Persistent_Diagram_SRU.ipynb

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      "source": [
        "#Importing Library\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
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              "         p1        p2        p3        p5\n",
              "0  0.201259  0.307293  0.450155  0.226632\n",
              "1  0.826302  0.502568  0.511527  0.702064\n",
              "2  0.850925  0.507837  0.461587  0.791158"
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              "      <td>0.201259</td>\n",
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              "      <td>0.450155</td>\n",
              "      <td>0.226632</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.826302</td>\n",
              "      <td>0.502568</td>\n",
              "      <td>0.511527</td>\n",
              "      <td>0.702064</td>\n",
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              "      <th>2</th>\n",
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              "  "
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      "source": [
        "#Importing synthetic data\n",
        "df_s=pd.read_csv('/content/drive/MyDrive/Dataset/SRU/Syn_SRU.csv')\n",
        "df_s=df_s.drop(['Unnamed: 0'], axis=1)\n",
        "df_s.head(3)"
      ]
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      "source": [
        "#importing real data\n",
        "df_r=pd.read_csv('/content/drive/MyDrive/Dataset/SRU/Real_SRU.csv')\n",
        "df_r= df_r.drop(['Unnamed: 0'], axis=1)\n",
        "df_r.head(3)"
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              "         p1        p2        p3        p5\n",
              "0  0.237302  0.229869  0.432285  0.205629\n",
              "1  0.386642  0.167125  0.611078  0.411539\n",
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              "      <th>1</th>\n",
              "      <td>0.386642</td>\n",
              "      <td>0.167125</td>\n",
              "      <td>0.611078</td>\n",
              "      <td>0.411539</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.344667</td>\n",
              "      <td>0.315774</td>\n",
              "      <td>0.440591</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "id": "f7fe8db9",
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        "id": "f7fe8db9"
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      "source": [
        "# col=df.columns\n",
        "# scaler.fit(df)\n",
        "# arr=scaler.transform(df)\n",
        "# df1=pd.DataFrame(arr)\n",
        "# df1.columns=col"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "id": "8dd52ff3",
      "metadata": {
        "id": "8dd52ff3"
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      "outputs": [],
      "source": [
        "# # df1=df1.abs()\n",
        "# df1.head(3)"
      ]
    },
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      "execution_count": null,
      "id": "718919d7",
      "metadata": {
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      "outputs": [],
      "source": [
        "# corr1=df.corr()"
      ]
    },
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      "execution_count": null,
      "id": "56362ee5",
      "metadata": {
        "id": "56362ee5"
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      "source": [
        "# sns.set(rc={'figure.figsize':(10,8)})\n",
        "# sns.heatmap(corr1, cmap='YlGnBu', annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Higly correlated features for synthetic data\n",
        "HC_s=df_s[['p1','p2' ]]\n",
        "HC_s=HC_s.head(n=1000)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "#Plotting HC_s\n",
        "HC_s.plot.scatter(x='p1', y='p2', s=20)"
      ],
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              "<Figure size 432x288 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a3ef6673",
      "metadata": {
        "id": "a3ef6673",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf9620d0-623d-4e87-c899-ebb90ede122f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "#Standarizing synthetic data\n",
        "scaler.fit(HC_s) \n",
        "HC_s_array=scaler.transform(HC_s)\n",
        "HC_s_array.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# col=HC_s.columns\n",
        "# HC_s_arr_df=pd.DataFrame(HC_s_array, columns=col)\n",
        "# HC_s_arr_df.plot.scatter(x='fixed acidity', y='citric acid', s=20)"
      ],
      "metadata": {
        "id": "T012-QPfpzyN"
      },
      "id": "T012-QPfpzyN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Highly correlated features for real data\n",
        "HC_r=df_r[['p1','p2' ]]\n",
        "HC_r=HC_r.head(n=1000)"
      ],
      "metadata": {
        "id": "Eyf7rkX4bBdH"
      },
      "id": "Eyf7rkX4bBdH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting real\n",
        "HC_r.plot.scatter(x='p1', y='p2', s=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "fcaiD8Zuqw3m",
        "outputId": "59eb796b-2213-4096-e6d1-c4432fcb97d4"
      },
      "id": "fcaiD8Zuqw3m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c94979d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Standarizing real data\n",
        "scaler.fit(HC_r) #Real\n",
        "HC_r_array=scaler.transform(HC_r)\n",
        "HC_r_array.shape #Real"
      ],
      "metadata": {
        "id": "-gSVm5DObQiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a1eacfc-34f9-4198-f474-b2bf57eee3d2"
      },
      "id": "-gSVm5DObQiH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# corr_hcr=HC_r.corr()\n",
        "# sns.set(rc={'figure.figsize':(10,8)})\n",
        "# sns.heatmap(corr_hcr, cmap='YlGnBu', annot=True)"
      ],
      "metadata": {
        "id": "rZOSStL3Gaa8"
      },
      "id": "rZOSStL3Gaa8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ee9495e",
      "metadata": {
        "id": "8ee9495e"
      },
      "outputs": [],
      "source": [
        "#HC.plot.scatter(x='fixed acidity', y='citric acid', s=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c52f8da",
      "metadata": {
        "id": "0c52f8da"
      },
      "outputs": [],
      "source": [
        "#Low correlated features for Synthetic data\n",
        "LC_s=df_s[['p5','p3' ]]\n",
        "LC_s=LC_s.head(n=1000)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LC_s.plot.scatter(x='p5', y='p3', s=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "1dwB4UlUrB72",
        "outputId": "e0491bee-68f4-4c15-cc08-7e48f1bb3402"
      },
      "id": "1dwB4UlUrB72",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c947b8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standarizing low correlated features for synthetic data\n",
        "scaler.fit(LC_s)\n",
        "LC_s_array=scaler.transform(LC_s)\n",
        "LC_s_array.shape"
      ],
      "metadata": {
        "id": "aINDXD8ftBN-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "942681be-728e-40cc-eca2-06263c38d415"
      },
      "id": "aINDXD8ftBN-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Low correlated features for real data\n",
        "\n",
        "LC_r=df_r[['p5','p3' ]]\n",
        "LC_r=LC_r.head(n=1000)"
      ],
      "metadata": {
        "id": "f_8d5clCn47O"
      },
      "id": "f_8d5clCn47O",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LC_r.plot.scatter(x='p5', y='p3', s=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "lcZ6zu6GrPwl",
        "outputId": "dced56ef-a8df-47df-f8cc-858df5ea00d1"
      },
      "id": "lcZ6zu6GrPwl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c93e37d0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standarizing low correlated features for real data\n",
        "scaler.fit(LC_r)\n",
        "LC_r_array=scaler.transform(LC_r)\n",
        "LC_r_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khMuUaBGoCLl",
        "outputId": "1646dc6b-e615-48b5-fab6-2cc31c03c6d5"
      },
      "id": "khMuUaBGoCLl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9be2dedb",
      "metadata": {
        "id": "9be2dedb"
      },
      "outputs": [],
      "source": [
        "#LC.plot.scatter(x='citric acid', y='total sulfur dioxide', s=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "efc53892",
      "metadata": {
        "id": "efc53892"
      },
      "outputs": [],
      "source": [
        "#Negatively correlated features for Synthetic data\n",
        "NC_s=df_s[['p1', 'p3']]\n",
        "NC_s=NC_s.head(1000)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NC_s.plot.scatter(x='p1', y='p3', s=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dakOFz-FrX5E",
        "outputId": "9cd04e62-5de6-4b4c-9e0c-77cae5ecad79"
      },
      "id": "dakOFz-FrX5E",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c9354bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standarizing negatively correlated features for synthetic data\n",
        "scaler.fit(NC_s)\n",
        "NC_s_array=scaler.transform(NC_s)\n",
        "NC_s_array.shape"
      ],
      "metadata": {
        "id": "1CwGsO3XJ0qr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d4225f6-803a-4229-c95e-66d8f77aa465"
      },
      "id": "1CwGsO3XJ0qr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Negatively correlated features for real data\n",
        "NC_r=df_r[['p1', 'p3']]\n",
        "NC_r=NC_r.head(1000)"
      ],
      "metadata": {
        "id": "eR3XxrrDo5Cm"
      },
      "id": "eR3XxrrDo5Cm",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NC_r.plot.scatter(x='p1', y='p3', s=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "a7olMLeurgqy",
        "outputId": "60f7a033-db23-4fd6-d340-fb556a018cc1"
      },
      "id": "a7olMLeurgqy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c92c3e90>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standarizing negatively correlated features for real data\n",
        "scaler.fit(NC_r)\n",
        "NC_r_array=scaler.transform(NC_r)\n",
        "NC_r_array.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efhNAyuFpB8m",
        "outputId": "94776dcc-fc27-4591-8e49-1c9333e64cdf"
      },
      "id": "efhNAyuFpB8m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ed48c92",
      "metadata": {
        "id": "1ed48c92"
      },
      "outputs": [],
      "source": [
        "#NC.plot.scatter(x='fixed acidity', y='pH', s=10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -U giotto-tda"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbXMU8fErCec",
        "outputId": "4a609b1b-8848-42a1-956c-bb1b603399c0"
      },
      "id": "HbXMU8fErCec",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting giotto-tda\n",
            "  Downloading giotto_tda-0.5.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (1.1.0)\n",
            "Collecting python-igraph>=0.8.2\n",
            "  Downloading python-igraph-0.9.11.tar.gz (9.5 kB)\n",
            "Collecting pyflagser>=0.4.3\n",
            "  Downloading pyflagser-0.4.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (458 kB)\n",
            "\u001b[K     |████████████████████████████████| 458 kB 64.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19.1 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn>=0.23.1 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (1.0.2)\n",
            "Requirement already satisfied: plotly>=4.8.2 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (5.5.0)\n",
            "Requirement already satisfied: ipywidgets>=7.5.1 in /usr/local/lib/python3.7/dist-packages (from giotto-tda) (7.7.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (3.6.1)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (5.3.4)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (5.1.1)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (7.9.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->giotto-tda) (3.0.2)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->giotto-tda) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->giotto-tda) (5.1.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.1->giotto-tda) (2.6.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.1->giotto-tda) (0.2.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.1->giotto-tda) (57.4.0)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.1->giotto-tda) (4.8.0)\n",
            "Collecting jedi>=0.10\n",
            "  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 68.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0->ipywidgets>=7.5.1->giotto-tda) (0.7.5)\n",
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            "Collecting igraph==0.9.11\n",
            "  Downloading igraph-0.9.11-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 60.8 MB/s \n",
            "\u001b[?25hCollecting texttable>=1.6.2\n",
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            "Building wheels for collected packages: python-igraph\n",
            "  Building wheel for python-igraph (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-igraph: filename=python_igraph-0.9.11-py3-none-any.whl size=9074 sha256=cd12a568a7722e5eed33482c0584c67854d8633017f98ce931905fd3a0822aa2\n",
            "  Stored in directory: /root/.cache/pip/wheels/7d/e4/0c/0c83f70bd0b99ce8aea47a21f8e52502169e7dd17808d12f30\n",
            "Successfully built python-igraph\n",
            "Installing collected packages: jedi, texttable, igraph, python-igraph, pyflagser, giotto-tda\n",
            "Successfully installed giotto-tda-0.5.1 igraph-0.9.11 jedi-0.18.1 pyflagser-0.4.5 python-igraph-0.9.11 texttable-1.6.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#import gudhi as gd\n",
        "from gtda.diagrams import PersistenceEntropy\n",
        "from gtda.homology import VietorisRipsPersistence\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from gtda.diagrams import Scaler\n",
        "from gtda.plotting import plot_diagram\n",
        "from gtda.diagrams import PairwiseDistance as pdDist\n",
        "#from gudhi.wasserstein import wasserstein_distance as wd"
      ],
      "metadata": {
        "id": "upuhOwyKrYa2"
      },
      "id": "upuhOwyKrYa2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def DataReshape(data):\n",
        "    d=data\n",
        "    d=d.reshape(1, 1000, 2) \n",
        "    return d\n"
      ],
      "metadata": {
        "id": "YAykx8yNrs7z"
      },
      "id": "YAykx8yNrs7z",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate Persistent Diagram from the datasets (Origial data and Synthetic data)\n",
        "def PerDgm(OrgData, SynData):\n",
        "    odata=OrgData\n",
        "    sdata=SynData\n",
        "    scaler1 = Scaler()\n",
        "    \n",
        "#     od_re = odata.reshape(1,300,3)\n",
        "#     sd_re = sdata.reshape(1,550,2)\n",
        "    \n",
        "    od_re= DataReshape(odata) #Converting 2D array --> 3D array (Original Data)\n",
        "    sd_re= DataReshape(sdata) #Converting 2D array --> 3D array (Synthetic Data)\n",
        "    \n",
        "    VR = VietorisRipsPersistence(homology_dimensions=[0, 1, 2])  # Declaring Vietoris-Rips Complex \n",
        "    \n",
        "    od_dgm= VR.fit_transform(od_re) # Computing Persistent Diagram for Original Data\n",
        "    sd_dgm= VR.fit_transform(sd_re) # Computing Persistent Diagram for Synthetic Data\n",
        "    \n",
        "    od_dgm1 = scaler1.fit_transform(od_dgm) # Sclaing the persitent diagram\n",
        "    sd_dgm1 = scaler1.fit_transform(sd_dgm)  # Sclaing the persitent diagram\n",
        "    \n",
        "    return od_dgm1, sd_dgm1"
      ],
      "metadata": {
        "id": "tN77XHiMrwQN"
      },
      "id": "tN77XHiMrwQN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr=[]# Persistent diagram for Highly correlated features\n",
        "\n",
        "arr=PerDgm(HC_s_array,HC_r_array)"
      ],
      "metadata": {
        "id": "dGz6V81zr0O0"
      },
      "id": "dGz6V81zr0O0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1=arr[0]#Persistent diagram for synthetic data\n",
        "d2=arr[1]#Persistent diagram for original data"
      ],
      "metadata": {
        "id": "u5TuNRdVunbA"
      },
      "id": "u5TuNRdVunbA",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ziv2X2qgj-n",
        "outputId": "45f27553-25fe-407d-9b0f-0096a1e3683b"
      },
      "id": "0ziv2X2qgj-n",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.        , 0.00557743, 0.        ],\n",
              "        [0.        , 0.00578715, 0.        ],\n",
              "        [0.        , 0.00842208, 0.        ],\n",
              "        ...,\n",
              "        [0.11792024, 0.13854221, 1.        ],\n",
              "        [0.11386424, 0.1487883 , 1.        ],\n",
              "        [0.        , 0.        , 2.        ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt(\"SRU-HC-Syn.txt\", d1[0], delimiter =\", \")"
      ],
      "metadata": {
        "id": "F7jJlgP_wTMT"
      },
      "id": "F7jJlgP_wTMT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt(\"SRU-HC-Real.txt\", d2[0], delimiter =\", \")"
      ],
      "metadata": {
        "id": "qMXO8zFew3ZG"
      },
      "id": "qMXO8zFew3ZG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_diagram(d1[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "w2BKxcc8uwJm",
        "outputId": "2a4db041-7cfd-40c0-e55e-93fcca2cf449"
      },
      "id": "w2BKxcc8uwJm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "source": [
        "plot_diagram(d2[0])"
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      "id": "AO-m18rSu0Fc",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# p=1--> Absolute difference between two persistent diagram\n",
        "# p=2--> Euclidean difference between two persistent diagram"
      ],
      "metadata": {
        "id": "w46OO0i7137C"
      },
      "id": "w46OO0i7137C",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dist = pdDist(metric = 'wasserstein',order=None,metric_params={'delta':0.01, 'p':2.})\n",
        "dwgi = dist.fit(X=d1)\n",
        "dwgi1 = dwgi.transform(d2)\n",
        "print(f\"Giotto wass: {dwgi1}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1LFMULvu6ZB",
        "outputId": "46147873-3b89-48ab-b7b9-88c39882694a"
      },
      "id": "Q1LFMULvu6ZB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Giotto wass: [[[3.83539682 1.41767874 0.01166989]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr1=[]# Persistent diagram for Low correlated features\n",
        "\n",
        "arr1=PerDgm(LC_s_array,LC_r_array)"
      ],
      "metadata": {
        "id": "TD49lgud0Bo2"
      },
      "id": "TD49lgud0Bo2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1_LC=arr1[0]#Persistent diagram for synthetic data\n",
        "d2_LC=arr1[1]#Persistent diagram for original data"
      ],
      "metadata": {
        "id": "U0ZSMI092Srq"
      },
      "id": "U0ZSMI092Srq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt(\"SRU-LC-Syn.txt\", d1_LC[0], delimiter =\", \")\n",
        "np.savetxt(\"SRU-LC-Real.txt\", d2_LC[0], delimiter =\", \")"
      ],
      "metadata": {
        "id": "jTWrS2kt2aD9"
      },
      "id": "jTWrS2kt2aD9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_diagram(d1_LC[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "4r_HprxC2n3R",
        "outputId": "6ef1e8c2-5d75-4402-ec2a-0a24496bf6f1"
      },
      "id": "4r_HprxC2n3R",
      "execution_count": null,
      "outputs": [
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        "plot_diagram(d2_LC[0])"
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      "id": "LB0eh0AD2sqc",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dist_LC = pdDist(metric = 'wasserstein',order=None,metric_params={'delta':0.01, 'p':2.})\n",
        "dwgi_LC = dist_LC.fit(X=d1_LC)\n",
        "dwgi1_LC = dwgi_LC.transform(d2_LC)\n",
        "print(f\"Giotto wass: {dwgi1_LC}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JI0FUVO73BJx",
        "outputId": "dc0d2265-4223-4994-a21f-42659e7a8fe1"
      },
      "id": "JI0FUVO73BJx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Giotto wass: [[[1.74629258e+00 4.42073456e-01 4.55793879e-04]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cEV029_A9hxO"
      },
      "id": "cEV029_A9hxO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arr2=[]# Persistent diagram for Low correlated features\n",
        "\n",
        "arr2=PerDgm(NC_s_array,NC_r_array)"
      ],
      "metadata": {
        "id": "21uHrFQv3Yoh"
      },
      "id": "21uHrFQv3Yoh",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1_NC=arr2[0]#Persistent diagram for synthetic data\n",
        "d2_NC=arr2[1]#Persistent diagram for original data"
      ],
      "metadata": {
        "id": "gwlzcgDN8vNF"
      },
      "id": "gwlzcgDN8vNF",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.savetxt(\"SRU-NC-Syn.txt\", d1_NC[0], delimiter =\", \")\n",
        "np.savetxt(\"SRU-NC-Real.txt\", d2_NC[0], delimiter =\", \")"
      ],
      "metadata": {
        "id": "IFNCveLL80QW"
      },
      "id": "IFNCveLL80QW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_diagram(d1_NC[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "EhSNEC_Z841O",
        "outputId": "b2c90ee1-af6b-4d80-9d37-0a5597b6999e"
      },
      "id": "EhSNEC_Z841O",
      "execution_count": null,
      "outputs": [
        {
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        "plot_diagram(d2_NC[0])"
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      "id": "emeH34Si86H1",
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    {
      "cell_type": "code",
      "source": [
        "dist_NC = pdDist(metric = 'wasserstein',order=None,metric_params={'delta':0.01, 'p':2.})\n",
        "dwgi_NC = dist_NC.fit(X=d1_NC)\n",
        "dwgi1_NC = dwgi_NC.transform(d2_NC)\n",
        "print(f\"Giotto wass: {dwgi1_NC}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tHQpjc89jaX",
        "outputId": "e5856cd4-bf7c-4adf-e4ee-8310d2927efe"
      },
      "id": "4tHQpjc89jaX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Giotto wass: [[[4.03835353 1.05204137 0.01010775]]]\n"
          ]
        }
      ]
    }
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