Time_Series_Feature_Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alpha Feature Selector for Time Series Data\n",
    "\n",
    "## Introduction\n",
    "\n",
    "`alpha_Feat_Selector` is a Python function designed for effective feature selection in time series data. It combines several statistical methods and machine learning techniques to identify the most significant features for time series forecasting models.\n",
    "\n",
    "## Features\n",
    "\n",
    "- **Lag Feature Creation**: Generates lagged versions of the target variable to capture temporal dependencies.\n",
    "- **Rolling Window Statistics**: Computes rolling statistics like mean and standard deviation as potential features.\n",
    "- **Time Series Decomposition**: Extracts trend, seasonality, and residuals from the time series.\n",
    "- **Feature Importance Analysis**: Uses Random Forest to determine the importance of each feature.\n",
    "- **Visualization Dashboard**: Provides insightful visualizations for time series analysis and feature importance.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "Ensure you have the following libraries installed:\n",
    "- pandas\n",
    "- numpy\n",
    "- matplotlib\n",
    "- seaborn\n",
    "- sklearn\n",
    "- statsmodels\n",
    "- tqdm\n",
    "\n",
    "You can install them using pip:\n",
    "\n",
    "```bash\n",
    "pip install pandas numpy matplotlib seaborn sklearn statsmodels tqdm\n",
    "```\n",
    "\n",
    "## Usage\n",
    "\n",
    "Here's how to use the alpha_Feat_Selector function:\n",
    "\n",
    "Import the Function:\n",
    "Ensure the alpha_Feat_Selector function is in your Python path.\n",
    "\n",
    "Prepare Your Data:\n",
    "Load your time series data into a Pandas DataFrame.\n",
    "\n",
    "Run the Feature Selector:\n",
    "Call the alpha_Feat_Selector function with your DataFrame, the name of the file where you want to save the processed data, and the name of the target column.\n",
    "\n",
    "Example:\n",
    "\n",
    "```bash\n",
    "df_clean, features_selected, analysis_explanation = alpha_Feat_Selector(your_dataframe, 'processed_data.csv', 'your_target_column', 'importance_percentile')\n",
    "\n",
    "df_clean, features_selected, analysis_explanation = alpha_Feat_Selector(df_raw, 'cleaned_data.csv', 'Close', importance_percentile=70)\n",
    "```\n",
    "\n",
    "## View the Results:\n",
    "The function returns a cleaned DataFrame and displays a dashboard of visualizations.\n",
    "\n",
    "\n",
    "## Example\n",
    "\n",
    "Here's a simple example of using alpha_Feat_Selector:\n",
    "\n",
    "```bash\n",
    "\n",
    "import pandas as pd\n",
    "# Load your data\n",
    "df = pd.read_csv('your_time_series_data.csv')\n",
    "\n",
    "# Assuming 'Close' is your target column\n",
    "df_clean = alpha_Feat_Selector(df, 'cleaned_data.csv', 'Close')\n",
    "\n",
    "```\n",
    "## Conclusion\n",
    "\n",
    "The alpha_Feat_Selector function is a comprehensive tool for feature selection in time series data, making the process more intuitive and effective. It's suitable for both beginners and experienced practitioners in the field of time series analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Dataframe from csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Create raw df to be used for testing purposes\n",
    "# Load a csv file with the raw data \n",
    "\n",
    "df_raw = pd.read_csv(\"BTCUSDT_06_01_2024_1h_raw.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              dateTime       LTime       OpenTime      Open     High  \\\n",
      "0  2018-01-01 22:00:00  1514844000  1514844000000  13494.99  13599.7   \n",
      "\n",
      "        Low    Close      Volume      CloseTime  QuoteAssetVolume  ...  \\\n",
      "0  13450.96  13452.0  303.301385  1514847599999      4.101907e+06  ...   \n",
      "\n",
      "      lag_3  rolling_mean  rolling_std  day_of_week  hour_of_day        RSI  \\\n",
      "0  13240.37      13249.66    198.14436            0           22  48.561459   \n",
      "\n",
      "   Smooth_Close  Number  High_Trendline  Low_Trendline  \n",
      "0  13365.347105      30    15740.326965   15319.874754  \n",
      "\n",
      "[1 rows x 31 columns]\n",
      "Index(['dateTime', 'LTime', 'OpenTime', 'Open', 'High', 'Low', 'Close',\n",
      "       'Volume', 'CloseTime', 'QuoteAssetVolume', 'NumberOfTrades',\n",
      "       'TakerBuyBaseVolume', 'TakerBuyQuoteVolume', 'MA_7', 'MA_30', 'EMA12',\n",
      "       'EMA26', 'MACD', 'Signal_Line', 'lag_1', 'lag_2', 'lag_3',\n",
      "       'rolling_mean', 'rolling_std', 'day_of_week', 'hour_of_day', 'RSI',\n",
      "       'Smooth_Close', 'Number', 'High_Trendline', 'Low_Trendline'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_raw.head(1))\n",
    "print(df_raw.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install dependency in case your env not have them already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install feature_engine seaborn tqdm sklearn matplotlib statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The function alpha_Feat_Selector is a function that will help you in the feature selection \n",
    "I added a loading bar beacuse on very large dataset it may take quirte long. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from feature_engine.selection import DropConstantFeatures, DropDuplicateFeatures\n",
    "from tqdm.notebook import tqdm\n",
    "import sys\n",
    "import re\n",
    "\n",
    "def is_datetime_column(col):\n",
    "    if pd.api.types.is_string_dtype(col):\n",
    "        first_non_null = col.dropna().iloc[0] if not col.dropna().empty else ''\n",
    "        return bool(re.search(r'\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}', first_non_null))\n",
    "    return False\n",
    "\n",
    "def explain_results(df_clean, features_selected, feature_importances):\n",
    "    explanation = \"Time Series Feature Selection Analysis:\\n\"\n",
    "    explanation += \"---------------------------------------\\n\\n\"\n",
    "\n",
    "    explanation += f\"Total Features Selected: {len(features_selected)}\\n\\n\"\n",
    "    for feature in features_selected:\n",
    "        importance = feature_importances[feature]\n",
    "        explanation += f\"Feature: {feature}, Importance: {importance}\\n\"\n",
    "\n",
    "    explanation += \"\\nGeneral Insights:\\n\"\n",
    "    explanation += f\"  - Total Features Retrieved: {df_clean.shape[1]}\\n\"\n",
    "    explanation += f\"  - Total Observations in Cleaned Dataset: {df_clean.shape[0]}\\n\"\n",
    "\n",
    "    return explanation\n",
    "\n",
    "def alpha_Feat_Selector(df, filename, target_column, importance_percentile=20):\n",
    "    try:\n",
    "        pbar = tqdm(total=5, desc=\"Overall Progress\")\n",
    "\n",
    "        # Step 1: Exclude datetime columns\n",
    "        datetime_cols = [col for col in df.columns if is_datetime_column(df[col])]\n",
    "        df_excluded_dt = df.drop(columns=datetime_cols)\n",
    "        pbar.update(1)  # Increment progress bar by 1\n",
    "\n",
    "        df_lagged = df_excluded_dt.copy()\n",
    "        for lag in range(1, 4):\n",
    "            df_lagged[f'{target_column}_lag_{lag}'] = df_lagged[target_column].shift(lag)\n",
    "        df_lagged['rolling_mean'] = df_lagged[target_column].rolling(window=3).mean()\n",
    "        df_lagged.dropna(inplace=True)\n",
    "        pbar.update(1)\n",
    "\n",
    "\n",
    "        # Step 2: Preprocessing (lags, rolling mean, etc.)\n",
    "        drop_const_feat = DropConstantFeatures(tol=0.95)\n",
    "        drop_dup_feat = DropDuplicateFeatures()\n",
    "        df_lagged = drop_const_feat.fit_transform(df_lagged)\n",
    "        df_lagged = drop_dup_feat.fit_transform(df_lagged)\n",
    "        pbar.update(1)\n",
    "\n",
    "        # Step 4: Decompose and feature selection\n",
    "        decomposition = seasonal_decompose(df[target_column], model='additive', period=12)\n",
    "        df_lagged['trend'] = decomposition.trend\n",
    "        df_lagged['seasonality'] = decomposition.seasonal\n",
    "        df_lagged['residual'] = decomposition.resid\n",
    "        df_lagged.dropna(inplace=True)\n",
    "        pbar.update(1)\n",
    "\n",
    "        X = df_lagged.drop(target_column, axis=1)\n",
    "        y = df_lagged[target_column]\n",
    "        model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "        model.fit(X, y)\n",
    "        importances = model.feature_importances_\n",
    "        # Calculate the threshold based on the percentile\n",
    "        threshold = np.percentile(importances, importance_percentile)\n",
    "        features_selected = X.columns[importances >= threshold].tolist()\n",
    "        pbar.update(1)\n",
    "\n",
    "        # Visualization Dashboard\n",
    "        plt.figure(figsize=(18, 7))\n",
    "\n",
    "        # Time Series Plot\n",
    "        plt.subplot(1, 2, 1)\n",
    "        df[target_column].plot(title='Time Series Plot')\n",
    "        plt.xlabel('Time')\n",
    "        plt.ylabel(target_column)\n",
    "\n",
    "        # Feature Importances Plot\n",
    "        plt.subplot(1, 2, 2)\n",
    "        filtered_importances = importances[X.columns.isin(features_selected)]\n",
    "        sns.barplot(x=filtered_importances, y=X.columns[X.columns.isin(features_selected)], orient='h')\n",
    "        plt.title('Feature Importances')\n",
    "        plt.xlabel('Importance')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        # Save the cleaned dataframe if more than 3 features are selected\n",
    "        df_clean = df_lagged.copy()\n",
    "        if len(features_selected) >= 3:\n",
    "            df_clean.to_csv(filename, index=False)\n",
    "        \n",
    "        feature_importances = {feature: importances[i] for i, feature in enumerate(X.columns) if importances[i] >= threshold}\n",
    "        analysis_explanation = explain_results(df_clean, features_selected, feature_importances)\n",
    "        pbar.close()\n",
    "    \n",
    "\n",
    "        return df_clean, features_selected, analysis_explanation\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred: \", e)\n",
    "        return None, None, None\n",
    "\n",
    "# Example usage\n",
    "# df_clean, features_selected, analysis_explanation = alpha_Feat_Selector(df_raw, 'cleaned_data.csv', 'Close', importance_percentile=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4018cd054d1744b3a07a45fbb3b61e35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Overall Progress:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Open', 'High', 'Low', 'Volume', 'QuoteAssetVolume', 'NumberOfTrades', 'TakerBuyBaseVolume', 'TakerBuyQuoteVolume', 'MA_7', 'EMA12', 'MACD', 'Signal_Line', 'lag_1', 'lag_2', 'lag_3', 'rolling_mean', 'rolling_std', 'hour_of_day', 'RSI', 'Number', 'High_Trendline', 'Low_Trendline', 'Close_lag_1', 'Close_lag_2', 'Close_lag_3', 'trend', 'seasonality', 'residual']\n",
      "Time Series Feature Selection Analysis:\n",
      "---------------------------------------\n",
      "\n",
      "Total Features Selected: 28\n",
      "\n",
      "Feature: Open, Importance: 5.883423325003375e-06\n",
      "Feature: High, Importance: 0.6110767386665453\n",
      "Feature: Low, Importance: 0.24501287537016062\n",
      "Feature: Volume, Importance: 6.607930915961125e-07\n",
      "Feature: QuoteAssetVolume, Importance: 6.208724955043466e-07\n",
      "Feature: NumberOfTrades, Importance: 9.441342159879737e-07\n",
      "Feature: TakerBuyBaseVolume, Importance: 7.71019070233988e-07\n",
      "Feature: TakerBuyQuoteVolume, Importance: 7.266449854603417e-07\n",
      "Feature: MA_7, Importance: 7.449176695718434e-07\n",
      "Feature: EMA12, Importance: 5.23047470584845e-07\n",
      "Feature: MACD, Importance: 6.864265463413395e-07\n",
      "Feature: Signal_Line, Importance: 8.600826961262765e-07\n",
      "Feature: lag_1, Importance: 1.4666077350976458e-06\n",
      "Feature: lag_2, Importance: 9.39929885069093e-07\n",
      "Feature: lag_3, Importance: 5.451806716733622e-07\n",
      "Feature: rolling_mean, Importance: 0.14226525226796258\n",
      "Feature: rolling_std, Importance: 1.1668720251213922e-06\n",
      "Feature: hour_of_day, Importance: 7.523869830312138e-07\n",
      "Feature: RSI, Importance: 1.5399785770147966e-06\n",
      "Feature: Number, Importance: 7.532054689382032e-07\n",
      "Feature: High_Trendline, Importance: 5.599807618147173e-07\n",
      "Feature: Low_Trendline, Importance: 5.762391698315326e-07\n",
      "Feature: Close_lag_1, Importance: 1.431173574276613e-06\n",
      "Feature: Close_lag_2, Importance: 9.204798435615233e-07\n",
      "Feature: Close_lag_3, Importance: 5.968252789774441e-07\n",
      "Feature: trend, Importance: 0.0016098619559252077\n",
      "Feature: seasonality, Importance: 6.760801322923551e-07\n",
      "Feature: residual, Importance: 8.089062969904703e-06\n",
      "\n",
      "General Insights:\n",
      "  - Total Features Retrieved: 36\n",
      "  - Total Observations in Cleaned Dataset: 49896\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_clean, features_selected, analysis_explanation = alpha_Feat_Selector(df_raw, 'cleaned_data.csv', 'Close', importance_percentile=20)\n",
    "\n",
    "# print(df_clean.head(1)) # Check the cleaned dataframe \n",
    "print(features_selected) # Check the selected features \n",
    "print(analysis_explanation) # Check the analysis explanation Des-Tri-Ct6-969-969-969"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AIFlow_Prophet",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}