diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb index 52e963d..36b1ad7 100644 --- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb +++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb @@ -1439,9 +1439,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", + " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", + " hidden_layer_sizes=(16, 16), learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", + " nesterovs_momentum=True, power_t=0.5, random_state=4222,\n", + " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", + " verbose=False, warm_start=False)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clf_3 = MLPClassifier(hidden_layer_sizes=(16,16), random_state=4222)\n", "clf_3.fit(vec_train_3, y3)" @@ -1449,9 +1466,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.9997144488863506'\n", + "Confusion matrix, without normalization\n", + "array([[3367, 1],\n", + " [ 1, 3635]])\n", + "'score: 0.7714856762158561'\n", + "Confusion matrix, without normalization\n", + "array([[1136, 343],\n", + " [ 343, 1180]])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- train\", Xt=vec_train_3, yt=y3, clf=clf_3)\n", "cm_4=test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- test\", Xt=vec_test_3, yt=yt3, clf=clf_3)" @@ -1466,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1481,7 +1533,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1491,7 +1543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1510,7 +1562,7 @@ " }\n", " self.g = Graph()\n", "\n", - " for i in nsp.items():\n", + " for i in self.nsp.items():\n", " self.g.bind(i[0],i[1])\n", " \n", " self.exp = self.nsp[\"this\"][name_exp]\n", @@ -1550,7 +1602,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [