2_Tech_Treassure_for_REE_minerals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "| Section                      | Subsections                |\n",
    "|-------------------------------|----------------------------|\n",
    "|[Introduction](#introduction)| [Initial Ideas](#iniitial-ideas) |\n",
    "| [Exploratory Data Analysis](#exploratory-data-analysis) | [Categorical Variables](#categorical-variables), [Numerical Variable](#numerical-variables) |\n",
    "| [Feature Engineering](#feature-engineering) | [PCA](#), [K-Means](#), [Transformations](#transformations) |\n",
    "|[Random Forest](#random-forest)| [Model Performance and Interpretation](#interpretationmodel-performance) |\n",
    "|[Conclusion](#conclusion)| --|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:orange;\">Please note that while all cells in the notebook will run efficiently, the dashboard will not be displayed within the notebook itself. The dashboard has been implemented using Streamlit, so to view it, you need to run the `dash.py` script locally or access it online at [dashboard link](https://reemineralexploration.streamlit.app/).</span>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Critical minerals are crucial for technologies that drive global energy, security, and economic systems. This exploratory analysis will focus on Rare Earth Elements (REEs), highlighting their unique and essential properties. Here are some key points to consider:\n",
    "\n",
    "- The concentration of REE production primarily in China ([figure 1](https://unctad.org/publication/digital-economy-report-2024)) exposes global supply chains to geopolitical and logistical vulnerabilities, underscoring the need for diversified supply sources [[INN](https://investingnews.com/rare-earths-forecast/)].\n",
    "\n",
    "- According to the IEA’s Global Critical Minerals Outlook, the major concern in the rare earths sector is not the demand-supply gap but the high geographical concentration of mining and refining activities. This concentration significantly heightens the risk of supply disruptions in the market [[INN](https://investingnews.com/rare-earths-forecast/)].\n",
    "\n",
    "- Strategically, governments are increasingly viewing REEs as critical minerals, prompting investments across the exploration, extraction, and processing stages of the rare earth supply chain [[MN](https://www.miningnewswire.com/three-key-trends-that-could-impact-rare-earths-in-2024/)].\n",
    "\n",
    "### Iniitial ideas\n",
    "- The model would incorporate raster data and linear features, such as the distance to the nearest fault, nearest geological contact, age, and geochemical data. This approach was inspired by several previous studies and analyses such as the [Prospectivity Mapping of Ni-Co Laterites in the Lachlan Orogen](https://github.com/EarthByte/MPM_Lachlan_Laterite), and [Tin-Tungsten Prospecting with Machine Learning in NE Tasmania, Australia](https://colab.research.google.com/drive/168PSo21-Jkwdz8xOmr5-rX9_DL3SInCN?usp=sharing).\n",
    "\n",
    "- The goal was to develop an ensemble model with a broad understanding of REE deposits, allowing for generalized predictions over larger areas. However, challenges arose related to the scale of available data for each deposit, as well as limitations in computational resources. After further consideration, I decided to adopt a simpler approach focused on the British Columbia region, with the intention of conducting a more regional study.\n",
    "\n",
    "- A `PCA` was performed on the raster data<sup>1</sup> to reduce dimensionality, followed by `unsupervised models` like `K-means` clustering. Unfortunately, these models did not produce any meaningful clusters, likely due to the complexity of the underlying spatial patterns or the limited number of training samples. \n",
    "\n",
    "- Afterward, a `Random Forest` model was applied to the dataset, but it performed poorly. The analysis focused on 65 known points of Critical Mineral showings and prospecting areas in British Columbia. For REE commodity types 1, 2, and 3, 38 points were labeled as mineralized with varying confidence levels (high/low), while the remaining points were categorized as \"non-mineralized\" (at least for REE prospects). While this initially seemed to provide a reasonably balanced dataset for the Random Forest model, it may have introduced additional complexity. As a result, the model will be revisited outside of the challenge using only two categories and the `grid_no_bc` grid (check the Legacy notebook for further understanding), with the goal of reducing complexity and improving performance.\n",
    "\n",
    "<sup>1</sup>  _Please note that the RAD raster data might not have been transformed accurately from the source_\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "In the Data_Cleaninng_Preprocessing notebook all the standarization for geospatial consistency was made, however, here I'll explore for potential correlation or patterns in the data, as well as some transformation necesary in order to try to implement a simple machine learning model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import re\n",
    "from matplotlib.colors import ListedColormap\n",
    "import rasterio\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Variables\n",
    "So far our shp\\geodataframe will have only categorical variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the deposit points\n",
    "deposits = gpd.read_file(r\"datasets\\shp\\cmBC.shp\") #all critical minerals ocurrences in BC\n",
    "#deposits_all = gpd.read_file(r\"datasets\\shp\\cmBC.shp\") # When selecting RS deposits\n",
    "\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS') | (deposits_all['COMMOD_C2']=='RS') | (deposits_all['COMMOD_C3']=='RS')]\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS') | (deposits_all['COMMOD_C2']=='RS')]\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1600 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_columns = deposits.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "n_cols = 3\n",
    "n_rows = (len(categorical_columns) + n_cols - 1) // n_cols #number of rows\n",
    "\n",
    "# subplots\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, n_rows * 4))\n",
    "\n",
    "#flatten the axis\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, col in enumerate(categorical_columns):\n",
    "    sns.countplot(y=col, data=deposits, ax=axes[i], color='skyblue')\n",
    "   # axes[i].set_title(f'Distribution {col}', fontsize=12)\n",
    "    axes[i].set_xlabel('Freq')\n",
    "    axes[i].set_ylabel(col)\n",
    "\n",
    "# delete empty plots\n",
    "for j in range(i + 1, len(axes)):\n",
    "    fig.delaxes(axes[j])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some categories in the dataset show similar frequency distributions to other attributes, suggesting potential correlations that could simplify the predictive model without significantly compromising the informational value. The analysis will specifically focus on:\n",
    "\n",
    "- `DEPTYPE_D1`: This category provides insights into the types of geological deposits present, which directly influence the mineralization processes and potential. It's unique in its distribution.\n",
    "\n",
    "- `Rock Class`: It shows a frequency distribution similar to 'age_min' and 'age_max'. Given that these age categories might not add significant value beyond what 'rock_class' can provide in a regional system, it simplifies the model to keep 'rock_class' and omit the others.\n",
    "\n",
    "- `Belt`: Although it features only two categories, it is retained for its ability to capture crucial regional geological distinctions that are significant for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['COMMOD_C1', 'COMMOD_C2', 'COMMOD_C3', 'DEPTYPE_D1', 'rock_class',\n",
       "       'belt', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deposits.drop(columns=['STATUS_D','strat_unit', 'rock_type', 'age_max', 'age_min'], inplace=True)\n",
    "deposits.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical Variables\n",
    "\n",
    "The numerical variables in this analysis are derived directly from raster datasets. To effectively summarize and reduce the dimensionality of these complex data sets, we will employ Principal Component Analysis (PCA), and this will help to identify the most significant patterns and variations within the rasters.\n",
    "\n",
    "#### PCA Analysis for points CM REE\n",
    "Multiple PCA tests were conducted, considering different numbers of components ranging from 2 to 5. These tests were applied to three distinct subsets of our data:\n",
    "\n",
    "- `All Samples` (65 in total): This includes the entire dataset to ensure a comprehensive analysis across all variables.\n",
    "- `Samples with REE in Any Commodity` (38 samples): This subset focuses on samples where Rare Earth Elements (REE) appear in any of the commodity classifications, providing insights into the specific behaviors of REE-associated variables.\n",
    "- `Samples with REE as the Primary Commodity` (6 samples): This targeted analysis concentrates on samples where REE is the main commodity, allowing for a detailed study of its predominant effects.\n",
    "\n",
    "In general, the percentage of variance explained and the characteristics identified by the PCA remained consistent across all analyses. Notably, the first three principal components (PC1 to PC3) account for approximately 90% of the variance, capturing the majority of the dataset's underlying patterns. The introduction of the fourth component (PC4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"If you want to try the diffences of PCA with REE commodities you can uncomment the following lines. \n",
    "The code runned is following the all togheter approach with deposits = all records for critical minerals\n",
    "\"\"\"\n",
    "#deposits_all = deposits.copy()\n",
    "\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS') | (deposits_all['COMMOD_C2']=='RS') | (deposits_all['COMMOD_C3']=='RS')]\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS') | (deposits_all['COMMOD_C2']=='RS')]\n",
    "#deposits=deposits_all[(deposits_all['COMMOD_C1']=='RS')]\n",
    "\n",
    "# Define the raster files list\n",
    "raster_paths = [os.path.join(r'datasets\\raster\\resampled', f) for f in os.listdir(r'datasets\\raster\\resampled') if f.lower().endswith('.tif')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance by component: [45.10100529 22.55422077 14.00534342  8.76245242], which represent up to 90.42% of the total data\n"
     ]
    }
   ],
   "source": [
    "# Initialize an array to hold raster values for each deposit point\n",
    "features = np.zeros((len(deposits), len(raster_paths)))\n",
    "\n",
    "# Extract raster values at deposit locations\n",
    "for i, raster_path in enumerate(raster_paths):\n",
    "    with rasterio.open(raster_path) as src:\n",
    "        # Use sample to extract raster values at points\n",
    "        raster_values = [x[0] for x in src.sample([(point.x, point.y) for point in deposits.geometry])]\n",
    "        features[:, i] = raster_values\n",
    "\n",
    "features_df = pd.DataFrame(data=features, columns=[raster_path.split('/')[-1] for raster_path in raster_paths])\n",
    "\n",
    "# Initialize PCA, let's reduce to 5 principal components for visualization\n",
    "pca = PCA(n_components=4)\n",
    "\n",
    "# Fit and transform the features\n",
    "principal_components = pca.fit_transform(features)\n",
    "\n",
    "#Convert coefficients to a DataFrame for better visualization\n",
    "loadings = pca.components_\n",
    "loadings_df = pd.DataFrame(data=loadings, columns=features_df.columns, index=['PC1', 'PC2','PC3','PC4'])#,'PC5'])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(loadings_df, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=.5)\n",
    "plt.title('PCA Loadings Heatmap')\n",
    "plt.show()\n",
    "\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "print(f\"Explained variance by component: {(100*explained_variance)}, which represent up to {round(sum(explained_variance)*100,2)}% of the total data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can be confident that with all the rasters, except for the `RAD` ones and `Gravitiy Anomnalies`, we can achieve a high level of explained variance and reduce dimensionality. Although the contribution of K is small, it will still be included in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['datasets\\\\raster\\\\resampled\\\\resampledRAD_eU-K.tif', 'datasets\\\\raster\\\\resampled\\\\resampled_GRAV_GravityAnomalies.tif', 'datasets\\\\raster\\\\resampled\\\\resampled_RAD _eTh-K.tif', 'datasets\\\\raster\\\\resampled\\\\resampled_RAD_Equivalent Uranium_eU.tif', 'datasets\\\\raster\\\\resampled\\\\resampled_RAD_EquivalentThorium_eTh.tif', 'datasets\\\\raster\\\\resampled\\\\resampled_RAD_eU-eTh.tif']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COMMOD_C1</th>\n",
       "      <th>COMMOD_C2</th>\n",
       "      <th>COMMOD_C3</th>\n",
       "      <th>DEPTYPE_D1</th>\n",
       "      <th>rock_class</th>\n",
       "      <th>belt</th>\n",
       "      <th>geometry</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_GRAV_1stVerticalDerivative.tif</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_GRAV_HorizontalGradient.tif</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_GRAV_IsostaticResidual.tif</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_MAG.tif</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_MAG1VD.tif</th>\n",
       "      <th>datasets\\raster\\resampled\\resampled_RAD_PotassiumK.tif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CU</td>\n",
       "      <td>AU</td>\n",
       "      <td>WO</td>\n",
       "      <td>Polymetallic veins Ag-Pb-Zn+/-Au</td>\n",
       "      <td>sedimentary rocks</td>\n",
       "      <td>Omineca</td>\n",
       "      <td>POINT (1712345.013 508793.306)</td>\n",
       "      <td>165.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>142.107132</td>\n",
       "      <td>229.92099</td>\n",
       "      <td>21.412943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  COMMOD_C1 COMMOD_C2 COMMOD_C3                        DEPTYPE_D1  \\\n",
       "0        CU        AU        WO  Polymetallic veins Ag-Pb-Zn+/-Au   \n",
       "\n",
       "          rock_class     belt                        geometry  \\\n",
       "0  sedimentary rocks  Omineca  POINT (1712345.013 508793.306)   \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_GRAV_1stVerticalDerivative.tif  \\\n",
       "0                                              165.0                    \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_GRAV_HorizontalGradient.tif  \\\n",
       "0                                              245.0                 \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_GRAV_IsostaticResidual.tif  \\\n",
       "0                                              166.0                \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_MAG.tif  \\\n",
       "0                                   142.107132   \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_MAG1VD.tif  \\\n",
       "0                                       229.92099   \n",
       "\n",
       "   datasets\\raster\\resampled\\resampled_RAD_PotassiumK.tif  \n",
       "0                                          21.412943       "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting columns to drop in the dataset\n",
    "columns = [i for i in features_df.columns if 'RAD' in i and 'Potassium' not in i or 'Gravity'in i] \n",
    "print(columns)\n",
    "features_important=features_df.drop(columns=columns)\n",
    "\n",
    "\n",
    "#Join this DataFrame with the deposits DataFrame to keep geographic info\n",
    "final_df = pd.concat([deposits.reset_index(drop=True), features_important], axis=1)\n",
    "final_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Clustering Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values\n",
    "In the initial phase of feature engineering, null values in the dataset were addressed. After preprocessing, several strategies were explored to label the data, starting with K-means clustering to identify natural groupings. However, the elbow method did not reveal clear clusters, and further clustering attempts using Gaussian Mixture Models (GMM) and DBSCAN also resulted in weak separations, with low silhouette scores across the models.\n",
    "\n",
    "Despite these efforts, the lack of clear clustering suggests that unsupervised methods may not be suitable for this dataset. Therefore, the analysis will proceed with Random Forest, leveraging domain knowledge to predict mineralization labels and evaluate feature importance in a supervised context.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COMMOD_C1                                                             0\n",
       "COMMOD_C2                                                             0\n",
       "COMMOD_C3                                                             6\n",
       "DEPTYPE_D1                                                            8\n",
       "rock_class                                                            0\n",
       "belt                                                                  0\n",
       "geometry                                                              0\n",
       "datasets\\raster\\resampled\\resampled_GRAV_1stVerticalDerivative.tif    0\n",
       "datasets\\raster\\resampled\\resampled_GRAV_HorizontalGradient.tif       0\n",
       "datasets\\raster\\resampled\\resampled_GRAV_IsostaticResidual.tif        0\n",
       "datasets\\raster\\resampled\\resampled_MAG.tif                           0\n",
       "datasets\\raster\\resampled\\resampled_MAG1VD.tif                        0\n",
       "datasets\\raster\\resampled\\resampled_RAD_PotassiumK.tif                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values in these fields likely indicate the absence of a third commodity (`COMMOD_C3`) and unknown characteristics regarding the origin of the deposits (`DEPTYPE_D1`). Therefore, 'None' will be assigned to `COMMOD_C3` to accurately reflect the absence of a third commodity. For `DEPTYPE_D1`, an `'Unknown'` category will be used to represent the undetermined origins of the deposits in the datase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEPTYPE_D1\n",
       "Carbonatite-hosted deposits                    25\n",
       "Unknown                                         9\n",
       "Upwelling-type phosphate                        6\n",
       "Surficial placers                               6\n",
       "Rare element pegmatite - NYF family             6\n",
       "Polymetallic veins Ag-Pb-Zn+/-Au                2\n",
       "Five-element veins Ni-Co-As-Ag+/-(Bi-U)         2\n",
       "Rare element pegmatite - LCT family             2\n",
       "Sedimentary exhalative Zn-Pb-Ag                 2\n",
       "Paleoplacer U-Au-PGE-Sn-Ti-diam-mag-gar-zir     1\n",
       "PEGMATITE                                       1\n",
       "Volcanic-hosted U                               1\n",
       "Nepheline syenite                               1\n",
       "Kyanite-sillimanite schists                     1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['DEPTYPE_D1'] = final_df['DEPTYPE_D1'].fillna('Unknown')\n",
    "final_df['DEPTYPE_D1'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label_1\n",
       "0    27\n",
       "1    21\n",
       "2    17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Conditions:\n",
    "1: \"High Confidence\" REE are present as commoditie 1 or 2\n",
    "2: \"Low Confindece\" REE are present as commmoditie 3\n",
    "0: \"No mineralizacion\" REE are no present, however those are still points for critical minerals\n",
    "\"\"\"\n",
    "final_df['label_1'] = np.where(\n",
    "    # Condition for high confidence (hc): either COMMOD_C1 or COMMOD_C2 has 'RS'\n",
    "    (final_df['COMMOD_C1'] == 'RS') | (final_df['COMMOD_C2'] == 'RS'),\n",
    "    1,  # Assign 'mineralized_hc' if the condition is met\n",
    "    # Otherwise, check for low confidence (lc) or no mineralized\n",
    "    np.where(\n",
    "        # Condition for low confidence (lc): COMMOD_C3 has 'RS'\n",
    "        final_df['COMMOD_C3'] == 'RS',\n",
    "        2,  # Assign 'mineralized_lc' if this condition is met\n",
    "        0  # Assign 'no_mineralized' if none of the conditions are met\n",
    "    )\n",
    ")\n",
    "\n",
    "final_df['label_1'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling\n",
    "before amply kmeans we need to scale everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##Not using the categorical columns\n",
    "# final_df.drop(columns=['COMMOD_C1', 'COMMOD_C2', 'COMMOD_C3', 'DEPTYPE_D1', 'rock_class',\n",
    "#        'belt'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['COMMOD_C1', 'COMMOD_C2', 'COMMOD_C3', 'DEPTYPE_D1', 'rock_class',\n",
       "       'belt', 'geometry', 'GRAV_1stVerticalDerivative',\n",
       "       'GRAV_HorizontalGradient', 'GRAV_IsostaticResidual', 'MAG', 'MAG1VD',\n",
       "       'RAD_PotassiumK'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to clean column names by extracting the desired part\n",
    "def clean_column_name(column_name):\n",
    "    #regex to capture GRAV, MAG, RAD followed by any characters and stop before \".tif\"\n",
    "    match = re.search(r'(GRAV|MAG|RAD).*?(?=\\.tif|$)', column_name)\n",
    "    if match:\n",
    "        return match.group(0)  # Return the matched part\n",
    "    else:\n",
    "        return column_name  # Return the original name if no match is found\n",
    "\n",
    "# Apply the cleaning function to all column names\n",
    "final_df.columns = [clean_column_name(col) for col in final_df.columns]\n",
    "\n",
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#categorical = ['COMMOD_C1', 'COMMOD_C2', 'COMMOD_C3', 'DEPTYPE_D1', 'rock_class','belt']  # Categorical columns\n",
    "numerical = ['GRAV_1stVerticalDerivative',\n",
    "       'GRAV_HorizontalGradient', 'GRAV_IsostaticResidual', 'MAG', 'MAG1VD']\n",
    "       #'RAD_PotassiumK']  # Numerical-rasters columns\n",
    "\n",
    "# Apply one-hot encoding to categorical columns\n",
    "#categorical_encoded = pd.get_dummies(final_df[categorical])\n",
    "\n",
    "# Combine numerical and categorical encoded columns\n",
    "#combined_data = pd.concat([final_df[numerical], categorical_encoded], axis=1)\n",
    "combined_data = final_df[numerical]\n",
    "\n",
    "# Step to scale the combined data\n",
    "scaler = StandardScaler()\n",
    "combined_data_scaled = scaler.fit_transform(combined_data)  # Scaled feature set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `K-Means`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, random_state=42, n_init='auto')\n",
    "# Fit the K-means model on the scaled data\n",
    "kmeans.fit(combined_data_scaled)\n",
    "# Assign the cluster labels to the original DataFrame\n",
    "final_df['kmeans_label'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters=i, random_state=42)\n",
    "    kmeans.fit(combined_data_scaled)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "# Plot the WCSS to find the elbow\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, 11), wcss, marker='o')\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `GMM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Define the number of components (clusters) for GMM\n",
    "gmm = GaussianMixture(n_components=3, random_state=42)\n",
    "\n",
    "# Fit the GMM model to the scaled data\n",
    "gmm.fit(combined_data_scaled)\n",
    "\n",
    "# Predict the cluster labels for each data point\n",
    "final_df['gmm_label'] = gmm.predict(combined_data_scaled)\n",
    "\n",
    "# Optional: Get the probabilities for each data point's assignment to clusters\n",
    "final_df['gmm_prob'] = gmm.predict_proba(combined_data_scaled).max(axis=1)  # Maximum probability for each point\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average silhouette score for GMM clustering is 0.270\n"
     ]
    }
   ],
   "source": [
    "# Calculate silhouette score\n",
    "silhouette_avg = silhouette_score(combined_data_scaled, final_df['gmm_label'])\n",
    "print(f\"The average silhouette score for GMM clustering is {silhouette_avg:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_components = 2, Silhouette Score = 0.269\n",
      "n_components = 3, Silhouette Score = 0.270\n",
      "n_components = 4, Silhouette Score = 0.282\n",
      "n_components = 5, Silhouette Score = 0.345\n"
     ]
    }
   ],
   "source": [
    "for n in range(2, 6):  # Testing for clusters from 2 to 5\n",
    "    gmm = GaussianMixture(n_components=n, random_state=42)\n",
    "    gmm.fit(combined_data_scaled)\n",
    "    labels = gmm.predict(combined_data_scaled)\n",
    "    silhouette_avg = silhouette_score(combined_data_scaled, labels)\n",
    "    print(f\"n_components = {n}, Silhouette Score = {silhouette_avg:.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "\n",
    "3-way Data Split:\n",
    "Training Set: Used for training the model.\n",
    "\n",
    "Validation Set: Used for tuning hyperparameters and evaluating model performance during development. This set is for try different model configurations and evaluate improvements.\n",
    "\n",
    "Holdout (Final Test) Set: This is the portion of the data that do not use until you are completely done with model development. This provides a completely unbiased final evaluation of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "X = combined_data_scaled  # Features (scaled numerical) + NO categorical)\n",
    "y = final_df['label_1']  # Target (labels)\n",
    "\n",
    "# Split into training+validation and holdout (final test)\n",
    "X_train_val, X_holdout, y_train_val, y_holdout = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#Further split the training+validation set into training and validation\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42)  # 0.25 of 0.8 is 0.2 of the total data\n",
    "\n",
    "# Now we have:\n",
    "# - X_train, y_train: For training the model\n",
    "# - X_val, y_val: For tuning and evaluating the model during development\n",
    "# - X_holdout, y_holdout: For the final evaluation after we're done with all tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.40      0.50         5\n",
      "           1       0.33      0.50      0.40         4\n",
      "           2       0.75      0.75      0.75         4\n",
      "\n",
      "    accuracy                           0.54        13\n",
      "   macro avg       0.58      0.55      0.55        13\n",
      "weighted avg       0.59      0.54      0.55        13\n",
      "\n",
      "Confusion Matrix:\n",
      "[[2 3 0]\n",
      " [1 2 1]\n",
      " [0 1 3]]\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Random Forest classifier\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = rf_model.predict(X_val)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(confusion_matrix(y_val, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Precision: 0.48\n",
      "Average Recall: 0.46\n",
      "Average F1-Score: 0.45\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "from sklearn.metrics import make_scorer, precision_recall_fscore_support\n",
    "import numpy as np\n",
    "\n",
    "# Define your features and labels\n",
    "X = combined_data_scaled  # Features (scaled numerical)\n",
    "y = final_df['label_1']   # Target (labels)\n",
    "\n",
    "# Initialize the Random Forest model\n",
    "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# stratified k-fold cross-validation (to ensure each fold has a similar class distribution)\n",
    "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# custom scoring function to get precision, recall, and f1-score for each fold\n",
    "scoring = make_scorer(precision_recall_fscore_support, average='weighted')\n",
    "\n",
    "# Perform cross-validation\n",
    "precision_scores = []\n",
    "recall_scores = []\n",
    "f1_scores = []\n",
    "\n",
    "for train_index, test_index in kf.split(X, y):\n",
    "    X_train, X_val = X[train_index], X[test_index]\n",
    "    y_train, y_val = y[train_index], y[test_index]\n",
    "    \n",
    "    rf_model.fit(X_train, y_train)\n",
    "    y_pred = rf_model.predict(X_val)\n",
    "    \n",
    "    # Get precision, recall, and F1 for the current fold\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(y_val, y_pred, average='weighted')\n",
    "    precision_scores.append(precision)\n",
    "    recall_scores.append(recall)\n",
    "    f1_scores.append(f1)\n",
    "\n",
    "# Calculate the average precision, recall, and F1 scores\n",
    "avg_precision = np.mean(precision_scores)\n",
    "avg_recall = np.mean(recall_scores)\n",
    "avg_f1 = np.mean(f1_scores)\n",
    "\n",
    "print(f\"Average Precision: {avg_precision:.2f}\")\n",
    "print(f\"Average Recall: {avg_recall:.2f}\")\n",
    "print(f\"Average F1-Score: {avg_f1:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpretation/Model performance\n",
    "\n",
    "- The metrics performance from the cross-validation analysis are all around 0.50, which indicates that the model is struggling to generalize well during cross-validation, and `performance is generally low`.\n",
    "\n",
    "- `Class 2` has the highest precision of all the classes (Precision: 0.75) meaning predictions for `'Mineralizacion_lc'` are more accurate.\n",
    "\n",
    "\n",
    "<span style=\"color:orange;\">I understand this is not a good model, however, I'm trying to meet the deadline with this one and then continue with the initial idea because it's a super interesting problem!</span>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAG: 0.2913\n",
      "MAG1VD: 0.2426\n",
      "GRAV_1stVerticalDerivative: 0.1699\n",
      "GRAV_HorizontalGradient: 0.1605\n",
      "GRAV_IsostaticResidual: 0.1357\n"
     ]
    }
   ],
   "source": [
    "importances = rf_model.feature_importances_\n",
    "feature_names = combined_data.columns\n",
    "\n",
    "# Sort and print feature importances\n",
    "sorted_indices = np.argsort(importances)[::-1]\n",
    "for idx in sorted_indices:\n",
    "    print(f\"{feature_names[idx]}: {importances[idx]:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing in rasters for prospectivity maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# List of raster paths for the new data\n",
    "raster_paths = [r'datasets\\raster\\aligned\\aligned_GRAV - 1st Vertical Derivative_float.tif',\n",
    "                r'datasets\\raster\\aligned\\aligned_resampled_cropped_reprojected_AGG_Canada 2 km - GRAV - Horizontal Gradient_float.tif',\n",
    "                r'datasets\\raster\\aligned\\aligned_resampled_cropped_reprojected_AGG_Canada 2 km - GRAV - Isostatic Residual_float.tif',\n",
    "                r'datasets\\raster\\aligned\\aligned_resampled_cropped_reprojected_Canada_1km_MAG_float.tif',\n",
    "                r'datasets\\raster\\aligned\\aligned_resampled_cropped_reprojected_Canada_1km_MAG1VD_float.tif'\n",
    "                #r'datasets\\raster\\aligned\\aligned_resampled_cropped_reprojected_Canada - 250m - RAD - eU-K - 2012 - Geotif_float.tif'\n",
    "                ]  # Add paths to your rasters\n",
    "\n",
    "\n",
    "# Initialize an empty list to hold the raster values (features for each pixel)\n",
    "raster_features = []\n",
    "\n",
    "# Loop through the rasters and extract values for each pixel\n",
    "for raster_path in raster_paths:\n",
    "    with rasterio.open(raster_path) as src:\n",
    "        raster_values = src.read(1).flatten()  # Read and flatten the raster values into a 1D array\n",
    "        raster_features.append(raster_values)\n",
    "\n",
    "# Combine all raster values into a DataFrame (each column corresponds to a raster)\n",
    "raster_data = np.stack(raster_features, axis=1)\n",
    "raster_df = pd.DataFrame(raster_data, columns=['GRAV_1stVerticalDerivative', 'GRAV_HorizontalGradient', 'GRAV_IsostaticResidual','MAG','MAG1VD'])#,'RAD_K'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GRAV_1stVerticalDerivative    1233597\n",
       "GRAV_HorizontalGradient       1233597\n",
       "GRAV_IsostaticResidual        1233597\n",
       "MAG                           1232098\n",
       "MAG1VD                        1232098\n",
       "dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raster_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the rasters cover areas outside British Columbia (rectangular extened), which are marked as NaN, I applied a mask to exclude these regions. This ensures that predictions are made only for valid data within British Columbia, avoiding erroneous predictions in areas with no data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of valid rows: 941485\n"
     ]
    }
   ],
   "source": [
    "# Create a combined mask where all raster columns have valid (non-NaN) values\n",
    "combined_valid_mask = raster_df.notna().all(axis=1)\n",
    "\n",
    "# Check how many valid rows you have (should match 946539)\n",
    "print(f\"Number of valid rows: {combined_valid_mask.sum()}\")\n",
    "\n",
    "# Use the combined mask to filter out rows with any NaN values\n",
    "raster_data_filtered = raster_df[combined_valid_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appling the same StandardScaler that was used during training\n",
    "raster_data_scaled = scaler.transform(raster_data_filtered)\n",
    "#predictions using the scaled data\n",
    "raster_predictions = rf_model.predict(raster_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models\\\\random_forest_model.pkl']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Save the model for further use\n",
    "joblib.dump(rf_model, r'models\\random_forest_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reconstruct the full raster map with NaNs in invalid areas\n",
    "with rasterio.open(raster_paths[0]) as src:\n",
    "    raster_shape = src.shape  # (1366, 1596)\n",
    "\n",
    "predicted_map_full = np.full(raster_shape, np.nan)  # Initialize with NaNs\n",
    "predicted_map_full[combined_valid_mask.values.reshape(raster_shape)] = raster_predictions\n",
    "# Replace NaN values with a distinct integer (e.g., -1 for areas outside British Columbia)\n",
    "predicted_map_full[np.isnan(predicted_map_full)] = -1\n",
    "\n",
    "#Save the predicted map as a new raster\n",
    "with rasterio.open(\n",
    "    'predicted_prospectivity_map_discrete.tif', 'w',\n",
    "    driver='GTiff',\n",
    "    height=predicted_map_full.shape[0],\n",
    "    width=predicted_map_full.shape[1],\n",
    "    count=1,\n",
    "    dtype=rasterio.int32,  # Adjust the data type as needed\n",
    "    crs=src.crs,  # Use the same CRS as the original raster\n",
    "    transform=src.transform  # Use the same transform (georeferencing)\n",
    ") as dst:\n",
    "    dst.write(predicted_map_full.astype(np.int32), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value 0.0 occurs 489347 times\n",
      "Value 1.0 occurs 198394 times\n",
      "Value 2.0 occurs 253744 times\n",
      "Value nan occurs 1238651 times\n"
     ]
    }
   ],
   "source": [
    "unique_values, counts = np.unique(predicted_map_full, return_counts=True)\n",
    "\n",
    "# Print the counts for each value\n",
    "for value, count in zip(unique_values, counts):\n",
    "    print(f\"Value {value} occurs {count} times\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Open the predicted map file\n",
    "with rasterio.open('predicted_prospectivity_map_discrete.tif') as src:\n",
    "    predicted_map = src.read(1)  # Read the first band\n",
    "\n",
    "# Create a discrete colormap with 3 colors, one for each class (0, 1, 2)\n",
    "colors = ['white', 'gray', 'skyblue']  # Define colors for the classes\n",
    "cmap = ListedColormap(colors)\n",
    "\n",
    "# Plot the raster using the discrete colormap\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.imshow(predicted_map, cmap=cmap, vmin=0, vmax=2)\n",
    "plt.colorbar(ticks=[0, 1, 2], label='Predicted Classes')  # Add colorbar with class labels\n",
    "plt.title('Predicted Prospectivity Map (Discrete)')\n",
    "plt.xlabel('X Coordinate')\n",
    "plt.ylabel('Y Coordinate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a result of a low performance model. \n",
    "\n",
    "## Conclusion\n",
    "\n",
    "This project involved a significant amount of work, much of which may not be immediately visible in the final product. A large portion of the effort was dedicated to understanding the complex domain of rare earth element (REE) exploration (types, geodynamics, pathfinders), which took substantial time at the beginning. Furthermore, the nuances and challenges of working with geospatial data are critical to producing meaningful results.\n",
    "\n",
    "Machine Learning (ML) is a powerful tool, and it's exciting to apply it to this field. However, the success of ML models depends heavily on the quality and richness of the data. In hindsight, attempting to tackle such a complex problem within a month, as a single person, and without the necessary computational resources was an ambitious goal. Nevertheless, the experience has been incredibly enriching, and I'm grateful for the opportunity to deepen my understanding of REE exploration and ML applications. This project has laid important groundwork for my professional future development.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "geo_env",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}