notebooks/How_To_Use_Datasets_From_HF.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 KB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 KB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 KB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qq datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When you want to access some dataset, lets say `human_nontata_promoters`, you just need to add a prefix `katarinagresova/Genomic_Benchmarks_` to get the path to Hugging Face dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "Kv2xxkMvhBUA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "a9ccdfd2cce24027a47b30cc1e13b088",
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            "649715cf3f5f48f8b20771d17db70357",
            "482668c256e244d7a8f9e4e987507c44",
            "f0dd985e75fc4f94b30cead6ed23ad4d",
            "988e9b059b0a4ca584f46d3c5dc2213f",
            "e34d55d81c614795b48a89776a44a5ef",
            "3d6f988c6a264846bc79a09c46ef3959",
            "d5c5b77351e64c10a9198a53463bfca4",
            "10a4a354cea7487f9b548b190d9eff9c"
          ]
        },
        "id": "fU_OOgoxhh3q",
        "outputId": "3bd406b5-e08a-4818-ec38-bc20379447cf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"katarinagresova/Genomic_Benchmarks_human_nontata_promoters\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265,
          "referenced_widgets": [
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            "53be243bb98e48c09fa41d7ecc3fa69a",
            "5e29f63de208495faf52730744f8be1f",
            "d766a73d780a415daf8b31cad3562d41",
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            "5dfeb6c78fbf462081a842c64e1a1b2b",
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            "279d7f4475144aeaae8415caeb24e038",
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            "b1cc37d06a634faa93e66925e9223738",
            "4de0e3c5dc9d4f9bb9bbdb1f0e6c91c9",
            "cefbcc12b4074cc1b08ba1ec42e19c85",
            "545446ec7ab24173b6cd88d7e33684b8",
            "602153db9f0d4850b86204ca4e5ad161",
            "d84d8695d8fd4db9a07e3a6f67c31a33",
            "bd9a7f09bff148479a182e6eab2163b1",
            "96b12728a4e64c528ea502d01e2d7e0d",
            "eb905f150f344c8a9ea35664d94724d7",
            "95f2dfd3b79e4c50b45f4a07938aad73",
            "f9e799c7719140d5b7de16e5af42a3b0",
            "bf50bedc6e244f2b96c68e18754124cd",
            "c6f639425d5a40dd91de797976c5872e",
            "04679ec400ef407a806f5bbec8d6886a",
            "176a3c6cc83a4c869ca8aaf8b6bc746b",
            "a98fa2659951470cb5c052cf6eae0011",
            "197fb8da79e041a8ab329b7500c9e512",
            "e3652564c3b9490c867be39b6fc79a84",
            "f9f6135cb56041e9b760dd8222314a31",
            "6f6fccb2332345c984cfad98f185bf4e"
          ]
        },
        "id": "jQzjHtA5gRWu",
        "outputId": "0d8cd872-b8f1-4e45-caa6-f22e219c01ae"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/472 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b24ad8612d74df7919f369ed4cdc740"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/katarinagresova___parquet/katarinagresova--Genomic_Benchmarks_human_nontata_promoters-9d8ebae779f9ff53/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae23df51fd634ce9b243953629a1f969"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1883f1e4cdff451e9315658b29a17d30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92a78df17609437cb6aafff9c1f69108"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20ad5884e8904d39a1a8b5e7d49f233c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/27097 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dfeb6c78fbf462081a842c64e1a1b2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/9034 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "279d7f4475144aeaae8415caeb24e038"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/katarinagresova___parquet/katarinagresova--Genomic_Benchmarks_human_nontata_promoters-9d8ebae779f9ff53/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95f2dfd3b79e4c50b45f4a07938aad73"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When loading the dataset, data are downloaded and stored in the huggingface cache, but the easiest way to work with them is trough `dataset` variable. It is of type `DatasetDict` (so something like a python dictionary) with splits as keys and data as values. "
      ],
      "metadata": {
        "id": "ifqFsqMxheCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVMJGSG9hdUt",
        "outputId": "39a2ffb5-4bdd-4c10-9b92-19d09986a9d4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['seq', 'label'],\n",
              "        num_rows: 27097\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['seq', 'label'],\n",
              "        num_rows: 9034\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dset = dataset[\"train\"]\n",
        "train_dset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRLf93E-iZzG",
        "outputId": "c72f765f-d921-4b18-e498-53b9cc5057be"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['seq', 'label'],\n",
              "    num_rows: 27097\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TensorFlow"
      ],
      "metadata": {
        "id": "olN9xA91iHg8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By default, datasets return regular Python objects: integers, floats, strings, lists, etc.\n",
        "\n",
        "To get TensorFlow tensors instead, you can set the format of the dataset to tf:"
      ],
      "metadata": {
        "id": "pL6wzJRkjKrt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds = train_dset.with_format(\"tf\")\n",
        "ds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOoPz3OJiI0w",
        "outputId": "ff51e2a2-b799-49ee-d732-5d8d46cd8c2b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'seq': <tf.Tensor: shape=(), dtype=string, numpy=b'CAAGGGTGTAGTGCCCTGAGGGTGGCAATAGTTCCTGAGGCCATAACTGTTCTGAGCCCTTGCTGGGTGCCAGGCACAGTGCTGCTAGTGCGCTCTGCAGAGCTGATCTCACAATAACTTTTGGAGGTGCAAATACTCTATCCAGTTTATGAATGAGGAAACTGAGGCACAAAGTGGCTCCATGACTTGCCTGAGTCCCCACAGCTAGTAAGGGATGCCAGCAGGCGTTGAACCTCAACCCTAGAGCCTGC'>,\n",
              " 'label': <tf.Tensor: shape=(), dtype=int64, numpy=0>}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although you can load individual samples and batches just by indexing into your dataset, this won’t work if you want to use Keras methods like fit() and predict(). You could write a generator function that shuffles and loads batches from your dataset and fit() on that, but that sounds like a lot of unnecessary work. Instead, if you want to stream data from your dataset on-the-fly, we recommend converting your dataset to a tf.data.Dataset using the to_tf_dataset() method."
      ],
      "metadata": {
        "id": "0RqQ0tXHjQbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf_ds = train_dset.to_tf_dataset(\n",
        "            columns=[\"seq\"],\n",
        "            label_cols=[\"label\"],\n",
        "            batch_size=2,\n",
        "            shuffle=True\n",
        "            )"
      ],
      "metadata": {
        "id": "BFP6cR9LiwQm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pytorch"
      ],
      "metadata": {
        "id": "c5vf48jRjaRE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To get PyTorch tensors instead, you can set the format of the dataset to pytorch using Dataset.with_format():"
      ],
      "metadata": {
        "id": "uKgSZ7d8kIJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ds = train_dset.with_format(\"torch\")\n",
        "ds[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJw24-J2jcJg",
        "outputId": "fdb5cf02-cdbf-4a60-a74f-9c50f89715f5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'seq': 'CAAGGGTGTAGTGCCCTGAGGGTGGCAATAGTTCCTGAGGCCATAACTGTTCTGAGCCCTTGCTGGGTGCCAGGCACAGTGCTGCTAGTGCGCTCTGCAGAGCTGATCTCACAATAACTTTTGGAGGTGCAAATACTCTATCCAGTTTATGAATGAGGAAACTGAGGCACAAAGTGGCTCCATGACTTGCCTGAGTCCCCACAGCTAGTAAGGGATGCCAGCAGGCGTTGAACCTCAACCCTAGAGCCTGC',\n",
              " 'label': tensor(0)}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like torch.utils.data.Dataset objects, a Dataset can be passed directly to a PyTorch DataLoader:"
      ],
      "metadata": {
        "id": "Srgaoekgj8Pd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "dataloader = DataLoader(ds, batch_size=4)\n",
        "for batch in dataloader:\n",
        "    print(batch)   \n",
        "    break                                                                                         "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awkPW0H8jlJm",
        "outputId": "a52d0f2d-11e6-42c4-bd94-c089329433e4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'seq': ['CAAGGGTGTAGTGCCCTGAGGGTGGCAATAGTTCCTGAGGCCATAACTGTTCTGAGCCCTTGCTGGGTGCCAGGCACAGTGCTGCTAGTGCGCTCTGCAGAGCTGATCTCACAATAACTTTTGGAGGTGCAAATACTCTATCCAGTTTATGAATGAGGAAACTGAGGCACAAAGTGGCTCCATGACTTGCCTGAGTCCCCACAGCTAGTAAGGGATGCCAGCAGGCGTTGAACCTCAACCCTAGAGCCTGC', 'TGCAGTTAGGAGGGCAGGCCAGGGAGGATCCCACAGTGGCCCAGGGGTTTGAGATTTGAGCAGCAAATAAGAGAAAATGTGTGGATCTGAAATGTAGAAAGACGGAGGATTGAACCTCAAGGGGAACAAGGTGGCTGACGTGAGTGGAACAGGAGTAAAGAAGGGGAGGTGAGGCTTGAACCGCGAGGTGCCATGTGGGGAGCTTATGCAGAGGCTGGGGCATCTCAGGATGCATACCCAAGATGTTCTTG', 'CCCCCAATTTATCCTAGCTCCTCGTAGGACCTGACCTCCTCTTTATTCTGATTATTCCATCTGGGTTTTGTTGTTTTCTTAAGAAAACAATTTTTTTTCCTACTTGGCTGGTCTAGTTTTTTGAGGGAGAGCCAATCTTTTATCAGCTGAACCAAAATAATAATGGCTTTGGTTGCTAACTTCTCTGTGTCATGTAGGACCTTGGTTTGCTGCCAAGGACTGGAGTAGAAAAAAGGGGAACGAGATGCAGG', 'CCCGATGCCATCGTGCTGGCCGAGGAGGCCCTGGACAAAGCCCAGGAAGTGCTGGAGTTCCACCAAAGCCTGGGGGCCTTGGTGGAGGGCACAGGGCACCTGCTGGAGGCCCACTATGCTCGGCCAGAGGTCGTGGGGCAGACCAGTGCCCTCCTGCGGGCCAAGCTGGCCCAGGGCGCCTACCGCACAGCTGTGGACTTGGAGTCTCTGGCCTCTCAGCTCACAGCAGACCTCCAGGAGGTGTCTGGGGA'], 'label': tensor([0, 0, 0, 0])}\n"
          ]
        }
      ]
    }
  ]
}