Added PandasDataSource (facebookresearch#1098)

Summary:
Pull Request resolved: facebookresearch#1098

DataSource implementation which is a simple wrapper of Pandas DataFrame

Reviewed By: chenyangyu1988

Differential Revision: D18266156

fbshipit-source-id: 3a7bce83ec7aa07309074a44eb44d97dd613408a

Author: seayoung1112

docs_requirements.txt

@@ -10,3 +10,4 @@ pytorch-pretrained-bert
 requests
 torchtext
 tensorboard==1.14
+pandas

pytext/data/sources/__init__.py

@@ -2,8 +2,15 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 
 from .data_source import DataSource, RawExample
+from .pandas import PandasDataSource
 from .squad import SquadDataSource
 from .tsv import TSVDataSource
 
 
-__all__ = ["DataSource", "RawExample", "SquadDataSource", "TSVDataSource"]
+__all__ = [
+    "DataSource",
+    "RawExample",
+    "SquadDataSource",
+    "TSVDataSource",
+    "PandasDataSource",
+]

pytext/data/sources/pandas.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+from typing import Optional
+
+from pandas import DataFrame
+from pytext.data.sources.data_source import RootDataSource
+
+
+class PandasDataSource(RootDataSource):
+    """
+    DataSource which loads data from a pandas DataFrame.
+
+    Inputs:
+
+        train_df: DataFrame for training
+        eval_df: DataFrame for evalu
+        test_df: DataFrame for test
+        schema: same as base DataSource, define the list of output values with their types
+        column_mapping: maps the column names in DataFrame to the name defined in schema
+
+    Example:
+
+    ds = PandasDataSource(
+        train_df=pd.DataFrame({"c1": [10, 20, 30], "c2": [40, 50, 60]}),
+        eval_df=pd.DataFrame({"c1": [11, 21, 31], "c2": [41, 51, 61]}),
+        test_df=pd.DataFrame({"c1": [12, 22, 32], "c2": [42, 52, 62]}),
+        schema={"feature1": float, "feature2": float},
+        column_mapping={"c1": "feature1", "c2": "feature2"},
+    )
+
+    for row in ds.train:
+        print(row)
+
+    will print out:
+
+    {"feature1": 10, "feature2": 40}
+    {"feature1": 20, "feature2": 50}
+    {"feature1": 30, "feature2": 60}
+
+    """
+
+    def __init__(
+        self,
+        train_df: Optional[DataFrame] = None,
+        eval_df: Optional[DataFrame] = None,
+        test_df: Optional[DataFrame] = None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.train_df = train_df
+        self.eval_df = eval_df
+        self.test_df = test_df
+
+    @staticmethod
+    def raw_generator(df: Optional[DataFrame]):
+        if df is None:
+            yield from ()
+        else:
+            for _, row in df.iterrows():
+                yield row
+
+    def raw_train_data_generator(self):
+        return self.raw_generator(self.train_df)
+
+    def raw_eval_data_generator(self):
+        return self.raw_generator(self.eval_df)
+
+    def raw_test_data_generator(self):
+        return self.raw_generator(self.test_df)

pytext/data/test/pandas_data_source_test.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+import unittest
+
+import pandas as pd
+from pytext.data.sources import PandasDataSource
+
+
+class PandasDataSourceTest(unittest.TestCase):
+    def test_create_data_source(self):
+        ds = PandasDataSource(
+            train_df=pd.DataFrame({"c1": [10, 20, 30], "c2": [40, 50, 60]}),
+            eval_df=pd.DataFrame({"c1": [11, 21, 31], "c2": [41, 51, 61]}),
+            test_df=pd.DataFrame({"c1": [12, 22, 32], "c2": [42, 52, 62]}),
+            schema={"feature1": float, "feature2": float},
+            column_mapping={"c1": "feature1", "c2": "feature2"},
+        )
+        self.assertEqual({"feature1": 10, "feature2": 40}, next(iter(ds.train)))
+        self.assertEqual({"feature1": 11, "feature2": 41}, next(iter(ds.eval)))
+        self.assertEqual({"feature1": 12, "feature2": 42}, next(iter(ds.test)))
+        self.assertEqual(3, len(list(ds.train)))
+
+    def test_empty_data(self):
+        ds = PandasDataSource(schema={"feature1": float, "feature2": float})
+        self.assertEqual(0, len(list(ds.train)))