{"cells":[{"metadata":{},"cell_type":"markdown","source":"This kernel was created to the [LANL Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction) competition. Here we will plot 295 features vs time to failure."},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt","execution_count":7,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features_df = pd.read_csv('../input/features/features.csv', index_col=0)","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features_df.head()","execution_count":9,"outputs":[{"output_type":"execute_result","execution_count":9,"data":{"text/plain":"   abs_max  abs_q95    ...     q99_roll_mean_5000       ttf\n0    104.0     12.0    ...                 5.2732  1.430797\n1    181.0     12.0    ...                 5.2732  1.423396\n2    181.0     12.0    ...                 5.2732  1.414899\n3    181.0     12.0    ...                 5.2414  1.407397\n4    181.0     12.0    ...                 5.0068  1.399996\n\n[5 rows x 296 columns]","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>abs_max</th>\n      <th>abs_q95</th>\n      <th>abs_q75</th>\n      <th>abs_q25</th>\n      <th>abs_q05</th>\n      <th>abs_min</th>\n      <th>Moving_average_50_mean</th>\n      <th>Moving_average_15000_mean</th>\n      <th>MA_50MA_std_mean</th>\n      <th>MA_15000MA_std_mean</th>\n      <th>kstat_1</th>\n      <th>kstat_3</th>\n      <th>moment_1</th>\n      <th>moment_3</th>\n      <th>abs_energy</th>\n      <th>abs_sum_of_changes</th>\n      <th>count_above_mean</th>\n      <th>count_below_mean</th>\n      <th>range_m1000_0</th>\n      <th>range_0_p1000</th>\n      <th>autocorrelation_10</th>\n      <th>autocorrelation_1000</th>\n      <th>c3_10</th>\n      <th>c3_100</th>\n      <th>long_strk_above_mean</th>\n      <th>long_strk_below_mean</th>\n      <th>cid_ce_0</th>\n      <th>cid_ce_1</th>\n      <th>binned_entropy_10</th>\n      <th>binned_entropy_100</th>\n      <th>num_crossing_0</th>\n      <th>num_peaks_100</th>\n      <th>num_peaks_500</th>\n      <th>num_peaks_1000</th>\n      <th>num_peaks_2000</th>\n      <th>spkt_welch_density_1</th>\n      <th>spkt_welch_density_50</th>\n      <th>time_rev_asym_stat_1</th>\n      <th>time_rev_asym_stat_50</th>\n      <th>max_roll_std_10</th>\n      <th>...</th>\n      <th>q75_roll_mean_2500</th>\n      <th>q80_roll_mean_2500</th>\n      <th>q85_roll_mean_2500</th>\n      <th>q90_roll_mean_2500</th>\n      <th>q95_roll_mean_2500</th>\n      <th>q97_roll_mean_2500</th>\n      <th>q99_roll_mean_2500</th>\n      <th>max_roll_std_5000</th>\n      <th>min_roll_std_5000</th>\n      <th>q01_roll_std_5000</th>\n      <th>q03_roll_std_5000</th>\n      <th>q05_roll_std_5000</th>\n      <th>q10_roll_std_5000</th>\n      <th>q15_roll_std_5000</th>\n      <th>q20_roll_std_5000</th>\n      <th>q25_roll_std_5000</th>\n      <th>q75_roll_std_5000</th>\n      <th>q80_roll_std_5000</th>\n      <th>q85_roll_std_5000</th>\n      <th>q90_roll_std_5000</th>\n      <th>q95_roll_std_5000</th>\n      <th>q97_roll_std_5000</th>\n      <th>q99_roll_std_5000</th>\n      <th>max_roll_mean_5000</th>\n      <th>min_roll_mean_5000</th>\n      <th>q01_roll_mean_5000</th>\n      <th>q03_roll_mean_5000</th>\n      <th>q05_roll_mean_5000</th>\n      <th>q10_roll_mean_5000</th>\n      <th>q15_roll_mean_5000</th>\n      <th>q20_roll_mean_5000</th>\n      <th>q25_roll_mean_5000</th>\n      <th>q75_roll_mean_5000</th>\n      <th>q80_roll_mean_5000</th>\n      <th>q85_roll_mean_5000</th>\n      <th>q90_roll_mean_5000</th>\n      <th>q95_roll_mean_5000</th>\n      <th>q97_roll_mean_5000</th>\n      <th>q99_roll_mean_5000</th>\n      <th>ttf</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>104.0</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.883969</td>\n      <td>4.881488</td>\n      <td>4.011743</td>\n      <td>4.415607</td>\n      <td>4.884113</td>\n      <td>-132.520775</td>\n      <td>0.0</td>\n      <td>-3.193751</td>\n      <td>10247.0</td>\n      <td>391980.0</td>\n      <td>81408.0</td>\n      <td>68592.0</td>\n      <td>11570.0</td>\n      <td>138430.0</td>\n      <td>-0.469692</td>\n      <td>-0.004982</td>\n      <td>33.216202</td>\n      <td>118.804773</td>\n      <td>27.0</td>\n      <td>27.0</td>\n      <td>160.530365</td>\n      <td>257.823936</td>\n      <td>0.608435</td>\n      <td>2.031982</td>\n      <td>17111.0</td>\n      <td>481.0</td>\n      <td>93.0</td>\n      <td>43.0</td>\n      <td>22.0</td>\n      <td>10.274043</td>\n      <td>15.608981</td>\n      <td>0.259257</td>\n      <td>-0.595751</td>\n      <td>78.313047</td>\n      <td>...</td>\n      <td>5.0912</td>\n      <td>5.1288</td>\n      <td>5.1788</td>\n      <td>5.2320</td>\n      <td>5.2772</td>\n      <td>5.3072</td>\n      <td>5.3644</td>\n      <td>15.334598</td>\n      <td>2.736178</td>\n      <td>2.805620</td>\n      <td>2.850267</td>\n      <td>2.908203</td>\n      <td>3.066138</td>\n      <td>3.193116</td>\n      <td>3.270433</td>\n      <td>3.410167</td>\n      <td>5.069850</td>\n      <td>5.248088</td>\n      <td>5.505753</td>\n      <td>5.979518</td>\n      <td>6.436335</td>\n      <td>7.431773</td>\n      <td>15.161128</td>\n      <td>5.2966</td>\n      <td>4.2208</td>\n      <td>4.2582</td>\n      <td>4.3308</td>\n      <td>4.3688</td>\n      <td>4.4888</td>\n      <td>4.5612</td>\n      <td>4.7232</td>\n      <td>4.7598</td>\n      <td>5.0858</td>\n      <td>5.1326</td>\n      <td>5.1670</td>\n      <td>5.2004</td>\n      <td>5.2332</td>\n      <td>5.2484</td>\n      <td>5.2732</td>\n      <td>1.430797</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>181.0</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.811084</td>\n      <td>4.816016</td>\n      <td>3.937294</td>\n      <td>4.958577</td>\n      <td>4.811160</td>\n      <td>-183.260776</td>\n      <td>0.0</td>\n      <td>62.284436</td>\n      <td>-12244.0</td>\n      <td>388029.0</td>\n      <td>80278.0</td>\n      <td>69722.0</td>\n      <td>11234.0</td>\n      <td>138766.0</td>\n      <td>-0.345196</td>\n      <td>0.019970</td>\n      <td>46.101474</td>\n      <td>111.112470</td>\n      <td>29.0</td>\n      <td>30.0</td>\n      <td>NaN</td>\n      <td>243.999301</td>\n      <td>0.132079</td>\n      <td>1.660743</td>\n      <td>17190.0</td>\n      <td>480.0</td>\n      <td>94.0</td>\n      <td>42.0</td>\n      <td>21.0</td>\n      <td>10.966529</td>\n      <td>16.013670</td>\n      <td>-0.593288</td>\n      <td>-6.003569</td>\n      <td>103.997009</td>\n      <td>...</td>\n      <td>5.0060</td>\n      <td>5.0772</td>\n      <td>5.1420</td>\n      <td>5.2056</td>\n      <td>5.2696</td>\n      <td>5.3024</td>\n      <td>5.3644</td>\n      <td>18.213082</td>\n      <td>2.736178</td>\n      <td>2.800454</td>\n      <td>2.837377</td>\n      <td>2.895527</td>\n      <td>2.998999</td>\n      <td>3.154210</td>\n      <td>3.215890</td>\n      <td>3.315551</td>\n      <td>4.898883</td>\n      <td>5.085674</td>\n      <td>5.473451</td>\n      <td>6.098863</td>\n      <td>7.393787</td>\n      <td>17.104358</td>\n      <td>18.099710</td>\n      <td>5.2966</td>\n      <td>4.2208</td>\n      <td>4.2582</td>\n      <td>4.3304</td>\n      <td>4.3688</td>\n      <td>4.4772</td>\n      <td>4.5214</td>\n      <td>4.5636</td>\n      <td>4.6062</td>\n      <td>5.0244</td>\n      <td>5.0876</td>\n      <td>5.1436</td>\n      <td>5.1956</td>\n      <td>5.2332</td>\n      <td>5.2484</td>\n      <td>5.2732</td>\n      <td>1.423396</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>181.0</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.796527</td>\n      <td>4.780726</td>\n      <td>4.228768</td>\n      <td>5.280648</td>\n      <td>4.796347</td>\n      <td>-317.745497</td>\n      <td>0.0</td>\n      <td>120.478245</td>\n      <td>-22004.0</td>\n      <td>398782.0</td>\n      <td>80026.0</td>\n      <td>69974.0</td>\n      <td>12486.0</td>\n      <td>137514.0</td>\n      <td>-0.420062</td>\n      <td>0.029088</td>\n      <td>35.673043</td>\n      <td>110.976856</td>\n      <td>29.0</td>\n      <td>30.0</td>\n      <td>174.223999</td>\n      <td>222.825892</td>\n      <td>0.159299</td>\n      <td>1.716610</td>\n      <td>17396.0</td>\n      <td>472.0</td>\n      <td>94.0</td>\n      <td>46.0</td>\n      <td>24.0</td>\n      <td>11.297068</td>\n      <td>16.280527</td>\n      <td>-2.864492</td>\n      <td>-4.231408</td>\n      <td>122.978273</td>\n      <td>...</td>\n      <td>5.0096</td>\n      <td>5.0712</td>\n      <td>5.1420</td>\n      <td>5.2056</td>\n      <td>5.2696</td>\n      <td>5.3024</td>\n      <td>5.3644</td>\n      <td>18.213082</td>\n      <td>2.736178</td>\n      <td>2.822321</td>\n      <td>2.885262</td>\n      <td>2.922007</td>\n      <td>3.112363</td>\n      <td>3.209036</td>\n      <td>3.303483</td>\n      <td>3.406311</td>\n      <td>5.339492</td>\n      <td>6.039251</td>\n      <td>6.370517</td>\n      <td>6.897598</td>\n      <td>16.861070</td>\n      <td>17.104358</td>\n      <td>18.099710</td>\n      <td>5.2966</td>\n      <td>4.2208</td>\n      <td>4.2582</td>\n      <td>4.3304</td>\n      <td>4.3688</td>\n      <td>4.4772</td>\n      <td>4.5212</td>\n      <td>4.5550</td>\n      <td>4.5888</td>\n      <td>4.9924</td>\n      <td>5.0870</td>\n      <td>5.1436</td>\n      <td>5.1956</td>\n      <td>5.2332</td>\n      <td>5.2484</td>\n      <td>5.2732</td>\n      <td>1.414899</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>181.0</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.699436</td>\n      <td>4.681025</td>\n      <td>4.160284</td>\n      <td>5.644621</td>\n      <td>4.699480</td>\n      <td>-295.543732</td>\n      <td>0.0</td>\n      <td>125.203298</td>\n      <td>3218.0</td>\n      <td>397429.0</td>\n      <td>78319.0</td>\n      <td>71681.0</td>\n      <td>12690.0</td>\n      <td>137310.0</td>\n      <td>-0.414093</td>\n      <td>0.028282</td>\n      <td>33.697886</td>\n      <td>103.555247</td>\n      <td>29.0</td>\n      <td>30.0</td>\n      <td>109.494293</td>\n      <td>226.269665</td>\n      <td>0.148544</td>\n      <td>1.702149</td>\n      <td>18171.0</td>\n      <td>474.0</td>\n      <td>84.0</td>\n      <td>45.0</td>\n      <td>22.0</td>\n      <td>11.405980</td>\n      <td>16.251211</td>\n      <td>-2.427679</td>\n      <td>0.098786</td>\n      <td>122.978273</td>\n      <td>...</td>\n      <td>4.8296</td>\n      <td>4.8600</td>\n      <td>4.9148</td>\n      <td>4.9888</td>\n      <td>5.0700</td>\n      <td>5.1352</td>\n      <td>5.3476</td>\n      <td>18.213082</td>\n      <td>2.736178</td>\n      <td>2.869458</td>\n      <td>2.916394</td>\n      <td>2.957323</td>\n      <td>3.149982</td>\n      <td>3.211176</td>\n      <td>3.305352</td>\n      <td>3.395788</td>\n      <td>5.090700</td>\n      <td>5.500328</td>\n      <td>5.921970</td>\n      <td>6.529097</td>\n      <td>16.861070</td>\n      <td>17.104358</td>\n      <td>18.099710</td>\n      <td>5.2924</td>\n      <td>4.2208</td>\n      <td>4.2582</td>\n      <td>4.3304</td>\n      <td>4.3688</td>\n      <td>4.4284</td>\n      <td>4.4838</td>\n      <td>4.5230</td>\n      <td>4.5524</td>\n      <td>4.8134</td>\n      <td>4.8322</td>\n      <td>4.8594</td>\n      <td>4.9182</td>\n      <td>5.0154</td>\n      <td>5.1028</td>\n      <td>5.2414</td>\n      <td>1.407397</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>181.0</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>4.672099</td>\n      <td>4.675822</td>\n      <td>4.248711</td>\n      <td>5.884745</td>\n      <td>4.672107</td>\n      <td>-321.216093</td>\n      <td>0.0</td>\n      <td>129.241105</td>\n      <td>13412.0</td>\n      <td>401030.0</td>\n      <td>77909.0</td>\n      <td>72091.0</td>\n      <td>13147.0</td>\n      <td>136853.0</td>\n      <td>-0.425920</td>\n      <td>0.027747</td>\n      <td>23.863795</td>\n      <td>94.942623</td>\n      <td>29.0</td>\n      <td>30.0</td>\n      <td>NaN</td>\n      <td>222.532551</td>\n      <td>0.160287</td>\n      <td>1.722670</td>\n      <td>18352.0</td>\n      <td>485.0</td>\n      <td>79.0</td>\n      <td>43.0</td>\n      <td>18.0</td>\n      <td>12.891395</td>\n      <td>16.792648</td>\n      <td>-4.072374</td>\n      <td>3.089787</td>\n      <td>122.978273</td>\n      <td>...</td>\n      <td>4.8328</td>\n      <td>4.8548</td>\n      <td>4.8956</td>\n      <td>4.9512</td>\n      <td>5.0088</td>\n      <td>5.0368</td>\n      <td>5.0628</td>\n      <td>18.213082</td>\n      <td>2.711841</td>\n      <td>2.758718</td>\n      <td>2.823437</td>\n      <td>2.880966</td>\n      <td>2.931693</td>\n      <td>3.053939</td>\n      <td>3.208997</td>\n      <td>3.336063</td>\n      <td>5.639486</td>\n      <td>6.159511</td>\n      <td>6.486022</td>\n      <td>7.086858</td>\n      <td>16.861070</td>\n      <td>17.104358</td>\n      <td>18.099710</td>\n      <td>5.0390</td>\n      <td>4.2208</td>\n      <td>4.2582</td>\n      <td>4.3304</td>\n      <td>4.3688</td>\n      <td>4.4160</td>\n      <td>4.4632</td>\n      <td>4.4998</td>\n      <td>4.5334</td>\n      <td>4.8154</td>\n      <td>4.8352</td>\n      <td>4.8708</td>\n      <td>4.9206</td>\n      <td>4.9684</td>\n      <td>4.9876</td>\n      <td>5.0068</td>\n      <td>1.399996</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plot_feature_ttf(feature):\n    fig, ax1 = plt.subplots(figsize=(12, 5))\n    plt.title('{} vs time to failure'.format(feature))\n    plt.plot(features_df[feature], color='r')\n    ax1.set_xlabel('training samples')\n    ax1.set_ylabel('{}'.format(feature), color='r')\n    plt.legend(['{}'.format(feature)], loc=(0.01, 0.95))\n\n    ax2 = ax1.twinx()\n    plt.plot(features_df['ttf'], color='b')\n    ax2.set_ylabel('time to failure', color='b')\n    plt.legend(['time to failure'], loc=(0.01, 0.9))\n    \n    plt.grid(True)\n    plt.show()","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features = [col for col in features_df.columns if col not in ['ttf']]\nfor feature in features:\n    plot_feature_ttf(feature)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.6.7"}},"nbformat":4,"nbformat_minor":1}