Upload durhamtrees.py

durhamtrees.py

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+# -*- 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
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import io
+# 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='PNG')  # Use 'PNG' instead of '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()
+
+# Iterate through the dataset and display images
+for example in dataset['train']:
+    if "image" in example and example["image"] is not None and "bytes" in example["image"]:
+        # Display the image
+        image_data = base64.b64decode(example["image"]["bytes"])
+        img = mpimg.imread(io.BytesIO(image_data), format='PNG')
+        plt.imshow(img)
+        plt.show()
+
+# 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()
+
+# Iterate through the dataset for the new instance and display images
+for example in dataset_another['train']:
+    if "image" in example and example["image"] is not None and "bytes" in example["image"]:
+        # Display the image
+        image_data = base64.b64decode(example["image"]["bytes"])
+        img = mpimg.imread(io.BytesIO(image_data), format='PNG')
+        plt.imshow(img)
+        plt.show()