Day_2/2.2_interpretability_complete.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"https://github.com/icomse/5th_workshop_MachineLearning/blob/main/Day_2/2.2_interpretability_complete.ipynb","timestamp":1689111484500},{"file_id":"https://github.com/icomse/5th_workshop_MachineLearning/blob/main/Day_2/2.2_interpretability_complete.ipynb","timestamp":1689111148769},{"file_id":"1OP8MzKqteWqulYz1qZqIn64PhFjsrCDa","timestamp":1688918282883}],"authorship_tag":"ABX9TyNBVPkI0ePto5P9KYOrup6W"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":9,"metadata":{"id":"p62IXYb0EEqZ","executionInfo":{"status":"ok","timestamp":1689111452741,"user_tz":300,"elapsed":3557,"user":{"displayName":"","userId":""}},"outputId":"bd6c2b44-83ad-4ce4-a008-70e7925eb6d3","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into '5th_workshop_MachineLearning'...\n","remote: Enumerating objects: 124, done.\u001b[K\n","remote: Counting objects: 100% (124/124), done.\u001b[K\n","remote: Compressing objects: 100% (98/98), done.\u001b[K\n","remote: Total 124 (delta 49), reused 73 (delta 20), pack-reused 0\u001b[K\n","Receiving objects: 100% (124/124), 15.27 MiB | 9.60 MiB/s, done.\n","Resolving deltas: 100% (49/49), done.\n"]}],"source":["!git clone https://github.com/icomse/5th_workshop_MachineLearning.git\n","import os\n","os.chdir('5th_workshop_MachineLearning/data')\n","data_dir = os.getcwd()"]},{"cell_type":"markdown","source":["# Make sure you can see the files we'll need"],"metadata":{"id":"eUxx6WwvGf0V"}},{"cell_type":"code","source":["os.listdir(data_dir)"],"metadata":{"id":"yHJZLXIyE7hH","executionInfo":{"status":"ok","timestamp":1689111452741,"user_tz":300,"elapsed":10,"user":{"displayName":"","userId":""}},"outputId":"1b272b13-ea75-41bd-abe2-d0c7a40d4c46","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['nomad',\n"," 'HCEPDB_100K_cleaned.csv',\n"," 'stockholm_td_adj.dat',\n"," 'configs',\n"," 'DES_SurfaceTension.xlsx',\n"," 'perovskite_data.csv',\n"," 'linmod.csv']"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["# Import the python modules we'll use and configure matplotlib\n","\n"],"metadata":{"id":"JXUV9PdHGlNO"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import os\n","\n","from sklearn.tree import DecisionTreeClassifier, plot_tree\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.inspection import permutation_importance\n","\n","plt.style.use(os.path.join(data_dir, 'configs', 'plot_style.mplstyle'))"],"metadata":{"id":"pK8Nqa2IE89M","executionInfo":{"status":"ok","timestamp":1689111452742,"user_tz":300,"elapsed":5,"user":{"displayName":"","userId":""}}},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":["# load the data from the [\"New Tolerance Factor\" paper](https://www.science.org/doi/10.1126/sciadv.aav0693)"],"metadata":{"id":"DGHIUbRdG0lv"}},{"cell_type":"code","source":["fcsv = os.path.join(data_dir, 'perovskite_data.csv')\n","df = pd.read_csv(fcsv)\n","df.head()"],"metadata":{"id":"k5_s1zrWG139","executionInfo":{"status":"ok","timestamp":1689111452937,"user_tz":300,"elapsed":9,"user":{"displayName":"","userId":""}},"outputId":"dfe1b290-a3cb-44a5-d1de-2f2826c308fc","colab":{"base_uri":"https://localhost:8080/","height":206}},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      ABX3  exp_label  is_train   A   B   X  nA  nB  nX  rA (Ang)  rB (Ang)  \\\n","0   AgBrO3       -1.0         1  Ag  Br   O   1   5  -2      1.28      0.31   \n","1  AgCdBr3       -1.0         1  Ag  Cd  Br   1   2  -1      1.28      0.95   \n","2  PbAgBr3       -1.0         1  Pb  Ag  Br   2   1  -1      1.49      1.15   \n","3  AgCaCl3       -1.0         1  Ag  Ca  Cl   1   2  -1      1.28      1.00   \n","4   AgClO3       -1.0         1  Ag  Cl   O   1   5  -2      1.28      0.12   \n","\n","   rX (Ang)     t    tau  t_pred  tau_pred  tau_prob  \n","0      1.40  1.11   6.43      -1        -1      0.05  \n","1      1.96  0.79   5.58      -1        -1      0.12  \n","2      1.96  0.78   7.71      -1        -1      0.01  \n","3      1.81  0.78   6.00      -1        -1      0.07  \n","4      1.40  1.25  15.17      -1        -1      0.00  "],"text/html":["\n","\n","  <div id=\"df-ee4b10ed-3d78-422d-b2a1-ee66a9afb966\">\n","    <div class=\"colab-df-container\">\n","      <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>ABX3</th>\n","      <th>exp_label</th>\n","      <th>is_train</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>X</th>\n","      <th>nA</th>\n","      <th>nB</th>\n","      <th>nX</th>\n","  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<td>0.79</td>\n","      <td>5.58</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.12</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PbAgBr3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Pb</td>\n","      <td>Ag</td>\n","      <td>Br</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>1.49</td>\n","      <td>1.15</td>\n","      <td>1.96</td>\n","      <td>0.78</td>\n","      <td>7.71</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.01</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AgCaCl3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Ca</td>\n","      <td>Cl</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>1.28</td>\n","      <td>1.00</td>\n","      <td>1.81</td>\n","      <td>0.78</td>\n","      <td>6.00</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.07</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AgClO3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Cl</td>\n","      <td>O</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-2</td>\n","      <td>1.28</td>\n","      <td>0.12</td>\n","      <td>1.40</td>\n","      <td>1.25</td>\n","      <td>15.17</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4b10ed-3d78-422d-b2a1-ee66a9afb966')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 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(Ang)']\n","df.head()"],"metadata":{"id":"qxfajMnwG5uN","executionInfo":{"status":"ok","timestamp":1689111452937,"user_tz":300,"elapsed":6,"user":{"displayName":"","userId":""}},"outputId":"15f83a6d-f536-48c1-9674-3a3190e6167d","colab":{"base_uri":"https://localhost:8080/","height":206}},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      ABX3  exp_label  is_train   A   B   X  nA  nB  nX  rA (Ang)  rB (Ang)  \\\n","0   AgBrO3       -1.0         1  Ag  Br   O   1   5  -2      1.28      0.31   \n","1  AgCdBr3       -1.0         1  Ag  Cd  Br   1   2  -1      1.28      0.95   \n","2  PbAgBr3       -1.0         1  Pb  Ag  Br   2   1  -1      1.49      1.15   \n","3  AgCaCl3       -1.0         1  Ag  Ca  Cl   1   2  -1      1.28      1.00   \n","4   AgClO3       -1.0         1  Ag  Cl   O   1   5  -2      1.28      0.12   \n","\n","   rX (Ang)     t    tau  t_pred  tau_pred  tau_prob        mu  \n","0      1.40  1.11   6.43      -1        -1      0.05  0.221429  \n","1      1.96  0.79   5.58      -1        -1      0.12  0.484694  \n","2      1.96  0.78   7.71      -1        -1      0.01  0.586735  \n","3      1.81  0.78   6.00      -1        -1      0.07  0.552486  \n","4      1.40  1.25  15.17      -1        -1      0.00  0.085714  "],"text/html":["\n","\n","  <div id=\"df-86cf2587-e460-4991-ad22-f13c27b470b4\">\n","    <div class=\"colab-df-container\">\n","      <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>ABX3</th>\n","      <th>exp_label</th>\n","      <th>is_train</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>X</th>\n","      <th>nA</th>\n","      <th>nB</th>\n","      <th>nX</th>\n","      <th>rA (Ang)</th>\n","      <th>rB (Ang)</th>\n","      <th>rX (Ang)</th>\n","      <th>t</th>\n","      <th>tau</th>\n","      <th>t_pred</th>\n","      <th>tau_pred</th>\n","      <th>tau_prob</th>\n","      <th>mu</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>AgBrO3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Br</td>\n","      <td>O</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-2</td>\n","      <td>1.28</td>\n","      <td>0.31</td>\n","      <td>1.40</td>\n","      <td>1.11</td>\n","      <td>6.43</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.05</td>\n","      <td>0.221429</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AgCdBr3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Cd</td>\n","      <td>Br</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>1.28</td>\n","      <td>0.95</td>\n","      <td>1.96</td>\n","      <td>0.79</td>\n","      <td>5.58</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.12</td>\n","      <td>0.484694</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PbAgBr3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Pb</td>\n","      <td>Ag</td>\n","      <td>Br</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>1.49</td>\n","      <td>1.15</td>\n","      <td>1.96</td>\n","      <td>0.78</td>\n","      <td>7.71</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.01</td>\n","      <td>0.586735</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AgCaCl3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Ca</td>\n","      <td>Cl</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>1.28</td>\n","      <td>1.00</td>\n","      <td>1.81</td>\n","      <td>0.78</td>\n","      <td>6.00</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.07</td>\n","      <td>0.552486</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AgClO3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Cl</td>\n","      <td>O</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-2</td>\n","      <td>1.28</td>\n","      <td>0.12</td>\n","      <td>1.40</td>\n","      <td>1.25</td>\n","      <td>15.17</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.00</td>\n","      <td>0.085714</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86cf2587-e460-4991-ad22-f13c27b470b4')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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df.get(df.is_train == 1)\n","\n","# plot perovskite (+1) class\n","t_perov = df_train.t.get((df_train.exp_label == 1)).values\n","mu_perov = df_train.mu.get((df_train.exp_label == 1)).values\n","ax = plt.scatter(t_perov, mu_perov, color='blue', alpha=0.2, marker='o', lw=0, label='perovskite')\n","\n","# plot nonperovskite (-1) class\n","t_nonperov = df_train.t.get((df_train.exp_label == -1)).values\n","mu_nonperov = df_train.mu.get((df_train.exp_label == -1)).values\n","ax = plt.scatter(t_nonperov, mu_nonperov, color='red', alpha=0.2, marker='^', lw=0, label='nonperovskite')\n","\n","# label\n","ax = plt.ylabel(r'$\\mu=\\frac{r_B}{r_X}$')\n","ax = plt.xlabel(r'$t=\\frac{r_A+r_X}{\\sqrt{2}(r_B+r_X)}$')\n","plt.legend()"],"metadata":{"id":"S5K045UiHA1O","executionInfo":{"status":"ok","timestamp":1689111455995,"user_tz":300,"elapsed":2909,"user":{"displayName":"","userId":""}},"outputId":"069e1bea-acd8-400d-a2d4-f0cbe23292b8","colab":{"base_uri":"https://localhost:8080/","height":736}},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f960fdf61a0>"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"text/plain":["<Figure size 900x750 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# **Hands-on**: Which feature (t or mu) is more important for this classification problem?\n","\n","## Guidelines\n","- determine (quantitatively) whether t or mu is more valuable in separating perovskites from nonperovskites\n","- consider a decision tree that can only make two decisions (i.e., two linear decision boundaries are drawn)\n","- plot the decision boundaries\n","\n","## Hints\n","- information gain will be useful\n","- you might restrict the `max_leaf_nodes`\n","- decision tree models can be visualized with `sklearn.tree.plot_tree`"],"metadata":{"id":"SDawO379IyVk"}},{"cell_type":"markdown","source":["### First, we'll prepare our feature matrix (X_train) and target array (y_train)"],"metadata":{"id":"7xsr1SGuJBVq"}},{"cell_type":"code","source":["y_train = df_train.exp_label.values\n","X_train = df_train[['t', 'mu']].values\n","\n","print(y_train.shape)\n","print(X_train.shape)"],"metadata":{"id":"njhHlNmUJAl9","executionInfo":{"status":"ok","timestamp":1689111455996,"user_tz":300,"elapsed":5,"user":{"displayName":"","userId":""}},"outputId":"3eb379af-add8-4d17-d78f-7d422653195d","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["(460,)\n","(460, 2)\n"]}]},{"cell_type":"markdown","source":["### Now, fit a decision tree"],"metadata":{"id":"TTkwtLzDJJ-2"}},{"cell_type":"code","source":["dt = DecisionTreeClassifier(criterion='log_loss', max_leaf_nodes=3)\n","dt.fit(X_train, y_train)"],"metadata":{"id":"78ISCEYYJD9m","executionInfo":{"status":"ok","timestamp":1689111455996,"user_tz":300,"elapsed":4,"user":{"displayName":"","userId":""}},"outputId":"b63b0e6e-0453-4d8b-d675-2141b19790d3","colab":{"base_uri":"https://localhost:8080/","height":75}},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(criterion='log_loss', max_leaf_nodes=3)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;log_loss&#x27;, max_leaf_nodes=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;log_loss&#x27;, max_leaf_nodes=3)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["### Let's see what our learned model looks like"],"metadata":{"id":"mu5EnKNCJVJC"}},{"cell_type":"code","source":["plot_tree(dt)"],"metadata":{"id":"ZWcX16r1JRQt","executionInfo":{"status":"ok","timestamp":1689111457826,"user_tz":300,"elapsed":1833,"user":{"displayName":"","userId":""}},"outputId":"d2e7d1f3-7530-43fe-ad21-3c2fb3a15733","colab":{"base_uri":"https://localhost:8080/","height":656}},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[Text(0.4, 0.8333333333333334, 'x[1] <= 0.427\\nlog_loss = 0.998\\nsamples = 460\\nvalue = [218, 242]'),\n"," Text(0.2, 0.5, 'log_loss = 0.608\\nsamples = 154\\nvalue = [131, 23]'),\n"," Text(0.6, 0.5, 'x[0] <= 0.835\\nlog_loss = 0.861\\nsamples = 306\\nvalue = [87, 219]'),\n"," Text(0.4, 0.16666666666666666, 'log_loss = 0.776\\nsamples = 70\\nvalue = [54, 16]'),\n"," Text(0.8, 0.16666666666666666, 'log_loss = 0.584\\nsamples = 236\\nvalue = [33, 203]')]"]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Now, let's quantify the importance of each feature (t and mu)"],"metadata":{"id":"law4Ru-XJqGC"}},{"cell_type":"code","source":["def impurity(split):\n","  \"\"\"\n","  Calculate impurity for binary classification problem (0, 1)\n","\n","  Args:\n","    split (2-item tuple):\n","      split[0] = number of samples in class 0\n","      split[1] = number of samples in class 1\n","\n","  Returns:\n","    impurity = -(p0*ln(p0) + p1*ln(p1))\n","  \"\"\"\n","  n_class0, n_class1 = split\n","  n_samples = n_class0 + n_class1\n","  frac_class0 = n_class0 / n_samples\n","  frac_class1 = 1 - frac_class0\n","\n","  log_frac_class0 = np.log(frac_class0) if frac_class0 != 0 else 0\n","  log_frac_class1 = np.log(frac_class1) if frac_class1 != 0 else 0\n","\n","  return -(frac_class0*log_frac_class0 + frac_class1*log_frac_class1)"],"metadata":{"id":"lofnKECGJYNu","executionInfo":{"status":"ok","timestamp":1689111458010,"user_tz":300,"elapsed":190,"user":{"displayName":"","userId":""}}},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":["### Recall our previous splits (A and B)"],"metadata":{"id":"8WV6PolaKOSg"}},{"cell_type":"code","source":["before_split = (40, 40)\n","\n","A_left = (30, 10)\n","A_right = (10, 30)\n","\n","B_left = (20, 40)\n","B_right = (20, 0)"],"metadata":{"id":"Dm6C1Fw1KHOg","executionInfo":{"status":"ok","timestamp":1689111458010,"user_tz":300,"elapsed":8,"user":{"displayName":"","userId":""}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["impurity(before_split)"],"metadata":{"id":"hWs-dXPdLV-u","executionInfo":{"status":"ok","timestamp":1689111458010,"user_tz":300,"elapsed":7,"user":{"displayName":"","userId":""}},"outputId":"55d2dd19-7735-44b1-cbe6-3f0914a62db0","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6931471805599453"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["impurity(A_left)"],"metadata":{"id":"DxW-dy2ZKYeG","executionInfo":{"status":"ok","timestamp":1689111458010,"user_tz":300,"elapsed":7,"user":{"displayName":"","userId":""}},"outputId":"8413256e-a817-4135-e377-b94e26a518b6","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5623351446188083"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["impurity(A_right)"],"metadata":{"id":"k_xeD1E3KaGc","executionInfo":{"status":"ok","timestamp":1689111458011,"user_tz":300,"elapsed":6,"user":{"displayName":"","userId":""}},"outputId":"7f514da3-720e-4659-93c8-9e9b913b07f7","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.5623351446188083"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["impurity(B_left)"],"metadata":{"id":"4W0i-aqOKfhc","executionInfo":{"status":"ok","timestamp":1689111458011,"user_tz":300,"elapsed":5,"user":{"displayName":"","userId":""}},"outputId":"8682e369-a9f1-43a2-aa86-4ca485b7bad0","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6365141682948128"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["impurity(B_right)"],"metadata":{"id":"D_AHi9BiKg52","executionInfo":{"status":"ok","timestamp":1689111458011,"user_tz":300,"elapsed":4,"user":{"displayName":"","userId":""}},"outputId":"3a26a0ed-e30c-4e60-a39a-28bbfffb3b76","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-0.0"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["def information_gain(before_split, after_split_left, after_split_right):\n","  \"\"\"\n","  Calculate information gain (weighted impurity decrease) for binary classification\n","\n","  Args:\n","    before_split (2-item tuple):\n","      (n_class0, n_class1) before splitting\n","\n","    after_split_left (2-item tuple):\n","      (n_class0, n_class1) resulting from the <= criterion of the split\n","\n","    after_split_right (2-item tuple):\n","      (n_class0, n_class1) resulting from the > criterion of the split\n","\n","  Returns:\n","    information gain by splitting\n","  \"\"\"\n","\n","  impurity_before = impurity(before_split)\n","  impurity_left = impurity(after_split_left)\n","  impurity_right = impurity(after_split_right)\n","\n","  n_left = np.sum(after_split_left)\n","  n_right = np.sum(after_split_right)\n","  n_total = np.sum(before_split)\n","\n","  return n_total * (impurity_before - n_left / n_total * impurity_left - n_right / n_total * impurity_right)"],"metadata":{"id":"XnJopZSEKii-","executionInfo":{"status":"ok","timestamp":1689111458011,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["information_gain(before_split, A_left, A_right)"],"metadata":{"id":"38AvREfrK3pA","executionInfo":{"status":"ok","timestamp":1689111458011,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}},"outputId":"cc4310dd-6173-4b24-ef9f-81bb557c1dcf","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10.46496287529096"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","source":["information_gain(before_split, B_left, B_right)"],"metadata":{"id":"kRZC_BiWLaK5","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":158,"user":{"displayName":"","userId":""}},"outputId":"e08fb505-3398-4a08-ece9-68a987e1b189","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["17.260924347106858"]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","source":["### OK, now back to the perovskites"],"metadata":{"id":"uLtSvVf4Lozs"}},{"cell_type":"code","source":["before_mu_split = (np.count_nonzero(y_train == -1), np.count_nonzero(y_train == 1))"],"metadata":{"id":"zZSqiwFbLnqY","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":4,"user":{"displayName":"","userId":""}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["before_mu_split"],"metadata":{"id":"tqU8xRadMEB5","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}},"outputId":"458a4ad8-78b7-43c7-f0cc-059e3276622a","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(218, 242)"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["mu_boundary = 0.427\n","\n","after_mu_split_right = (np.count_nonzero((X_train[:, 1] > mu_boundary) & (y_train == -1)),\n","                        np.count_nonzero((X_train[:, 1] > mu_boundary) & (y_train == 1)))\n","\n","after_mu_split_left = (np.count_nonzero((X_train[:, 1] <= mu_boundary) & (y_train == -1)),\n","                       np.count_nonzero((X_train[:, 1] <= mu_boundary) & (y_train == 1)))\n"],"metadata":{"id":"nfPcs2HLMEgf","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["after_mu_split_left"],"metadata":{"id":"qf188afYMshg","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}},"outputId":"5862580f-29eb-468e-e1ad-a41e241f575b","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(131, 23)"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["after_mu_split_right"],"metadata":{"id":"MVTyRrLMMtWi","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":2,"user":{"displayName":"","userId":""}},"outputId":"07a64c64-b952-475a-a0f1-76af733aa0f5","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(87, 219)"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["before_t_split = after_mu_split_right"],"metadata":{"id":"b6txGOlhMuYn","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":2,"user":{"displayName":"","userId":""}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["t_boundary = 0.835\n","\n","after_t_split_right = (np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == -1)),\n","                       np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == 1)))\n","\n","after_t_split_left = (np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] <= t_boundary) &\n","                                         (y_train == -1)),\n","                       np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] <= t_boundary) &\n","                                         (y_train == 1)))\n"],"metadata":{"id":"sdwm3AVGNYiR","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":2,"user":{"displayName":"","userId":""}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["IG_mu = information_gain(before_mu_split, after_mu_split_right, after_mu_split_left)"],"metadata":{"id":"I-FHLwv7NtT7","executionInfo":{"status":"ok","timestamp":1689111458166,"user_tz":300,"elapsed":2,"user":{"displayName":"","userId":""}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["IG_mu"],"metadata":{"id":"yor1i4eqNzuq","executionInfo":{"status":"ok","timestamp":1689111458328,"user_tz":300,"elapsed":163,"user":{"displayName":"","userId":""}},"outputId":"d6616c99-6c98-4443-f7cb-a08bb8f48ca8","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["70.62152123366151"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["IG_t = information_gain(before_t_split, after_t_split_right, after_t_split_left)"],"metadata":{"id":"qmd9wPmCN0Mt","executionInfo":{"status":"ok","timestamp":1689111458329,"user_tz":300,"elapsed":4,"user":{"displayName":"","userId":""}}},"execution_count":37,"outputs":[]},{"cell_type":"code","source":["IG_t"],"metadata":{"id":"PVLlKOvtN5gq","executionInfo":{"status":"ok","timestamp":1689111458329,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}},"outputId":"bf769d63-a4b8-43f2-c11c-c85b55082bda","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["49.54947445935094"]},"metadata":{},"execution_count":38}]},{"cell_type":"markdown","source":["### Alternatively, sklearn nicely does this for us already"],"metadata":{"id":"sutW5Tc4ORdk"}},{"cell_type":"code","source":["features = ['t', 'mu']\n","importances = dt.feature_importances_\n","print(dict(zip(features, importances)))"],"metadata":{"id":"skqpngbDOQJw","executionInfo":{"status":"ok","timestamp":1689111458511,"user_tz":300,"elapsed":184,"user":{"displayName":"","userId":""}},"outputId":"5b147b6c-351d-4b34-c8c4-f89bc9ed9b44","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["{'t': 0.41232473920686724, 'mu': 0.5876752607931328}\n"]}]},{"cell_type":"markdown","source":["### Why the difference compared to our numbers?"],"metadata":{"id":"_9-mluUiO3Vs"}},{"cell_type":"code","source":["our_importances = [IG_t / (IG_mu + IG_t), IG_mu / (IG_mu + IG_t)]\n","print(dict(zip(features, our_importances)))"],"metadata":{"id":"eRb7dTb5N6Jw","executionInfo":{"status":"ok","timestamp":1689111458512,"user_tz":300,"elapsed":5,"user":{"displayName":"","userId":""}},"outputId":"30f4edc9-a3c9-47b4-d440-a19086705188","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["{'t': 0.41232473920686735, 'mu': 0.5876752607931326}\n"]}]},{"cell_type":"markdown","source":["### Visualize our learned decision boundary"],"metadata":{"id":"cOW5D96tO18_"}},{"cell_type":"code","source":["fig = plt.figure(figsize=(3,2.5))\n","ax = plt.subplot(111)\n","\n","# just look at original training set\n","df_train = df.get(df.is_train == 1)\n","\n","# plot perovskite (+1) class\n","t_perov = df_train.t.get((df_train.exp_label == 1)).values\n","mu_perov = df_train.mu.get((df_train.exp_label == 1)).values\n","ax = plt.scatter(t_perov, mu_perov, color='blue', alpha=0.2, marker='o', lw=0, label='perovskite')\n","\n","# plot nonperovskite (-1) class\n","t_nonperov = df_train.t.get((df_train.exp_label == -1)).values\n","mu_nonperov = df_train.mu.get((df_train.exp_label == -1)).values\n","ax = plt.scatter(t_nonperov, mu_nonperov, color='red', alpha=0.2, marker='^', lw=0, label='nonperovskite')\n","\n","# plot_boundaries\n","ax = plt.plot([0.835, 1000], [0.427, 0.427], color='black', ls='--')\n","ax = plt.plot([0.835, 0.835], [0.427, 1000], color='black', ls='--')\n","\n","# label\n","ax = plt.ylabel(r'$\\mu=\\frac{r_B}{r_X}$')\n","ax = plt.xlabel(r'$t=\\frac{r_A+r_X}{\\sqrt{2}(r_B+r_X)}$')\n","ax = plt.ylim([df.mu.min(), df.mu.max()])\n","ax = plt.xlim([df.t.min(), df.t.max()])\n","\n","plt.legend()"],"metadata":{"id":"4OkPaLFbOjPa","executionInfo":{"status":"ok","timestamp":1689111460326,"user_tz":300,"elapsed":1818,"user":{"displayName":"","userId":""}},"outputId":"76ac3517-c3b5-4401-ca7d-534c2de73eec","colab":{"base_uri":"https://localhost:8080/","height":736}},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f960bd720e0>"]},"metadata":{},"execution_count":41},{"output_type":"display_data","data":{"text/plain":["<Figure size 900x750 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Would the information gain have been the same if t made the first split instead of mu?"],"metadata":{"id":"xt43fh0wPoev"}},{"cell_type":"code","source":["before_t_split2 = before_mu_split\n","\n","after_t_split_right2 = (np.count_nonzero((X_train[:, 0] > t_boundary) & (y_train == -1)), np.count_nonzero((X_train[:, 0] > t_boundary) & (y_train == 1)))\n","after_t_split_left2 = (np.count_nonzero((X_train[:, 0] <= t_boundary) & (y_train == -1)), np.count_nonzero((X_train[:, 0] <= t_boundary) & (y_train == 1)))\n","\n","before_mu_split2 = after_t_split_right2\n","\n","after_mu_split_right2 = (np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == -1)),\n","                       np.count_nonzero((X_train[:, 1] > mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == 1)))\n","after_mu_split_left2 = (np.count_nonzero((X_train[:, 1] <= mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == -1)),\n","                       np.count_nonzero((X_train[:, 1] <= mu_boundary) &\n","                                        (X_train[:, 0] > t_boundary) &\n","                                         (y_train == 1)))"],"metadata":{"id":"7JCdVcB6O8yO","executionInfo":{"status":"ok","timestamp":1689111460327,"user_tz":300,"elapsed":9,"user":{"displayName":"","userId":""}}},"execution_count":42,"outputs":[]},{"cell_type":"code","source":["IG_t_2 = information_gain(before_t_split2, after_t_split_right2, after_t_split_left2)\n","IG_mu_2 = information_gain(before_mu_split2, after_mu_split_right2, after_mu_split_left2)"],"metadata":{"id":"XKBhW9OnPnvy","executionInfo":{"status":"ok","timestamp":1689111460327,"user_tz":300,"elapsed":8,"user":{"displayName":"","userId":""}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["IG_t_2"],"metadata":{"id":"7TgMlYrBQaxc","executionInfo":{"status":"ok","timestamp":1689111460327,"user_tz":300,"elapsed":7,"user":{"displayName":"","userId":""}},"outputId":"7826aa8f-8335-4135-9166-8c40542d0b1a","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["26.584765811993993"]},"metadata":{},"execution_count":44}]},{"cell_type":"code","source":["IG_mu_2"],"metadata":{"id":"NvcB0pjsQbWk","executionInfo":{"status":"ok","timestamp":1689111460327,"user_tz":300,"elapsed":6,"user":{"displayName":"","userId":""}},"outputId":"f1f02d02-b61e-4f8d-c5a9-fe63696531ab","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["92.62111096785485"]},"metadata":{},"execution_count":45}]},{"cell_type":"markdown","source":["### Not the same! Part of instability of decision trees"],"metadata":{"id":"eJ-8Z9NAQ44h"}},{"cell_type":"markdown","source":[],"metadata":{"id":"7BWLHyxVRFv4"}},{"cell_type":"markdown","source":[],"metadata":{"id":"Tzf3PAs8RF0T"}},{"cell_type":"markdown","source":[],"metadata":{"id":"DTmawe5fRF4r"}},{"cell_type":"markdown","source":["# Now, lets consider importances for an ensemble of decision trees (random forest)"],"metadata":{"id":"2vTzoWUSRGNb"}},{"cell_type":"markdown","source":["## This time, we'll use the ionic features (oxidation states and radii)"],"metadata":{"id":"lm-ngUT5RNWZ"}},{"cell_type":"code","source":["ions = ['A', 'B', 'X']\n","df = df.rename(columns={'r%s (Ang)' % v : 'r%s' % v for v in ions})\n","df_train = df.get((df.is_train == 1))\n","features = ['n%s' % ion for ion in ions] + ['r%s' % ion for ion in ions]\n"],"metadata":{"id":"gHQWXkLXRK4T","executionInfo":{"status":"ok","timestamp":1689111460327,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}}},"execution_count":46,"outputs":[]},{"cell_type":"code","source":["features"],"metadata":{"id":"-_KP45vTRR2t","executionInfo":{"status":"ok","timestamp":1689111460496,"user_tz":300,"elapsed":172,"user":{"displayName":"","userId":""}},"outputId":"5c02e913-c51d-4299-c591-a199a54364c9","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['nA', 'nB', 'nX', 'rA', 'rB', 'rX']"]},"metadata":{},"execution_count":47}]},{"cell_type":"markdown","source":["# Fit a generic random forest to these features (again predicting perovskite vs nonperovskite)"],"metadata":{"id":"B-LzwUESRZ_L"}},{"cell_type":"code","source":["rf = RandomForestClassifier(random_state=44)\n","\n","X_train, y_train = df_train[features].values, df_train['exp_label'].values\n","rf.fit(X_train, y_train)"],"metadata":{"id":"bnMTvYOHRSqP","executionInfo":{"status":"ok","timestamp":1689111461060,"user_tz":300,"elapsed":565,"user":{"displayName":"","userId":""}},"outputId":"69f0d1d9-3412-42dc-c159-dfa53f4a3e15","colab":{"base_uri":"https://localhost:8080/","height":75}},"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(random_state=44)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=44)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=44)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":48}]},{"cell_type":"markdown","source":["# Use sklearn to compute the information gain over this forest of decision trees"],"metadata":{"id":"FiQQI5SxRi0A"}},{"cell_type":"code","source":["rf_importances = rf.feature_importances_\n","print(features)\n","print(rf_importances)"],"metadata":{"id":"-Ez8VzrTRZMN","executionInfo":{"status":"ok","timestamp":1689111461060,"user_tz":300,"elapsed":1,"user":{"displayName":"","userId":""}},"outputId":"ab04bc4a-c83f-4b60-80e3-441f7e3a1ab3","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["['nA', 'nB', 'nX', 'rA', 'rB', 'rX']\n","[0.10751281 0.06349688 0.01603049 0.27927925 0.38836561 0.14531496]\n"]}]},{"cell_type":"markdown","source":["# Let's visualize these"],"metadata":{"id":"lAZ7BipiRqeY"}},{"cell_type":"code","source":["def plot_importances(features_and_their_importances, ylabel='importance'):\n","    \"\"\"\n","    Args:\n","        features_and_their_importances (dict):\n","            {feature (str) : importance (float)}\n","    Returns:\n","        matplotlib bar chart of sorted importances\n","    \"\"\"\n","    axis_width = 1.5\n","    maj_tick_len = 6\n","    fontsize = 14\n","    bar_color = 'lightblue'\n","    align = 'center'\n","    label = '__nolegend__'\n","\n","    n_features = len(features_and_their_importances)\n","    sorted_features = sorted(features_and_their_importances,\n","                             key=features_and_their_importances.get,\n","                             reverse=True)\n","    sorted_importances = [features_and_their_importances[f] for f in sorted_features]\n","\n","    ax = plt.bar(range(n_features), sorted_importances,\n","                 color=bar_color, align=align, label=label)\n","    ax = plt.xticks(range(n_features), sorted_features, rotation=90)\n","    ax = plt.xlim([-1, n_features])\n","    ax = plt.ylabel(ylabel, fontsize=fontsize)\n","    ax = plt.tick_params('both', length=maj_tick_len, width=axis_width,\n","                         which='major', right=True, top=True)\n","    ax = plt.xticks(fontsize=fontsize)\n","    ax = plt.yticks(fontsize=fontsize)\n","    ax = plt.tight_layout()\n","    return ax"],"metadata":{"id":"wBiI5nYwRlvd","executionInfo":{"status":"ok","timestamp":1689111461061,"user_tz":300,"elapsed":2,"user":{"displayName":"","userId":""}}},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["features_and_their_importances = dict(zip(features, rf_importances))\n","\n","fig = plt.figure()\n","ax = plt.subplot(111)\n","ax = plot_importances(features_and_their_importances)"],"metadata":{"id":"bo2xzEgURpS6","executionInfo":{"status":"ok","timestamp":1689111462123,"user_tz":300,"elapsed":1063,"user":{"displayName":"","userId":""}},"outputId":"e66060d9-24fc-4553-ff54-b22885b0bd32","colab":{"base_uri":"https://localhost:8080/","height":569}},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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**Hands-on**: Are these reliable??\n","\n","## Guidelines:\n","- include a random variable among your features and repeat this process\n","- train, compute importances, plot"],"metadata":{"id":"_6Sup-jGSi7r"}},{"cell_type":"code","source":["np.random.seed(44)\n","\n","df['random'] = np.random.rand(len(df))\n","df_train = df.get(df.is_train == 1)\n"],"metadata":{"id":"8L2ihCS2SIQO","executionInfo":{"status":"ok","timestamp":1689111462123,"user_tz":300,"elapsed":1,"user":{"displayName":"","userId":""}}},"execution_count":52,"outputs":[]},{"cell_type":"code","source":["df.head()"],"metadata":{"id":"4i-FhzTQSxcB","executionInfo":{"status":"ok","timestamp":1689111462269,"user_tz":300,"elapsed":147,"user":{"displayName":"","userId":""}},"outputId":"f616994a-f864-4a69-b0a1-241b2d4ba879","colab":{"base_uri":"https://localhost:8080/","height":206}},"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      ABX3  exp_label  is_train   A   B   X  nA  nB  nX    rA    rB    rX  \\\n","0   AgBrO3       -1.0         1  Ag  Br   O   1   5  -2  1.28  0.31  1.40   \n","1  AgCdBr3       -1.0         1  Ag  Cd  Br   1   2  -1  1.28  0.95  1.96   \n","2  PbAgBr3       -1.0         1  Pb  Ag  Br   2   1  -1  1.49  1.15  1.96   \n","3  AgCaCl3       -1.0         1  Ag  Ca  Cl   1   2  -1  1.28  1.00  1.81   \n","4   AgClO3       -1.0         1  Ag  Cl   O   1   5  -2  1.28  0.12  1.40   \n","\n","      t    tau  t_pred  tau_pred  tau_prob        mu    random  \n","0  1.11   6.43      -1        -1      0.05  0.221429  0.834842  \n","1  0.79   5.58      -1        -1      0.12  0.484694  0.104796  \n","2  0.78   7.71      -1        -1      0.01  0.586735  0.744640  \n","3  0.78   6.00      -1        -1      0.07  0.552486  0.360501  \n","4  1.25  15.17      -1        -1      0.00  0.085714  0.359311  "],"text/html":["\n","\n","  <div id=\"df-a508e645-ab79-4549-8028-028a432f9c06\">\n","    <div class=\"colab-df-container\">\n","      <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>ABX3</th>\n","      <th>exp_label</th>\n","      <th>is_train</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>X</th>\n","      <th>nA</th>\n","      <th>nB</th>\n","      <th>nX</th>\n","      <th>rA</th>\n","      <th>rB</th>\n","      <th>rX</th>\n","      <th>t</th>\n","      <th>tau</th>\n","      <th>t_pred</th>\n","      <th>tau_pred</th>\n","      <th>tau_prob</th>\n","      <th>mu</th>\n","      <th>random</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>AgBrO3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Br</td>\n","      <td>O</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-2</td>\n","      <td>1.28</td>\n","      <td>0.31</td>\n","      <td>1.40</td>\n","      <td>1.11</td>\n","      <td>6.43</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.05</td>\n","      <td>0.221429</td>\n","      <td>0.834842</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AgCdBr3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Cd</td>\n","      <td>Br</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>1.28</td>\n","      <td>0.95</td>\n","      <td>1.96</td>\n","      <td>0.79</td>\n","      <td>5.58</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.12</td>\n","      <td>0.484694</td>\n","      <td>0.104796</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PbAgBr3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Pb</td>\n","      <td>Ag</td>\n","      <td>Br</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>-1</td>\n","      <td>1.49</td>\n","      <td>1.15</td>\n","      <td>1.96</td>\n","      <td>0.78</td>\n","      <td>7.71</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.01</td>\n","      <td>0.586735</td>\n","      <td>0.744640</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AgCaCl3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Ca</td>\n","      <td>Cl</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>-1</td>\n","      <td>1.28</td>\n","      <td>1.00</td>\n","      <td>1.81</td>\n","      <td>0.78</td>\n","      <td>6.00</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.07</td>\n","      <td>0.552486</td>\n","      <td>0.360501</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AgClO3</td>\n","      <td>-1.0</td>\n","      <td>1</td>\n","      <td>Ag</td>\n","      <td>Cl</td>\n","      <td>O</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>-2</td>\n","      <td>1.28</td>\n","      <td>0.12</td>\n","      <td>1.40</td>\n","      <td>1.25</td>\n","      <td>15.17</td>\n","      <td>-1</td>\n","      <td>-1</td>\n","      <td>0.00</td>\n","      <td>0.085714</td>\n","      <td>0.359311</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a508e645-ab79-4549-8028-028a432f9c06')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 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RandomForestClassifier(random_state=44)\n","rf2.fit(X_train, y_train)"],"metadata":{"id":"xWDOXnKjSzQg","executionInfo":{"status":"ok","timestamp":1689111462567,"user_tz":300,"elapsed":300,"user":{"displayName":"","userId":""}},"outputId":"be05660d-6943-4731-b1f1-b16afaf9fc89","colab":{"base_uri":"https://localhost:8080/","height":75}},"execution_count":55,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(random_state=44)"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=44)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=44)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","source":["features_and_their_importances2 = dict(zip(features, rf2.feature_importances_))"],"metadata":{"id":"ecw_VBMqS6RY","executionInfo":{"status":"ok","timestamp":1689111462567,"user_tz":300,"elapsed":1,"user":{"displayName":"","userId":""}}},"execution_count":56,"outputs":[]},{"cell_type":"code","source":["fig = plt.figure(figsize=(5,2.5))\n","ax = plt.subplot(111)\n","ax = plot_importances(features_and_their_importances2)"],"metadata":{"id":"BR9cFHzCS-_K","executionInfo":{"status":"ok","timestamp":1689111463066,"user_tz":300,"elapsed":499,"user":{"displayName":"","userId":""}},"outputId":"ced7aafe-b57d-4e3d-dab4-e156c4c373aa","colab":{"base_uri":"https://localhost:8080/","height":643}},"execution_count":57,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x750 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Concerning! Let's discuss another way to compute importances"],"metadata":{"id":"ifcVl2WxTK6a"}},{"cell_type":"markdown","source":[],"metadata":{"id":"RQaGJ2_BTPCa"}},{"cell_type":"markdown","source":[],"metadata":{"id":"EVHM2qI-TPFF"}},{"cell_type":"markdown","source":[],"metadata":{"id":"Rqd8fbykTPH1"}},{"cell_type":"markdown","source":["# OK, now let's give permutation importances a try"],"metadata":{"id":"_PJkdvneTPaU"}},{"cell_type":"code","source":["pm_importances = permutation_importance(rf2, X_train, y_train, random_state=44)['importances_mean']"],"metadata":{"id":"2dOoeUY8TBjG","executionInfo":{"status":"ok","timestamp":1689111463499,"user_tz":300,"elapsed":434,"user":{"displayName":"","userId":""}}},"execution_count":58,"outputs":[]},{"cell_type":"code","source":["features_to_pm_importances = dict(zip(features, pm_importances))"],"metadata":{"id":"AMZVmXaSTT4i","executionInfo":{"status":"ok","timestamp":1689111463499,"user_tz":300,"elapsed":1,"user":{"displayName":"","userId":""}}},"execution_count":59,"outputs":[]},{"cell_type":"code","source":["fig = plt.figure(figsize=(5,2.5))\n","ax = plt.subplot(111)\n","ax = plot_importances(features_to_pm_importances)"],"metadata":{"id":"KUTAxI-4TVgC","executionInfo":{"status":"ok","timestamp":1689111464112,"user_tz":300,"elapsed":614,"user":{"displayName":"","userId":""}},"outputId":"9f6cea71-af2b-45c9-b39d-eddc6245e27a","colab":{"base_uri":"https://localhost:8080/","height":643}},"execution_count":60,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x750 with 1 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Looks better, but still makes me a bit uncomfortable!\n","- what is this telling us?\n","- what's something else we can try?"],"metadata":{"id":"hqljJUtDTYkZ"}},{"cell_type":"code","source":["X_test = df[features].get(df.is_train == -1).values\n","y_test = df['exp_label'].get(df.is_train == -1).values\n","\n","pm_importances_test = permutation_importance(rf2, X_test, y_test, random_state=44, n_repeats=5)['importances_mean']\n","features_to_pm_importances_test = dict(zip(features, pm_importances_test))"],"metadata":{"id":"v4SKGzJVTWnr","executionInfo":{"status":"ok","timestamp":1689111464388,"user_tz":300,"elapsed":278,"user":{"displayName":"","userId":""}}},"execution_count":61,"outputs":[]},{"cell_type":"code","source":["fig = plt.figure(figsize=(5,2.5))\n","ax = plt.subplot(111)\n","ax = plot_importances(features_to_pm_importances_test)"],"metadata":{"id":"_vvOgkldTgoN","executionInfo":{"status":"ok","timestamp":1689111464980,"user_tz":300,"elapsed":593,"user":{"displayName":"","userId":""}},"outputId":"1cfb3fb5-65e4-4880-e4c1-038ea06cfe34","colab":{"base_uri":"https://localhost:8080/","height":643}},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x750 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"E5tTdW4YTh2q","executionInfo":{"status":"ok","timestamp":1689111464980,"user_tz":300,"elapsed":3,"user":{"displayName":"","userId":""}}},"execution_count":62,"outputs":[]}]}