.size()); + for (std::size_t i = 0; i < metrizable_types_.size(); ++i) { + auto const& type = metrizable_types_[i]; + auto* ptr = type->Allocate(); + type->ValueFromStr(ptr, str_values[i]); + values[i] = ptr; + } + return values; +} + +std::vector MetricBasedDomain::GetDestructors() const { + std::vector destructors; + destructors.reserve(metrizable_types_.size()); + std::ranges::transform(metrizable_types_, std::back_inserter(destructors), + std::mem_fn(&::model::Type::GetDestructor)); + return destructors; +} + +void MetricBasedDomain::SetTypes(std::vector&& types) { + metrizable_types_ = std::vector(types.size()); + for (std::size_t i = 0; i < types.size(); ++i) { + auto const* metrizable_type = dynamic_cast(types[i]); + if (!metrizable_type) { + throw config::ConfigurationError("Cannot use metric-based domain, because column #" + + std::to_string(i) + " has type " + + types[i]->ToString() + ", which is not metrizable"); + } + metrizable_types_[i] = metrizable_type; + } + + // All leveling coefficients that are not specified are 1 + leveling_coeffs_.insert(leveling_coeffs_.end(), types.size() - leveling_coeffs_.size(), 1); + + tuple_type_ = std::make_shared(std::move(types)); + ConvertValues(); +} + +double MetricBasedDomain::DistFromDomain(Tuple const& value) const { + assert(value.size() == metrizable_types_.size()); + ThrowIfEmpty(); + + return DistFromDomainInternal(value); +} +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/metric_based_domain.h b/src/core/algorithms/pac/model/default_domains/metric_based_domain.h new file mode 100644 index 0000000000..b50436f9a0 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/metric_based_domain.h @@ -0,0 +1,48 @@ +#pragma once + +#include +#include +#include +#include + +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/model/tuple.h" +#include "core/model/types/imetrizable_type.h" +#include "core/model/types/type.h" + +namespace pac::model { +/// @brief Utility base class for Domains based on IMetrizableType +class MetricBasedDomain : public IDomain { +private: + std::vector leveling_coeffs_; + + void ThrowIfEmpty() const; + +protected: + std::vector<::model::IMetrizableType const*> metrizable_types_ = {}; + + /// @brief Distance between two std::byte pointers of type metrizable_types[type_num]. + /// Consider using this function instead of direct calls to metrizable_types[i]->Dist, + /// because it takes into account @c dist_from_null_is_infiniy and @c leveling_coefficients. + double DistBetweenBytes(std::size_t type_num, std::byte const* x, std::byte const* y) const; + + virtual double DistFromDomainInternal(Tuple const& value) const = 0; + std::vector AllocateValues(std::vector const&) const; + + // MetricBasedDomain's destructor is called before derived classes' destructors, so value + // destructors have to be stored in derived classes + std::vector<::model::Type::Destructor> GetDestructors() const; + + /// @brief Convert all needed values after types are set + virtual void ConvertValues() {} + +public: + /// @param leveling_coefficients -- distances between individual coordinates (attributes) are + /// multiplied by these coefficients. + explicit MetricBasedDomain(std::vector&& leveling_coefficients = {}) + : leveling_coeffs_(std::move(leveling_coefficients)) {} + + virtual void SetTypes(std::vector<::model::Type const*>&& types) override; + virtual double DistFromDomain(Tuple const& value) const override; +}; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/parallelepiped.cpp b/src/core/algorithms/pac/model/default_domains/parallelepiped.cpp new file mode 100644 index 0000000000..8f3c576302 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/parallelepiped.cpp @@ -0,0 +1,71 @@ +#include "core/algorithms/pac/model/default_domains/parallelepiped.h" + +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/config/exceptions.h" +#include "core/model/types/builtin.h" + +namespace pac::model { +double Parallelepiped::DistFromDomainInternal(Tuple const& value) const { + // d(x, D) = min{rho(x, y)}, where y in D, rho is Chebyshev dist: + // rho(x, y) = max{|x[i] - y[i]|}, i = 1, ..., n + // For parallelepiped only points on bounds need to be considered, i. e. y in dD + // For each dimension bounds are first_[i] and last_[i] + // Distance from bounds on one dimension is min{|x[i] - first_[i]|, |x[i] - last_[i]|} + // Thus, distance becomes + // d(x, D) = max{min{|x[i] - first_[i]|, |x[i] - last_[i]|}}, i = 1, ..., n + double max_dist = 0; + for (std::size_t i = 0; i < metrizable_types_.size(); ++i) { + double one_dim_dist = 0; + if (tuple_type_->CompareBytes(i, value[i], first_[i]) == ::model::CompareResult::kLess) { + one_dim_dist = DistBetweenBytes(i, value[i], first_[i]); + } else if (tuple_type_->CompareBytes(i, last_[i], value[i]) == + ::model::CompareResult::kLess) { + one_dim_dist = DistBetweenBytes(i, value[i], last_[i]); + } + max_dist = std::max(max_dist, one_dim_dist); + } + return max_dist; +} + +void Parallelepiped::ConvertValues() { + first_ = AllocateValues(first_str_); + last_ = AllocateValues(last_str_); + destructors_ = GetDestructors(); + + // Check that first_ <= last_ + for (std::size_t i = 0; i < metrizable_types_.size(); ++i) { + if (tuple_type_->CompareBytes(i, first_[i], last_[i]) == ::model::CompareResult::kGreater) { + throw config::ConfigurationError( + "Lower bound of Parallelepiped must be less or equal than the upper bound."); + } + } +} + +Parallelepiped::~Parallelepiped() { + for (std::size_t i = 0; i < first_.size(); ++i) { + auto const& destroy = destructors_[i]; + if (destroy) { + destroy(first_[i]); + destroy(last_[i]); + } else { + delete[] first_[i]; + delete[] last_[i]; + } + } +} + +std::string Parallelepiped::ToString() const { + // tuple_type_->ValueToString() would be clearer, but domain isn't guaranteed to be instantiated + // with types by here. + std::ostringstream oss; + oss << '[' << StringValueToString(first_str_) << ", " << StringValueToString(last_str_) << ']'; + return oss.str(); +} +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/parallelepiped.h b/src/core/algorithms/pac/model/default_domains/parallelepiped.h new file mode 100644 index 0000000000..002e05577a --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/parallelepiped.h @@ -0,0 +1,47 @@ +#pragma once + +#include +#include +#include + +#include "core/algorithms/pac/model/default_domains/metric_based_domain.h" +#include "core/algorithms/pac/model/tuple.h" +#include "core/config/exceptions.h" + +namespace pac::model { +/// @brief Closed n-ary parallelepiped, defined by two corners. D = [@c first, @c last] = +/// [@c first[1], @c last[1]] x ... x [@c first[n], @c last[n]] +class Parallelepiped final : public MetricBasedDomain { +private: + Tuple first_; + std::vector first_str_; + Tuple last_; + std::vector last_str_; + std::vector<::model::Type::Destructor> destructors_; + +protected: + /// @brief Compare with Chebyshev distance + virtual double DistFromDomainInternal(Tuple const& value) const override; + virtual void ConvertValues() override; + +public: + Parallelepiped(std::vector&& first_str, std::vector&& last_str, + std::vector&& leveling_coefficients = {}) + : MetricBasedDomain(std::move(leveling_coefficients)), + first_str_(std::move(first_str)), + last_str_(std::move(last_str)) { + if (first_str_.size() != last_str_.size()) { + throw config::ConfigurationError( + "Lower and upper bounds of Parallelepiped must contain the same number of " + "values"); + } + } + + // 1D parallelepiped is an interval on a single column, so this syntactic sugar will be useful + Parallelepiped(std::string&& single_first_str, std::string&& single_last_str) + : first_str_{single_first_str}, last_str_{single_last_str} {} + + virtual ~Parallelepiped(); + virtual std::string ToString() const override; +}; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/default_domains/untyped_domain.h b/src/core/algorithms/pac/model/default_domains/untyped_domain.h new file mode 100644 index 0000000000..272eb63968 --- /dev/null +++ b/src/core/algorithms/pac/model/default_domains/untyped_domain.h @@ -0,0 +1,55 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" + +namespace pac::model { +using StringValues = std::vector; +using StringDistFromDomain = std::function; + +/// @brief Domain that uses string value representations instead of typed values. +/// Useful when original type information is not needed (e. g. values to some specific type) +/// or cannot be obtained (e. g. Python bindings) +class UntypedDomain final : public IDomain { +private: + StringDistFromDomain string_dist_; + std::string name_; + + StringValues ValuesToStrings(Tuple const& values) const { + StringValues result(values.size()); + for (std::size_t i = 0; i < values.size(); ++i) { + result[i] = tuple_type_->ByteToString(i, values[i]); + } + return result; + } + +public: + /// @param string_dist_from_domain -- function that calculates the distance from this domain to + /// its argument + /// @param name -- displayed name of this domain; should be short, but informative. + UntypedDomain(StringDistFromDomain&& string_dist_from_domain, + std::string&& name = "Untyped Domain") + : string_dist_(std::move(string_dist_from_domain)), name_(std::move(name)) {} + + virtual double DistFromDomain(Tuple const& value) const override { + return string_dist_(ValuesToStrings(value)); + } + + virtual std::string ToString() const override { + return name_; + } + + virtual void SetTypes(std::vector<::model::Type const*>&& types) override { + tuple_type_ = std::make_shared(std::move(types)); + } +}; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/idomain.h b/src/core/algorithms/pac/model/idomain.h new file mode 100644 index 0000000000..5863bfa155 --- /dev/null +++ b/src/core/algorithms/pac/model/idomain.h @@ -0,0 +1,41 @@ +#pragma once + +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/util/export.h" + +namespace pac::model { +/// @brief Ordered domain in a metric space of attribute values. +/// Despite the name, it can be a closed set. +/// @note that @c tuple_type must agree with Domain, i. e. +/// x in D iff exist a, b in D such that a <= x <= b +class DESBORDANTE_EXPORT IDomain { +protected: + std::shared_ptr tuple_type_; + bool dist_from_null_is_infty_ = false; + +public: + virtual ~IDomain() = default; + + /// @brief Distance from this Domain @c value. + /// Must be zero when value is in Domain + virtual double DistFromDomain(Tuple const& value) const = 0; + virtual std::string ToString() const = 0; + + // Types are set by algorithm after input table is read, so they cannot be set in constructor. + virtual void SetTypes(std::vector<::model::Type const*>&&) {} + + // Dist_from_null is algorithm parameter, so it cannot be set in constructor. + void SetDistFromNullIsInfinity(bool value) { + dist_from_null_is_infty_ = value; + } + + std::shared_ptr GetTupleTypePtr() const { + return tuple_type_; + } +}; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/tuple.h b/src/core/algorithms/pac/model/tuple.h new file mode 100644 index 0000000000..aea3718328 --- /dev/null +++ b/src/core/algorithms/pac/model/tuple.h @@ -0,0 +1,9 @@ +#pragma once + +#include +#include + +namespace pac::model { +using Tuple = std::vector; +using Tuples = std::vector; +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/tuple_type.cpp b/src/core/algorithms/pac/model/tuple_type.cpp new file mode 100644 index 0000000000..f391f33471 --- /dev/null +++ b/src/core/algorithms/pac/model/tuple_type.cpp @@ -0,0 +1,59 @@ +#include "core/algorithms/pac/model/tuple_type.h" + +#include +#include +#include +#include +#include +#include + +#include "core/model/types/builtin.h" + +namespace pac::model { +::model::CompareResult TupleType::CompareBytes(std::size_t const type_num, std::byte const* x, + std::byte const* y) { + assert(type_num < types_.size()); + + if (x == y) { + return ::model::CompareResult::kEqual; + } + if (x == nullptr) { + return ::model::CompareResult::kLess; + } + if (y == nullptr) { + return ::model::CompareResult::kGreater; + } + return types_[type_num]->Compare(x, y); +} + +std::string TupleType::ValueToString(Tuple const& value) const { + assert(value.size() == types_.size()); + + if (types_.size() == 1) { + return ByteToString(0, value.front()); + } + std::ostringstream oss; + oss << '{'; + for (std::size_t i = 0; i < types_.size(); ++i) { + if (i > 0) { + oss << ", "; + } + oss << ByteToString(i, value[i]); + } + oss << '}'; + return oss.str(); +} + +std::string StringValueToString(std::vector const& strings) { + using namespace std::string_literals; + + if (strings.size() == 1) { + return strings.front(); + } + return std::accumulate(std::next(strings.begin()), strings.end(), "{"s + strings.front(), + [](std::string&& acc, std::string const& val) { + return std::move(acc) + ", "s + val; + }) + + "}"s; +} +} // namespace pac::model diff --git a/src/core/algorithms/pac/model/tuple_type.h b/src/core/algorithms/pac/model/tuple_type.h new file mode 100644 index 0000000000..0d64d11d86 --- /dev/null +++ b/src/core/algorithms/pac/model/tuple_type.h @@ -0,0 +1,49 @@ +#pragma once + +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/model/types/builtin.h" +#include "core/model/types/type.h" + +namespace pac::model { +/// @brief Provides operations for fixed-size tuples of values on a fixed set of attributes +class TupleType { +private: + std::vector<::model::Type const*> types_; + +public: + TupleType(std::vector<::model::Type const*>&& types) : types_(std::move(types)) {} + + /// @brief Convert @c std::byte* of type @c types[type_num] to string. + /// Consider using this function rather than directly calling @c types[type_num]->ValueToString, + /// because it correctly handles NULLs + std::string ByteToString(std::size_t type_num, std::byte const* value) const { + assert(type_num < types_.size()); + + if (value == nullptr) { + return "NULL"; + } + return types_[type_num]->ValueToString(value); + } + + /// @brief Compare two @c std::byte pointers of type @c types[type_num]. + /// Consider using this function rather than directly call @c types[type_num]->Compare, because + /// it correctly handles NULLs (NULL is considered less than any value). + ::model::CompareResult CompareBytes(std::size_t const type_num, std::byte const* x, + std::byte const* y); + + std::string ValueToString(Tuple const& value) const; + + std::vector<::model::Type const*> const& GetTypes() const { + return types_; + } +}; + +/// @brief Converts vector of strings of the same format as ComparableTupleType::ValueToString() +/// does +std::string StringValueToString(std::vector const& strings); +} // namespace pac::model diff --git a/src/core/algorithms/pac/pac.h b/src/core/algorithms/pac/pac.h new file mode 100644 index 0000000000..455aa5101c --- /dev/null +++ b/src/core/algorithms/pac/pac.h @@ -0,0 +1,90 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include "core/model/table/relational_schema.h" + +namespace model { + +/// @brief Probabilistic Approximate Constraints are dependencies that generalize exact +/// dependencies. Each PAC has two common parameters: list of epsilons and list of deltas (separate +/// for each attribute, or the same for all attributes). Epsilon is a measure of "proximity", which +/// shows how much PAC deviates from exact dependency. Delta is a probability at which approximate +/// dependency holds. +class PAC { +private: + std::shared_ptr rel_schema_; + std::vector epsilons_; + std::vector deltas_; + +public: + PAC() = default; + + PAC(std::shared_ptr rel_schema, double epsilon, double delta) + : rel_schema_(std::move(rel_schema)), epsilons_({epsilon}), deltas_({delta}) {} + + PAC(std::shared_ptr rel_schema, std::vector&& epsilons, + std::vector&& deltas) + : rel_schema_(std::move(rel_schema)), + epsilons_(std::move(epsilons)), + deltas_(std::move(deltas)) {} + + PAC(PAC const&) = default; + PAC(PAC&&) = default; + PAC& operator=(PAC const&) = default; + PAC& operator=(PAC&&) = default; + + virtual ~PAC() = default; + + virtual std::string ToShortString() const = 0; + virtual std::string ToLongString() const = 0; + + double GetEpsilon() const { + if (epsilons_.size() != 1) { + throw std::logic_error("Cannot get epsilon, because PAC has " + + std::to_string(epsilons_.size()) + " different epsilons"); + } + return epsilons_.front(); + } + + std::vector const& GetEpsilons() const { + return epsilons_; + } + + void SetEpsilon(double const new_value) { + SetEpsilons({new_value}); + } + + void SetEpsilons(std::vector&& new_values) { + epsilons_ = std::move(new_values); + } + + double GetDelta() const { + if (deltas_.size() != 1) { + throw std::logic_error("Cannot get delta, because PAC has " + + std::to_string(epsilons_.size()) + " different deltas"); + } + return deltas_.front(); + } + + std::vector const& GetDeltas() const { + return deltas_; + } + + void SetDelta(double const new_value) { + SetDeltas({new_value}); + } + + void SetDeltas(std::vector&& new_values) { + deltas_ = std::move(new_values); + } + + std::shared_ptr GetRelSchema() const { + return rel_schema_; + } +}; +} // namespace model diff --git a/src/core/algorithms/pac/pac_verifier/CMakeLists.txt b/src/core/algorithms/pac/pac_verifier/CMakeLists.txt new file mode 100644 index 0000000000..5107086b07 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/CMakeLists.txt @@ -0,0 +1,15 @@ +add_subdirectory(domain_pac_verifier) + +set(NAME pac.verifier) +desbordante_add_lib(NAME OBJECT) +target_sources( + ${NAME} + PRIVATE pac_verifier.cpp + PUBLIC FILE_SET HEADERS BASE_DIRS "${PROJECT_SOURCE_DIR}/src" +) +target_link_libraries( + ${NAME} + PUBLIC ${DESBORDANTE_PREFIX}::algos ${DESBORDANTE_PREFIX}::config + ${DESBORDANTE_PREFIX}::model::table + PRIVATE Boost::headers better-enums spdlog::spdlog_header_only +) diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/CMakeLists.txt b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/CMakeLists.txt new file mode 100644 index 0000000000..f0db4bc365 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/CMakeLists.txt @@ -0,0 +1,14 @@ +set(NAME pac.verifier.domain_pac_verifier) +desbordante_add_lib(NAME) +target_sources( + ${NAME} + PRIVATE domain_pac_verifier_base.cpp domain_pac_verifier_cli_adapter.cpp + PUBLIC FILE_SET HEADERS BASE_DIRS "${PROJECT_SOURCE_DIR}/src" +) +target_link_libraries( + ${NAME} + PUBLIC ${DESBORDANTE_PREFIX}::pac::model ${DESBORDANTE_PREFIX}::model::table + ${DESBORDANTE_PREFIX}::model::types ${DESBORDANTE_PREFIX}::config + PRIVATE ${DESBORDANTE_PREFIX}::pac::model::default_domains ${DESBORDANTE_PREFIX}::pac::verifier + ${DESBORDANTE_PREFIX}::util Boost::headers better-enums spdlog::spdlog_header_only +) diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h new file mode 100644 index 0000000000..9da16b2c25 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h @@ -0,0 +1,68 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/model/types/type.h" + +namespace algos::pac_verifier { +/// @brief Values that violate Domain PAC with given epsilon +class DomainPACHighlight { +private: + using Tuples = std::vector; + using TuplesIter = Tuples::iterator; + + std::shared_ptr tuple_type_; + std::shared_ptr original_value_tuples_; + std::vector highlighted_tuples_; + +public: + DomainPACHighlight(std::shared_ptr tuple_type, + std::shared_ptr original_value_tuples, + std::vector&& highlighted_tuples) + : tuple_type_(std::move(tuple_type)), + original_value_tuples_(std::move(original_value_tuples)), + highlighted_tuples_(std::move(highlighted_tuples)) {} + + /// @brief Get row numbers of highlighted values + std::vector GetRowNums() const { + std::vector indices(highlighted_tuples_.size()); + std::ranges::transform(highlighted_tuples_, indices.begin(), + [begin = original_value_tuples_->begin()](auto const it) { + return std::distance(begin, it); + }); + return indices; + } + + /// @brief Get @c Types of columns associated with this @c Highlight + std::vector const& GetTypes() const { + return tuple_type_->GetTypes(); + } + + /// @brief Get highlighted values as pointers to @c std::byte, that can be used with types (see + /// @c GetTypes()) + Tuples GetByteData() const { + Tuples tuples; + tuples.reserve(highlighted_tuples_.size()); + std::ranges::transform(highlighted_tuples_, std::back_inserter(tuples), + [](auto const it) { return *it; }); + return tuples; + } + + /// @brief Get highlighted values as strings + std::vector GetStringData() const { + std::vector strings; + strings.reserve(highlighted_tuples_.size()); + std::ranges::transform(highlighted_tuples_, std::back_inserter(strings), + [this](auto const it) { return tuple_type_->ValueToString(*it); }); + return strings; + } +}; +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier.h b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier.h new file mode 100644 index 0000000000..d35adc0039 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier.h @@ -0,0 +1,23 @@ +#pragma once + +#include + +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h" +#include "core/config/descriptions.h" +#include "core/config/names.h" +#include "core/config/option_using.h" + +namespace algos::pac_verifier { +/// @brief Domain Probabilistic Approximate Constraint verifier. +/// Takes pointer to IDomain as domain option. +/// This version is considered to be used in C++. +class DomainPACVerifier final : public DomainPACVerifierBase { +public: + DomainPACVerifier() : DomainPACVerifierBase() { + DESBORDANTE_OPTION_USING; + + RegisterOption(Option(&domain_, kDomain, kDDomain)); + MakeOptionsAvailable({kDomain}); + } +}; +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.cpp b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.cpp new file mode 100644 index 0000000000..9f2756f883 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.cpp @@ -0,0 +1,156 @@ +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/domain_pac.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h" +#include "core/algorithms/pac/pac_verifier/util/make_tuples.h" +#include "core/config/column_index/validate_index.h" +#include "core/util/bitset_utils.h" +#include "core/util/logger.h" + +namespace algos::pac_verifier { + +void DomainPACVerifierBase::ProcessPACTypeOptions() { + auto max_idx = *std::ranges::max_element(column_indices_); + config::ValidateIndex(max_idx, TypedRelation().GetNumColumns()); + + std::vector types(column_indices_.size()); + auto const& col_data = TypedRelation().GetColumnData(); + std::ranges::transform(column_indices_, types.begin(), [&col_data](auto const col_idx) { + return &col_data[col_idx].GetType(); + }); + + domain_->SetTypes(std::move(types)); + domain_->SetDistFromNullIsInfinity(DistFromNullIsInfty()); + tuple_type_ = domain_->GetTupleTypePtr(); +} + +void DomainPACVerifierBase::PreparePACTypeData() { + original_value_tuples_ = + pac::util::MakeTuples(TypedRelation().GetColumnData(), column_indices_); + + dists_from_domain_ = {}; + dists_from_domain_.reserve(original_value_tuples_->size()); + for (auto it = original_value_tuples_->begin(); it != original_value_tuples_->end(); ++it) { + dists_from_domain_.emplace_back(it, domain_->DistFromDomain(*it)); + } + std::ranges::sort(dists_from_domain_, {}, [](auto const& p) { return p.second; }); +} + +std::vector> DomainPACVerifierBase::FindEpsilons() const { + auto total_tuples_num = original_value_tuples_->size(); + // Tuples number needed to satisfy min_delta + std::size_t min_tuples_num = std::ceil(MinDelta() * total_tuples_num); + std::size_t tuples_step; + if (DeltaSteps() <= 1) { + tuples_step = total_tuples_num - min_tuples_num; + } else { + tuples_step = static_cast(total_tuples_num - min_tuples_num) / (DeltaSteps() - 1); + } + if (tuples_step == 0) { + // min_tuples ~ total_tuples => make a few iterations + tuples_step = 1; + } + LOG_TRACE("Min tuples num: {}, total tuples num: {}, tuples step: {}, delta steps: {}", + min_tuples_num, total_tuples_num, tuples_step, DeltaSteps()); + + auto end = domain_end_; + auto domain_size = std::distance(dists_from_domain_.begin(), end); + std::vector> result; + std::size_t curr_size = domain_size; + + result.emplace_back(0, static_cast(domain_size) / total_tuples_num); + + for (auto needed_tuples_num = min_tuples_num; needed_tuples_num <= total_tuples_num; + needed_tuples_num += tuples_step) { + if (needed_tuples_num <= curr_size) { + continue; + } + + // Find eps_i + auto need_to_add = needed_tuples_num - curr_size; + auto actually_add = + std::min(need_to_add, + static_cast(std::distance(end, dists_from_domain_.end()))); + std::advance(end, actually_add); + curr_size += actually_add; + auto eps_i = end == dists_from_domain_.begin() ? 0 : std::prev(end)->second; + LOG_TRACE("Eps for {} tuples: {}", needed_tuples_num, eps_i); + + // Refine delta_i + while (end != dists_from_domain_.end() && + end->second - eps_i < PACVerifier::kDistThreshold) { + std::advance(end, 1); + ++curr_size; + } + assert(curr_size == + static_cast(std::distance(dists_from_domain_.begin(), end))); + LOG_TRACE("Refined size: {}", curr_size); + auto delta_i = static_cast(curr_size) / total_tuples_num; + + result.emplace_back(eps_i, delta_i); + } + return result; +} + +void DomainPACVerifierBase::PACTypeExecuteInternal() { + std::ostringstream oss; + oss << '{'; + for (auto it = column_indices_.begin(); it != column_indices_.end(); ++it) { + if (it != column_indices_.begin()) { + oss << ", "; + } + oss << *it; + } + oss << '}'; + LOG_INFO("Verifying Domain PAC on columns {} with domain {}", oss.str(), domain_->ToString()); + + domain_end_ = std::ranges::partition_point( + dists_from_domain_, [](auto const& p) { return std::abs(p.second) < kDistThreshold; }); + + LOG_TRACE("Distances from domain:"); + for ([[maybe_unused]] auto const& [it, dist] : dists_from_domain_) { + LOG_TRACE("\t{}: {}", tuple_type_->ValueToString(*it), dist); + } + LOG_TRACE("Initial domain size: {}", std::distance(dists_from_domain_.cbegin(), domain_end_)); + + auto empirical_probabilities = FindEpsilons(); + auto [eps, delta] = FindEpsilonDelta(std::move(empirical_probabilities)); + + auto const schema = TypedRelation().GetSharedPtrSchema(); + MakePAC( + schema, eps, delta, domain_, + schema->GetVertical(util::IndicesToBitset(column_indices_, schema->GetNumColumns()))); + + LOG_INFO("Result: {}", GetPAC().ToLongString()); +} + +DomainPACHighlight DomainPACVerifierBase::GetHighlights(double eps_1, double eps_2) const { + if (eps_2 < 0) { + eps_2 = GetPAC().GetEpsilon(); + } + + if (eps_2 <= eps_1) { + return DomainPACHighlight{tuple_type_, original_value_tuples_, std::vector{}}; + } + + auto first_end = std::ranges::upper_bound(domain_end_, dists_from_domain_.end(), eps_1, {}, + [](auto const& p) { return p.second; }); + auto second_end = std::ranges::upper_bound(domain_end_, dists_from_domain_.end(), eps_2, {}, + [](auto const& p) { return p.second; }); + + std::vector highlighted_tuples; + highlighted_tuples.reserve(std::distance(first_end, second_end)); + std::ranges::transform(first_end, second_end, std::back_inserter(highlighted_tuples), + [](auto const& p) { return p.first; }); + return DomainPACHighlight{tuple_type_, original_value_tuples_, std::move(highlighted_tuples)}; +} +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h new file mode 100644 index 0000000000..c74f2dcf71 --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h @@ -0,0 +1,60 @@ +#pragma once + +#include +#include +#include + +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h" +#include "core/algorithms/pac/pac_verifier/pac_verifier.h" +#include "core/config/descriptions.h" +#include "core/config/indices/type.h" +#include "core/config/names.h" +#include "core/config/option_using.h" + +namespace algos::pac_verifier { +/// @brief Base class for Domain Probabilistic Approximate Constraints verifier. +/// Domain option is handled in concrete verifiers. +class DomainPACVerifierBase : public PACVerifier { +private: + using Tuples = std::vector; + using TuplesIter = Tuples::iterator; + using TuplesIteratorsIter = std::vector::const_iterator; + + config::IndicesType column_indices_; + + std::shared_ptr original_value_tuples_; + // Distances from domain to each value, sorted by distance + std::vector> dists_from_domain_; + std::shared_ptr tuple_type_; + // Iterator to after-last value that falls into domain itself (with eps = 0) + std::vector>::const_iterator domain_end_; + + /// @brief For each of @c delta_steps delta_i values in [@c min_delta, 1] find eps_i such that + /// Pr(eps < eps_i) >= delta_i + /// Also refine deltas, i. e. find max acceptable delta_i + /// @return (eps_i, refined delta_i) pairs + std::vector> FindEpsilons() const; + +protected: + std::shared_ptr domain_; + + virtual void ProcessPACTypeOptions() override; + virtual void PreparePACTypeData() override; + void PACTypeExecuteInternal() override; + +public: + DomainPACVerifierBase() : PACVerifier() { + DESBORDANTE_OPTION_USING; + + // config::IndicesOption cannot be used here, because it would require indices to be execute + // option, but this would make data preparation impossible + RegisterOption(Option(&column_indices_, kColumnIndices, kDColumnIndices)); + MakeOptionsAvailable({kColumnIndices}); + } + + DomainPACHighlight GetHighlights(double eps_1 = 0, double eps_2 = -1) const; +}; +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.cpp b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.cpp new file mode 100644 index 0000000000..eeb67c477c --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.cpp @@ -0,0 +1,76 @@ +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h" + +#include +#include +#include +#include + +#include "core/algorithms/pac/model/default_domains/ball.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/model/default_domains/parallelepiped.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h" +#include "core/config/descriptions.h" +#include "core/config/exceptions.h" +#include "core/config/names.h" +#include "core/config/option.h" +#include "core/config/option_using.h" + +namespace algos::pac_verifier { +using namespace pac::model; + +void DomainPACVerifierCLIAdapter::RegisterOptions() { + DESBORDANTE_OPTION_USING; + using namespace std::string_literals; + + auto make_opt_condition = [](DomainType needed_type, + std::vector&& options_to_activate) { + return std::make_pair( + [needed_type](DomainType const& d_type) { return d_type == needed_type; }, + std::move(options_to_activate)); + }; + + RegisterOption(Option(&domain_type_, kDomainType, kDDomainType) + .SetConditionalOpts({ + make_opt_condition(DomainType::ball, + {kLevelingCoeffs, kCenter, kRadius}), + make_opt_condition(DomainType::parallelepiped, + {kLevelingCoeffs, kFirst, kLast}), + }) + .SetValueCheck([](DomainType const d_type) { + if (d_type == +DomainType::custom_domain) { + throw config::ConfigurationError( + "Custom domain is not supported in CLI. Consider using " + "Python interface or C++ " + "library."); + } + })); + + // These options will be made available when domain type is selected + RegisterOption( + Option(&leveling_coeffs_, kLevelingCoeffs, kDLevelingCoeffs, std::vector{})); + RegisterOption(Option(¢er_str_, kCenter, kDCenter)); + RegisterOption(Option(&radius_, kRadius, kDPACRadius)); + RegisterOption(Option(&first_str_, kFirst, kDFirst)); + RegisterOption(Option(&last_str_, kLast, kDLast)); +} + +void DomainPACVerifierCLIAdapter::ProcessPACTypeOptions() { + using namespace pac::model; + + switch (domain_type_) { + case DomainType::ball: + domain_ = std::make_shared(std::move(center_str_), radius_, + std::move(leveling_coeffs_)); + break; + case DomainType::parallelepiped: + domain_ = std::make_shared(std::move(first_str_), std::move(last_str_), + std::move(leveling_coeffs_)); + break; + case DomainType::custom_domain: + __builtin_unreachable(); + break; + } + + algos::pac_verifier::DomainPACVerifierBase::ProcessPACTypeOptions(); +} +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h new file mode 100644 index 0000000000..a69585f85d --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h @@ -0,0 +1,36 @@ +#pragma once + +#include +#include + +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_base.h" +#include "core/config/names.h" + +namespace algos::pac_verifier { +/// @brief Adapts DomainPACVerifier options to simplify its usage in CLI +class DomainPACVerifierCLIAdapter final : public DomainPACVerifierBase { +private: + pac::model::DomainType domain_type_ = pac::model::DomainType::ball; + std::vector leveling_coeffs_; + // Ball options + std::vector center_str_; + double radius_; + // Parallelepiped options + std::vector first_str_; + std::vector last_str_; + + void RegisterOptions(); + +protected: + virtual void ProcessPACTypeOptions() override; + +public: + DomainPACVerifierCLIAdapter() : DomainPACVerifierBase() { + using namespace config::names; + + RegisterOptions(); + MakeOptionsAvailable({kDomainType}); + } +}; +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/pac_verifier.cpp b/src/core/algorithms/pac/pac_verifier/pac_verifier.cpp new file mode 100644 index 0000000000..02f73a3f8c --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/pac_verifier.cpp @@ -0,0 +1,197 @@ +#include "core/algorithms/pac/pac_verifier/pac_verifier.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/config/descriptions.h" +#include "core/config/exceptions.h" +#include "core/config/names.h" +#include "core/config/option_using.h" +#include "core/config/tabular_data/input_table/option.h" +#include "core/model/table/column_layout_typed_relation_data.h" +#include "core/util/logger.h" + +namespace algos::pac_verifier { +void PACVerifier::RegisterOptions() { + DESBORDANTE_OPTION_USING; + + RegisterOption(config::kTableOpt(&input_table_)); + RegisterOption(Option(&distance_from_null_is_infinity_, kDistFromNullIsInfinity, + kDDistFromNullIsInfinity, false)); + + RegisterOption(Option(&min_epsilon_, kMinEpsilon, kDMinEpsilon, -1.0)); + RegisterOption(Option(&max_epsilon_, kMaxEpsilon, kDMaxEpsilon, -1.0)); + RegisterOption(Option(&min_delta_, kMinDelta, kDMinDelta, -1.0).SetValueCheck([](double x) { + return x <= 1; + })); + RegisterOption(Option(&delta_steps_, kDeltaSteps, kDDeltaSteps, 0ul)); + RegisterOption(Option(&diagonal_threshold_, kDiagonalThreshold, kDDiagonalThreshold, + kDefaultDiagonalThreshold)); +} + +void PACVerifier::ProcessCommonExecuteOpts() { + if (min_delta_ < 0) { + if (min_epsilon_ >= 0 || max_epsilon_ >= 0) { + min_delta_ = 0; + } else { + min_delta_ = kDefaultMinDelta; + } + } + + if (delta_steps_ == 0) { + delta_steps_ = std::round((1 - min_delta_) * 1000); + } + + // Ignore min_delta if min_eps == max_eps ("validation") + if (min_epsilon_ >= 0 && min_epsilon_ == max_epsilon_) { + min_delta_ = 0; + } + + if (max_epsilon_ > 0 && max_epsilon_ < min_epsilon_) { + throw config::ConfigurationError("Min epsilon must be less or equal to max epsilon"); + } +} + +unsigned long long PACVerifier::ExecuteInternal() { + ProcessCommonExecuteOpts(); + + auto start = std::chrono::system_clock::now(); + PACTypeExecuteInternal(); + auto elapsed = std::chrono::duration_cast( + std::chrono::system_clock::now() - start) + .count(); + LOG_INFO("Validation took {}ms", elapsed); + return elapsed; +} + +void PACVerifier::LoadDataInternal() { + typed_relation_ = model::ColumnLayoutTypedRelationData::CreateFrom(*input_table_, true); + input_table_->Reset(); + if (typed_relation_->GetColumnData().empty()) { + throw std::runtime_error("Got an empty dataset: PAC validation is meaningless."); + } + + ProcessPACTypeOptions(); + PreparePACTypeData(); +} + +void PACVerifier::MakeExecuteOptsAvailable() { + using namespace config::names; + + MakeOptionsAvailable({kMinEpsilon, kMaxEpsilon, kMinDelta, kDeltaSteps, kDiagonalThreshold}); +} + +std::pair PACVerifier::FindEpsilonDelta( + std::vector>&& empirical_probabilities) const { + assert(!empirical_probabilities.empty()); + + LOG_TRACE("Empirical probabilities:"); + for ([[maybe_unused]] auto const& [eps, delta] : empirical_probabilities) { + LOG_TRACE("\t{}, {}", eps, delta); + } + + if (empirical_probabilities.size() == 1) { + return empirical_probabilities.front(); + } + + auto begin = empirical_probabilities.begin(); + auto end = empirical_probabilities.end(); + + // First pair is always (0, ??), others have their delta >= min_delta... + if (begin->first < kDistThreshold && std::next(begin)->second > min_delta_ - kDistThreshold) { + // ...but there is a corner case: ?? >= min_delta + if (begin->second < min_delta_ - kDistThreshold) { + std::advance(begin, 1); + } + } else { + // Be careful: ??) is a trigraph for ] + LOG_WARN( + "First two empirical probability pairs must be (0, xx) and (xx, {}), got ({}, {}) " + "and ({}, {})", + min_delta_, begin->first, begin->second, std::next(begin)->first, + std::next(begin)->second); + begin = std::ranges::lower_bound(begin, end, min_delta_, {}, + [](auto const& p) { return p.second; }); + // Empirical probabilities must always contain (??, 1) + assert(begin != end); + } + + if (max_epsilon_ >= 0) { + // Special case: max_eps and min_delta cannot be both satisfied. + // Return (??, min_delta) so that user can see that parameters are contradictory. + // NOTE: This should be checked before min_epsilon, because (??, min_epsilon) will always + // have its first <= max_epsilon + if (begin->first > max_epsilon_) { + LOG_TRACE( + "Max eps and min delta cannot be both satisfied. Taking pair with min delta."); + return *begin; + } + + // Take all values that have eps < max_eps + // New pair is not added, because it would be stripped out anyway + end = std::ranges::upper_bound( + begin, end, max_epsilon_, {}, + [](std::pair const& pair) { return pair.first; }); + assert(begin != end); + } + if (min_epsilon_ >= 0) { + // Take all values that have eps > min_eps, and add (min_eps, delta_{j - 1}) to beginning + // (where j is the index of the first "good" element) + begin = std::ranges::upper_bound( + begin, end, min_epsilon_, {}, + [](std::pair const& pair) { return pair.first; }); + // Don't add (min_eps, delta_{j - 1}) if j == 0, because delta_{-1} is less than min_delta + if (begin != empirical_probabilities.begin()) { + std::advance(begin, -1); + begin->first = min_epsilon_; + begin->second = std::max(begin->second, min_delta_); + } + + // If begin != empirical_probs.begin, a new pair is added. + // If begin == empirical_probs.begin, then begin != empirical_probs.end (because + // empirical_probs is not empty here) + assert(begin != end); + } + + // Due to the delta refinement, there may be several epsilons with near deltas + auto extra_pairs = std::ranges::unique( + begin, end, + [threshold{diagonal_threshold_}](double a, double b) { return b - a < threshold; }, + [](std::pair const& pair) { return pair.second; }); + // For convenience only + auto stripped_emp_prob = std::ranges::subrange(begin, extra_pairs.begin()); + + if (stripped_emp_prob.size() == 1) { + return stripped_emp_prob.front(); + } + + LOG_TRACE("Stripped empirical probabilities:"); + for ([[maybe_unused]] auto const& [eps, delta] : stripped_emp_prob) { + LOG_TRACE("\t{}, {}", eps, delta); + } + + double max_eps_diff = -1; + std::size_t best_eps_idx = 0; + for (std::size_t i = 0; i < stripped_emp_prob.size() - 1; ++i) { + auto eps_diff = stripped_emp_prob[i + 1].first - stripped_emp_prob[i].first; + // Take the least eps_i such that (eps_{i + 1} - eps_i) is maximal + if (eps_diff > max_eps_diff) { + max_eps_diff = eps_diff; + best_eps_idx = i; + } + } + + if (max_eps_diff < 0) { + return stripped_emp_prob.back(); + } + + return stripped_emp_prob[best_eps_idx]; +} +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/pac_verifier.h b/src/core/algorithms/pac/pac_verifier/pac_verifier.h new file mode 100644 index 0000000000..3e1f7575fa --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/pac_verifier.h @@ -0,0 +1,115 @@ +#pragma once + +#include +#include +#include +#include + +#include "core/algorithms/algorithm.h" +#include "core/algorithms/pac/pac.h" +#include "core/config/names.h" +#include "core/config/tabular_data/input_table/option.h" +#include "core/config/tabular_data/input_table_type.h" +#include "core/model/table/column_layout_typed_relation_data.h" + +namespace algos::pac_verifier { +/// @brief Base class for Probabilistic Approximate Constrains verifiers +class PACVerifier : public Algorithm { +private: + constexpr static double kDefaultMinDelta = 0.9; + // One extra value on table containing 10^5 rows + constexpr static double kDefaultDiagonalThreshold = 1e-5; + + config::InputTable input_table_; + bool distance_from_null_is_infinity_; + double min_epsilon_; + double max_epsilon_; + double min_delta_; + double diagonal_threshold_; + unsigned long delta_steps_; + + std::shared_ptr typed_relation_; + std::shared_ptr pac_; + + void RegisterOptions(); + + /// @brief Process execute options that are common for all PAC types. + void ProcessCommonExecuteOpts(); + + unsigned long long ExecuteInternal() final; + +protected: + // Threshold for floating-point comparison of distances + constexpr static double kDistThreshold = 1e-12; + + bool DistFromNullIsInfty() const { + return distance_from_null_is_infinity_; + } + + double MinDelta() const { + return min_delta_; + } + + std::size_t DeltaSteps() const { + return delta_steps_; + } + + model::ColumnLayoutTypedRelationData const& TypedRelation() const { + return *typed_relation_; + } + + template + void MakePAC(Args&&... args) { + pac_ = std::make_shared(std::forward(args)...); + } + + virtual void LoadDataInternal() override; + virtual void MakeExecuteOptsAvailable() override; + + /// @brief Process options for concrete PAC type + virtual void ProcessPACTypeOptions() {} + + /// @brief Prepare data for validating concrete PAC type. + /// Called after processing options. + virtual void PreparePACTypeData() {} + + /// @brief ExecuteInternal for concrete PAC types. + virtual void PACTypeExecuteInternal() = 0; + + /// @brief Find PAC's epsilon and delta using elbow method on ECDF defined by @c + /// empirical_probabilities (epsilon-delta pairs) + /// @return epsilon, delta + std::pair FindEpsilonDelta( + std::vector>&& empirical_probabilities) const; + + void ResetState() override { + pac_ = nullptr; + } + +public: + PACVerifier() : Algorithm({}) { + RegisterOptions(); + MakeOptionsAvailable({config::kTableOpt.GetName(), config::names::kDistFromNullIsInfinity}); + } + + virtual ~PACVerifier() = default; + + model::PAC const& GetPAC() const { + if (!pac_) { + throw std::runtime_error("Cannot get PAC: it's nullptr"); + } + return *pac_; + } + + model::PAC& GetPAC() { + if (!pac_) { + throw std::runtime_error("Cannot get PAC: it's nullptr"); + } + return *pac_; + } + + std::shared_ptr GetPACPtr() { + return pac_; + } +}; +} // namespace algos::pac_verifier diff --git a/src/core/algorithms/pac/pac_verifier/util/make_tuples.h b/src/core/algorithms/pac/pac_verifier/util/make_tuples.h new file mode 100644 index 0000000000..fc4f0b337a --- /dev/null +++ b/src/core/algorithms/pac/pac_verifier/util/make_tuples.h @@ -0,0 +1,32 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/model/tuple.h" +#include "core/config/indices/type.h" +#include "core/model/table/typed_column_data.h" + +namespace pac::util { +inline std::shared_ptr MakeTuples( + std::vector<::model::TypedColumnData> const& col_data, config::IndicesType const& indices) { + std::vector const*> columns_data; + columns_data.reserve(indices.size()); + std::ranges::transform( + indices, std::back_inserter(columns_data), + [&col_data](auto const col_idx) { return &col_data[col_idx].GetData(); }); + + auto num_rows = col_data.front().GetNumRows(); + auto tuples = std::make_shared(num_rows); + for (std::size_t row_idx = 0; row_idx < num_rows; ++row_idx) { + auto& tuple = (*tuples)[row_idx]; + tuple = model::Tuple(indices.size()); + std::ranges::transform(columns_data, tuple.begin(), + [row_idx](auto const* col_data) { return (*col_data)[row_idx]; }); + } + return tuples; +} +} // namespace pac::util diff --git a/src/core/config/descriptions.h b/src/core/config/descriptions.h index 2a3b769a6f..6f2a06b976 100644 --- a/src/core/config/descriptions.h +++ b/src/core/config/descriptions.h @@ -10,6 +10,7 @@ #include "core/algorithms/metric/enums.h" #include "core/algorithms/nar/des/enums.h" #include "core/algorithms/od/fastod/od_ordering.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" #include "core/util/enum_to_available_values.h" namespace config::descriptions { @@ -36,6 +37,8 @@ std::string const kDDifferentialStrategyString = util::EnumToAvailableValues(); std::string const kDODLeftOrdering = "Ordering of the left attribute of OC or OD to use\n" + util::EnumToAvailableValues(); +std::string const kDDomainTypeString = + "Domain type\n" + util::EnumToAvailableValues(); } // namespace details // Common @@ -189,16 +192,51 @@ auto const kDMetric = details::kDMetricString.c_str(); auto const kDAFDMetric = details::kDAFDMetricString.c_str(); constexpr auto kDRhsIndex = "RHS column index"; constexpr auto kDParameter = "metric FD parameter"; +auto const kDMetricAlgorithm = details::kDMetricAlgorithmString.c_str(); +constexpr auto kDQGramLength = "q-gram length for cosine metric"; +// Metric verifier, PAC constexpr auto kDDistFromNullIsInfinity = "specify whether distance from NULL value is infinity " "(if not, it is 0)"; -auto const kDMetricAlgorithm = details::kDMetricAlgorithmString.c_str(); -constexpr auto kDQGramLength = "q-gram length for cosine metric"; // ND constexpr auto kDNDWeight = "Weight of ND to verify (positive integer)"; +// PAC +constexpr auto kDCenter = "N-ary ball center."; +constexpr auto kDColumnIndices = "Column indices."; +constexpr auto kDDeltaSteps = + "Select how many delta values to check while verifying PAC. " + "0 has special meaning: make (1 - min_delta) * 1000 steps, i. e. 10 steps per cent. " + "Default is 0."; +constexpr auto kDDiagonalThreshold = + "Maximal k such that diagonal segment of ECDF with slope coefficient of k is considered " + "horizontal, i. e. maximal ratio m/N such that an addition of m values on a table " + "containing N rows is not considered a change (default is 1e-5)."; +constexpr auto kDDomain = "Ordered domain for Domain PAC. "; +constexpr auto kDDomainName = + "Optional name for custom domain. This name is displayed as a part of Domain PAC's string " + "reprsentation, so short unique names are preferrable."; +auto const kDDomainType = details::kDDomainTypeString.c_str(); +constexpr auto kDFirst = "Lower bound of n-ary parallelepiped."; +constexpr auto kDLast = "Upper bound of n-ary parallelepiped."; +constexpr auto kDLevelingCoeffs = + "Coefficients by which distances between individual coordinates are multiplied (for " + "domains based on coordinate-wise metrics). Default is [1, 1, ..., 1]."; +constexpr auto kDMaxEpsilon = + "Maximal value of epsilon, which shows how much values deviate from exact dependency " + "(default is +infinity)."; +constexpr auto kDMinEpsilon = + "Minimal value of epsilon, which shows how much values deviate from exact dependency " + "(default is 0)."; +constexpr auto kDMinDelta = + "Minimal value of delta, which is a probability at which values satisfy the dependency " + "(default is 0 if min_eps or max_eps is passed, 0.9 otherwise)."; +constexpr auto kDPACRadius = "Radius of n-ary ball."; +constexpr auto kDStringDistFromDomain = + "Custom metric for n-ary value tuples. Must return distance from domain to its argument."; // Pyro constexpr auto kDCustomRandom = - "seed for the custom random generator. Used for consistency of results across platforms."; + "seed for the custom random generator. Used for consistency of results across " + "platforms."; // Spider constexpr auto kDMemLimitMB = "memory limit im MBs"; // Split diff --git a/src/core/config/names.h b/src/core/config/names.h index d6aa0f6e61..7f2b355b55 100644 --- a/src/core/config/names.h +++ b/src/core/config/names.h @@ -95,11 +95,29 @@ constexpr auto kRightTable = "right_table"; constexpr auto kCsvConfigs = "csv_configs"; constexpr auto kTables = "tables"; // Metric verifier -constexpr auto kDistFromNullIsInfinity = "dist_from_null_is_infinity"; constexpr auto kMetric = "metric"; constexpr auto kMetricAlgorithm = "metric_algorithm"; constexpr auto kParameter = "parameter"; constexpr auto kQGramLength = "q"; +// Metric verifier, PAC +constexpr auto kDistFromNullIsInfinity = "dist_from_null_is_infinity"; +// PAC +constexpr auto kCenter = "center"; +constexpr auto kColumnIndices = "column_indices"; +constexpr auto kDeltaSteps = "delta_steps"; +constexpr auto kDiagonalThreshold = "diagonal_threshold"; +constexpr auto kDomain = "domain"; +constexpr auto kDomainName = "domain_name"; +constexpr auto kDomainType = "domain_type"; +constexpr auto kFirst = "lower_bound"; +constexpr auto kLast = "upper_bound"; +constexpr auto kLevelingCoeffs = "leveling_coefficients"; +constexpr auto kMinDelta = "min_delta"; +constexpr auto kMaxEpsilon = "max_epsilon"; +constexpr auto kMinEpsilon = "min_epsilon"; +constexpr auto kStringDistFromDomain = "dist_from_domain"; +// PAC, Typo miner +constexpr auto kRadius = "radius"; // Pyro constexpr auto kCustomRandom = "custom_random_seed"; // Spider @@ -115,7 +133,6 @@ constexpr auto kPfdErrorMeasure = "pfd_error_measure"; // Typo miner constexpr auto kApproximateAlgorithm = "approximate_algorithm"; constexpr auto kPreciseAlgorithm = "precise_algorithm"; -constexpr auto kRadius = "radius"; constexpr auto kRatio = "ratio"; // UCC verifier constexpr auto kUCCIndices = "ucc_indices"; diff --git a/src/core/model/types/type.h b/src/core/model/types/type.h index 47a250627e..67aed24031 100644 --- a/src/core/model/types/type.h +++ b/src/core/model/types/type.h @@ -13,7 +13,7 @@ class Type { TypeId type_id_; public: - using Destructor = std::function; + using Destructor = std::function; explicit Type(TypeId const type_id) noexcept : type_id_(type_id) {} diff --git a/src/python_bindings/CMakeLists.txt b/src/python_bindings/CMakeLists.txt index 87280afb73..13a836e1d9 100644 --- a/src/python_bindings/CMakeLists.txt +++ b/src/python_bindings/CMakeLists.txt @@ -239,6 +239,19 @@ desbordante_add_bind( Boost::headers ) +desbordante_add_bind( + pac + SRCS + pac/bind_pac.cpp + pac/bind_pac_verification.cpp + LIBS + Desbordante::pac::verifier::domain_pac_verifier + Desbordante::pac::verifier + better-enums + spdlog::spdlog_header_only + Boost::headers +) + desbordante_add_bind( pfd SRCS diff --git a/src/python_bindings/bindings.cpp b/src/python_bindings/bindings.cpp index 9dad0540d4..f3355a98d1 100644 --- a/src/python_bindings/bindings.cpp +++ b/src/python_bindings/bindings.cpp @@ -29,6 +29,8 @@ #include "python_bindings/nd/bind_nd_verification.h" #include "python_bindings/od/bind_od.h" #include "python_bindings/od/bind_od_verification.h" +#include "python_bindings/pac/bind_pac.h" +#include "python_bindings/pac/bind_pac_verification.h" #include "python_bindings/pfd/bind_pfd_verification.h" #include "python_bindings/py_util/logging.h" #include "python_bindings/sfd/bind_sfd.h" @@ -70,7 +72,9 @@ PYBIND11_MODULE(desbordante, module, pybind11::mod_gil_not_used()) { BindGfd, BindCFDVerification, BindDDVerification, - BindAODVerification}) { + BindAODVerification, + BindPAC, + BindPACVerification}) { bind_func(module); } } diff --git a/src/python_bindings/pac/bind_pac.cpp b/src/python_bindings/pac/bind_pac.cpp new file mode 100644 index 0000000000..c0cb2000e7 --- /dev/null +++ b/src/python_bindings/pac/bind_pac.cpp @@ -0,0 +1,122 @@ +#include "python_bindings/pac/bind_pac.h" + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include "core/algorithms/pac/domain_pac.h" +#include "core/algorithms/pac/model/default_domains/ball.h" +#include "core/algorithms/pac/model/default_domains/parallelepiped.h" +#include "core/algorithms/pac/model/default_domains/untyped_domain.h" +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/pac.h" + +namespace py = pybind11; + +namespace { +/// @brief Convert Domain PAC to Python tuple, which first element is string representation of +/// Domain, then follow column indices. +/// Epsilon and delta are not included in tuple, because there's no proper way to hash doubles +py::tuple DomainPACToTuple(model::DomainPAC const& d_pac) { + auto const column_indices = d_pac.GetColumns().GetColumnIndicesAsVector(); + py::tuple result(column_indices.size() + 1); + // It's unlikely that set of Domain PACs will contain PACs on different domains, so using + // Domain's string representation shouldn't lead to a lot of collisions. + result[0] = d_pac.GetDomain().ToString(); + // Cannot use std::copy, because py::tuple provides only const iterators + for (std::size_t i = 0; i < column_indices.size(); ++i) { + result[i] = column_indices[i]; + } + return result; +} +} // namespace + +namespace python_bindings { +void BindPAC(py::module& main_module) { + using namespace model; + using namespace pac::model; + using namespace pybind11::literals; + + constexpr static double kEpsilon = 1e-12; + + auto pac_module = main_module.def_submodule("pac"); + + // PACs + py::class_(pac_module, "PAC") + .def("to_short_string", &PAC::ToShortString) + .def("to_long_string", &PAC::ToLongString) + .def("__str__", &PAC::ToLongString); + + // Domain PACs cannot be pickled, because they contain user-defined metrics + py::class_(pac_module, "DomainPAC") + .def_property_readonly("epsilon", &PAC::GetEpsilon) + .def_property_readonly("delta", &PAC::GetDelta) + .def_property_readonly("domain", &DomainPAC::GetDomain, + pybind11::return_value_policy::reference) + .def_property_readonly("column_indices", + [](model::DomainPAC const& d_pac) { + return d_pac.GetColumns().GetColumnIndicesAsVector(); + }) + .def_property_readonly("column_names", &model::DomainPAC::GetColumnNames) + .def("__eq__", + [](DomainPAC const& a, DomainPAC const& b) { + return a.GetDomain().ToString() == b.GetDomain().ToString() && + a.GetColumns() == b.GetColumns() && + std::abs(a.GetEpsilon() - b.GetEpsilon()) < kEpsilon && + std::abs(a.GetDelta() - b.GetDelta()) < kEpsilon; + }) + .def("__hash__", + [](DomainPAC const& d_pac) { return py::hash(DomainPACToTuple(d_pac)); }); + + // Domains + auto domains_module = pac_module.def_submodule("domains"); + py::class_>(domains_module, "IDomain") + .def("__str__", &IDomain::ToString) + .doc() = + "Abstract base class for domains. Cannot be used directly.\n" + "Currently available domain types: Ball, Parallelepiped, CustomDomain.\n"; + py::class_>(domains_module, "Ball") + .def(py::init, double, std::vector>(), "center"_a, + "radius"_a, "leveling_coefficients"_a = std::vector{}) + .doc() = + "A closed n-ary ball, defined by center and radius, i. e. D = {x : dist(center, x) <= " + "radius}.\n" + "Uses Euclidean distance:\n" + "dist(x, y) = sqrt((d_1(x[1], y[1]))^2 + ... + (d_n(x[n], y[n]))^2), where " + "d_i is a default\n" + "metric for i-th column's data type multiplied by leveling_coefficients[i] (which " + "defaults to 1).\n"; + py::class_>(domains_module, + "Parallelepiped") + .def(py::init, std::vector, + std::vector>(), + "lower_bound"_a, "upper_bound"_a, + "leveling_coefficients"_a = std::vector{}) + .def(py::init(), "lower_bound"_a, "upper_bound"_a) + .doc() = + "A closed n-ary parallelepiped, defined by lower and upper bounds, i. e.\n" + "D = [lower[1], upper[1]] x ... x [lower[n], upper[n]].\n" + "Uses Chebyshev distance: dist(x, y) = max{d_1(x[1], y[1]), ..., d_n(x[n], y[n])}, " + "where d_i is a default\n" + "metric for i-th column's data type multiplied by leveling_coefficients[i] (which " + "defaults to 1).\n"; + py::class_>(domains_module, + "CustomDomain") + .def(py::init const&)>, std::string>(), + "dist_from_domain"_a, "name"_a = "Custom domain") + .doc() = + "Custom domain, defined by metric:\n" + "\tdist_from_domain: must return distance from domain to a value, passed as list of " + "strings.\n" + "Second (optional) argument is domain name, which is used to make Domain PAC's string " + "representation.\n"; +} +} // namespace python_bindings diff --git a/src/python_bindings/pac/bind_pac.h b/src/python_bindings/pac/bind_pac.h new file mode 100644 index 0000000000..0d50488551 --- /dev/null +++ b/src/python_bindings/pac/bind_pac.h @@ -0,0 +1,7 @@ +#pragma once + +#include + +namespace python_bindings { +void BindPAC(pybind11::module_& main_module); +} // namespace python_bindings diff --git a/src/python_bindings/pac/bind_pac_verification.cpp b/src/python_bindings/pac/bind_pac_verification.cpp new file mode 100644 index 0000000000..e15ffaedd8 --- /dev/null +++ b/src/python_bindings/pac/bind_pac_verification.cpp @@ -0,0 +1,95 @@ +#include "python_bindings/pac/bind_pac_verification.h" + +#include +#include +#include +#include +#include + +#include +#include +#include + +#include "core/algorithms/algorithm.h" +#include "core/algorithms/pac/domain_pac.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_highlight.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h" +#include "core/algorithms/pac/pac_verifier/pac_verifier.h" +#include "python_bindings/py_util/bind_primitive.h" + +namespace py = pybind11; + +namespace python_bindings { +/// @brief Register concrete PAC verifier. +/// Inheritance cannot be used for this purpose, because abstract PAC cannot be copied (and PAC +/// *must* be copied out from algorithm, because algorithm can be executed again and overwrite PAC) +template +auto BindPACVerifier(py::module_& algos_module, auto&& name) { + // BindPrimitiveNoBase cannot be used here, because it cannot bind different methods for + // different classes. + return detail::RegisterAlgorithm(algos_module, + std::move(name)) + .def( + "get_pac", + [](VerifierT const& ver) { return dynamic_cast(ver.GetPAC()); }, + py::return_value_policy::copy); +} + +void BindPACVerification(py::module_& main_module) { + using namespace algos::pac_verifier; + using namespace model; + using namespace std::string_literals; + using namespace pybind11::literals; + + auto pac_verification_module = main_module.def_submodule("pac_verification"); + + auto algos_module = pac_verification_module.def_submodule("algorithms"); + py::class_(algos_module, "PACVerifier") + // Just define interface. It will be overwritten in derived classes. + .def("get_pac", []() { + throw py::attribute_error{"Cannot call get_pac on abstract class PACVerifier"}; + }); + auto cli_module = algos_module.def_submodule("cli"); + + BindDomainPACVerification(pac_verification_module, algos_module, cli_module); +} + +void BindDomainPACVerification(py::module_& pac_verification_module, py::module_& algos_module, + py::module_& cli_module) { + using namespace algos::pac_verifier; + using namespace pybind11::literals; + using namespace std::string_literals; + + py::class_(pac_verification_module, "DomainPACHighlight") + .def_property_readonly("indices", &DomainPACHighlight::GetRowNums) + .def_property_readonly("string_data", &DomainPACHighlight::GetStringData) + .def("__str__", [](DomainPACHighlight const& hl) -> std::string { + auto strings = hl.GetStringData(); + if (strings.empty()) { + return ""; + } + return "["s + + std::accumulate(std::next(strings.begin()), strings.end(), strings.front(), + [](std::string&& a, std::string const& b) { + return std::move(a) + ", " + b; + }) + + "]"s; + }); + + auto domain_pac_verifier = + BindPACVerifier(algos_module, "DomainPACVerifier") + .def("get_highlights", &DomainPACVerifier::GetHighlights, "eps_1"_a = -1, + "eps_2"_a = -1); + algos_module.attr("Default") = domain_pac_verifier; + + auto domain_pac_verifier_cli = + detail::RegisterAlgorithm( + cli_module, "DomainPACVerifierCLI"); + domain_pac_verifier_cli.doc() = + "NOTE: This algorithm is a wrapper around DomainPACVerifer with a restricted set of " + "options, which should be used only in CLI.\n" + "Consider using desbordante.pac_verification.algorithms.DomainPACVerifer in Python.\n" + + domain_pac_verifier_cli.doc().cast(); +} +} // namespace python_bindings diff --git a/src/python_bindings/pac/bind_pac_verification.h b/src/python_bindings/pac/bind_pac_verification.h new file mode 100644 index 0000000000..8be8814527 --- /dev/null +++ b/src/python_bindings/pac/bind_pac_verification.h @@ -0,0 +1,9 @@ +#pragma once + +#include + +namespace python_bindings { +void BindPACVerification(pybind11::module_& main_module); +void BindDomainPACVerification(pybind11::module_& pac_verification_module, + pybind11::module_& algos_module, pybind11::module_& cli_module); +} // namespace python_bindings diff --git a/src/python_bindings/py_util/get_py_type.cpp b/src/python_bindings/py_util/get_py_type.cpp index 7b9b9f4064..4cce400333 100644 --- a/src/python_bindings/py_util/get_py_type.cpp +++ b/src/python_bindings/py_util/get_py_type.cpp @@ -6,6 +6,7 @@ #include #include +#include #include #include "core/algorithms/association_rules/ar_algorithm_enums.h" @@ -16,6 +17,8 @@ #include "core/algorithms/md/md_verifier/column_similarity_classifier.h" #include "core/algorithms/metric/enums.h" #include "core/algorithms/od/fastod/od_ordering.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/model/idomain.h" #include "core/config/custom_random_seed/type.h" #include "core/config/error_measure/type.h" #include "core/config/tabular_data/input_table_type.h" @@ -116,6 +119,11 @@ py::tuple GetPyType(std::type_index type_index) { PyTypePair, kPyList, kPyStr>, PyTypePair, kPySet, kPyInt>, PyTypePair, + PyTypePair, kPyList, kPyStr>, + PyTypePair, kPyList, kPyFloat>, + PyTypePair, + {typeid(std::shared_ptr), + []() { return MakeTypeTuple(py::type::of()); }}, }; return type_map.at(type_index)(); } diff --git a/src/python_bindings/py_util/opt_to_py.cpp b/src/python_bindings/py_util/opt_to_py.cpp index 5873b31d18..8b595d54ba 100644 --- a/src/python_bindings/py_util/opt_to_py.cpp +++ b/src/python_bindings/py_util/opt_to_py.cpp @@ -11,6 +11,8 @@ #include "core/algorithms/md/hymd/enums.h" #include "core/algorithms/metric/enums.h" #include "core/algorithms/od/fastod/od_ordering.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/model/idomain.h" #include "core/config/custom_random_seed/type.h" #include "core/config/equal_nulls/type.h" #include "core/config/error/type.h" @@ -37,17 +39,23 @@ std::unordered_map const kConverters{ normal_conv_pair, normal_conv_pair, normal_conv_pair, + normal_conv_pair, normal_conv_pair, normal_conv_pair, normal_conv_pair, normal_conv_pair, normal_conv_pair, normal_conv_pair, + normal_conv_pair>, + normal_conv_pair>, + normal_conv_pair>, enum_conv_pair, enum_conv_pair, enum_conv_pair, enum_conv_pair, - enum_conv_pair}; + enum_conv_pair, + enum_conv_pair, +}; } // namespace namespace python_bindings { diff --git a/src/python_bindings/py_util/py_to_any.cpp b/src/python_bindings/py_util/py_to_any.cpp index c144d1ee79..f720443e67 100644 --- a/src/python_bindings/py_util/py_to_any.cpp +++ b/src/python_bindings/py_util/py_to_any.cpp @@ -16,6 +16,8 @@ #include "core/algorithms/md/md_verifier/column_similarity_classifier.h" #include "core/algorithms/metric/enums.h" #include "core/algorithms/od/fastod/od_ordering.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/model/idomain.h" #include "core/config/custom_random_seed/type.h" #include "core/config/error_measure/type.h" #include "core/config/exceptions.h" @@ -146,7 +148,11 @@ std::unordered_map const kConverters{ kNormalConvPair, kNormalConvPair, kNormalConvPair>>, - kNormalConvPair>}; + kNormalConvPair>, + kEnumConvPair, + kNormalConvPair>, + kNormalConvPair>, +}; } // namespace diff --git a/src/python_bindings/test_bindings.py b/src/python_bindings/test_bindings.py index 29de865b89..4500586f24 100644 --- a/src/python_bindings/test_bindings.py +++ b/src/python_bindings/test_bindings.py @@ -122,6 +122,33 @@ def check_metric_verifier_failure(dataset, options) -> bool: }, ), ]), + (desb.pac_verification.algorithms.DomainPACVerifier, [ + OptionContainer( + "TestLong.csv", + { + "column_indices": [0, 1], + "domain": desb.pac.domains.Parallelepiped(["0", "0"], ["5", "5"], [1, 1.2]), + }, + { + "min_epsilon": 0, + "max_epsilon": 7, + "min_delta": 0.8, + "diagonal_threshold": 1e-10, + }, + ), + OptionContainer( + "TestLong.csv", + { + "column_indices": [1], + "domain": desb.pac.domains.CustomDomain( + lambda val: int(val[0]) < 7, "[-\\infty, 7]"), + }, + { + "max_epsilon": 7, + "min_delta": 0.8, + }, + ) + ]), ] METRIC_VERIFIER_FAILURE_CASES = [ diff --git a/src/tests/common/all_csv_configs.cpp b/src/tests/common/all_csv_configs.cpp index c70a6855e5..26416ae670 100644 --- a/src/tests/common/all_csv_configs.cpp +++ b/src/tests/common/all_csv_configs.cpp @@ -25,6 +25,7 @@ CSVConfig const kCIPublicHighway10k = CreateCsvConfig("CIPublicHighway10k.csv", CSVConfig const kCIPublicHighway20attr55k = CreateCsvConfig("CIPublicHighway20attr55k.csv", ',', true); CSVConfig const kCIPublicHighway700 = CreateCsvConfig("CIPublicHighway700.csv", ',', true); +CSVConfig const kCustomMetricBalls = CreateCsvConfig("pac_data/custom_metric_balls.csv", ',', true); CSVConfig const kEpicMeds = CreateCsvConfig("EpicMeds.csv", '|', true); CSVConfig const kEpicVitals = CreateCsvConfig("EpicVitals.csv", '|', true); CSVConfig const kFdReduced30 = CreateCsvConfig("fd-reduced-30.csv", ',', true); @@ -46,6 +47,7 @@ CSVConfig const kIris = CreateCsvConfig("iris.csv", ',', false); CSVConfig const kLegacyPayors = CreateCsvConfig("LegacyPayors.csv", '|', true); CSVConfig const kLetter = CreateCsvConfig("letter.csv", ',', false); CSVConfig const kLineItem = CreateCsvConfig("LineItem.csv", '|', true); +CSVConfig const kMixedWithNulls = CreateCsvConfig("MixedWithNulls.csv", ',', true); CSVConfig const kMushroom = CreateCsvConfig("cfd_data/mushroom.csv", ',', true); CSVConfig const kMushroomPlus2attr1500 = CreateCsvConfig("mushroom+2attr1500.csv", ',', true); CSVConfig const kMushroomPlus3attr1300 = CreateCsvConfig("mushroom+3attr1300.csv", ',', true); diff --git a/src/tests/common/all_csv_configs.h b/src/tests/common/all_csv_configs.h index 26b58ed1b8..c508b45407 100644 --- a/src/tests/common/all_csv_configs.h +++ b/src/tests/common/all_csv_configs.h @@ -14,6 +14,7 @@ extern CSVConfig const kCIPublicHighway; extern CSVConfig const kCIPublicHighway10k; extern CSVConfig const kCIPublicHighway20attr55k; extern CSVConfig const kCIPublicHighway700; +extern CSVConfig const kCustomMetricBalls; extern CSVConfig const kEpicMeds; extern CSVConfig const kEpicVitals; extern CSVConfig const kFdReduced30; @@ -36,6 +37,7 @@ extern CSVConfig const kIris; extern CSVConfig const kLegacyPayors; extern CSVConfig const kLetter; extern CSVConfig const kLineItem; +extern CSVConfig const kMixedWithNulls; extern CSVConfig const kMushroom; extern CSVConfig const kMushroomPlus2attr1500; extern CSVConfig const kMushroomPlus3attr1300; diff --git a/src/tests/unit/CMakeLists.txt b/src/tests/unit/CMakeLists.txt index 79b1a507c2..e184d45a4b 100644 --- a/src/tests/unit/CMakeLists.txt +++ b/src/tests/unit/CMakeLists.txt @@ -554,6 +554,21 @@ desbordante_add_test( ${DESBORDANTE_PREFIX}::model::types ) +# --- PAC --- +desbordante_add_test( + pac.domain_pac_verifier + SRCS + test_domain_pac_verifier.cpp + LIBS + ${DESBORDANTE_PREFIX}::pac::verifier::domain_pac_verifier + ${DESBORDANTE_PREFIX}::testlib::common + spdlog::spdlog_header_only + better-enums + gmock + Boost::headers + ${DESBORDANTE_PREFIX}::model::types +) + # --- UCC --- desbordante_add_test( ucc.algos diff --git a/src/tests/unit/test_domain_pac_verifier.cpp b/src/tests/unit/test_domain_pac_verifier.cpp new file mode 100644 index 0000000000..53791674f2 --- /dev/null +++ b/src/tests/unit/test_domain_pac_verifier.cpp @@ -0,0 +1,333 @@ +#include +#include +#include +#include + +#include +#include + +#include "core/algorithms/algo_factory.h" +#include "core/algorithms/pac/model/default_domains/ball.h" +#include "core/algorithms/pac/model/default_domains/domain_type.h" +#include "core/algorithms/pac/model/default_domains/parallelepiped.h" +#include "core/algorithms/pac/model/default_domains/untyped_domain.h" +#include "core/algorithms/pac/model/idomain.h" +#include "core/algorithms/pac/model/tuple.h" +#include "core/algorithms/pac/model/tuple_type.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier.h" +#include "core/algorithms/pac/pac_verifier/domain_pac_verifier/domain_pac_verifier_cli_adapter.h" +#include "core/config/indices/type.h" +#include "core/config/names.h" +#include "core/model/types/builtin.h" +#include "core/model/types/mixed_type.h" +#include "core/parser/csv_parser/csv_parser.h" +#include "tests/common/all_csv_configs.h" + +namespace tests { +using namespace config::names; +using namespace pac::model; + +// Threshold for algo results (epsilon and delta) +constexpr static auto kThreshold = 1e-3; + +struct DomainPACVerifyingParams { + algos::StdParamsMap params; + double exp_epsilon; + double exp_delta; + + DomainPACVerifyingParams(CSVConfig const& csv_config, config::IndicesType&& col_indices, + std::shared_ptr&& domain, double expected_epsilon, + double expected_delta, double min_delta = -1, double min_eps = -1, + double max_eps = -1, unsigned long delta_steps = 0, + bool dist_from_null_is_infinity = false, + double diagonal_threshold = 1e-5) + : params({{kCsvConfig, csv_config}, + {kColumnIndices, std::move(col_indices)}, + {kDomain, std::move(domain)}, + {kMinDelta, min_delta}, + {kMinEpsilon, min_eps}, + {kMaxEpsilon, max_eps}, + {kDeltaSteps, delta_steps}, + {kDistFromNullIsInfinity, dist_from_null_is_infinity}, + {kDiagonalThreshold, diagonal_threshold}}), + exp_epsilon(expected_epsilon), + exp_delta(expected_delta) {} +}; + +class TestDomainPACVerifier : public ::testing::TestWithParam {}; + +TEST_P(TestDomainPACVerifier, DefaultTest) { + auto const& p = GetParam(); + auto params_map = p.params; + auto verifier = + algos::CreateAndLoadAlgorithm(params_map); + verifier->Execute(); + + auto const& pac = verifier->GetPAC(); + EXPECT_NEAR(pac.GetEpsilon(), p.exp_epsilon, kThreshold); + EXPECT_NEAR(pac.GetDelta(), p.exp_delta, kThreshold); +} + +struct DomainPACVerifyingPythonParams { + algos::StdParamsMap params; + double exp_epsilon; + double exp_delta; + + DomainPACVerifyingPythonParams( + CSVConfig const& csv_config, config::IndicesType&& col_indices, double expected_epsilon, + double expected_delta, DomainType domain_type, + std::vector&& leveling_coeffs = {}, std::vector&& center = {}, + double radius = 0, std::vector&& first = {}, + std::vector&& last = {}, + StringDistFromDomain&& string_dist_from_domain = nullptr, + std::string&& domain_name = "", double min_delta = -1, unsigned long delta_steps = 0, + bool dist_from_domain_is_infty = false, double diagonal_threshold = 1e-5) + : params({{kCsvConfig, csv_config}, + {kColumnIndices, std::move(col_indices)}, + {kDomainType, domain_type}, + {kMinDelta, min_delta}, + {kDeltaSteps, delta_steps}, + {kDistFromNullIsInfinity, dist_from_domain_is_infty}, + {kDiagonalThreshold, diagonal_threshold}}), + exp_epsilon(expected_epsilon), + exp_delta(expected_delta) { + switch (domain_type) { + case pac::model::DomainType::ball: + params[kLevelingCoeffs] = std::move(leveling_coeffs); + params[kCenter] = std::move(center); + params[kRadius] = radius; + break; + case pac::model::DomainType::parallelepiped: + params[kLevelingCoeffs] = std::move(leveling_coeffs); + params[kFirst] = std::move(first); + params[kLast] = std::move(last); + break; + case pac::model::DomainType::custom_domain: + params[kStringDistFromDomain] = std::move(string_dist_from_domain); + params[kDomainName] = std::move(domain_name); + break; + } + } +}; + +class TestDomainPACVerifierPython + : public ::testing::TestWithParam {}; + +TEST_P(TestDomainPACVerifierPython, DefaultTest) { + auto const& p = GetParam(); + auto params_map = p.params; + auto verifier = algos::CreateAndLoadAlgorithm( + params_map); + verifier->Execute(); + + auto const& pac = verifier->GetPAC(); + EXPECT_NEAR(pac.GetEpsilon(), p.exp_epsilon, kThreshold); + EXPECT_NEAR(pac.GetDelta(), p.exp_delta, kThreshold); +} + +/// @brief Is single-column mixed-typed value a null +inline bool IsNull(Tuple const& value) { + auto const* real_value = model::MixedType::RetrieveValue(value[0]); + auto const type_id = model::MixedType::RetrieveTypeId(value[0]); + return type_id == +model::TypeId::kNull || real_value == nullptr; +} + +/// @brief x in D iff x is not null. +/// Works on a single column of mixed type. +class NotNullDomain final : public IDomain { +private: + model::MixedType mixed_type_; + +public: + NotNullDomain() : mixed_type_(true) { + tuple_type_ = std::make_shared(std::vector{&mixed_type_}); + } + + virtual double DistFromDomain(Tuple const& value) const override { + return IsNull(value) ? 1 : 0; + } + + virtual std::string ToString() const override { + return "{x : x is not null}"; + } +}; + +using Strings = std::vector; + +DomainPACVerifyingParams CustomMetricBallsIntervalsParams(double min_eps, double max_eps, + double expected_eps, + double expected_delta, + double min_delta = -1, + unsigned long delta_steps = 0) { + return {kCustomMetricBalls, + {0, 1}, + std::make_shared(Strings{"0", "0"}, 5), + expected_eps, + expected_delta, + min_delta, + min_eps, + max_eps, + delta_steps}; +} + +INSTANTIATE_TEST_SUITE_P( + DomainPACVerifierTests, TestDomainPACVerifier, + ::testing::Values( + // -- "Refinement" -- + // (finding optimal eps, delta) + // A simple test on 1D-array + // #0 + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared("0", "5"), 0, 0.9, 0.8), + // A simple test on 1D-array, but values don't fall into domain + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared("5", "7"), 4, 1), + // A ball in R^2 with center (0, 0) and radius 5 + DomainPACVerifyingParams(kCustomMetricBalls, {0, 1}, + std::make_shared(Strings{"0", "0"}, 5), 6.217, 0.999, + 0.7), + // A square-shaped domain in R^2 with corners (-5, -5) and (5, 5) + DomainPACVerifyingParams(kCustomMetricBalls, {2, 3}, + std::make_shared(Strings{"-5", "-5"}, + Strings{"5", "5"}), + 4.379, 0.999, 0.7), + // Check not-metrizable type: + // a. only not-null values + DomainPACVerifyingParams(kMixedWithNulls, {0}, std::make_shared(), 0, + 0.8, 0.7), + // b. all values + DomainPACVerifyingParams(kMixedWithNulls, {0}, std::make_shared(), 1, + 1, 0.9), + // Special cases: + // a. +\infty + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared("11", "11"), 8, 0.4, 0.3), + // b. -\infty + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared("0", "0"), 4, 0.9, 0.3), + // Test leveling coefficients + DomainPACVerifyingParams(kTestDC1, {1, 2}, + std::make_shared(Strings{"3500", "0.2"}, 1, + std::vector{1e-3, 10}), + 0.803, 0.8, 0.7), + // Test Untyped Domain + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared( + [](Strings const& value) { + auto val = std::stoi(value.front()); + if (val < 0) { + return -val; + } + if (val > 5) { + return val - 5; + } + return 0; + }, + "[\"0\", \"5\"]"), + 0, 0.9, 0.8), + // -- Paramethrized "refinement" -- + // (finding optimal eps, delta on some segment of ECDF) + // Example from Comparison slides + // (https://github.com/p-senichenkov/Domain-PAC-validation-comparison/blob/main/slides/slides.pdf) + // #10 + CustomMetricBallsIntervalsParams(0, 1, 0.631, 0.625), + CustomMetricBallsIntervalsParams(1.2, 1.3, 1.225, 0.73), + CustomMetricBallsIntervalsParams(2, 5, 4.709, 0.991), + // (min_eps, ??) is considered a point too, and here it wins + DomainPACVerifyingParams(kSimpleTypos, {1}, + std::make_shared("5", "7"), 1, 0.5, 0.5, + 1), + // Min_delta is greater than delta for max_eps => should return (??, min_delta) + CustomMetricBallsIntervalsParams(0, 1, 2.60487, 0.9, 0.9), + // -- Validation -- + // (finding delta for the given eps) + // #15 + CustomMetricBallsIntervalsParams(1.5, 1.5, 1.5, 0.774), + // Min delta is greater than actual delta => should return actual delta + CustomMetricBallsIntervalsParams(1.5, 1.5, 1.5, 0.774, 0.9, 1000))); + +// Same, but Python versions +INSTANTIATE_TEST_SUITE_P( + DomainPACVeriferPythonTests, TestDomainPACVerifierPython, + ::testing::Values( + // 1D array + DomainPACVerifyingPythonParams(kSimpleTypos, {1}, 0, 0.9, + DomainType::parallelepiped, {}, {}, 0, {"0"}, {"5"}, + nullptr, "", 0.8), + // 2D ball + DomainPACVerifyingPythonParams(kCustomMetricBalls, {0, 1}, 6.217, 0.999, + DomainType::ball, {}, {"0", "0"}, 5, {}, {}, nullptr, + "", 0.7), + // 2D rectangle + DomainPACVerifyingPythonParams(kCustomMetricBalls, {2, 3}, 4.379, 0.999, + DomainType::parallelepiped, {}, {}, 0, {"-5", "-5"}, + {"5", "5"}, nullptr, "", 0.7))); + +using Epsilons = std::pair; +using HighlightValues = std::vector; + +struct DomainPACHighlightParams { + algos::StdParamsMap params; + std::map expected_highlights; + + DomainPACHighlightParams(CSVConfig const& csv_config, config::IndicesType&& col_indices, + std::shared_ptr&& domain, + std::map&& expected_highlights, + bool dist_from_null_is_infinity = false) + : params({{kCsvConfig, csv_config}, + {kColumnIndices, std::move(col_indices)}, + {kDomain, std::move(domain)}, + {kDistFromNullIsInfinity, dist_from_null_is_infinity}}), + expected_highlights(std::move(expected_highlights)) {} +}; + +class TestDomainPACHighlight : public ::testing::TestWithParam {}; + +TEST_P(TestDomainPACHighlight, DefaultTest) { + auto const& p = GetParam(); + auto algo = algos::CreateAndLoadAlgorithm(p.params); + algo->Execute(); + + for (auto const& [epsilons, expected_values] : p.expected_highlights) { + auto actual_highlight = algo->GetHighlights(epsilons.first, epsilons.second); + EXPECT_THAT(actual_highlight.GetStringData(), + ::testing::UnorderedElementsAreArray(expected_values)); + } +} + +INSTANTIATE_TEST_SUITE_P( + DomainPACVerifierHighlightsTests, TestDomainPACHighlight, + ::testing::Values( + // Some values fall into domain, highlighed values on both sides of domain + DomainPACHighlightParams(kTest1, {0}, std::make_shared("4", "6"), + {{{0.1, 1.1}, {"3", "7"}}, + {{1.1, 2.1}, {"2", "2", "2", "8"}}, + {{2.1, 3.1}, {"1", "1", "1", "1", "9"}}}, + true), + // No values fall into domain, highlighted values on both sides of domain + DomainPACHighlightParams(kSimpleTypos, {2}, + std::make_shared(Strings{"17"}, 4), + {{ + {0.1, 1.1}, + {"22", "22"}, + }, + {{1.1, 2.1}, {"11", "11", "11", "11"}}, + {{2.1, 5.1}, {"10"}}, + {{5.1, 25.1}, {"33", "34", "44"}}}), + // Custom domain + DomainPACHighlightParams(kSimpleTypos, {1}, + std::make_shared( + [](Strings const& value) { + auto val = std::stoi(value.front()); + if (val < 3) { + return 3 - val; + } + if (val > 8) { + return val - 8; + } + return 0; + }, + "[\"3\", \"8\"]"), + {{{0.1, 1.1}, {"2", "2"}}, + {{1.1, 2.1}, {"1", "1", "1", "1", "10"}}}))); +} // namespace tests diff --git a/test_input_data/MixedWithNulls.csv b/test_input_data/MixedWithNulls.csv new file mode 100644 index 0000000000..0d0eaa58f1 --- 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