autokeras-classification-notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"autokeras_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"FXesBs4K3D1k","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":609},"executionInfo":{"status":"ok","timestamp":1601034818918,"user_tz":-330,"elapsed":7226,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"6dc0ccbf-c27d-4226-ce5f-95b6e4efdb5f"},"source":["!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1\n","  Cloning https://github.com/keras-team/keras-tuner.git (to revision 1.0.2rc1) to 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Stored in directory: /tmp/pip-ephem-wheel-cache-hm645udj/wheels/af/c9/7c/6ea01f9753a5dd1484136b4cb7b33a0a7fba253e5c74ade5af\n","  Building wheel for terminaltables (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for terminaltables: filename=terminaltables-3.1.0-cp36-none-any.whl size=15356 sha256=bf20a97cc4d3f5951f2d575bcca15e4d653794ed38aeb74f2efd6e81e6a2bed3\n","  Stored in directory: /root/.cache/pip/wheels/30/6b/50/6c75775b681fb36cdfac7f19799888ef9d8813aff9e379663e\n","Successfully built keras-tuner terminaltables\n","Installing collected packages: terminaltables, colorama, keras-tuner\n","Successfully installed colorama-0.4.3 keras-tuner-1.0.2rc1 terminaltables-3.1.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"46qJh79hkwoR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":886},"executionInfo":{"status":"ok","timestamp":1601034821963,"user_tz":-330,"elapsed":9605,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"8821b8e3-8f0c-4697-ecb8-31464a334d59"},"source":["!pip install autokeras"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting autokeras\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/12/01b893f0e2f5865f0fdd2c47a99fb0a96ad3263e3eaa1be04f9ec9979dc4/autokeras-1.0.8-py3-none-any.whl (119kB)\n","\r\u001b[K     |██▊                             | 10kB 21.3MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 20kB 2.9MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 30kB 3.8MB/s eta 0:00:01\r\u001b[K     |███████████                     | 40kB 4.1MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 51kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 61kB 3.7MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 71kB 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train_test_split"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"ha6PEi87zW5a","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1601034854044,"user_tz":-330,"elapsed":918,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["df = pd.read_excel(\"Data.xlsx\")"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"xFHbdozczW8b","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":222},"executionInfo":{"status":"ok","timestamp":1601034857410,"user_tz":-330,"elapsed":951,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"ccb2fa4d-bd4e-4ba2-d888-16644b89b3d8"},"source":["df.head()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Gender</th>\n","      <th>Married</th>\n","      <th>Dependents</th>\n","      <th>Education</th>\n","      <th>Self_Employed</th>\n","      <th>ApplicantIncome</th>\n","      <th>CoapplicantIncome</th>\n","      <th>LoanAmount</th>\n","      <th>Loan_Amount_Term</th>\n","      <th>Credit_History</th>\n","      <th>Property_Area</th>\n","      <th>Loan_Status</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5849</td>\n","      <td>0.0</td>\n","      <td>126</td>\n","      <td>360.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>4583</td>\n","      <td>1508.0</td>\n","      <td>128</td>\n","      <td>360.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3000</td>\n","      <td>0.0</td>\n","      <td>66</td>\n","      <td>360.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2583</td>\n","      <td>2358.0</td>\n","      <td>120</td>\n","      <td>360.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>6000</td>\n","      <td>0.0</td>\n","      <td>141</td>\n","      <td>360.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Gender  Married  Dependents  ...  Credit_History  Property_Area  Loan_Status\n","0       1        0           0  ...               1              2            1\n","1       1        1           1  ...               1              0            0\n","2       1        1           0  ...               1              2            1\n","3       1        1           0  ...               1              2            1\n","4       1        0           0  ...               1              2            1\n","\n","[5 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"aR1rkt8KzW-_","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1601034902197,"user_tz":-330,"elapsed":982,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["target_col = \"Loan_Status\"\n","X = df.loc[:, df.columns != target_col]\n","y = df.loc[:, target_col]"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"KfwOZEfq0NgT","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1601034917108,"user_tz":-330,"elapsed":1068,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, \n","                                                    test_size=0.20, \n","                                                    random_state=2021)"],"execution_count":7,"outputs":[]},{"cell_type":"code","metadata":{"id":"qP96_Q2J0ZOK","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1601034988890,"user_tz":-330,"elapsed":1021,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}}},"source":["search = StructuredDataClassifier(max_trials = 20)"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"3DD5hmwL0uMn","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":329},"executionInfo":{"status":"ok","timestamp":1601035115441,"user_tz":-330,"elapsed":107698,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"39a65997-9819-4494-8d97-9c7fa1ab8295"},"source":["search.fit(x=X_train, y=y_train)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Trial 20 Complete [00h 00m 04s]\n","val_accuracy: 0.7599999904632568\n","\n","Best val_accuracy So Far: 0.7599999904632568\n","Total elapsed time: 00h 01m 38s\n","INFO:tensorflow:Oracle triggered exit\n","Epoch 1/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.8880 - accuracy: 0.4501\n","Epoch 2/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.7845 - accuracy: 0.4949\n","Epoch 3/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.7312 - accuracy: 0.5784\n","Epoch 4/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.6934 - accuracy: 0.6191\n","Epoch 5/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.6248 - accuracy: 0.7026\n","Epoch 6/6\n","16/16 [==============================] - 0s 4ms/step - loss: 0.6162 - accuracy: 0.6986\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"OaztLYky1tYY","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1601035171525,"user_tz":-330,"elapsed":1406,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"a909e78f-878f-4b3e-e630-d09f2bc38134"},"source":["loss, acc = search.evaluate(X_train, y_train, verbose=0)\n","print('Accuracy is {}'.format(acc))"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Accuracy is 0.7637474536895752\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"RcgHFIEP3xLI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":158},"executionInfo":{"status":"ok","timestamp":1601035209294,"user_tz":-330,"elapsed":1435,"user":{"displayName":"Bhavesh Bhatt","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ggn1iyaPhugkkpQYFRw42nt9ToNB-Rit7YeWtI4Zw=s64","userId":"01561702845917398436"}},"outputId":"5724b2fd-4614-4096-f904-3b28ace84e06"},"source":["loss, acc = search.evaluate(X_test, y_test, verbose=0)\n","print('Accuracy is {}'.format(acc))"],"execution_count":11,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate\n","WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n","Accuracy is 0.7154471278190613\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-eAW1y0j2WrP","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"08T2EYg_2Wt7","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}