lit | Learning Interpretability Tool : Interactively analyze ML | Machine Learning library

Spark for some reason doesn't distinguish your c1 and c2 columns correctly. This is the fix for df3 to have your expected result:

First, it's good to note the difference between equivalence and equisatisfiability. In general, converting an arbitrary boolean formula (say, something in DNF) to CNF can result in a exponential blow-up in size.

This blow-up is the issue with your from_dnf approach: whenever you handle another product term, each of the literals in that product demands a new copy of the current cnf clause set (to which it will add itself in every clause). If you have n product terms of size k, the growth is O(k^n).

In your case n is actually a function of k!. What's kept as a product term is filtered to those satisfying the view constraint, but overall the runtime of your program is roughly in the region of O(k^f(k!)). Even if f grows logarithmically, this is still O(k^(k lg k)) and not quite ideal!

Because you're asking "is this satisfiable?", you don't need an equivalent formula but merely an equisatisfiable one. This is some new formula that is satisfiable if and only if the original is, but which might not be satisfied by the same assignments.

For example, (a ∨ b) and (a ∨ c) ∧ (¬b) are each obviously satisfiable, so they are equisatisfiable. But setting b true satisfies the first and falsifies the second, so they are not equivalent. Furthermore the first doesn't even have c as a variable, again making it not equivalent to the second.

This relaxation is enough to replace this exponential blow-up with a linear-sized translation instead.

The critical idea is the use of extension variables. These are fresh variables (i.e., not already present in the formula) that allow us to abbreviate expressions, so we don't end up making multiple copies of them in the translation. Since the new variable is not present in the original, we'll no longer have an equivalent formula; but because the variable will be true if and only if the expression is, it will be equisatisfiable.

If we wanted to use x as an abbreviation of y, we'd state x ≡ y. This is the same as x → y and y → x, which is the same as (¬x ∨ y) ∧ (¬y ∨ x), which is already in CNF.

Consider the abbreviation for a product term: x ≡ (a ∧ b). This is x → (a ∧ b) and (a ∧ b) → x, which works out to be three clauses: (¬x ∨ a) ∧ (¬x ∨ b) ∧ (¬a ∨ ¬b ∨ x). In general, abbreviating a product term of k literals with x will produce k binary clauses expressing that x implies each of them, and one (k+1)-clause expressing that all together they imply x. This is linear in k.

To really see why this helps, try converting (a ∧ b ∧ c) ∨ (d ∧ e ∧ f) ∨ (g ∧ h ∧ i) to an equivalent CNF with and without an extension variable for the first product term. Of course, we won't just stop with one term: if we abbreviate each term then the result is precisely a single CNF clause: (x ∨ y ∨ z) where these each abbreviate a single product term. This is a lot smaller!

This approach can be used to turn any circuit into an equisatisfiable formula, linear in size and in CNF. This is called a Tseitin transformation. Your DNF formula is simply a circuit composed of a bunch of arbitrary fan-in AND gates, all feeding into a single arbitrary fan-in OR gate.

Best of all, although this formula is not equivalent due to additional variables, we can recover an assignment for the original formula by simply dropping the extension variables. It is sort of a 'best case' equisatisfiable formula, being a strict superset of the original.

To patch this into your code, I added:

As it turns out, this behavior is not caused by @incremental. It can be observed in a regular transformation too:

Awk suites this better:

Use the instr function to determine whether the rust column contains _, and then use the when function to process.

1. current_timestamp() returns a TimestampType column, the value of which is evaluated at query time as described in the docs. So that is 'computed' each time your call show.

Returns the current timestamp at the start of query evaluation as a TimestampType column. All calls of current_timestamp within the same query return the same value.

1. Passing this column to a lit call doesn't change anything, if you check the source code you can see lit simply returns the column you called it with.

return col if isinstance(col, Column) else _invoke_function("lit", col)

1. If you cal lit with something else than a column, e.g. a datetime object then a new column is created with this literal value. The literal being the datetime object returned from datetime.now(). This is a static value representing the time the datetime.now function was called.

The documentation for weekofyear spark function has the answer:

Extracts the week number as an integer from a given date/timestamp/string. A week is considered to start on a Monday and week 1 is the first week with more than 3 days, as defined by ISO 8601.

It means that every year actually has 52 weeks plus n days, where n < 7. For that reason, to_date considers 53/2020 as an incorrect date and returns null. For the same reason, to_date considers 01/2020 as invalid date because 01/2020 is actually 53th week of 2019 year.

Evan You (Vite.js and Vue.js creator) has added the inline toggle which fixes the problem of styles also being added to the main CSS bundle when importing.

You can use conditional sum aggregation over a window ordered by sequence like this: