initial implementation for fluid interface check

refurb/checks/readability/fluid_interface.py

@@ -0,0 +1,107 @@
+from dataclasses import dataclass
+
+from mypy.nodes import (
+    Block,
+    Statement,
+    AssignmentStmt,
+    MypyFile,
+    CallExpr,
+    MemberExpr,
+    NameExpr,
+)
+
+from refurb.checks.common import check_block_like
+from refurb.error import Error
+
+
+@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:
+        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', F.lit('green'))
+        return df
+    ```
+
+    Good:
+
+    ```python
+    def get_tensors(device: str) -> torch.Tensor:
+        a = (
+            torch.ones(2, 1)
+            .long()
+            .to(device)
+        )
+        return a
+
+    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: Block | MypyFile, errors: list[Error]) -> None:
+    check_block_like(check_stmts, node, errors)
+
+
+def check_call(node) -> bool:
+    match node:
+        # Single chain
+        case CallExpr(callee=MemberExpr(expr=NameExpr(name=x), name=y)):
+            return True
+        # Nested
+        case CallExpr(callee=MemberExpr(expr=call_node, name=y)):
+            return check_call(call_node)
+
+    return False
+
+
+def check_stmts(stmts: list[Statement], errors: list[Error]) -> None:
+    last = ""
+
+    for stmt in stmts:
+        match stmt:
+            case AssignmentStmt(lvalues=[NameExpr(name=name)], rvalue=rvalue):
+                if last and f"{last}'" == name and check_call(rvalue):
+                    errors.append(
+                        ErrorInfo.from_node(
+                            stmt,
+                            f"Assignment statements should be chained",
+                        )
+                    )
+
+                last = name
+
+            case _:
+                last = ""

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()
+
+# 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"))
+
+
+# these will not
+def no_match():
+    y = 10
+    y = transform(y)
+    return y

test/data/err_184.txt

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+test/data/err_184.py:53:5 [FURB184]: Assignment statements should be chained
+test/data/err_184.py:54:5 [FURB184]: Assignment statements should be chained
+test/data/err_184.py:61:5 [FURB184]: Assignment statements should be chained
+test/data/err_184.py:64:5 [FURB184]: Assignment statements should be chained