Delete durhamtrees.py

durhamtrees.py

@@ -1,256 +0,0 @@
-# -*- coding: utf-8 -*-
-"""DurhamTrees
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv
-"""
-
-# -*- coding: utf-8 -*-
-"""DurhamTrees
-Automatically generated by Colaboratory.
-Original file is located at
-    https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv
-"""
-import pyarrow.parquet as pq
-import pandas as pd
-import geopandas as gpd
-from datasets import (
-    GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split,
-    Features, Value, BuilderConfig, DatasetInfo
-)
-import matplotlib.pyplot as plt
-import seaborn as sns
-import csv
-import json
-from shapely.geometry import Point
-import base64
-
-# URL definitions
-_URLS = {
-    "first_domain1": {
-        "csv_file": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
-        "geojson_file": "https://drive.google.com/uc?export=download&id=1cbn7EY7RofXN7c6Ph0eIGFIZowPZ5lKE",
-        "parquet_file": "https://drive.google.com/uc?export=download&id=1RNDLJLoSSV9RJptVyfWFhPra0nh-i_CN",
-    },
-    "first_domain2": {
-        "csv_file2": "https://drive.google.com/uc?export=download&id=1RVdaI5dSTPStjhOHO40ypDv2cAQZpi_Y",
-    },
-}
-
-# Combined Dataset Class
-class DurhamTrees(GeneratorBasedBuilder):
-    VERSION = Version("1.0.0")
-
-    class MyConfig(BuilderConfig):
-        def __init__(self, **kwargs):
-            super().__init__(**kwargs)
-
-    BUILDER_CONFIGS = [
-        MyConfig(name="class1_domain1", description="this is combined of csv and geojson"),
-        MyConfig(name="class2_domain1", description="this is csv file"),
-    ]
-
-    def _info(self):
-        return DatasetInfo(
-            description="This dataset combines information from both classes, with additional processing for csv_file2.",
-            features=Features({
-                "feature1_from_class1": Value("string"),
-                "geometry":Value("string"),
-                "OBJECTID": Value("int64"),
-                "X": Value("float64"),
-                "Y": Value("float64"),
-                "image": Value("binary"),
-                "label": Value("int64"),
-                "feature1_from_class2": Value("string"),
-                "streetaddress": Value("string"),
-                "city": Value("string"),
-                "facilityid": Value("int64"),
-                "present": Value("string"),
-                "genus": Value("string"),
-                "species": Value("string"),
-                "commonname": Value("string"),
-                "diameterin": Value("float64"),
-                "condition": Value("string"),
-                "neighborhood": Value("string"),
-                "program": Value("string"),
-                "plantingw": Value("string"),
-                "plantingcond": Value("string"),
-                "underpwerlins": Value("string"),
-                "GlobalID": Value("string"),
-                "created_user": Value("string"),
-                "last_edited_user": Value("string"),
-                "isoprene": Value("float64"),
-                "monoterpene": Value("float64"),
-                "monoterpene_class2": Value("float64"),
-                "vocs": Value("float64"),
-                "coremoved_ozperyr": Value("float64"),
-                "coremoved_dolperyr": Value("float64"),
-                "o3removed_ozperyr": Value("float64"),
-                "o3removed_dolperyr": Value("float64"),
-                "no2removed_ozperyr": Value("float64"),
-                "no2removed_dolperyr": Value("float64"),
-                "so2removed_ozperyr": Value("float64"),
-                "so2removed_dolperyr": Value("float64"),
-                "pm10removed_ozperyr": Value("float64"),
-                "pm10removed_dolperyr": Value("float64"),
-                "pm25removed_ozperyr": Value("float64"),
-                "o2production_lbperyr": Value("float64"),
-                "replacevalue_dol": Value("float64"),
-                "carbonstorage_lb": Value("float64"),
-                "carbonstorage_dol": Value("float64"),
-                "grosscarseq_lbperyr": Value("float64"),
-                "grosscarseq_dolperyr": Value("float64"),
-                "avoidrunoff_ft2peryr": Value("float64"),
-                "avoidrunoff_dol2peryr": Value("float64"),
-                "polremoved_ozperyr": Value("float64"),
-                "polremoved_dolperyr": Value("float64"),
-                "totannbenefits_dolperyr": Value("float64"),
-                "leafarea_sqft": Value("float64"),
-                "potevapotran_cuftperyr": Value("float64"),
-                "evaporation_cuftperyr": Value("float64"),
-                "transpiration_cuftperyr": Value("float64"),
-                "h2ointercept_cuftperyr": Value("float64"),
-                "carbonavoid_lbperyr": Value("float64"),
-                "carbonavoid_dolperyr": Value("float64"),
-                "heating_mbtuperyr": Value("float64"),
-                "heating_dolperyrmbtu": Value("float64"),
-                "heating_kwhperyr": Value("float64"),
-                "heating_dolperyrmwh": Value("float64"),
-                "cooling_kwhperyr": Value("float64"),
-                "cooling_dolperyr": Value("float64"),
-                "totalenerg_dolperyr": Value("float64"),
-            }),
-            supervised_keys=("image", "label"),
-            homepage="https://github.com/AuraMa111?tab=repositories",
-            citation="Citation for the combined dataset",
-        )
-
-    def _split_generators(self, dl_manager):
-          downloaded_files = dl_manager.download_and_extract(_URLS)
-
-          return [
-              SplitGenerator(
-                  name=Split.TRAIN,
-                  gen_kwargs={
-                      "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
-                      "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
-                      "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
-                      "parquet_file": downloaded_files["first_domain1"]["parquet_file"],
-                      "split": Split.TRAIN,
-                  },
-              ),
-          ]
-
-
-
-
-    def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, parquet_file, split):
-        class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file))
-        class2_examples = list(self._generate_examples_from_class2(class2_data_file))
-
-        # Load Parquet file
-        parquet_data = pq.read_table(parquet_file).to_pandas()
-        class1_examples += list(self._generate_examples_from_parquet(parquet_data))
-
-        examples = class1_examples + class2_examples
-        df = pd.DataFrame(examples)
-
-        for id_, example in enumerate(examples):
-            if not isinstance(example, dict):
-                # Convert the example to a dictionary if it's not
-                example = {"example": example}
-            yield id_, example
-
-    def _generate_examples_from_class1(self, csv_filepath, geojson_filepath):
-          columns_to_extract = ["OBJECTID", "X", "Y"]  # Remove "geometry" from columns_to_extract
-          csv_data = pd.read_csv(csv_filepath)
-
-          with open(geojson_filepath, 'r') as file:
-              geojson_dict = json.load(file)
-          gdf = gpd.GeoDataFrame.from_features(geojson_dict['features'], crs="EPSG:4326")  # Specify the CRS if known
-          merged_data = gdf.merge(csv_data, on='OBJECTID')
-          final_data = merged_data[columns_to_extract + ['geometry']]  # Include 'geometry' in the final_data
-          for id_, row in final_data.iterrows():
-              example = row.to_dict()
-              yield id_, example
-
-
-
-
-
-    def _generate_examples_from_class2(self, csv_filepath2):
-        csv_data2 = pd.read_csv(csv_filepath2)
-
-
-        columns_to_extract = [
-            "streetaddress", "city", "facilityid", "present", "genus", "species",
-            "commonname", "diameterin", "condition",  "neighborhood", "program", "plantingw",
-            "plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene",
-             "monoterpene", "vocs", "coremoved_ozperyr", "coremoved_dolperyr",
-            "o3removed_ozperyr", "o3removed_dolperyr", "no2removed_ozperyr", "no2removed_dolperyr",
-            "so2removed_ozperyr", "so2removed_dolperyr", "pm10removed_ozperyr", "pm10removed_dolperyr",
-            "pm25removed_ozperyr", "o2production_lbperyr", "replacevalue_dol", "carbonstorage_lb",
-            "carbonstorage_dol", "grosscarseq_lbperyr", "grosscarseq_dolperyr", "polremoved_ozperyr", "polremoved_dolperyr",
-            "totannbenefits_dolperyr", "leafarea_sqft", "potevapotran_cuftperyr", "evaporation_cuftperyr",
-            "transpiration_cuftperyr", "h2ointercept_cuftperyr",
-             "carbonavoid_lbperyr", "carbonavoid_dolperyr", "heating_mbtuperyr",
-            "heating_dolperyrmbtu", "heating_kwhperyr", "heating_dolperyrmwh", "cooling_kwhperyr",
-            "cooling_dolperyr", "totalenerg_dolperyr",
-        ]
-
-        final_data = csv_data2[columns_to_extract]
-        for id_, row in final_data.iterrows():
-            example = row.to_dict()
-            non_empty_example = {key: value for key, value in example.items() if pd.notna(value)}
-            yield id_, example
-
-
-    def _generate_examples_from_parquet(self, parquet_data):
-        for id_, row in parquet_data.iterrows():
-            # Check if the "image" column is present and not empty
-            if "image" in row and "bytes" in row["image"]:
-                # Decode the base64-encoded image bytes
-                image_data = base64.b64decode(row["image"]["bytes"])
-                example = {"image": image_data, "label": row["label"]}
-
-                # Display the image
-                image_bytes = example.get('image', None)
-                if image_bytes:
-                    img = mpimg.imread(io.BytesIO(image_bytes), format='JPG')
-                    plt.imshow(img)
-                    plt.show()
-
-                yield id_, example
-            else:
-                print(f"Skipping example {id_} as it has missing or invalid image data")
-
-
-
-    def _correlation_analysis(self, df):
-        correlation_matrix = df.corr()
-        sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5)
-        plt.title("Correlation Analysis")
-        plt.show()
-
-
-
-
-# Create an instance of the DurhamTrees class
-durham_trees_dataset = DurhamTrees(name='class1_domain1')
-
-# Build the dataset
-durham_trees_dataset.download_and_prepare()
-
-# Access the dataset
-dataset = durham_trees_dataset.as_dataset()
-
-# Create an instance of the DurhamTrees class for another configuration
-durham_trees_dataset_another = DurhamTrees(name='class2_domain1')
-
-# Build the dataset for the new instance
-durham_trees_dataset_another.download_and_prepare()
-
-# Access the dataset for the new instance
-dataset_another = durham_trees_dataset_another.as_dataset()