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Minimum Substring Partition of Equal Character Frequency

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StringsGreedy Algorithms

Given a string s, you need to partition it into one or more balanced substrings. For example, if s == "ababcc" then ("abab", "c", "c"), ("ab", "abc", "c"), and ("ababcc") are all valid partitions, but ("a", "bab", "cc"), ("aba", "bc", "c"), and ("ab", "abcc") are not. The unbalanced substrings are bolded.

Return the minimum number of substrings that you can partition s into.

Note: A balanced string is a string where each character in the string occurs the same number of times.

Example 1:

Input: s = "fabccddg"

Output: 3

Explanation:

We can partition the string s into 3 substrings in one of the following ways: ("fab, "ccdd", "g"), or ("fabc", "cd", "dg").

Example 2:

Input: s = "abababaccddb"

Output: 2

Explanation:

We can partition the string s into 2 substrings like so: ("abab", "abaccddb").

Constraints:

  • 1 <= s.length <= 1000
  • s consists only of English lowercase letters.

Solution


Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

  1. What is the expected range of characters in the input string? Are we dealing with only lowercase English letters, or could it include uppercase letters, numbers, or other special characters?
  2. What should I return if the input string is empty or null? Is an empty array/list the correct response, or should I throw an exception?
  3. If there are multiple valid partitions resulting in the minimum number of substrings, is any one of them acceptable, or is there a specific criteria to choose among them?
  4. Could you define more formally what constitutes a 'substring partition'? Specifically, does each character need to appear exactly the same number of times across *all* substrings, or only within *each individual* substring?
  5. Is the input guaranteed to have at least one valid partition, or is it possible that there is no way to partition the string such that each substring has equal character frequency?

Brute Force Solution

Approach

The core idea is to try every single possible way to split the given string into smaller pieces. We check each split to see if it meets our criteria for having equal character frequencies in each piece.

Here's how the algorithm would work step-by-step:

  1. Start by considering a partition with just one piece, which is the entire input string.
  2. Next, try all possible ways to split the string into two pieces. For example, the first piece could be just the first character, and the second piece is the rest of the string. Then, the first piece is the first two characters, and the second piece is the remainder, and so on.
  3. Continue by trying all possible ways to split the string into three pieces, then four pieces, and so on, up to the length of the string (where each piece is just a single character).
  4. For each possible way of splitting the string, check if each piece has the same number of occurrences of each character.
  5. If a split satisfies the equal frequency requirement for every piece, save the number of pieces in that split.
  6. After checking every possible split, find the smallest number of pieces among all the valid splits. This smallest number is the answer.

Code Implementation

def minimum_substring_partition_equal_freq_brute_force(input_string):
    string_length = len(input_string)
    minimum_partitions = string_length + 1

    for number_of_partitions in range(1, string_length + 1):
        for partition_indices in find_all_partitions(string_length, number_of_partitions):
            is_valid_partition = True

            for partition_index in range(number_of_partitions):
                start_index = partition_indices[partition_index - 1] if partition_index > 0 else 0
                end_index = partition_indices[partition_index]
                substring = input_string[start_index:end_index]

                if not has_equal_character_frequency(substring):
                    is_valid_partition = False
                    break

            if is_valid_partition:
                minimum_partitions = min(minimum_partitions, number_of_partitions)

    return minimum_partitions if minimum_partitions <= string_length else -1

def find_all_partitions(string_length, number_of_partitions):
    if number_of_partitions == 1:
        yield [string_length]
        return

    if number_of_partitions == string_length:
        yield list(range(1, string_length + 1))
        return

    def generate_partitions(current_partition, remaining_partitions):
        if remaining_partitions == 1:
            yield current_partition + [string_length]
            return

        start_index = current_partition[-1] if current_partition else 0
        for index in range(start_index + 1, string_length - remaining_partitions + 2):
            yield from generate_partitions(current_partition + [index], remaining_partitions - 1)

    yield from generate_partitions([], number_of_partitions)

def has_equal_character_frequency(substring):
    if not substring:
        return True

    character_counts = {}
    for char in substring:
        character_counts[char] = character_counts.get(char, 0) + 1

    # If only one character in substring it is automatically true
    if len(character_counts) <= 1:
        return True

    first_frequency = list(character_counts.values())[0]

    # Check if all character frequencies are equal to first frequency
    for frequency in character_counts.values():
        if frequency != first_frequency:
            return False

    return True

# The core idea is to try every single possible way to split the given string
# We check each split to see if it meets our criteria for equal char frequencies
# Steps:
# 1. Start by considering a partition with just one piece
# 2. Next, try all possible ways to split the string into two pieces.
# 3. Continue by trying all possible ways to split the string into three pieces
# 4. For each possible way of splitting the string, check if each piece has the same number of occurrences of each character.
# 5. If a split satisfies the equal frequency requirement for every piece, save the number of pieces in that split.
# 6. After checking every possible split, find the smallest number of pieces among all the valid splits. This smallest number is the answer.

Big(O) Analysis

Time Complexity
O(n * 2^n)The algorithm explores all possible partitions of the input string of length n. Generating all possible partitions takes O(2^n) time because each character has a choice to either start a new partition or be part of the existing one. For each of these partitions, we need to check if the character frequencies are equal within each substring, which takes O(n) time in the worst case (iterating through each character in all substrings). Combining these factors, the overall time complexity is approximately O(n * 2^n).
Space Complexity
O(N)The algorithm explores all possible partitions of the string. To check the character frequencies of each substring within a partition, a frequency map (e.g., a dictionary or array) needs to be created for each substring. In the worst-case scenario where we have N single-character substrings, we might need to store up to N frequency maps, each potentially of constant size (depending on the character set size), but cumulatively contributing to O(N) space. The depth of the recursion stack could reach N, further contributing to O(N) auxiliary space. Thus, the auxiliary space complexity is O(N).

Optimal Solution

Approach

The goal is to split a string into the fewest parts possible, where each part has all its characters appearing the same number of times. We find the earliest possible valid cut point, and then repeat the process on the rest of the string. This avoids exploring unnecessary partitions.

Here's how the algorithm would work step-by-step:

  1. Start at the beginning of the string.
  2. Consider progressively larger sections of the string, one character at a time.
  3. Keep track of how many times each character appears in the section you are considering.
  4. Check if, in the current section, every character that *does* appear, appears the *same* number of times. If so, we've found a valid split.
  5. If you've found a valid split, cut the string at that point, and increase the number of parts you've found.
  6. Repeat the entire process, starting from the character immediately after your previous cut, until you have reached the end of the string.
  7. The total number of splits you've made is the minimum number of parts needed.

Code Implementation

def min_substring_partition(input_string):
    number_of_parts = 0
    start_index = 0
    string_length = len(input_string)

    while start_index < string_length:
        end_index = start_index
        character_counts = {}

        # This loop expands the current substring
        while end_index < string_length:
            current_char = input_string[end_index]
            character_counts[current_char] = character_counts.get(current_char, 0) + 1

            # Check if all chars have equal frequency
            frequencies = list(character_counts.values())

            if len(frequencies) > 0 and all(frequency == frequencies[0] for frequency in frequencies):
                # Found a valid split point
                number_of_parts += 1
                start_index = end_index + 1

                # Break to start a new substring from next char
                break

            end_index += 1

        # No valid split found from the start index.
        if end_index == string_length:
            number_of_parts += 1
            break

    return number_of_parts

Big(O) Analysis

Time Complexity
O(n²)The algorithm iterates through the string of length n, extending a substring one character at a time. For each starting position in the string, the algorithm potentially checks substrings up to the end of the string to find a valid partition point. This inner process of extending and checking frequency counts could, in the worst case, touch each of the remaining n characters from each starting point. As a result, the number of operations is proportional to n * (n + (n-1) + ... + 1) which simplifies to n * (n * (n+1)/2) which means roughly n * n/2. Thus, the time complexity is O(n²).
Space Complexity
O(1)The algorithm primarily uses a frequency counter to track the occurrences of each character in the current substring. Since the character set is limited (e.g., ASCII or Unicode), the frequency counter will have a maximum size independent of the input string length N. Other variables, such as loop counters and the number of parts, take up constant space. Therefore, the auxiliary space used remains constant regardless of the input size N.

Edge Cases

Empty string input
How to Handle:
Return an empty list as there are no substrings to partition.
String with a single character
How to Handle:
Return a list containing the single character string.
String where all characters are the same
How to Handle:
Return a list containing the entire string, as it already has equal frequency.
String where no partitioning is possible (e.g., 'aabbbcc')
How to Handle:
Return a list containing the original string, indicating that no valid partition exists.
Very long string (scalability)
How to Handle:
The solution should use an efficient algorithm (e.g., linear time complexity) to handle large input strings without exceeding memory limits.
String with only two distinct characters that do not divide evenly
How to Handle:
Return a list containing the original string because an equal partition cannot be made.
String with many possible valid partitions (multiple solutions)
How to Handle:
The algorithm should return any one of the valid partitions, not necessarily the shortest or longest.
String with Unicode characters
How to Handle:
The character frequency counting mechanism needs to correctly handle Unicode characters, possibly using a wider character type or appropriate encoding.