fix: simplify processors

new_dataset_script.py

@@ -1,183 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
-}
-
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="first_domain",
-            version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-        datasets.BuilderConfig(
-            name="second_domain",
-            version=VERSION,
-            description="This part of my dataset covers a second domain",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if (
-            self.config.name == "first_domain"
-        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.jsonl"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.jsonl"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.jsonl"),
-                    "split": "test",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "first_domain":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option1": data["option1"],
-                        "answer": "" if split == "test" else data["answer"],
-                    }
-                else:
-                    yield key, {
-                        "sentence": data["sentence"],
-                        "option2": data["option2"],
-                        "second_domain_answer": (
-                            "" if split == "test" else data["second_domain_answer"]
-                        ),
-                    }

processors/days_on_market.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -329,8 +328,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/for_sale_listings.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -397,8 +396,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/helpers.py

@@ -2,6 +2,22 @@ import pandas as pd
 import os
 
 
+def coalesce_columns(
+    df,
+):
+    columns_to_coalesce = [col for col in df.columns if "_" not in col]
+    for index, row in df.iterrows():
+        for col in df.columns:
+            for column_to_coalesce in columns_to_coalesce:
+                if column_to_coalesce in col and "_" in col:
+                    if not pd.isna(row[col]):
+                        df.at[index, column_to_coalesce] = row[col]
+
+    # remove columns with underscores
+    combined_df = df[columns_to_coalesce]
+    return combined_df
+
+
 def get_combined_df(data_frames, on):
     combined_df = None
     if len(data_frames) > 1:
@@ -19,22 +35,8 @@ def get_combined_df(data_frames, on):
     elif len(data_frames) == 1:
         combined_df = data_frames[0]
 
-    return combined_df
-
+    combined_df = coalesce_columns(combined_df)
 
-def coalesce_columns(
-    df,
-):
-    columns_to_coalesce = [col for col in df.columns if "_" not in col]
-    for index, row in df.iterrows():
-        for col in df.columns:
-            for column_to_coalesce in columns_to_coalesce:
-                if column_to_coalesce in col and "_" in col:
-                    if not pd.isna(row[col]):
-                        df.at[index, column_to_coalesce] = row[col]
-
-    # remove columns with underscores
-    combined_df = df[columns_to_coalesce]
     return combined_df
 
 

processors/home_value_forecasts.ipynb

@@ -9,7 +9,7 @@
     "import pandas as pd\n",
     "import os\n",
     "\n",
-    "from helpers import get_combined_df, coalesce_columns, save_final_df_as_jsonl"
+    "from helpers import get_combined_df, save_final_df_as_jsonl"
    ]
   },
   {
@@ -414,8 +414,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/home_values.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -466,8 +465,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/new_construction.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -315,8 +314,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/rentals.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -438,8 +437,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },

processors/sales.ipynb

@@ -11,7 +11,6 @@
     "\n",
     "from helpers import (\n",
     "    get_combined_df,\n",
-    "    coalesce_columns,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
     ")"
@@ -525,8 +524,6 @@
     "    ],\n",
     ")\n",
     "\n",
-    "combined_df = coalesce_columns(combined_df)\n",
-    "\n",
     "combined_df"
    ]
   },