research_tensile.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1Xe6w2MVbSl7dQExgZGlixCUR1LCcRcq8","timestamp":1665834214705}],"mount_file_id":"1iJyRaRf99KP6gd7KAWoqH1h2yRVlxssf","authorship_tag":"ABX9TyPY0I6lexoZpTmn8Bsu8jnh"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["# Use seaborn for pairplot\n","!pip install -q seaborn\n","\n","!pip install tensorflow-addons\n","import tensorflow_addons as tfa"],"metadata":{"id":"gizc6HsGgFu1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","\n","# Make NumPy printouts easier to read.\n","np.set_printoptions(precision=3, suppress=True)"],"metadata":{"id":"sv05nE-0gOFj","executionInfo":{"status":"ok","timestamp":1665852036060,"user_tz":-120,"elapsed":673,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["import tensorflow as tf\n","\n","from tensorflow import keras\n","from tensorflow.keras import layers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MMA2HkHykE4v","executionInfo":{"status":"ok","timestamp":1665852038795,"user_tz":-120,"elapsed":2736,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"outputId":"b9a6d57e-891f-41ad-e5b5-025fd043e0e4"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["2.9.2\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2o1pqV_mkmI1","executionInfo":{"status":"ok","timestamp":1665852050878,"user_tz":-120,"elapsed":12088,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"outputId":"aef644b1-9790-451a-a687-8326c0f96e83"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","execution_count":5,"metadata":{"id":"qrFozAkm6jms","executionInfo":{"status":"ok","timestamp":1665852053020,"user_tz":-120,"elapsed":2150,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"outputs":[],"source":["column_names = [\"Cement\", \"Sand\", \"Aggregate\", \"FA\", \"Cyclodextrin\", \"Water\", \"Days\", \"Tens\"]\n","url = '/content/drive/MyDrive/1 Projects/3_4 Research Project/Research data/tensile_3_data.csv'\n","\n","raw_dataset = pd.read_csv(url, names=column_names,\n","                          na_values='?', comment='\\t',\n","                          sep=' ', skipinitialspace=True)"]},{"cell_type":"code","source":["dataset = raw_dataset.copy()\n","# dataset.tail()"],"metadata":{"id":"Htnl-grQiPpf","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1665852053021,"user_tz":-120,"elapsed":6,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"outputId":"ace4fc1f-cab8-43b8-c040-3ee36ecc748f"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     Cement  Sand  Aggregate      FA  Cyclodextrin  Water  Days      Tens\n","283   260.0   650       1000  259.48          0.52  208.0    90  4.654398\n","284   260.0   650       1000  259.48          0.52  208.0    90  4.682692\n","285   260.0   650       1000  259.48          0.52  208.0   180  5.772019\n","286   260.0   650       1000  259.48          0.52  208.0   180  5.871049\n","287   260.0   650       1000  259.48          0.52  208.0   180  5.800313"],"text/html":["\n","  <div id=\"df-75e211f7-fc4a-4a92-b3a6-08c6892c5dab\">\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>Cement</th>\n","      <th>Sand</th>\n","      <th>Aggregate</th>\n","      <th>FA</th>\n","      <th>Cyclodextrin</th>\n","      <th>Water</th>\n","      <th>Days</th>\n","      <th>Tens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>283</th>\n","      <td>260.0</td>\n","      <td>650</td>\n","      <td>1000</td>\n","      <td>259.48</td>\n","      <td>0.52</td>\n","      <td>208.0</td>\n","      <td>90</td>\n","      <td>4.654398</td>\n","    </tr>\n","    <tr>\n","      <th>284</th>\n","      <td>260.0</td>\n","      <td>650</td>\n","      <td>1000</td>\n","      <td>259.48</td>\n","      <td>0.52</td>\n","      <td>208.0</td>\n","      <td>90</td>\n","      <td>4.682692</td>\n","    </tr>\n","    <tr>\n","      <th>285</th>\n","      <td>260.0</td>\n","      <td>650</td>\n","      <td>1000</td>\n","      <td>259.48</td>\n","      <td>0.52</td>\n","      <td>208.0</td>\n","      <td>180</td>\n","      <td>5.772019</td>\n","    </tr>\n","    <tr>\n","      <th>286</th>\n","      <td>260.0</td>\n","      <td>650</td>\n","      <td>1000</td>\n","      <td>259.48</td>\n","      <td>0.52</td>\n","      <td>208.0</td>\n","      <td>180</td>\n","      <td>5.871049</td>\n","    </tr>\n","    <tr>\n","      <th>287</th>\n","      <td>260.0</td>\n","      <td>650</td>\n","      <td>1000</td>\n","      <td>259.48</td>\n","      <td>0.52</td>\n","      <td>208.0</td>\n","      <td>180</td>\n","      <td>5.800313</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75e211f7-fc4a-4a92-b3a6-08c6892c5dab')\"\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 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-75e211f7-fc4a-4a92-b3a6-08c6892c5dab button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-75e211f7-fc4a-4a92-b3a6-08c6892c5dab');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["train_dataset = dataset.sample(frac=0.6, random_state=0)\n","test_dataset = dataset.drop(train_dataset.index)"],"metadata":{"id":"l_lY692G9ySD","executionInfo":{"status":"ok","timestamp":1665852059232,"user_tz":-120,"elapsed":616,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["train_features = train_dataset.copy()\n","test_features = test_dataset.copy()\n","\n","train_labels = train_features.pop(\"Tens\")\n","test_labels = test_features.pop(\"Tens\")"],"metadata":{"id":"5kEzwWtEmO9T","executionInfo":{"status":"ok","timestamp":1665852064283,"user_tz":-120,"elapsed":2,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["normalizer = tf.keras.layers.Normalization(axis=-1)"],"metadata":{"id":"05PMHzM0_Tx-","executionInfo":{"status":"ok","timestamp":1665852069636,"user_tz":-120,"elapsed":2,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["normalizer.adapt(np.array(train_features))"],"metadata":{"id":"Dn5E7LRe_Ymd","executionInfo":{"status":"ok","timestamp":1665852071193,"user_tz":-120,"elapsed":423,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["def plot_loss(history):\n","  plt.plot(history.history['loss'], label='loss')\n","  plt.plot(history.history['val_loss'], label='val_loss')\n","  plt.ylim([0, 0.1])\n","  plt.xlabel('Epoch')\n","  plt.ylabel('Error [Tens]')\n","  plt.legend()\n","  plt.grid(True)"],"metadata":{"id":"0sZ0m0TcAwi5","executionInfo":{"status":"ok","timestamp":1665852229034,"user_tz":-120,"elapsed":426,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}}},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":["#Regression using a DNN and multiple inputs"],"metadata":{"id":"m7EBnKSoFZEP"}},{"cell_type":"code","source":["def build_and_compile_model(norm):\n","  model = keras.Sequential([\n","      norm,\n","      layers.Dense(30, activation='sigmoid'),\n","      layers.Dense(1)\n","  ])\n","\n","  model.compile(loss='mean_squared_error',\n","                optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.5)\n","                )\n","  return model\n","\n","dnn_model = build_and_compile_model(normalizer)\n","dnn_model.summary()"],"metadata":{"id":"xVe543omFhPV","executionInfo":{"status":"ok","timestamp":1665856329033,"user_tz":-120,"elapsed":688,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"161efa6a-0a4e-45d7-c7fe-448a5ece3afb"},"execution_count":89,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_15\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," normalization (Normalizatio  (None, 7)                15        \n"," n)                                                              \n","                                                                 \n"," dense_30 (Dense)            (None, 30)                240       \n","                                                                 \n"," dense_31 (Dense)            (None, 1)                 31        \n","                                                                 \n","=================================================================\n","Total params: 286\n","Trainable params: 271\n","Non-trainable params: 15\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["%%time\n","history = dnn_model.fit(\n","    train_features,\n","    train_labels,\n","    validation_split=0.2,\n","    verbose=0, epochs=2000)\n","\n","plot_loss(history)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0},"id":"zNgaE_TDFqs_","executionInfo":{"status":"ok","timestamp":1665856412021,"user_tz":-120,"elapsed":82229,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"outputId":"c290f44d-0b9c-49b3-ff31-46cbf717b1f8"},"execution_count":90,"outputs":[{"output_type":"stream","name":"stdout","text":["CPU times: user 60 s, sys: 2.59 s, total: 1min 2s\n","Wall time: 1min 22s\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["test_predictions = dnn_model.predict(test_features).flatten()\n","\n","mae = tf.keras.losses.MeanAbsoluteError()\n","print(mae(test_labels, test_predictions).numpy())\n","\n","mape = tf.keras.losses.MeanAbsolutePercentageError()\n","print(mape(test_labels, test_predictions).numpy())\n","\n","mse = tf.keras.losses.MeanSquaredError()\n","print(mse(test_labels, test_predictions).numpy())\n","\n","metric = tfa.metrics.r_square.RSquare()\n","metric.update_state(test_labels, test_predictions)\n","result = metric.result()\n","print(result.numpy())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kjTyE7CWpHNA","executionInfo":{"status":"ok","timestamp":1665856412022,"user_tz":-120,"elapsed":14,"user":{"displayName":"Patrick Rimbault","userId":"11243556347484036399"}},"outputId":"3a568541-ad01-4b27-8da5-9a4714e6f7a1"},"execution_count":91,"outputs":[{"output_type":"stream","name":"stdout","text":["4/4 [==============================] - 0s 3ms/step\n","0.1528315\n","3.8964112\n","0.039299086\n","0.95170283\n"]}]}]}