{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "v_EzT33tC5FD" }, "outputs": [], "source": [ "import pandas as pd\n", "import scipy\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score\n", "import torch\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K7XznE7q8VZV", "outputId": "c998c723-c059-4e6a-ab2a-d07b258aa865" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ep-PKazOHNhn" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "filename = 'output2.csv'\n", "encoding = 'utf-8'\n", "\n", "\n", "successful_lines = []\n", "\n", "with open(filename, 'r', encoding=encoding) as file:\n", " for line in file:\n", " try:\n", " decoded_line = line.encode(encoding).decode(encoding)\n", " successful_lines.append(line)\n", " except UnicodeDecodeError:\n", " print(f\"Skipped line due to decoding error: {line}\")\n", "\n", "\n", "with open('filtered_' + filename, 'w', encoding=encoding) as file:\n", " file.writelines(successful_lines)\n", "\n", "data = pd.read_csv('filtered_' + filename)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DR4oNgW_HXIc" }, "outputs": [], "source": [ "selected_data = data[['Date', 'Content']]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "Rg57TOP4Ht5V", "outputId": "6ee01832-f0f8-4679-be05-448eb48cea00" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "selected_data" }, "text/html": [ "\n", "
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\n" ], "text/plain": [ " Date \\\n", "0 2023-03-20 00:00:30+00:00 \n", "1 2023-03-20 00:06:34+00:00 \n", "2 2023-03-20 00:54:59+00:00 \n", "3 2023-03-20 01:15:02+00:00 \n", "4 2023-03-20 01:39:41+00:00 \n", "\n", " Content positive negative \\\n", "0 You can report scammers in #πŸš¨β”ƒreport-scams 0.017093 0.077462 \n", "1 @Gama4T7 please don’t share that here, u can s... 0.027183 0.025469 \n", "2 I participated in the id project, but I don't ... 0.021391 0.034680 \n", "3 Everything will go to zero sooner or later exc... 0.024390 0.365387 \n", "4 Hii, where the commit bnb part button for ICO ... 0.043279 0.016605 \n", "\n", " neutral Hour Day \n", "0 0.905445 2023-03-20 01:00:00+00:00 2023-03-21 00:00:00+00:00 \n", "1 0.947348 2023-03-20 01:00:00+00:00 2023-03-21 00:00:00+00:00 \n", "2 0.943929 2023-03-20 01:00:00+00:00 2023-03-21 00:00:00+00:00 \n", "3 0.610223 2023-03-20 02:00:00+00:00 2023-03-21 00:00:00+00:00 \n", "4 0.940116 2023-03-20 02:00:00+00:00 2023-03-21 00:00:00+00:00 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "\n", "all_data['Hour'] = all_data['Date'].dt.ceil('H')\n", "all_data['Day'] = all_data['Date'].dt.ceil('D')\n", "\n", "all_data.head()\n" ] }, { "cell_type": "markdown", "metadata": { "id": "4i2Cwt6Yjfts" }, "source": [ "## hour sentiment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d2yjOIBJ3jzB" }, "outputs": [], "source": [ "hourly_sentiment = all_data.groupby(pd.Grouper(key='Hour', freq='H')).agg({\n", " 'positive': 'mean',\n", " 'negative': 'mean',\n", " 'neutral': 'mean'\n", "}).reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kd6H7dqk5Xqk" }, "outputs": [], "source": [ "hourly_sentiment = hourly_sentiment.dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fIv-Ls2bkYta" }, "outputs": [], "source": [ "hourly_sentiment['Hour'] = pd.to_datetime(hourly_sentiment['Hour'])\n", "hourly_sentiment['Hour'] = hourly_sentiment['Hour'].dt.tz_localize(None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TMbT9RkBo48-" }, "outputs": [], "source": [ "hourly_sentiment = hourly_sentiment.set_index('Hour')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x4j56BJ8ACkU" }, "outputs": [], "source": [ "complete_index = pd.date_range(start='2023-03-20', end='2023-04-01', freq='H')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "h5C1N0IpnjC3" }, "outputs": [], "source": [ "\n", "complete_df = pd.DataFrame(index=complete_index, columns=hourly_sentiment.columns)\n", "complete_df['positive'] = 0\n", "complete_df['negative'] = 0\n", "complete_df['neutral'] = 1\n", "\n", "for hour in complete_index:\n", " # Check if the hour exists in the original DataFrame\n", " if hour in hourly_sentiment.index:\n", " continue\n", " else:\n", " # Concatenate the corresponding row from complete_df\n", " hourly_sentiment = pd.concat([hourly_sentiment, complete_df.loc[[hour]]])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AhYv8Br6qu0J" }, "outputs": [], "source": [ "hourly_sentiment_sorted = hourly_sentiment.sort_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "CB1LDjMKsDtb", "outputId": "2f658d07-8b08-457c-c85c-01078d91b6b5" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"hourly_sentiment_sorted\",\n \"rows\": 289,\n \"fields\": [\n {\n \"column\": \"positive\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.05090518901914328,\n \"min\": 0.0,\n \"max\": 0.5098552703857422,\n \"num_unique_values\": 275,\n \"samples\": [\n 0.05327490717172623,\n 0.060727354139089584,\n 0.11413256824016571\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"negative\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06570704290354512,\n \"min\": 0.0,\n \"max\": 0.6354014277458191,\n \"num_unique_values\": 275,\n \"samples\": [\n 0.4941999912261963,\n 0.06461788713932037,\n 0.02846330963075161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"neutral\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07767777903213319,\n \"min\": 0.3047383427619934,\n \"max\": 1.0,\n \"num_unique_values\": 275,\n \"samples\": [\n 0.4525251090526581,\n 0.8746547698974609,\n 0.8574041128158569\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "hourly_sentiment_sorted" }, "text/html": [ "\n", "
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positivenegativeneutral
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............
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\n" ], "text/plain": [ " positive negative neutral\n", "2023-03-20 00:00:00 0.000000 0.000000 1.000000\n", "2023-03-20 01:00:00 0.021889 0.045871 0.932241\n", "2023-03-20 02:00:00 0.029899 0.113325 0.856775\n", "2023-03-20 03:00:00 0.121052 0.039788 0.839161\n", "2023-03-20 04:00:00 0.121189 0.029374 0.849437\n", "... ... ... ...\n", "2023-03-31 20:00:00 0.085038 0.065082 0.849880\n", "2023-03-31 21:00:00 0.120638 0.042057 0.837305\n", "2023-03-31 22:00:00 0.039514 0.103802 0.856685\n", "2023-03-31 23:00:00 0.079138 0.016252 0.904610\n", "2023-04-01 00:00:00 0.044507 0.098121 0.857373\n", "\n", "[289 rows x 3 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hourly_sentiment_sorted" ] }, { "cell_type": "markdown", "metadata": { "id": "Gcbrhv_hjijG" }, "source": [ "## day sentiment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_dwLQahFjjzC" }, "outputs": [], "source": [ "daily_sentiment = all_data.groupby(pd.Grouper(key='Date', freq='D')).agg({\n", " 'positive': 'mean',\n", " 'negative': 'mean',\n", " 'neutral': 'mean'\n", "}).reset_index()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "upPhxunWjl5S" }, "outputs": [], "source": [ "daily_sentiment = daily_sentiment.dropna()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "weCJ5h3Pjtq7" }, "outputs": [], "source": [ "daily_sentiment['Date'] = pd.to_datetime(daily_sentiment['Date'])\n", "daily_sentiment['Date'] = daily_sentiment['Date'].dt.tz_localize(None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mRmOpvj0kB7o" }, "outputs": [], "source": [ "daily_sentiment = daily_sentiment.set_index('Date')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TLSI7OXukIl0" }, "outputs": [], "source": [ "complete_index = pd.date_range(start='2023-05-31', end='2023-07-01', freq='D')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PG_GO5dOkm7d" }, "outputs": [], "source": [ "daily_sentiment_sorted = daily_sentiment.sort_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 457 }, "id": "Wp0z1P0Rkppi", "outputId": "d8d539d8-8529-45fa-e3c9-3ef8bec3f12f" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"daily_sentiment_sorted\",\n \"rows\": 12,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2023-03-20 00:00:00\",\n \"max\": \"2023-03-31 00:00:00\",\n \"num_unique_values\": 12,\n \"samples\": [\n \"2023-03-30 00:00:00\",\n \"2023-03-29 00:00:00\",\n \"2023-03-20 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"positive\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 12,\n \"samples\": [\n 0.07545065134763718,\n 0.08243799209594727,\n 0.0756571963429451\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"negative\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 12,\n \"samples\": [\n 0.07094075530767441,\n 0.09404300898313522,\n 0.06779993325471878\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"neutral\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 12,\n \"samples\": [\n 0.8536086082458496,\n 0.8235189914703369,\n 0.8565428853034973\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "daily_sentiment_sorted" }, "text/html": [ "\n", "
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positivenegativeneutral
Date
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2023-03-210.0980200.0715630.830417
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\n" ], "text/plain": [ " positive negative neutral\n", "Date \n", "2023-03-20 0.075657 0.067800 0.856543\n", "2023-03-21 0.098020 0.071563 0.830417\n", "2023-03-22 0.082404 0.068851 0.848745\n", "2023-03-23 0.068908 0.093714 0.837378\n", "2023-03-24 0.083128 0.100864 0.816008\n", "2023-03-25 0.111851 0.070841 0.817308\n", "2023-03-26 0.083284 0.089351 0.827366\n", "2023-03-27 0.074230 0.070798 0.854972\n", "2023-03-28 0.085851 0.079809 0.834340\n", "2023-03-29 0.082438 0.094043 0.823519\n", "2023-03-30 0.075451 0.070941 0.853609\n", "2023-03-31 0.080779 0.075942 0.843280" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "daily_sentiment_sorted" ] }, { "cell_type": "markdown", "metadata": { "id": "W9gm7aQJsY-t" }, "source": [ "## Combine with DNN" ] }, { "cell_type": "markdown", "metadata": { "id": "pcN-ZLVpscbe" }, "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cLr_Bz6ktl3I" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0S1PCZcNtnQc", "outputId": "f0877820-38a2-454e-a41f-98970108b6d8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3C2l1Hoztra2" }, "outputs": [], "source": [ "data=pd.read_csv('/content/drive/MyDrive/eth airdrop-biggest-2023/eth-airdrop-biggest-2023.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "o3vCtKIKtsti", "outputId": "5de25528-a804-47d7-9deb-831505dc10ef" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"data\",\n \"rows\": 78290,\n \"fields\": [\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 78290,\n \"samples\": [\n \"2023-03-31 18:01:35 UTC\",\n \"2023-03-30 11:18:11 UTC\",\n \"2023-03-27 22:06:59 UTC\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 22600,\n \"min\": 16872313,\n \"max\": 16950602,\n \"num_unique_values\": 78290,\n \"samples\": [\n 16948828,\n 16939719,\n 16921582\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gas_used\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5333450,\n \"min\": 0,\n \"max\": 29999975,\n \"num_unique_values\": 77974,\n \"samples\": [\n 21318392,\n 17787735,\n 14061188\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gas_limit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1343,\n \"min\": 29970677,\n \"max\": 30029295,\n \"num_unique_values\": 6,\n \"samples\": [\n 30000000,\n 29970705,\n 29999944\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_fee_per_gas\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10276555593,\n \"min\": 9259197929,\n \"max\": 108286933405,\n \"num_unique_values\": 78290,\n \"samples\": [\n 32517622739,\n 27660364426,\n 30779581713\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "data" }, "text/html": [ "\n", "
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\n" ], "text/plain": [ " timestamp number gas_used gas_limit base_fee_per_gas\n", "0 2023-03-21 00:00:11 UTC 16872313 29872569 30000000 14709149536\n", "1 2023-03-21 00:00:23 UTC 16872314 13937940 30000000 16532173214\n", "2 2023-03-21 00:00:35 UTC 16872315 19559490 30000000 16385855215\n", "3 2023-03-21 00:00:47 UTC 16872316 13826473 30000000 17008448073\n", "4 2023-03-21 00:00:59 UTC 16872317 20820719 30000000 16842115798" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wfCk-mYTt1Ea" }, "outputs": [], "source": [ "import pandas as pd\n", "new_df = data.drop_duplicates(subset='number', keep='first')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8mtQTUZvt4m4" }, "outputs": [], "source": [ "new_df=new_df[[\"timestamp\",\"number\",\"gas_used\",\"gas_limit\",\"base_fee_per_gas\"]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dmAYLYGit6Oq" }, "outputs": [], "source": [ "new_df[\"base_fee_per_gas\"] = new_df[\"base_fee_per_gas\"]*10**-9" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "-wswq0WOt7Xr", "outputId": "e0433734-b8ef-45a5-bd72-bb535eadd547" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"new_df\",\n \"rows\": 78290,\n \"fields\": [\n {\n \"column\": \"timestamp\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 78290,\n \"samples\": [\n \"2023-03-31 18:01:35 UTC\",\n \"2023-03-30 11:18:11 UTC\",\n \"2023-03-27 22:06:59 UTC\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 22600,\n \"min\": 16872313,\n \"max\": 16950602,\n \"num_unique_values\": 78290,\n \"samples\": [\n 16948828,\n 16939719,\n 16921582\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gas_used\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5333450,\n \"min\": 0,\n \"max\": 29999975,\n \"num_unique_values\": 77974,\n \"samples\": [\n 21318392,\n 17787735,\n 14061188\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gas_limit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1343,\n \"min\": 29970677,\n \"max\": 30029295,\n \"num_unique_values\": 6,\n \"samples\": [\n 30000000,\n 29970705,\n 29999944\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"base_fee_per_gas\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10.276555593473962,\n \"min\": 9.259197929,\n \"max\": 108.28693340500001,\n \"num_unique_values\": 78290,\n \"samples\": [\n 32.517622739000004,\n 27.660364426,\n 30.779581713000002\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "new_df" }, "text/html": [ "\n", "
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12023-03-21 00:00:23 UTC16872314139379403000000016.532173
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\n" ], "text/plain": [ " timestamp number gas_used gas_limit base_fee_per_gas\n", "0 2023-03-21 00:00:11 UTC 16872313 29872569 30000000 14.709150\n", "1 2023-03-21 00:00:23 UTC 16872314 13937940 30000000 16.532173\n", "2 2023-03-21 00:00:35 UTC 16872315 19559490 30000000 16.385855\n", "3 2023-03-21 00:00:47 UTC 16872316 13826473 30000000 17.008448\n", "4 2023-03-21 00:00:59 UTC 16872317 20820719 30000000 16.842116" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t2Y892bOt82n" }, "outputs": [], "source": [ "new_df['gas_fraction'] = new_df['gas_used'] / new_df['gas_limit']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YR_c5lNot_FT" }, "outputs": [], "source": [ "new_df['gas_target'] = (new_df['gas_used']-(new_df['gas_limit']/2)) / (new_df['gas_limit']/2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Erlqgzf8vgb8" }, "outputs": [], "source": [ "new_df['timestamp'] = pd.to_datetime(new_df['timestamp'], utc=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qdvugYkgwjeN" }, "outputs": [], "source": [ "new_df['Hour'] = new_df['timestamp']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "H6j7_PnRmiSd" }, "outputs": [], "source": [ "new_df['Date'] = new_df['timestamp']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zcdio0CgC2KZ" }, "outputs": [], "source": [ "new_df['Hour'] = new_df['Hour'].dt.tz_localize(None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5MNi1gKRmnPW" }, "outputs": [], "source": [ "new_df['Date'] = new_df['Date'].dt.tz_localize(None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "THITe17_DUX2" }, "outputs": [], "source": [ "new_df['Hour'] = new_df['Hour'].dt.floor('H')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bHeq5UXwmq3B" }, "outputs": [], "source": [ "new_df['Date'] = new_df['Date'].dt.floor('D')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IbczgijoxVup" }, "outputs": [], "source": [ "# Map sentiment values from hourly_sentiment_sorted to new_df using merge\n", "new_df = new_df.merge(hourly_sentiment_sorted, how='left', left_on='Hour', right_index=True)\n", "\n", "# Drop redundant 'Hour' column from new_df\n", "new_df.drop(columns='Hour', inplace=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-8fGJeDrmvJw" }, "outputs": [], "source": [ "new_df = new_df.merge(daily_sentiment_sorted, how='left', left_on='Date', right_index=True)\n", "\n", "# Drop redundant 'Hour' column from new_df\n", "new_df.drop(columns='Date', inplace=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 313 }, "id": "vrjvuPQ8m1BY", "outputId": 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timestampnumbergas_usedgas_limitbase_fee_per_gasgas_fractiongas_targetpositive_xnegative_xneutral_xpositive_ynegative_yneutral_y
02023-03-21 00:00:11+00:0016872313298725693000000014.7091500.9957520.9915050.059010.0657020.8752880.098020.0715630.830417
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42023-03-21 00:00:59+00:0016872317208207193000000016.8421160.6940240.3880480.059010.0657020.8752880.098020.0715630.830417
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"execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "74GHb-jCzWHh" }, "outputs": [], "source": [ "org_data=np.array(new_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y9ATPllz-b-A" }, "outputs": [], "source": [ "import copy\n", "x=[]\n", "y=[]\n", "for i in range(len(org_data)-3):\n", " gas_frc=org_data[i:i+3,5].tolist()\n", " base=org_data[i:i+4,4].tolist()\n", " pos=org_data[i+3,7]\n", " neg=org_data[i+3,8]\n", " neu=org_data[i+3,9]\n", " pos1=org_data[i+3,10]\n", " neg1=org_data[i+3,11]\n", " neu1=org_data[i+3,12]\n", " now=np.concatenate((gas_frc,base,[pos,neg,neu,pos1,neg1,neu1]),axis=0)\n", " now=now.tolist()\n", " x.append(now)\n", " y.append(org_data[i+3,6])\n", "x=np.array(x)\n", "y=np.array(y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qbfD2YRX_0u6" }, "outputs": [], "source": [ "import tensorflow.compat.v2 as tf\n", "tf.enable_v2_behavior()\n", "import models as nam_models\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UwD-k3hUXE8u" }, "outputs": [], "source": [ "spli=np.ones(len(x[0]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZoHdFb_WDc2D" }, "outputs": [], "source": [ "spli=list(spli)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IEs5ocXaDgAZ" }, "outputs": [], "source": [ "int_spli = [int(x) for x in spli]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VHZFNcLE_we4", "outputId": "ac552a01-32db-4862-9c53-52fcf1b49e0a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 149 } ], "source": [ "tf.compat.v1.reset_default_graph()\n", "#the change of the structure of NAM is in kwargs)\n", "#in this demo, first three features are strong monotonicity, thereby, they are combined in a DNN.\n", "Number_of_DNN=len(int_spli)\n", "Number_of_Unit=0\n", "Trainable=True\n", "Use_Shallow=False\n", "Model=nam_models.NAM(Number_of_DNN,Number_of_Unit,Trainable,Use_Shallow,feature_dropout = 0.0,dropout = 0.0,kwargs=[1,1,1,1,1,1,1,1,1,1,1,1,1])\n", "Model(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_gSagYRP0UOf" }, "outputs": [], "source": [ "from sklearn.model_selection import TimeSeriesSplit\n", "from sklearn import linear_model\n", "from sklearn.metrics import mean_squared_error\n", "tss = TimeSeriesSplit(n_splits=2)\n", "for train_index, test_index in tss.split(x):\n", " x_train, x_test = x[train_index, :], x[test_index,:]\n", " y_train, y_test = y[train_index], y[test_index]\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 706 }, "id": "NfO0IN34AAlY", "outputId": "e4d5957a-d23b-434f-9157-2a1b4e6a09ef" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/15\n", "408/408 [==============================] - 21s 19ms/step - loss: 0.1078 - MSE: 0.1078 - val_loss: 0.1017 - val_MSE: 0.1017 - lr: 0.0010\n", "Epoch 2/15\n", "408/408 [==============================] - 7s 17ms/step - loss: 0.1074 - MSE: 0.1074 - val_loss: 0.1013 - val_MSE: 0.1013 - lr: 0.0010\n", "Epoch 3/15\n", "408/408 [==============================] - 7s 18ms/step - loss: 0.1074 - MSE: 0.1074 - val_loss: 0.1012 - val_MSE: 0.1012 - lr: 0.0010\n", "Epoch 4/15\n", "408/408 [==============================] - ETA: 0s - loss: 0.1074 - MSE: 0.1074\n", "Epoch 4: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", "408/408 [==============================] - 7s 17ms/step - loss: 0.1074 - MSE: 0.1074 - val_loss: 0.1030 - val_MSE: 0.1030 - lr: 0.0010\n", "Epoch 5/15\n", "408/408 [==============================] - 7s 18ms/step - loss: 0.1065 - MSE: 0.1065 - val_loss: 0.1012 - val_MSE: 0.1012 - lr: 1.0000e-04\n", "Epoch 6/15\n", "406/408 [============================>.] - ETA: 0s - loss: 0.1064 - MSE: 0.1064\n", "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0001.\n", "408/408 [==============================] - 7s 17ms/step - loss: 0.1064 - MSE: 0.1064 - val_loss: 0.1012 - val_MSE: 0.1012 - lr: 1.0000e-04\n", "Epoch 7/15\n", "357/408 [=========================>....] - ETA: 0s - loss: 0.1065 - MSE: 0.1065" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m ]\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_callbacks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\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 66\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 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function._call_flat( # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0mflat_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m 1321\u001b[0m and executing_eagerly):\n\u001b[1;32m 1322\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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\u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 53\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 55\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "Model.compile(loss=tf.keras.losses.mean_squared_error,\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n", " metrics='MSE')\n", "\n", "training_callbacks = [\n", " tf.keras.callbacks.ReduceLROnPlateau(patience = 2, factor = 0.1, min_lr = 0.0001, verbose = 1),\n", " tf.keras.callbacks.EarlyStopping(patience =7, restore_best_weights = True),\n", "]\n", "\n", "Model.fit(x_train, y_train, batch_size=128, callbacks=training_callbacks,validation_data=(x_test,y_test),epochs=15)" ] }, { "cell_type": "markdown", "metadata": { "id": "ygfN9FCETIlN" }, "source": [ "## monotonicity" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nDJR0kTHBaex" }, "outputs": [], "source": [ "determine=[0,0.2,0.4,0.6,0.8,1.0]\n", "def descretize_2_pair():\n", " lower_bound=0\n", " upper_bound=5\n", " pair=[]\n", " pair1=[]\n", " for i in range(1,len(determine)-1):\n", " pair.append([determine[i],determine[i],determine[i]])\n", " pair1.append([determine[i+1],determine[i-1],determine[i]])\n", " pair.append([determine[i],determine[i],determine[i]])\n", " pair1.append([determine[i],determine[i+1],determine[i-1]])\n", " pair.append([determine[i],determine[i],determine[i]])\n", " pair1.append([determine[i+1],determine[i],determine[i-1]])\n", "\n", " return pair,pair1\n", "pair,pair1=descretize_2_pair()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "pD8QPdNqL7Tf", "outputId": "677736f9-19b1-43d1-9548-e370f1c6eb5d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(0.106346965, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10634696], shape=(1,), dtype=float32)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:5 out of the last 5 calls to triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "WARNING:tensorflow:6 out of the last 6 calls to triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "------------\n", "tf.Tensor(0.10764744, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10764744], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.10626717, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10626717], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.10754296, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10754296], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.10626165, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10626165], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.10753067, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10753067], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.106258556, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10625856], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.107519284, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10751928], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.10625583, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10625583], shape=(1,), dtype=float32)\n", "------------\n", "tf.Tensor(0.107508376, shape=(), dtype=float32)\n", "loss of strong pairwise monotonicity tf.Tensor([0.], shape=(1,), dtype=float32)\n", "overall loss tf.Tensor([0.10750838], shape=(1,), dtype=float32)\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\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 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m 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forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1325\u001b[0m \u001b[0margs\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/tensorflow/python/eager/polymorphic_function/atomic_function.py\u001b[0m in \u001b[0;36mcall_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcall_preflattened\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0;34m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m \u001b[0mflat_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_flat\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 217\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py\u001b[0m in \u001b[0;36mcall_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;32mwith\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36mcall_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcancellation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\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 1485\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1486\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 1487\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\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 1488\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_outputs\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/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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---> 53\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 54\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 55\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "each_epoch=100\n", "alpha_2=1\n", "learning_r=0.001\n", "for j in range(3):\n", " for i in range(each_epoch):\n", " Model.network_learn(x_train,y_train,None,None,None,pair,pair1,None,None,alpha_2,None,None,None,learning_r,num_fea=3)\n", " print(\"------------\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EG5z-GKe1-Gb", "outputId": "9272a5eb-d62c-4fcb-e9af-7d31c3c2094b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "816/816 [==============================] - 3s 4ms/step\n", "0.10099460947166662\n" ] } ], "source": [ "pred_reg=Model.predict(x_test)\n", "pred_reg=pred_reg\n", "print(mean_squared_error(y_test, pred_reg))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AeiTfon2_puf", "outputId": "665c2030-c8b1-469d-b453-7332d211c876" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "816/816 [==============================] - 4s 5ms/step\n" ] } ], "source": [ "pred=Model.predict(x_test)" ] }, { "cell_type": "code", "source": [ "pred1=Model.predict(x_test)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d1eJX745EKa9", "outputId": "3ea697ac-dd63-4955-fa4b-fa1af5188f0a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "816/816 [==============================] - 3s 3ms/step\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JVVWxL0M_RgQ" }, "outputs": [], "source": [ "plt.rcParams[\"font.family\"] = \"monospace\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "ubQM2HpBKacs", "outputId": "a08271f8-f710-4a58-98c0-e05243fb25be" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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