Untitled7.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "yvCisZsaLzcn"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from sklearn import preprocessing\n",
        "from torchsummary import summary\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm import tqdm\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from torch.distributions.categorical import Categorical\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "import shapely\n",
        "import os\n",
        "import pickle\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "tags": [],
        "id": "oZD5fYolMamq"
      },
      "source": [
        "# Classification Based"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "McswxzNrK2tX",
        "outputId": "b3b43da9-5bef-4744-a5c6-3410dee28d45"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Path to the Google Drive\n",
        "drive_path = '/content/drive/My Drive/'\n",
        "\n",
        "# List all files and folders in the specified path\n",
        "all_files = os.listdir(drive_path)\n",
        "\n",
        "# Display the list of files\n",
        "for file in all_files:\n",
        "    print(file)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGCIYlyGN8J7",
        "outputId": "3eb4bc6a-997e-4fe8-e608-203d29c0f518"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aadhar.jpg\n",
            "Classroom\n",
            "Untitled Jam.gjam\n",
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            "Keyur_resume (1).docx\n",
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            "WhatsApp Image 2022-01-20 at 9.31.54 PM.jpeg\n",
            "Tutorial1_Roughwork.pdf\n",
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            "Speech Outline.gdoc\n",
            "Assignment-1_corrected(Complex ).gdoc\n",
            "Engineering_mechanics_journal_final (3).gdoc\n",
            "Traditional Day 2K22\n",
            "Report.gdoc\n",
            "AlgorithmFlowchart.gslides\n",
            "Practical 2- Introduction to C and C-- programming.gdoc\n",
            "FUELS 3-sadri bomb caloriemeter.gdoc\n",
            "Colab Notebooks\n",
            "photo (1).jpg\n",
            "photo.jpg\n",
            "HOME ASSIGNMENT3-211080067 (1).gdoc\n",
            "HOME ASSIGNMENT3-211080067.gdoc\n",
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            "Formals Day '22\n",
            "211080067_LA_Ass-2.pdf\n",
            "Lecture-20-Sorting Algorithms-2.gslides\n",
            "FDS LAB\n",
            "debugthugs.pdf\n",
            "Spit Hackathon Abstract .gdoc\n",
            "os labb\n",
            "OSLAB1_Keyur_211080067.gdoc\n",
            "WhatsApp Image 2023-02-11 at 9.41.56 PM.jpeg\n",
            "dsy mail issue.gsheet\n",
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            "WhatsApp Image 2023-04-08 at 10.27.49 PM.jpeg\n",
            "aadhar card_2.pdf\n",
            "WhatsApp Image 2023-04-08 at 10.00.08 PM.jpeg\n",
            "WhatsApp Image 2023-04-08 at 7.41.47 PM.jpeg\n",
            "Netra Aadhar Card.pdf\n",
            "Screenshot 2023-04-14 125231.png\n",
            "WhatsApp Image 2023-04-14 at 2.02.43 PM.jpeg\n",
            "DBMS LAB EXP-4.gdoc\n",
            "DBMS LAB EXP-5.gdoc\n",
            "SYIT_DAA.gsheet\n",
            "FischerJordan Internship Application .gform\n",
            "FischerJordan Internship Application  (Responses).gsheet\n",
            "Domain Doubts.gform\n",
            "shortlisted.gsheet\n",
            "Fischer Jordan - 2.gsheet\n",
            "Resume_Keyur (3) (1).pdf\n",
            "Resume_Keyur (3).pdf\n",
            "Goldman Sachs IT (1).gsheet\n",
            "Goldman Sachs (Responses).gsheet\n",
            "Goldman Sachs IT.gsheet\n",
            "Goldman Sachs.gform\n",
            "Installation & Database Design.gdoc\n",
            "CITI Internship Application.gform\n",
            "CITI Internship Application (Responses).gsheet\n",
            "Untitled spreadsheet (9).gsheet\n",
            "211080067_WIM_Assign4\n",
            "NPCI Internship.gform\n",
            "Hilti Registraion Form (Responses).gsheet\n",
            "Hilti Technology Solutions India (Master Sheet).gsheet\n",
            "Hilti Technology Solutions India (Master Sheet).xlsx\n",
            "211080067_All Integrated_Assn \n",
            "Hilti Registraion Form.gform\n",
            "Interview Experiences\n",
            "Payment gateway exp WIM\n",
            "Keyur_Doshi_Resume (1).pdf\n",
            "Goldman Sachs.gsheet\n",
            "Untitled spreadsheet (8).gsheet\n",
            "Untitled document (4).gdoc\n",
            "Abstrac_Set_Bits.gdoc\n",
            "Abstrac_Set_Bits (1).pdf\n",
            "Idea-Presentation-SIH2023--SetBits.pdf\n",
            "Idea-Presentation-SIH2023-SetBits.pdf\n",
            "Untitled document (3).gdoc\n",
            "CP CLUB Round 2.gsheet\n",
            "The CP Club Contest Feedback.gform\n",
            "JAN TRIP TY CS-IT  (Responses).gsheet\n",
            "SAP Labs Internship.gform\n",
            "SAP Labs Internship (Responses).gsheet\n",
            "Untitled form.gform\n",
            "Untitled spreadsheet (7).gsheet\n",
            "Keyur_211080067_SGD_9.pdf\n",
            "WhatsApp Image 2023-11-23 at 5.25.39 PM.jpeg\n",
            "Untitled spreadsheet (6).gsheet\n",
            "Untitled spreadsheet (5).gsheet\n",
            "Untitled spreadsheet (4).gsheet\n",
            "SE - Sample Questions.gdoc\n",
            "Screenshot 2023-12-31 170059.png\n",
            "Suntory Summer Internship.gform\n",
            "Concession Documents\n",
            "Dhruv_Concession\n",
            "Computer Architecture & Parallel Processing_copy.pdf\n",
            "Smit\n",
            "2.py\n",
            "Introduction to Parallel Computing, Second Edition-Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar.gdoc\n",
            "CUDA.gdoc\n",
            "ml_qb.gdoc\n",
            "Judges_cancelled_cheques\n",
            "Untitled spreadsheet (3).gsheet\n",
            "UBS Internship.gform\n",
            "Sem_4_Marksheet.jpeg\n",
            "Untitled spreadsheet (2).gsheet\n",
            "IT Data (Responses).gsheet\n",
            "NPCI Interns.gsheet\n",
            "Schneider Master Sheet.gsheet\n",
            "SY IT TPO CR Nomination.gform\n",
            "SY IT TPO CR Elections (IT '26).gform\n",
            "211080067_WN_Assn_6.gdoc\n",
            "CoC Codehunt '23.gform\n",
            "BIS Internship Application (1).gform\n",
            "BIS Internship Application.gform\n",
            "Should Democracy exist or not?.gdoc\n",
            "PCS Speech.gdoc\n",
            "Meeting Reports\n",
            "Untitled document (2).gdoc\n",
            "ffk-sjhs-fec - May 25, 2024.gjam\n",
            "Untitled document (1).gdoc\n",
            "Untitled spreadsheet (1).gsheet\n",
            "Untitled document.gdoc\n",
            "GSSOC Info.gdoc\n",
            "BITS DATA.gsheet\n",
            "DoshiKeyur_Resume.gdoc\n",
            "ESE_marksdip.gsheet\n",
            "marks_MST.gsheet\n",
            "TPO.gsheet\n",
            "Internship Report Week 2.gdoc\n",
            "jash mock interview.gdoc\n",
            "Meet Recordings\n",
            "TPO Data 2.gsheet\n",
            "Internship Report Dhruvisha.gdoc\n",
            "Untitled spreadsheet.gsheet\n",
            "Axxela Withdrawal .gform\n",
            "Axxela Withdrawal  (Responses).gsheet\n",
            "Tata Cliq.gdoc\n",
            "bhoomi spons.gsheet\n",
            "Keyur_Doshi_Resume.pdf\n",
            "IT Data.gform\n",
            "SIH2024_IDEA_Presentation_Format- Set_Bits.pptx\n",
            "IT Internship Data 2023-24.gsheet\n",
            "shekhar home\n",
            "Rebel Foods Ideas.gdoc\n",
            "SPM Question Bank (1).gdoc\n",
            "SPM Question Bank.gdoc\n",
            "HI-Small_Trans.csv\n"
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    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LwXXpQkHMAD1"
      },
      "outputs": [],
      "source": [
        "class Environment:\n",
        "    def __init__(self, data, label):\n",
        "        self.label = label\n",
        "        self.dataset = data\n",
        "        self.current_pos = 0\n",
        "        self.max_pos = len(self.dataset) - 1\n",
        "        self.reward = 0\n",
        "        self.done = False\n",
        "        # self.bce_loss = nn.BCELoss()\n",
        "\n",
        "    def step(self, action):\n",
        "        actual_label = self.label.iloc[self.current_pos]\n",
        "        # actual_label_tensor = torch.FloatTensor([actual_label])\n",
        "\n",
        "        # bce_loss_value = self.bce_loss(action_prob, actual_label_tensor).item()\n",
        "        # reward = bce_loss_value\n",
        "        if action==1:\n",
        "            if actual_label==1:\n",
        "                reward = 100\n",
        "            else:\n",
        "                reward = -200\n",
        "        else:\n",
        "            if actual_label==0:\n",
        "                reward = 10\n",
        "            else:\n",
        "                reward = -50\n",
        "        # print(reward)\n",
        "\n",
        "        self.current_pos +=1\n",
        "        if self.current_pos>=self.max_pos:\n",
        "            self.done = True\n",
        "\n",
        "        next_state = self.dataset.iloc[self.current_pos] if not self.done else None\n",
        "        return next_state, reward, self.done\n",
        "\n",
        "    def reset(self):\n",
        "        self.current_pos = 0\n",
        "        self.done = False\n",
        "        return self.dataset.iloc[self.current_pos]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "E58bh9XEWwBP"
      },
      "outputs": [],
      "source": [
        "actor = nn.Sequential(\n",
        "    nn.Linear(14, 8),   # Input layer: from 10 features to 6 hidden units\n",
        "    nn.LeakyReLU(),\n",
        "    nn.Linear(8, 8),    # Hidden layer: from 6 to 6 hidden units\n",
        "    nn.LeakyReLU(),\n",
        "    nn.Linear(8, 2),    # Output layer: from 6 to 2 output units\n",
        "    nn.Softmax(dim=-1)  # Softmax for probabilities\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "XOMtUHYeXX9a"
      },
      "outputs": [],
      "source": [
        "critic = nn.Sequential(\n",
        "    nn.Linear(14, 8),\n",
        "    nn.LeakyReLU(),\n",
        "    nn.Linear(8, 8),\n",
        "    nn.LeakyReLU(),\n",
        "    nn.Linear(8, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kxjUCTIMams",
        "outputId": "6f7c02c1-9b94-4b78-9457-1ec3de28d320"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Linear-1                 [-1, 1, 8]             120\n",
            "         LeakyReLU-2                 [-1, 1, 8]               0\n",
            "            Linear-3                 [-1, 1, 8]              72\n",
            "         LeakyReLU-4                 [-1, 1, 8]               0\n",
            "            Linear-5                 [-1, 1, 2]              18\n",
            "           Softmax-6                 [-1, 1, 2]               0\n",
            "================================================================\n",
            "Total params: 210\n",
            "Trainable params: 210\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.00\n",
            "Params size (MB): 0.00\n",
            "Estimated Total Size (MB): 0.00\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "summary(actor, input_size = (1,14))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVySk0HYMamt",
        "outputId": "aa040474-4006-4584-e424-a58df586a07d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Linear-1                 [-1, 1, 8]             120\n",
            "         LeakyReLU-2                 [-1, 1, 8]               0\n",
            "            Linear-3                 [-1, 1, 8]              72\n",
            "         LeakyReLU-4                 [-1, 1, 8]               0\n",
            "            Linear-5                 [-1, 1, 1]               9\n",
            "================================================================\n",
            "Total params: 201\n",
            "Trainable params: 201\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.00\n",
            "Params size (MB): 0.00\n",
            "Estimated Total Size (MB): 0.00\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "summary(critic, input_size = (1,14))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "aABlvbByXkHm"
      },
      "outputs": [],
      "source": [
        "# Define the optimizers\n",
        "actor_optimizer = optim.Adam(actor.parameters(), lr=1e-4)\n",
        "critic_optimizer = optim.Adam(critic.parameters(), lr=1e-4)\n",
        "\n",
        "# Loss function for critic\n",
        "mse_loss = nn.MSELoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "MFhyXM-QX744"
      },
      "outputs": [],
      "source": [
        "# Function to select action\n",
        "def select_action(state):\n",
        "    state = torch.FloatTensor(state).unsqueeze(0)\n",
        "    # print(state)\n",
        "    probabilities = actor(state)\n",
        "    action = torch.multinomial(probabilities,num_samples = 1).item()\n",
        "    return action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "kGdjZd20Mamu"
      },
      "outputs": [],
      "source": [
        "def loss_calculations(state, action, reward, next_state, done, I = 0.9, gamma=0.99):\n",
        "    state = torch.FloatTensor(state).unsqueeze(0)\n",
        "    next_state = torch.FloatTensor(next_state).unsqueeze(0) if next_state is not None else torch.zeros_like(state)\n",
        "    action = torch.LongTensor([action])\n",
        "    reward = torch.FloatTensor([reward])\n",
        "    done = torch.FloatTensor([done])\n",
        "\n",
        "    # Update Critic\n",
        "    value = critic(state).squeeze()\n",
        "    next_value = critic(next_state).squeeze()\n",
        "    target = reward + (1.0 - done) * gamma * next_value - value\n",
        "    critic_loss = mse_loss(value, target.detach())\n",
        "\n",
        "    # Update Actor\n",
        "    advantage = target - value\n",
        "    log_prob = torch.log(actor(state).gather(1, action.unsqueeze(1)).squeeze() + 1e-6)\n",
        "    # actor_loss = -I * log_prob * advantage.detach()\n",
        "    actor_loss = - log_prob * advantage.detach()\n",
        "\n",
        "\n",
        "    return actor_loss, critic_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "_Nu7QVNLMamu"
      },
      "outputs": [],
      "source": [
        "def update_network(actor_loss, critic_loss):\n",
        "\n",
        "    # Update Critic Parameters\n",
        "    critic_optimizer.zero_grad()\n",
        "    critic_loss.backward()\n",
        "    critic_optimizer.step()\n",
        "\n",
        "    # Update Actor Parameter\n",
        "    actor_optimizer.zero_grad()\n",
        "    actor_loss.backward()\n",
        "    actor_optimizer.step()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQEilKgtMamu",
        "outputId": "89736e73-2936-4bc5-fc5c-a36462427b41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Complete Data Size : (5078345, 11) \n",
            "          Timestamp  From Bank    Account  To Bank  Account.1  \\\n",
            "0  2022/09/01 00:20         10  8000EBD30       10  8000EBD30   \n",
            "1  2022/09/01 00:20       3208  8000F4580        1  8000F5340   \n",
            "2  2022/09/01 00:00       3209  8000F4670     3209  8000F4670   \n",
            "3  2022/09/01 00:02         12  8000F5030       12  8000F5030   \n",
            "4  2022/09/01 00:06         10  8000F5200       10  8000F5200   \n",
            "\n",
            "   Amount Received Receiving Currency  Amount Paid Payment Currency  \\\n",
            "0          3697.34          US Dollar      3697.34        US Dollar   \n",
            "1             0.01          US Dollar         0.01        US Dollar   \n",
            "2         14675.57          US Dollar     14675.57        US Dollar   \n",
            "3          2806.97          US Dollar      2806.97        US Dollar   \n",
            "4         36682.97          US Dollar     36682.97        US Dollar   \n",
            "\n",
            "  Payment Format  Is Laundering  \n",
            "0   Reinvestment              0  \n",
            "1         Cheque              0  \n",
            "2   Reinvestment              0  \n",
            "3   Reinvestment              0  \n",
            "4   Reinvestment              0  \n"
          ]
        }
      ],
      "source": [
        "# Merging Synthetic Data and Original Data\n",
        "\n",
        "data = pd.read_csv('/content/drive/My Drive/HI-Small_Trans.csv')\n",
        "print(f'Complete Data Size : {data.shape} ')\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqfV7Ly8jpPH",
        "outputId": "de3f4815-d955-40c5-ca02-dbe617b96821"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5078345, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlEnW8mIMamv",
        "outputId": "f2d9eb55-3214-4fe2-c235-d6940abce423"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In Training Data\n",
            "Low : (45,) \n",
            "High : (46167,)\n",
            "In Testing Data\n",
            "Low : (19,) \n",
            "High : (19787,)\n"
          ]
        }
      ],
      "source": [
        "# Train Test Split\n",
        "data = data.sample(frac=0.013, random_state=42)\n",
        "X = data.drop('Is Laundering', axis = 1)\n",
        "y = data['Is Laundering']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=42)\n",
        "low = y_train[y_train==1]\n",
        "high = y_train[y_train==0]\n",
        "low2 = y_test[y_test==1]\n",
        "high2 = y_test[y_test==0]\n",
        "print(\"In Training Data\")\n",
        "print(f'Low : {low.shape} \\nHigh : {high.shape}' )\n",
        "print(\"In Testing Data\")\n",
        "print(f'Low : {low2.shape} \\nHigh : {high2.shape}' )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFA6LyceMamv",
        "outputId": "bacddd84-04f8-4c9d-ad31-3157e6338571"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(46212, 10)\n",
            "(46212, 2)\n",
            "(46212, 10)\n",
            "   Amount Received  Amount Paid         Timestamp  From Bank    Account  \\\n",
            "0        -0.117883    -0.118625  2022/09/02 03:35        124  813641EE1   \n",
            "1        -0.109976    -0.110675  2022/09/06 08:12         10  80005D980   \n",
            "2        32.397019    32.576616  2022/09/08 05:28        220  8100E43A0   \n",
            "3        -0.112523    -0.113236  2022/09/09 21:14      21611  80BFBE320   \n",
            "4         0.333882     0.335645  2022/09/09 00:09       6276  807A2AE40   \n",
            "\n",
            "   To Bank  Account.1 Receiving Currency Payment Currency Payment Format  \n",
            "0      124  813641EE1            Bitcoin          Bitcoin        Bitcoin  \n",
            "1       10  800947560          US Dollar        US Dollar    Credit Card  \n",
            "2    45279  8118383B0             Shekel           Shekel           Wire  \n",
            "3   130637  80FEAF310          US Dollar        US Dollar         Cheque  \n",
            "4   113798  8098B01D0          US Dollar        US Dollar            ACH  \n"
          ]
        }
      ],
      "source": [
        "os.makedirs('models', exist_ok=True)\n",
        "# Print the shape of the training data\n",
        "print(X_train.shape)\n",
        "\n",
        "# Select only numerical columns\n",
        "# X_numerical = X_train.select_dtypes(include=['float64', 'int64'])|\n",
        "X_numerical = X_train[['Amount Received','Amount Paid']]\n",
        "X_non_numerical = X_train.drop(['Amount Received','Amount Paid'], axis = 1)\n",
        "# Apply RobustScaler\n",
        "transformer = RobustScaler().fit(X_numerical)\n",
        "\n",
        "with open('models/robust_scaler_6M.pkl', 'wb') as f:\n",
        "    pickle.dump(transformer, f)\n",
        "\n",
        "normalized_numerical = pd.DataFrame(transformer.transform(X_numerical), columns=X_numerical.columns)\n",
        "print(normalized_numerical.shape)\n",
        "normalized_numerical.reset_index(drop=True, inplace=True)\n",
        "# X_non_numerical = X_train.select_dtypes(exclude=['float64', 'int64'])\n",
        "X_final = pd.concat([normalized_numerical, X_non_numerical.reset_index(drop=True)], axis=1)\n",
        "\n",
        "print(X_final.shape)\n",
        "print(X_final.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify non-datetime values in the 'Timestamp' column\n",
        "invalid_timestamps = X_train[~X_train['Timestamp'].str.match(r'\\d{4}/\\d{2}/\\d{2} \\d{2}:\\d{2}', na=False)]\n",
        "print(\"Invalid Timestamp entries:\")\n",
        "print(invalid_timestamps)\n",
        "\n",
        "# Remove rows with invalid 'Timestamp' values\n",
        "X_train = X_train[X_train['Timestamp'].str.match(r'\\d{4}/\\d{2}/\\d{2} \\d{2}:\\d{2}', na=False)]\n",
        "\n",
        "# Convert 'Timestamp' to datetime format after cleaning\n",
        "X_train['Timestamp'] = pd.to_datetime(X_train['Timestamp'], format='%Y/%m/%d %H:%M')\n",
        "\n",
        "# Extract features from 'Timestamp'\n",
        "X_train['Year'] = X_train['Timestamp'].dt.year\n",
        "X_train['Month'] = X_train['Timestamp'].dt.month\n",
        "X_train['Day'] = X_train['Timestamp'].dt.day\n",
        "X_train['Hour'] = X_train['Timestamp'].dt.hour\n",
        "X_train['Minute'] = X_train['Timestamp'].dt.minute\n",
        "\n",
        "# Drop the original 'Timestamp' column\n",
        "X_train.drop('Timestamp', axis=1, inplace=True)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GhSY4ahjC0i",
        "outputId": "acd4d605-1357-44af-a093-a501d18551a4"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Invalid Timestamp entries:\n",
            "Empty DataFrame\n",
            "Columns: [Timestamp, From Bank, Account, To Bank, Account.1, Amount Received, Receiving Currency, Amount Paid, Payment Currency, Payment Format]\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Convert all categorical columns to string type before applying LabelEncoder\n",
        "for col in ['From Bank', 'Account', 'Account.1', 'Receiving Currency', 'Payment Currency', 'Payment Format']:\n",
        "    X_train[col] = X_train[col].astype(str)\n",
        "\n",
        "# Now apply LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "for col in ['From Bank', 'Account', 'Account.1', 'Receiving Currency', 'Payment Currency', 'Payment Format']:\n",
        "    X_train[col] = label_encoder.fit_transform(X_train[col])\n",
        "\n",
        "# Fill missing values if any\n",
        "X_train.fillna(0, inplace=True)\n",
        "\n",
        "# Verify all columns are numerical now\n",
        "print(X_train.dtypes)\n",
        "# Proceed with your training loop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EQPYTGvnqwu",
        "outputId": "88318ac1-ac22-4208-81b9-cfe14bb0437e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "From Bank               int64\n",
            "Account                 int64\n",
            "To Bank                 int64\n",
            "Account.1               int64\n",
            "Amount Received       float64\n",
            "Receiving Currency      int64\n",
            "Amount Paid           float64\n",
            "Payment Currency        int64\n",
            "Payment Format          int64\n",
            "Year                    int32\n",
            "Month                   int32\n",
            "Day                     int32\n",
            "Hour                    int32\n",
            "Minute                  int32\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpJKRpYSrjyi",
        "outputId": "517d3b20-9324-46af-d98c-1414deac72f6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(46212, 14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "tsHdwvbqYclU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d9748a4-367b-4d60-e860-f516b62977cb"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Examples Shape :  (46212, 14)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [2:45:56<00:00, 99.57s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2h 45min 41s, sys: 29.6 s, total: 2h 46min 11s\n",
            "Wall time: 2h 45min 56s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "print(f'Training Examples Shape : ',X_train.shape)\n",
        "env = Environment(X_train, y_train)\n",
        "num_episodes = 100\n",
        "batch_size = 100\n",
        "rew = []\n",
        "actor_ls_episodic = []\n",
        "critic_ls_episodic = []\n",
        "gamma = 0.99\n",
        "\n",
        "# Assuming `env` is your environment object\n",
        "for episode in tqdm(range(1, num_episodes + 1)):\n",
        "    state = env.reset()\n",
        "    episode_reward = 0\n",
        "    done = False\n",
        "    size = len(X_train)\n",
        "    actor_loss=0\n",
        "    critic_loss = 0\n",
        "    iteration = 1\n",
        "    actor_ls = []\n",
        "    critic_ls = []\n",
        "    while not done:\n",
        "\n",
        "        # To get action, next_state, reward\n",
        "        action = select_action(state)\n",
        "        next_state, reward, done = env.step(action)\n",
        "\n",
        "        # To calculate Loss\n",
        "        ac_loss, ct_loss = loss_calculations(state, action, reward, next_state, done, gamma)\n",
        "        actor_loss += ac_loss\n",
        "        critic_loss += ct_loss\n",
        "\n",
        "        # To update parameters in batches\n",
        "        if iteration==batch_size:\n",
        "            al = actor_loss/batch_size\n",
        "            cl = critic_loss/batch_size\n",
        "            update_network(al, cl)\n",
        "\n",
        "            actor_ls.append(al)\n",
        "            critic_ls.append(cl)\n",
        "            actor_loss = 0\n",
        "            critic_loss = 0\n",
        "            iteration = 1\n",
        "\n",
        "        state = next_state\n",
        "        episode_reward += reward\n",
        "        iteration+=1\n",
        "        # I *= gamma\n",
        "\n",
        "        if done:\n",
        "            break\n",
        "\n",
        "    # For last remaining batch of examples\n",
        "    al = actor_loss/iteration\n",
        "    cl = critic_loss/iteration\n",
        "    update_network(al, cl)\n",
        "    actor_ls.append(al)\n",
        "    critic_ls.append(cl)\n",
        "\n",
        "    actor_ls_episodic.append((sum(actor_ls)/len(actor_ls)).item())\n",
        "    critic_ls_episodic.append((sum(critic_ls)/len(critic_ls)).item())\n",
        "    rew.append(episode_reward)\n",
        "    # Assuming you have hooks registered\n",
        "    # for hook in model.hooks:\n",
        "    #     hook.remove()\n",
        "\n",
        "    torch.save(actor, 'models/ACTOR_1M.pth')\n",
        "    torch.save(critic, 'models/CRITIC_1M.pth')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cIzawM7EMamw",
        "outputId": "8c016591-e9e6-48d0-86c6-de2e95c911e9"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/data/10episodes.png'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-89aef900acf3>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/10episodes.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;31m# Show the plots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1021\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1023\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Need this if 'transparent=True', to reset colors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3341\u001b[0m                         ax.patch._cm_set(facecolor='none', edgecolor='none'))\n\u001b[1;32m   3342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3343\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3345\u001b[0m     def ginput(self, n=1, timeout=30, show_clicks=True,\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2367\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \"bbox_inches_restore\"}\n\u001b[1;32m   2231\u001b[0m             \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m             print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m   2233\u001b[0m                 *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m   2234\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    456\u001b[0m         \"\"\"\n\u001b[1;32m    457\u001b[0m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m         mpl.image.imsave(\n\u001b[0m\u001b[1;32m    459\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m             dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1687\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"format\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1688\u001b[0m         \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dpi\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1689\u001b[0;31m         \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpil_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2561\u001b[0m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2562\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2563\u001b[0;31m                 \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w+b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2564\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2565\u001b[0m             \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mIO\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/data/10episodes.png'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1200 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axs = plt.subplots(3, 1, figsize=(10, 12))\n",
        "episodes = list(range(1,101))\n",
        "\n",
        "# Plot Reward vs Episodes\n",
        "axs[0].plot(episodes, rew, 'b-', label='Reward')\n",
        "axs[0].set_title('Reward vs Episodes')\n",
        "axs[0].set_xlabel('Episodes')\n",
        "axs[0].set_ylabel('Reward')\n",
        "axs[0].legend()\n",
        "\n",
        "# Plot Actor Loss vs Episodes\n",
        "axs[1].plot(episodes, actor_ls_episodic, 'r-', label='Actor Loss')\n",
        "axs[1].set_title('Actor Loss vs Episodes')\n",
        "axs[1].set_xlabel('Episodes')\n",
        "axs[1].set_ylabel('Actor Loss')\n",
        "axs[1].legend()\n",
        "\n",
        "# Plot Critic Loss vs Episodes\n",
        "axs[2].plot(episodes, critic_ls_episodic, 'g-', label='Critic Loss')\n",
        "axs[2].set_title('Critic Loss vs Episodes')\n",
        "axs[2].set_xlabel('Episodes')\n",
        "axs[2].set_ylabel('Critic Loss')\n",
        "axs[2].legend()\n",
        "\n",
        "# Adjust layout to prevent overlap\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig('data/10episodes.png')\n",
        "# Show the plots\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8ZS9OkWMamw",
        "outputId": "0d31c4f7-179d-4698-d145-a60bf8bbf268"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.weight: Parameter containing:\n",
            "tensor([[-0.0116,  0.0863,  0.0257, -0.0741,  0.0404, -0.1257, -0.0708,  0.2222,\n",
            "          0.0547, -0.0377,  0.1598, -0.1192, -0.0383, -0.1270],\n",
            "        [ 0.1038, -0.1873, -0.0268,  0.2428,  0.0758,  0.2271, -0.0243, -0.0366,\n",
            "         -0.0353, -0.1751,  0.0849,  0.2400, -0.2491,  0.0178],\n",
            "        [-0.0386,  0.1833,  0.0805, -0.1903, -0.0642,  0.1067,  0.0934, -0.2664,\n",
            "         -0.1367, -0.1336,  0.0905, -0.2151,  0.2419, -0.2522],\n",
            "        [-0.2513,  0.0005,  0.2000, -0.1143,  0.1597, -0.2310, -0.0861, -0.1927,\n",
            "         -0.0123, -0.1610, -0.2041,  0.0808,  0.1805, -0.1987],\n",
            "        [-0.1253,  0.0746, -0.0969,  0.1944, -0.1954, -0.1076, -0.2593, -0.1390,\n",
            "          0.1054, -0.0246, -0.2362, -0.0776, -0.0592,  0.1510],\n",
            "        [ 0.1267,  0.2317, -0.1835, -0.2094, -0.0062, -0.1683,  0.0798,  0.0243,\n",
            "         -0.2074,  0.1175,  0.1990,  0.1500, -0.2084, -0.0349],\n",
            "        [ 0.1865, -0.0615, -0.0615, -0.1990, -0.0224,  0.1731, -0.1309, -0.2302,\n",
            "         -0.2278,  0.0961,  0.0897,  0.2185,  0.2472,  0.1272],\n",
            "        [ 0.2627, -0.1489,  0.0871, -0.0231,  0.0669,  0.2236, -0.0533, -0.1104,\n",
            "         -0.0979,  0.2410, -0.0304, -0.0174, -0.2629, -0.2059]],\n",
            "       requires_grad=True)\n",
            "0.bias: Parameter containing:\n",
            "tensor([ 0.2343,  0.1165, -0.0546, -0.0151, -0.1217, -0.1821, -0.0200,  0.1949],\n",
            "       requires_grad=True)\n",
            "2.weight: Parameter containing:\n",
            "tensor([[ 9.7583e-02, -2.7591e-01, -3.1701e-01,  3.5409e-03,  1.7364e-01,\n",
            "         -8.5467e-03,  2.2678e-01, -4.6463e-02],\n",
            "        [-1.4169e-01,  2.3407e-01, -3.0323e-01,  5.0683e-02, -3.0637e-01,\n",
            "          2.7726e-01,  9.4276e-02, -2.0892e-01],\n",
            "        [-1.2644e-01,  2.1714e-01, -1.3127e-01,  2.3405e-01,  3.1890e-04,\n",
            "         -2.8732e-01, -3.2389e-01,  2.3166e-02],\n",
            "        [-2.0125e-01,  1.6398e-01, -3.5707e-01, -2.4832e-01,  2.9636e-01,\n",
            "         -2.6358e-01,  1.4306e-01, -1.3196e-02],\n",
            "        [ 1.5284e-02,  1.7837e-01,  3.0617e-01,  3.6318e-01,  3.1509e-02,\n",
            "          2.9119e-01,  2.5730e-01, -2.7722e-01],\n",
            "        [ 2.8735e-01, -5.3412e-02, -3.1236e-01, -1.2797e-01,  2.0001e-01,\n",
            "          7.0849e-02,  1.2656e-01,  8.5089e-02],\n",
            "        [ 7.6133e-02,  2.1013e-01,  6.5110e-02,  2.1124e-01,  1.3822e-01,\n",
            "          3.1908e-01,  5.9729e-04, -3.3856e-01],\n",
            "        [ 1.4992e-02, -2.0275e-01, -1.2023e-01, -1.3881e-01,  1.3585e-01,\n",
            "          2.6157e-01, -1.2485e-01,  5.3506e-02]], requires_grad=True)\n",
            "2.bias: Parameter containing:\n",
            "tensor([-0.2819, -0.1051,  0.1661,  0.2800,  0.2351,  0.1939,  0.3031,  0.2237],\n",
            "       requires_grad=True)\n",
            "4.weight: Parameter containing:\n",
            "tensor([[-0.0929, -0.1069, -0.2981,  0.1905, -0.1632,  0.0261, -0.2498,  0.2880],\n",
            "        [ 0.0586,  0.1228,  0.3381,  0.3192, -0.0406, -0.1901,  0.2929, -0.2460]],\n",
            "       requires_grad=True)\n",
            "4.bias: Parameter containing:\n",
            "tensor([ 0.3207, -0.3526], requires_grad=True)\n"
          ]
        }
      ],
      "source": [
        "for name,param in actor.named_parameters():\n",
        "        print(f'{name}: {param}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "IP7L-bOfMamw"
      },
      "outputs": [],
      "source": [
        "# Saving Model and state_dict()\n",
        "torch.save(actor.state_dict(), 'models/Actor_State_Dict.pth')\n",
        "torch.save(actor, 'models/ACTOR.pth')\n",
        "torch.save(critic.state_dict(), 'models/Critic_State_Dict.pth')\n",
        "torch.save(critic, 'models/CRITIC.pth')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cm_r9sAqMamw",
        "outputId": "c1d9bb61-b8ff-41c0-912b-132b2dbd2b75"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=14, out_features=8, bias=True)\n",
              "  (1): LeakyReLU(negative_slope=0.01)\n",
              "  (2): Linear(in_features=8, out_features=8, bias=True)\n",
              "  (3): LeakyReLU(negative_slope=0.01)\n",
              "  (4): Linear(in_features=8, out_features=2, bias=True)\n",
              "  (5): Softmax(dim=-1)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "actor.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJ2_67KsMamw",
        "outputId": "d321aec6-42d4-4127-dd76-b740a3584e12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=14, out_features=8, bias=True)\n",
              "  (1): LeakyReLU(negative_slope=0.01)\n",
              "  (2): Linear(in_features=8, out_features=8, bias=True)\n",
              "  (3): LeakyReLU(negative_slope=0.01)\n",
              "  (4): Linear(in_features=8, out_features=1, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "critic.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "dc9rcNI5Mamx"
      },
      "outputs": [],
      "source": [
        "actor = torch.load('models/ACTOR.pth')\n",
        "with open('models/robust_scaler_6M.pkl', 'rb') as f:\n",
        "    transformer_new = pickle.load(f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPuLuPV1Mamx",
        "outputId": "893e3e18-fd6e-40eb-f649-9c6da9b47afc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19806, 14)\n",
            "Invalid Timestamp entries:\n",
            "Empty DataFrame\n",
            "Columns: [Amount Received, Amount Paid, Timestamp, From Bank, Account, To Bank, Account.1, Receiving Currency, Payment Currency, Payment Format]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# Normalizing test data\n",
        "X_numerical_test = X_test[['Amount Received','Amount Paid']]\n",
        "X_non_numerical_test = X_test.drop(['Amount Received','Amount Paid'], axis = 1)\n",
        "normalized_numerical_test = pd.DataFrame(transformer.transform(X_numerical_test), columns = X_numerical_test.columns)\n",
        "# print(normalized_numerical_test.shape)\n",
        "normalized_numerical_test.reset_index(drop = True , inplace= True)\n",
        "X_non_numerical_test.reset_index(drop = True , inplace= True)\n",
        "X_test = pd.concat([normalized_numerical_test, X_non_numerical_test], axis = 1)\n",
        "print(X_test.shape)\n",
        "\n",
        "# Identify non-datetime values in the 'Timestamp' column\n",
        "# invalid_timestamps = X_test[~X_test['Timestamp'].str.match(r'\\d{4}/\\d{2}/\\d{2} \\d{2}:\\d{2}', na=False)]\n",
        "print(\"Invalid Timestamp entries:\")\n",
        "print(invalid_timestamps)\n",
        "\n",
        "# Remove rows with invalid 'Timestamp' values\n",
        "# X_test = X_test[X_test['Timestamp'].str.match(r'\\d{4}/\\d{2}/\\d{2} \\d{2}:\\d{2}', na=False)]\n",
        "\n",
        "# Convert 'Timestamp' to datetime format after cleaning\n",
        "# X_test['Timestamp'] = pd.to_datetime(X_test['Timestamp'], format='%Y/%m/%d %H:%M')\n",
        "\n",
        "# Extract features from 'Timestamp'\n",
        "# X_test['Year'] = X_test['Timestamp'].dt.year\n",
        "# X_test['Month'] = X_test['Timestamp'].dt.month\n",
        "# X_test['Day'] = X_test['Timestamp'].dt.day\n",
        "# X_test['Hour'] = X_test['Timestamp'].dt.hour\n",
        "# X_test['Minute'] = X_test['Timestamp'].dt.minute\n",
        "\n",
        "# Drop the original 'Timestamp' column\n",
        "# X_test.drop('Timestamp', axis=1, inplace=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMeCPH60Mamx",
        "outputId": "c8ec7400-870f-4bad-8130-55d94c15a7ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19806, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "import torch\n",
        "\n",
        "# Convert categorical variables in X_test to numerical format\n",
        "categorical_cols = ['From Bank', 'Account', 'Account.1', 'Receiving Currency', 'Payment Currency', 'Payment Format']\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "for col in categorical_cols:\n",
        "    if col in X_test.columns:\n",
        "        X_test[col] = label_encoder.fit_transform(X_test[col].astype(str))\n",
        "\n",
        "# Fill missing values if any\n",
        "X_test.fillna(0, inplace=True)\n",
        "\n",
        "# Testing on test dataset\n",
        "y_pred = []\n",
        "with torch.no_grad():\n",
        "    for i in range(len(X_test)):\n",
        "        test_state = torch.FloatTensor(X_test.iloc[i].values).unsqueeze(0)  # Use .values to convert to numpy array\n",
        "        probabilities = actor(test_state)\n",
        "        action = torch.multinomial(probabilities, num_samples=1).item()\n",
        "        y_pred.append(action)\n",
        "\n",
        "print(len(y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xyDhnELFoic",
        "outputId": "f9461d59-99f9-4984-f85a-1e450370e937"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19806\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC59vyIgMamy",
        "outputId": "f11cecac-2cb7-4ca3-9805-764ef1d574a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19806,)\n",
            "<class 'numpy.ndarray'>\n",
            "[1 1 1 1 1 1 1 1 1 1]\n"
          ]
        }
      ],
      "source": [
        "y_pred = np.array(y_pred)\n",
        "print(y_pred.shape)\n",
        "print(type(y_pred))\n",
        "print(y_pred[:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uaJnllU2Mamy",
        "outputId": "12042b5f-a302-4d93-ca31-ef755ab12b50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19806,)\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "y = y_test.to_numpy()\n",
        "print(y_test.shape)\n",
        "print(type(y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQ6Hy0B9Mamy",
        "outputId": "01b69d1d-bf3c-4833-9694-2192c24352dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.01      0.01     19787\n",
            "           1       0.00      0.95      0.00        19\n",
            "\n",
            "    accuracy                           0.01     19806\n",
            "   macro avg       0.50      0.48      0.01     19806\n",
            "weighted avg       0.99      0.01      0.01     19806\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "emlYdulAMamy",
        "outputId": "563b7614-aec8-45ed-f430-90da0b6c923b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "disp.plot()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5LTRgyKh8utA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}