{ "cells": [ { "cell_type": "code", "execution_count": 78, "id": "3521a852-eae8-4d4c-874e-b3f171b1eebd", "metadata": { "tags": [] }, "outputs": [], "source": [ "import mlflow\n", "import numpy as np\n", "import pandas as pd\n", "import csv\n", "from datetime import datetime\n", "import seaborn as sns\n", "import logging\n", "import mlflow\n", "from pandas import DataFrame, concat\n", "from tqdm import tqdm as tqdm_progress\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from dataclay import Client\n", "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error, mean_absolute_error, r2_score\n", "from xgboost import XGBRegressor\n", "\n", "import sys\n", "sys.path.insert(0,'/home/jaydeep/UCD_FUN_HORIZON_ICOS/src/icos_api/') " ] }, { "cell_type": "code", "execution_count": 79, "id": "19c8e38d-0c91-4abe-84bf-c243970f445c", "metadata": { "tags": [] }, "outputs": [], "source": [ "from utils.utils import *" ] }, { "cell_type": "code", "execution_count": 80, "id": "4f7e698c-90f5-44c0-a60c-5caf1b932895", "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(\"../dataset/cpu_data_custom.csv\")" ] }, { "cell_type": "code", "execution_count": 81, "id": "12edf2fe-1c80-42f3-bbb8-c92f6c00df78", "metadata": { "tags": [] }, "outputs": [], "source": [ "raw_data_clean = data_clean(raw_data)\n", "train_df, test_df = data_simple_split(raw_data_clean,test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 82, "id": "b8c836fe-afd5-45b4-b921-007d8a1494d1", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
| \n", " | date | \n", "TARGET | \n", "RAM | \n", "
|---|---|---|---|
| 34992 | \n", "2023-07-23 19:40:00 | \n", "57.8 | \n", "14.3 | \n", "
| 33738 | \n", "2023-07-22 22:03:05 | \n", "25.0 | \n", "14.3 | \n", "
| 12662 | \n", "2023-07-07 18:46:12 | \n", "85.1 | \n", "12.9 | \n", "
| 37591 | \n", "2023-07-25 16:27:52 | \n", "73.8 | \n", "14.3 | \n", "
| 32654 | \n", "2023-07-22 03:22:01 | \n", "0.4 | \n", "14.1 | \n", "
| \n", " | var1(t-12) | \n", "var1(t-11) | \n", "var1(t-10) | \n", "var1(t-9) | \n", "var1(t-8) | \n", "var1(t-7) | \n", "var1(t-6) | \n", "var1(t-5) | \n", "var1(t-4) | \n", "var1(t-3) | \n", "var1(t-2) | \n", "var1(t-1) | \n", "var1(t) | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34890 | \n", "57.8 | \n", "25.0 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "
| 19573 | \n", "25.0 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "
| 30662 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "
| 29027 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "56.9 | \n", "
| 43542 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "56.9 | \n", "36.2 | \n", "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
" num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
" num_parallel_tree=None, random_state=None, ...)| \n", " | var1(t-12) | \n", "var1(t-11) | \n", "var1(t-10) | \n", "var1(t-9) | \n", "var1(t-8) | \n", "var1(t-7) | \n", "var1(t-6) | \n", "var1(t-5) | \n", "var1(t-4) | \n", "var1(t-3) | \n", "var1(t-2) | \n", "var1(t-1) | \n", "var1(t) | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34890 | \n", "57.8 | \n", "25.0 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "
| 19573 | \n", "25.0 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "
| 30662 | \n", "85.1 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "
| 29027 | \n", "73.8 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "56.9 | \n", "
| 43542 | \n", "0.4 | \n", "79.5 | \n", "0.1 | \n", "52.7 | \n", "94.8 | \n", "0.1 | \n", "99.1 | \n", "71.4 | \n", "8.6 | \n", "95.0 | \n", "58.1 | \n", "56.9 | \n", "36.2 | \n", "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
" num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
" colsample_bylevel=None, colsample_bynode=None,\n",
" colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
" enable_categorical=False, eval_metric=None, feature_types=None,\n",
" gamma=None, grow_policy=None, importance_type=None,\n",
" interaction_constraints=None, learning_rate=None, max_bin=None,\n",
" max_cat_threshold=None, max_cat_to_onehot=None,\n",
" max_delta_step=None, max_depth=None, max_leaves=None,\n",
" min_child_weight=None, missing=nan, monotone_constraints=None,\n",
" multi_strategy=None, n_estimators=None, n_jobs=None,\n",
" num_parallel_tree=None, random_state=None, ...)