Initial implementation for fluent interface check

refurb/checks/readability/fluid_interface.py

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+from dataclasses import dataclass
+
+from mypy.nodes import (
+    AssignmentStmt,
+    CallExpr,
+    Expression,
+    FuncDef,
+    MemberExpr,
+    NameExpr,
+    ReturnStmt,
+    Statement,
+)
+
+from refurb.checks.common import ReadCountVisitor, check_block_like
+from refurb.error import Error
+from refurb.visitor import TraverserVisitor
+
+
+@dataclass
+class ErrorInfo(Error):
+    r"""
+    When an API has a Fluent Interface (the ability to chain multiple calls together), you should
+    chain those calls instead of repeatedly assigning and using the value.
+    Sometimes a return statement can be written more succinctly:
+
+    Bad:
+
+    ```python
+    def get_tensors(device: str) -> torch.Tensor:
+        t1 = torch.ones(2, 1)
+        t2 = t1.long()
+        t3 = t2.to(device)
+        return t3
+
+    def process(file_name: str):
+        common_columns = ["col1_renamed", "col2_renamed", "custom_col"]
+        df = spark.read.parquet(file_name)
+        df = df \
+            .withColumnRenamed('col1', 'col1_renamed') \
+            .withColumnRenamed('col2', 'col2_renamed')
+        df = df \
+            .select(common_columns) \
+            .withColumn('service_type', F.lit('green'))
+        return df
+    ```
+
+    Good:
+
+    ```python
+    def get_tensors(device: str) -> torch.Tensor:
+        t3 = (
+            torch.ones(2, 1)
+            .long()
+            .to(device)
+        )
+        return t3
+
+    def process(file_name: str):
+        common_columns = ["col1_renamed", "col2_renamed", "custom_col"]
+        df = (
+            spark.read.parquet(file_name)
+            .withColumnRenamed('col1', 'col1_renamed')
+            .withColumnRenamed('col2', 'col2_renamed')
+            .select(common_columns)
+            .withColumn('service_type', F.lit('green'))
+        )
+        return df
+    ```
+    """
+
+    name = "use-fluid-interface"
+    code = 184
+    categories = ("readability",)
+
+
+def check(node: FuncDef, errors: list[Error]) -> None:
+    check_block_like(check_stmts, node.body, errors)
+
+
+def check_call(node: Expression, name: str | None = None) -> bool:
+    match node:
+        # Single chain
+        case CallExpr(callee=MemberExpr(expr=NameExpr(name=x), name=_)):
+            if name is None or name == x:
+                # Exclude other references
+                x_expr = NameExpr(x)
+                x_expr.fullname = x
+                visitor = ReadCountVisitor(x_expr)
+                visitor.accept(node)
+                return visitor.read_count == 1
+            return False
+
+        # Nested
+        case CallExpr(callee=MemberExpr(expr=call_node, name=_)):
+            return check_call(call_node, name=name)
+
+    return False
+
+
+class NameReferenceVisitor(TraverserVisitor):
+    name: NameExpr
+    referenced: bool
+
+    def __init__(self, name: NameExpr, stmt: Statement | None = None) -> None:
+        super().__init__()
+        self.name = name
+        self.stmt = stmt
+        self.referenced = False
+
+    def visit_name_expr(self, node: NameExpr) -> None:
+        if not self.referenced and node.fullname == self.name.fullname:
+            self.referenced = True
+
+    @property
+    def was_referenced(self) -> bool:
+        return self.referenced
+
+
+def check_stmts(stmts: list[Statement], errors: list[Error]) -> None:
+    last = ""
+    visitors: list[NameReferenceVisitor] = []
+
+    for stmt in stmts:
+        for visitor in visitors:
+            visitor.accept(stmt)
+        # No need to track referenced variables anymore
+        visitors = [visitor for visitor in visitors if not visitor.referenced]
+
+        match stmt:
+            case AssignmentStmt(lvalues=[NameExpr(name=name)], rvalue=rvalue):
+                if last and check_call(rvalue, name=last):
+                    if f"{last}'" == name:
+                        errors.append(
+                            ErrorInfo.from_node(
+                                stmt,
+                                "Assignment statement should be chained",
+                            )
+                        )
+                    else:
+                        # We need to ensure that the variable is not referenced somewhere else
+                        name_expr = NameExpr(name=last)
+                        name_expr.fullname = last
+                        visitors.append(NameReferenceVisitor(name_expr, stmt))
+
+                last = name if name != "_" else ""
+            case ReturnStmt(expr=rvalue):
+                if last and rvalue is not None and check_call(rvalue, name=last):
+                    errors.append(
+                        ErrorInfo.from_node(
+                            stmt,
+                            "Return statement should be chained",
+                        )
+                    )
+            case _:
+                last = ""
+
+    # Ensure that variables are not referenced
+    errors.extend(
+        [
+            ErrorInfo.from_node(
+                visitor.stmt,
+                "Assignment statement should be chained",
+            )
+            for visitor in visitors
+            if not visitor.referenced and visitor.stmt is not None
+        ]
+    )

test/data/err_184.py

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+class torch:
+    @staticmethod
+    def ones(*args):
+        return torch
+
+    @staticmethod
+    def long():
+        return torch
+
+    @staticmethod
+    def to(device: str):
+        return torch.Tensor()
+
+    class Tensor:
+        pass
+
+
+def transform(x):
+    return x
+
+
+class spark:
+    class read:
+        @staticmethod
+        def parquet(file_name: str):
+            return spark.DataFrame()
+
+    class functions:
+        @staticmethod
+        def lit(constant):
+            return constant
+
+        @staticmethod
+        def col(col_name):
+            return col_name
+
+    class DataFrame:
+        @staticmethod
+        def withColumnRenamed(col_in, col_out):
+            return spark.DataFrame()
+
+        @staticmethod
+        def withColumn(col_in, col_out):
+            return spark.DataFrame()
+
+        @staticmethod
+        def select(*args):
+            return spark.DataFrame()
+
+class F:
+    @staticmethod
+    def lit(value):
+        return value
+
+
+# these will match
+def get_tensors(device: str) -> torch.Tensor:
+    a = torch.ones(2, 1)
+    a = a.long()
+    a = a.to(device)
+    return a
+
+
+def process(file_name: str):
+    common_columns = ["col1_renamed", "col2_renamed", "custom_col"]
+    df = spark.read.parquet(file_name)
+    df = df \
+        .withColumnRenamed('col1', 'col1_renamed') \
+        .withColumnRenamed('col2', 'col2_renamed')
+    df = df \
+        .select(common_columns) \
+        .withColumn('service_type', spark.functions.lit('green'))
+    return df
+
+
+def projection(df_in: spark.DataFrame) -> spark.DataFrame:
+    df = (
+        df_in.select(["col1", "col2"])
+        .withColumnRenamed("col1", "col1a")
+    )
+    return df.withColumn("col2a", spark.functions.col("col2").cast("date"))
+
+
+def assign_multiple(df):
+    df = df.select("column")
+    result_df = df.select("another_column")
+    final_df = result_df.withColumn("column2", F.lit("abc"))
+    return final_df
+
+
+# not yet supported
+def assign_alternating(df, df2):
+    df = df.select("column")
+    df2 = df2.select("another_column")
+    df = df.withColumn("column2", F.lit("abc"))
+    return df, df2
+
+
+# these will not
+def ignored(x):
+    _ = x.op1()
+    _ = _.op2()
+    return _
+
+def _(x):
+    y = x.m()
+    return y.operation(*[v for v in y])
+
+
+def assign_multiple_referenced(df, df2):
+    df = df.select("column")
+    result_df = df.select("another_column")
+    return df, result_df
+
+
+def invalid(df_in: spark.DataFrame, alternative_df: spark.DataFrame) -> spark.DataFrame:
+    df = (
+        df_in.select(["col1", "col2"])
+        .withColumnRenamed("col1", "col1a")
+    )
+    return alternative_df.withColumn("col2a", spark.functions.col("col2").cast("date"))
+
+
+def no_match():
+    y = 10
+    y = transform(y)
+    return y
+
+def f(x):
+    if x:
+        name = "alice"
+        stripped = name.strip()
+        print(stripped)
+    else:
+        name = "bob"
+    print(name)
+
+def g(x):
+    try:
+        name = "alice"
+        stripped = name.strip()
+        print(stripped)
+    except ValueError:
+        name = "bob"
+    print(name)
+
+def h(x):
+    for _ in (1, 2, 3):
+        name = "alice"
+        stripped = name.strip()
+        print(stripped)
+    else:
+        name = "bob"
+    print(name)
+
+def assign_multiple_try(df):
+    try:
+        df = df.select("column")
+        result_df = df.select("another_column")
+        final_df = result_df.withColumn("column2", F.lit("abc"))
+        return final_df
+    except ValueError:
+        return None

test/data/err_184.txt

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+test/data/err_184.py:59:5 [FURB184]: Assignment statement should be chained
+test/data/err_184.py:60:5 [FURB184]: Assignment statement should be chained
+test/data/err_184.py:67:5 [FURB184]: Assignment statement should be chained
+test/data/err_184.py:70:5 [FURB184]: Assignment statement should be chained
+test/data/err_184.py:81:5 [FURB184]: Return statement should be chained
+test/data/err_184.py:86:5 [FURB184]: Assignment statement should be chained
+test/data/err_184.py:87:5 [FURB184]: Assignment statement should be chained