From cc7ff51203aceae04b252bca4347cf613dba755f Mon Sep 17 00:00:00 2001 From: Carsten Date: Tue, 15 May 2018 10:28:16 +0200 Subject: [PATCH 1/2] more comments and maybe some mistakes --- .../Task 2 - Carsten Draschner.ipynb | 426 ++++++++++++------ 1 file changed, 297 insertions(+), 129 deletions(-) diff --git a/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb b/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb index dfd325a..0d10056 100644 --- a/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb +++ b/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb @@ -30,6 +30,7 @@ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import PassiveAggressiveClassifier\n", "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.neural_network import MLPClassifier\n", "from sklearn import metrics\n", "import matplotlib.pyplot as plt" ] @@ -186,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "store label from tob row of the table" + "store label column with the classification of each text" ] }, { @@ -223,8 +224,9 @@ "from skikit learn the function: train_test_split\n", "* in the dataframe get text column by df['text']\n", "* use stored y label df\n", - "* use seed 42\n", - "* determine split size: in this case 0.33" + "* use seed 4222\n", + "* determine split size: in this case 0.25\n", + "* **take care that these config is the same to get compareable results**" ] }, { @@ -241,7 +243,13 @@ "metadata": {}, "source": [ "## Load Dataset 2 - liar_dataset.zip\n", - "Read in File liar_dataset.zip" + "* Read in File liar_dataset.zip\n", + "* cause of three file read in each file on its own and assign it to train test vaidation \n", + "\n", + "#### So first train set\n", + "\n", + "* only use the text part and ignore the others\n", + "* it would also be possible to for example to concatiate the other information ito the text part but youll lose the robustness againstother datasets without these coulums and you'll mix up information" ] }, { @@ -344,6 +352,13 @@ "mul_y_train = df.y" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### second dataset: test set" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -444,6 +459,13 @@ "mul_y_test = df.y" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### as third dataset: validation set" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -510,7 +532,7 @@ "6335\n", "-- liar liar\n", "Index(['y', 'claim'], dtype='object')\n", - "{'mostly-true', 'pants-fire', 'true', 'half-true', 'false', 'barely-true'} 10240\n", + "{'true', 'barely-true', 'half-true', 'pants-fire', 'mostly-true', 'false'} 10240\n", "1676\n", "1995\n", "{'true', 'false'} 3671\n", @@ -564,50 +586,14 @@ " print(e)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vectorizer Classifiers\n", - "* tfids removes words in pregenerating the vectors by evaluating if this word appears more than 70% often in all articles\n", + "* tfids removes words in pregenerating the vectors by evaluating if this word appears more than 70% often in all articles (tfidf)\n", "* an immense naive approach would be to store a set of the over all occuring word in all the texts and for each text determining how often this word occurs.\n", - "* also testing some min df thresholds as lower bound for regarded occurences" + "* also testing some min df thresholds as lower bound for regarded occurences of words over all texts ... not easy to predict if this is a good idea" ] }, { @@ -617,32 +603,36 @@ "### Generate Vectorizer on Binary Classes" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "generate two different vectorizers" + ] + }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ + "#building the vectorspace\n", "bin_count_vectorizer = CountVectorizer(stop_words='english')\n", "bin_count_train = bin_count_vectorizer.fit_transform(bin_X_train)\n", + "#transform the other sets into same feature vector as trained on train data \n", "bin_count_test = bin_count_vectorizer.transform(bin_X_test)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "bin_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7, min_df=0.0005)\n", + "#building the vectorspace\n", + "bin_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)#, min_df=0.0005)\n", "bin_tfidf_train = bin_tfidf_vectorizer.fit_transform(bin_X_train)\n", + "#transform the other sets into same feature vector as trained on train data \n", "bin_tfidf_test = bin_tfidf_vectorizer.transform(bin_X_test)" ] }, @@ -706,7 +696,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Generate Vectorizer on Multilabel Classes" + "### Generate Vectorizer on Multilabel Classes\n", + "* same again\n", + "* only additional transform for validation set" ] }, { @@ -723,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -737,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -755,7 +747,7 @@ " 'zuckerbergs']" ] }, - "execution_count": 50, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -766,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -786,7 +778,7 @@ " 'zuckerbergs']" ] }, - "execution_count": 51, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -799,13 +791,106 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Confusion Matrix Code\n", - "copy paste by distributed notebook" + "### Generate Vectors on merged Sets\n", + "* again the same ..." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "concat_count_vectorizer = CountVectorizer(stop_words='english')\n", + "concat_count_train = concat_count_vectorizer.fit_transform(concat_X_train)\n", + "concat_count_test = concat_count_vectorizer.transform(concat_X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "concat_tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)#, min_df=0.0005)\n", + "concat_tfidf_train = concat_tfidf_vectorizer.fit_transform(concat_X_train)\n", + "concat_tfidf_test = concat_tfidf_vectorizer.transform(concat_X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['القادمون',\n", + " 'ایران',\n", + " 'جنگ',\n", + " 'سال',\n", + " 'عربي',\n", + " 'علیه',\n", + " 'مطالعاتی',\n", + " 'مورد',\n", + " 'کدآمایی',\n", + " 'ยงade']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "concat_tfidf_vectorizer.get_feature_names()[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['القادمون',\n", + " 'ایران',\n", + " 'جنگ',\n", + " 'سال',\n", + " 'عربي',\n", + " 'علیه',\n", + " 'مطالعاتی',\n", + " 'مورد',\n", + " 'کدآمایی',\n", + " 'ยงade']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "concat_count_vectorizer.get_feature_names()[-10:]#[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Confusion Matrix Code\n", + "* copy paste by distributed notebook\n", + "* thx for providing" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": { "collapsed": true }, @@ -849,15 +934,6 @@ " plt.xlabel('Predicted label')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -888,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": { "collapsed": true }, @@ -899,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -914,7 +990,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -932,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -947,7 +1023,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -975,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 34, "metadata": { "collapsed": true }, @@ -986,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1001,7 +1077,7 @@ "data": { "image/png": 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iuHc58FbEiZ0PKSZmALOBbwFU9ZSqlrXLF8BNIG0c/ZwHsolIZhHxxfpAUNUb\nwHERaWv3LyJSJo52DAaDC0nEGO93QPTYzwqs8GNp4DDg+JTNMQef0s3BPhUrxOhvF6fxJHc53gPA\niyKyByusMBWYBuzFistuj+Ped4CK9sTcfqBbLPVmARmxnO9DqOplYJM9mfdJDNdDgY+BbVgpnQ86\nXO4EdBGR3cCfQIs49BoMBheTGMvJVHU9cCWabbnDSqqtWOna49AhfkA6Vd1ij3JnAC2d9e3yUIOq\nngCeiuHSQLtEr1872vkloF0M9b7D+saKoAbwi6rG+lC5qnaMZlob7frnwEMzLqp6nHh8ixkMBvcQ\nz+VkWUQk0OH8K1X96hG6eQWY43BeQER2AjeAgaq6AcgFOD7WF2zb4iRZr+ONLyIyEWgCNE1qLQaD\nwcXEP8vwpYROronIAKx5n1m26SyQV1Uv2zHd30WkBDHvDOx0xcK/wvGq6ttJrcFgMLgHK8brwvZF\nXsSa46kXMUmmqveAe/ZxkIgcA4pgjXAdwxG5iboKK0bMXg0Gg8HDcL5PQ0KfbBORxkAf4H+qetvB\nnlVEvO3jgliTaH+p6lngpohUsR8C6wzMc9bPv2LEazAY/lskxgMUIjIbqI0VCw4GBmOtYvAFVth9\nRCwbqwV8LCJhQDjWA1URE3Pd+Wc52RK7xIlxvAaDwbNIpHW8qtohBvM3sdSdC8yN5VogUPJR+jaO\n12AweBTWtpCeHSU1jtdgMHgcHr5Vg3G8BoPB8/D0TXKM4zUYDB6FSPLPMOEM43gNBoPH4eED3tgd\nr4jEuUO0vXmMwWAwuB0vD/e8cY14/8R69M3xFUacK+C6nCkGg8EQBx7ud2N3vKoa/xQKBoPB4CZE\nwPu/EOMVkfZAQVUdISK5geyqGuRaaZ6NlwipfZNnCL1DueT7YyVjpbecV0pCTq6fkNQSDHj+qgan\nq5DFSqFeB3jBNt3GyuhgMBgMScJ/Ib17NVUtb+9DiapeEZGULtZlMBgMMSKAd3L3rE6Ij+MNFREv\n7D0mRSQz8MClqgwGgyE2PCB9uzPi88DzZKzNIbKKyEdY+dJGu1SVwWAwxMG/PtSgqjNEJAiob5va\nRk+dbjAYDO5C+I+sagC8gVCscINnbwtkMBg8nn99qMHOPTQbyImV1uJHEekX910Gg8HgGuITZkju\nfjk+I97ngQoRaTBEZDgQBIx0pTCDwWCIjf/CqoaT0er5AH+5Ro7BYDA4518bahCRCSIyHuuBiT9F\n5GsRmQbsBa65S6DBYDA4IoCXOC9O2xGZLiIXRGSfgy2TiKwQkSP234y2XUTkcxE5KiJ7RKS8wz0v\n2vWP2BmKnRLXiDdCzJ/AIgefzwWPAAAgAElEQVT71vg0bDAYDC4h8dbxfgdMAmY42PoCq1R1lIj0\ntc/7AE2wMgv7AwHAVCBARDJhJcmsiLX4IEhE5qvq1bg6jmuTnBiTvhkMBkNSkxgboavqehHJH83c\nAivzMMD3wFosx9sCmKGqCmwVkQwi4mfXXRGRcVhEVgCNsRYkxK7fmTgRKSQiP9nD68MRJZ6vzZAA\nvpj8OVUrlqFqxdJMnfQZAFevXKFVs0ZUKF2MVs0ace1qnF+oLuH1V18hb85sVCj7T0LV3bt2Uat6\nFQIqlKV6QEW2//GHSzV8MbgTJ1eNJPD/+kfaShfJxbrve7D1p75snNWbiiXyAVCzgj/n1n/C1p/6\nsvWnvvTr2hiA3NkzsPSrd9g5dyBBvwzgzQ61E13nu2+8xlMFc1EroGykbdTQwdSuWp661SvyXIum\nnDt7BoBrV6/yUsc21K5anka1q3Fgv3uXycf0uQ77eAgF8+UioEJZAiqUZemSxW7VFBePEGrIIiKB\nDqVrPJrPrqpnAey/2Wx7LuCUQ71g2xabPU7isyb3O+BbrNfbBPgZ+Cke9xkSwP4/9/H9t9+wav0W\nNmzdwbIlizh29AgTxo2mVu26BO05SK3adZkwzv0PD77w4kvMW7g0im1Av94M+HAw24J28eGQjxnQ\nr7dLNcxcsJUWb06OYhv+XkuGf7WEKu1HMXTqQoa/1zLy2qadx6jSfhRV2o9i5FeW9rDwB/Qd/yvl\nWg/j6c5jeb1dLYoVzJGoOtt36sxPvy6MYnvz3R6s3bKD1ZsCadC4KeNGDwfgs3GjKVmqDGu37GDS\nV9MZ2KdHompxRkyfK8Db777PtqBdbAvaReMmTd2qyRlihxviKsAlVa3oUL56nC5jsEXfr9zRHifx\ncbypVHUZgKoeU9WBWLuVGVzA4UMHqVQ5gFSpUuHj40P1mrVYOP93lixaQIdOnQHo0KkzixfOd7u2\nGjVrkSlTpig2EeHGDSsZyfXr1/HLmdOlGjbtOMaV67ej2FQhXeonAEif5knOXrweZxvnLt1g18Fg\nAEJu3+Pg8XPkzJohUXVWrV6TDBkzRrGlTfdPUpfbt29FxikPHzxAzdp1AfAvUoxTJ09y4cL5RNUT\nFzF9rskZEWs5mbOSQM7bIQTsvxdsezDguEd5buBMHPY4iY/jvSfWv5BjItJNRJrzz/DbkMgUf6oE\nmzdt4Mrly9y+fZsVy5Zw+nQwFy6cJ4efHwA5/Py4ePGCk5bcwyfjPqV/314ULpCHfn168vEw9y/v\n7jX2F0a815IjS4Yy8v1WDJo4L/JaQOkCbJvTl98ndad4DKPavH6ZKFs0N9v3nXCL1hEff0i54gWZ\n+/Nseg8YDMBTpUqxaP7vAOwI3E7wqZOcPX3aLXri4ospk6hUrjSvv/oKV5MgtBUXLnyAYj4QsTLh\nRWCeg72zvbqhCnDdDkUsAxqKSEZ7BURD2xYn8XG87wNpgHeA6sBrwCuP8koeFRHJ77jEIx71h4hI\nT/u4mIjsEpGdIlIoWr3aIlItsfUmJkWLFefdD3rRqnlj2rRsSolSZfDx9k5qWbHy1ZdTGTN2AkeP\nn2LM2Al079rF7Rq6tq1J73G/4t/kQ3qPncvUwZ0A2HXwFEWbfkhAu1FM/WkdP0+IGuJL/WRKZo99\nlV5j53Lz1l23aO0/aCg7D/xF6+c6MP3LKQC8835vrl+7St3qFfnmy8mUKl0WH5+k/cxfe707+w8d\nY1vQLnL4+dG3l3vDH86IZ6jBWRuzgS1AUREJFpEuwCiggYgcARrY5wCLsZ5fOApMA94Aa5tcYCiw\n3S4fR0y0xYVTx6uq21T1pqr+raovqOr/VHWT01eVdLQE5qlqOVU9Fu1abSBGxysiySZdxAsvvsK6\nzdtZvHwtGTNmpGBhf7Jly865s2cBOHf2LFmzJo8fHbNmfk/LVs8C0LpNWwK3u3ZyLSY6NQvg91W7\nAJi7Ymfk5NrNW3e5dec+AMs27ieFjzeZM6QGwMfHi9ljX2POkkDmrd7tds3Ptm3Pwvm/AVYI4rOp\nX7N6UyCTvvqWy5cvkTdfAbdrciR79ux4e3vj5eXFK11eIzDQ/Z9rbAiCt5fz4gxV7aCqfqqaQlVz\nq+o3qnpZVeupqr/994pdV1X1TVUtpKqlVDXQoZ3pqlrYLt/G5zXE9QDFbyLya2wlPo0/Jt4iMk1E\n/hSR5SLypIi8JiLbRWS3iMwVkVTRNDcF3gNeFZE10a7lB7oB79sj4poi8p2IjLfrjnYcOdv37ItY\nbiIiz4vIH/a9X4qIy4YkFy9YYYRTp/5m4fzfadO2PY2bNmP2LGu54exZM2jyTHNXdf9I+OXMyYb1\n6wBYu2Y1hQv7u13D2YvXqVnB6rd25SIc/fsiANkzp42sU7FEPrxEuHztFmCtjjh0/Byf/7DabTr/\nOnok8njZ4oX4FykKwPVr17h/3/qC+OH76VSpViNKPDgpOGt/yQPM+/03nipRMo7abuZfvlfDJLep\niBl/oIOqviYiPwOtgV9VdRqAiAwDugATI25Q1cUi8gUQoqpjHRtT1RPRr9k/LYoA9VU1XESGxCRE\nRIoD7YDqqhoqIlOATkRdeI29XKUrQO48Cc9r1rlTW65euYKPTwo+Gf85GTJm5P0efXj5hfb8MONb\ncufOw3c/zElw+wnW9XwHNqxby6VLlyiUPzcfDvqIyVOn0euDdwkLC8P3iSeYNPVxJo6d8/3Il6hZ\nwZ8sGdJwdOlQhn6xmDeH/sgnvdrg4+PFvXthvDXMWkLZqn45Xmtbk7DwcO7eDaVzP2swUq1sQTo1\nC2Dv4dNs/akvAIMnzWfZxv2JpvP1l59n88b1XLl8ibLFCtCr/yBWLV/C0SOH8fLyIneevHzyqbU6\n4/Chg7z9+it4e3tRpFhxJkxy7XsYnZg+1/Xr1rJn9y5EhHz58zNxypdu1eSMRHqAIskQaz1w8sIe\nZa5QVX/7vA+QAtgADAMyYMWdl6lqN9thhqjqWMfjGNqNck1EvgPWqOr3sVzfBzSzS3/+meF8Epit\nqkNiew3lylfUNRu3JfQtcClPpEy+MWOT7DLhpHsyRVJLiJHqARUJCgpMNE+ZvXBJbTf2F6f1JrYq\nHqSqFROr38Qk2cQ1Y+Cew3E4lrP7DmipqrtF5CX+ecIkRkTkTazJQIDYFiLecjgOI2r45YmIpoDv\nVdVsh2kwJAM8fB90j9vUPC1wVkRSYP3UjxNVnayqZe1yBrhptxEbJ4DyAPYmGBEzHKuANiKSzb6W\nSUTyJfxlGAyGxyExNslJSuLteEXE15VC4smHwDZgBXAwAfcvAFpFTK7FcH0ukElEdgHdgcMAqrof\nGAgsF5E9dv9+CejfYDA8Jtbk2eMvJ0tKnIYaRKQy8A2QHsgrImWAV1X1bVeJUtUTQEmHc8d47dQY\n6g+J6TiGeoeB0g6mDdGu38FaAB3TvXMA989oGQyGh/D2tN/q0YiP/M+xJpcuA6jqbswjwwaDIYmw\nNskRpyU5E5/JNS9VPRlt6B7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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1022,7 +1098,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 36, "metadata": { "collapsed": true }, @@ -1033,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1048,7 +1124,7 @@ "data": { "image/png": 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QD0DRAnHsSIlMazovfpo0gVq1a1O9eg2/QzmMiOumFewRDfLSgt0v7uviTxG5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\nBLwLdPMudg0AHlDVfH1BeWdAApwINBW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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1066,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "collapsed": true }, @@ -1077,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1090,9 +1166,9 @@ }, { "data": { - "image/png": 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ikl9EBojIWhHZKCI/iEgB5xtE5E5gKPCQiCxJkVcRa8vvJ0Rkg4i0EJEJIvKe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\n4WZAI9odh6J+6mK/NRtsiQPQFH0GWii5lS1MzfO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MMAhWIjDGmGAQxFqwxhgTLJZgjTEmSCzBGmNMMFgN1hhjgsdasMYYEwTWyWWMMUFkJYLT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\nPK9H0oWSXjuMcYpxSGWwxZhi+2BmgvdLek0eXmV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1119,7 +1195,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 40, "metadata": { "collapsed": true }, @@ -1130,43 +1206,18 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 true\n", - "1 false\n", - "2 false\n", - "3 half-true\n", - "4 pants-fire\n", - "5 true\n", - "6 true\n", - "7 barely-true\n", - "8 true\n", - "9 barely-true\n", - "10 barely-true\n", - "11 barely-true\n", - "12 pants-fire\n", - "13 false\n", - "14 half-true\n", - "15 true\n", - "16 pants-fire\n", - "17 half-true\n", - "18 true\n", - "19 false\n", - "20 mostly-true\n", - "21 half-true\n", - "22 pants-fire\n", - "23 mostly-true\n", - "24 false\n", - "25 true\n", - "26 barely-true\n", - "27 half-true\n", - "28 true\n", - "29 mostly-true\n", + "0 true\n", + "1 false\n", + "2 false\n", + "3 half-true\n", + "4 pants-fire\n", "Name: y, dtype: object\n" ] } @@ -1181,7 +1232,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 42, "metadata": { "collapsed": true }, @@ -1193,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1220,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 44, "metadata": { "scrolled": true }, @@ -1237,7 +1288,7 @@ "data": { "image/png": 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l70EdGXn7nzY5f9M1f6R9k13TpG5TXp0wntYtm9GyeVNuv21EuuVsgmsrmbsGdeCzO/vy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\nzFriBGtm1hInWDOzlvwCAdo7IVKwydUAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1253,19 +1304,19 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "clf = MultinomialNB()\n", - "from sklearn.neural_network import MLPClassifier\n", + "#from sklearn.neural_network import MLPClassifier\n", "\n", - "clf = MLPClassifier()" + "#clf = MLPClassifier(hidden_layer_sizes=(40,20))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1294,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1304,9 +1355,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['half-true' 'false' 'false' 'half-true' 'half-true' 'barely-true' 'false'\n", + " 'mostly-true' 'half-true' 'false' 'half-true' 'half-true' 'false' 'false'\n", + " 'false' 'half-true' 'half-true' 'half-true' 'false' 'true']\n" + ] + } + ], "source": [ "print(pred[:20])\n", "\n", @@ -1318,9 +1379,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.220\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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VyaYgaXIpYYHpdeDLajz/InBgDOPauYL2Z4AmMfHDqcDXAGY2HrgEeE3S2Dh/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\nxMpH1DMCAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "score = metrics.accuracy_score(bin_y_test, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", @@ -1346,6 +1426,94 @@ "* model c - test - [performance measures][0:4]" ] }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "clf = MultinomialNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.860\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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GpJkfN9BZ5JOo9zcIfCCpY9pb7QjMSsunA4Xh0QWYkZbvskL507UdRBH186uH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\nMuZgNTPLmIPVzCxjDlYzs4z9P3Wjv5Jr3ijvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clf.fit(concat_tfidf_train, concat_y_train)\n", + "pred = clf.predict(concat_tfidf_train)\n", + "score = metrics.accuracy_score(concat_y_train, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(concat_y_train, pred, labels=['true', 'false'])\n", + "plot_confusion_matrix(cm, classes=['true', 'false'])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "clf = MultinomialNB()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.749\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clf.fit(concat_tfidf_train, concat_y_train)\n", + "pred = clf.predict(concat_tfidf_test)\n", + "score = metrics.accuracy_score(concat_y_test, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(concat_y_test, pred, labels=['true', 'false'])\n", + "plot_confusion_matrix(cm, classes=['true', 'false'])" + ] + }, { "cell_type": "code", "execution_count": null, From 5fba30ef0b6b026cb8725f641216569ce2226929 Mon Sep 17 00:00:00 2001 From: Carsten Date: Thu, 31 May 2018 11:49:28 +0200 Subject: [PATCH 2/2] task 2 and 3 and a naive approach test image --- .../Task 2 - Carsten Draschner.ipynb | 588 ++++++++++++++---- .../Task 3 - Carsten Draschner.ipynb | 518 +++++++++++++++ Project/Images/Naive_approach_test_image.png | Bin 0 -> 252624 bytes 3 files changed, 983 insertions(+), 123 deletions(-) create mode 100644 Carsten_Solutions/Task 3 - Carsten Draschner.ipynb create mode 100644 Project/Images/Naive_approach_test_image.png diff --git a/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb b/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb index 0d10056..93792e4 100644 --- a/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb +++ b/Carsten_Solutions/Task 2 - Carsten Draschner.ipynb @@ -32,7 +32,9 @@ "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn import metrics\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "#https://github.com/kjam/random_hackery/blob/master/Attempting%20to%20detect%20fake%20news.ipynb" ] }, { @@ -53,7 +55,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/fake_or_real_news.csv')" @@ -193,7 +197,9 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#store label before dropping it\n", @@ -211,7 +217,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "df = df.drop('label', axis=1)" @@ -232,7 +240,9 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "bin_X_train, bin_X_test, bin_y_train, bin_y_test = train_test_split(df['text'], bin_y, test_size=0.25, random_state=4222)" @@ -255,7 +265,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#training data file\n", @@ -362,7 +374,9 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#test data file\n", @@ -532,7 +546,7 @@ "6335\n", "-- liar liar\n", "Index(['y', 'claim'], dtype='object')\n", - "{'true', 'barely-true', 'half-true', 'pants-fire', 'mostly-true', 'false'} 10240\n", + "{'true', 'pants-fire', 'false', 'barely-true', 'mostly-true', 'half-true'} 10240\n", "1676\n", "1995\n", "{'true', 'false'} 3671\n", @@ -613,7 +627,9 @@ { "cell_type": "code", "execution_count": 18, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#building the vectorspace\n", @@ -626,7 +642,9 @@ { "cell_type": "code", "execution_count": 19, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "#building the vectorspace\n", @@ -651,16 +669,7 @@ { "data": { "text/plain": [ - "['zoomed',\n", - " 'zooming',\n", - " 'zor',\n", - " 'zucker',\n", - " 'zuckerberg',\n", - " 'zuesse',\n", - " 'zurich',\n", - " 'zwick',\n", - " 'état',\n", - " 'œthe']" + "['تنجح', 'حلب', 'عن', 'لم', 'ما', 'محاولات', 'من', 'هذا', 'والمرضى', 'ยงade']" ] }, "execution_count": 20, @@ -704,7 +713,9 @@ { "cell_type": "code", "execution_count": 22, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "mul_count_vectorizer = CountVectorizer(stop_words='english')\n", @@ -934,6 +945,42 @@ " plt.xlabel('Predicted label')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dictionary definition for export to rdf in the end" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "example_configuration = {\n", + " \"name\":\"experiment1\",\n", + " \n", + " \"model\":\"generic_model_1_name\",\n", + " \"model-name\":\"deepNN\",\n", + "\n", + "\n", + " \"dataset\":\"X1\",\n", + " \"dataset-name\":\"fake_or_real_news\",\n", + " \"dataset-link\":\"'/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/fact checking/fake_or_real_news.csv'\",\n", + " \n", + " \"measurement-name\":\"measurement_configuration1\",\n", + " \n", + " \"execution-name\":\"execution_configuration1\",\n", + " \n", + " \"precision\":0.33,\n", + " \"recall\":0.73,\n", + " \"accuracy\":0.55\n", + "}" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -964,46 +1011,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "accuracy: 0.914\n", - "Confusion matrix, without normalization\n" - ] - }, - { - "data": { - "image/png": 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ypS3ceaWIqHQMZSFpCvBupeMog27A1EoHYUulLf/MNoiINbI8oKT7Sf7Nipka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\nWM3MMubEamaWMSdWM7OMObGamWXs/wP8oyQamEghrgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf.fit(bin_tfidf_train, bin_y_train)\n", - "pred = clf.predict(bin_tfidf_train)\n", - "score = metrics.accuracy_score(bin_y_train, pred)\n", - "print(\"accuracy: %0.3f\" % score)\n", - "cm = metrics.confusion_matrix(bin_y_train, pred, labels=['FAKE', 'REAL'])\n", - "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])" + "clf_model_a = MultinomialNB()" ] }, { @@ -1015,15 +1029,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "accuracy: 0.872\n", + "accuracy: 0.887\n", "Confusion matrix, without normalization\n" ] }, { "data": { - "image/png": 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xmVnNeRZ+KSlJ7gJ8q6ksIt6TdC+wR2HdiJgoqT9wq6QvpuLCc6CPR8QhHRG3mXW8nOTH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\nX2YbE4AJ6bUv+jf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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1031,12 +1045,84 @@ } ], "source": [ - "clf.fit(bin_tfidf_train, bin_y_train)\n", - "pred = clf.predict(bin_tfidf_test)\n", + "clf_model_a.fit(bin_tfidf_train, bin_y_train)\n", + "pred = clf_model_a.predict(bin_tfidf_train)\n", + "score = metrics.accuracy_score(bin_y_train, pred)\n", + "print(\"accuracy: %0.3f\" % score)\n", + "cm = metrics.confusion_matrix(bin_y_train, pred, labels=['FAKE', 'REAL'])\n", + "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.826\n", + "Confusion matrix, without normalization\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#clf_model_a.fit(bin_tfidf_train, bin_y_train)\n", + "pred = clf_model_a.predict(bin_tfidf_test)\n", "score = metrics.accuracy_score(bin_y_test, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", "cm = metrics.confusion_matrix(bin_y_test, pred, labels=['FAKE', 'REAL'])\n", - "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])" + "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])\n", + "\n", + "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n", + "recall = cm[1,1]/(cm[1,1]+cm[1,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "configuration_1 = {\n", + " \"name\":\"configuration1\",\n", + " \n", + " \"model\":\"model_a\",\n", + " \"model-name\":\"multinomialNB\",\n", + "\n", + "\n", + " \"dataset\":\"X1\",\n", + " \"dataset-name\":\"fake_or_real_news\",\n", + " \"dataset-link\":\"unknown\",\n", + " \n", + " \"measurement-name\":\"measurement_config1\",\n", + " \n", + " \"execution-name\":\"execution_config1\",\n", + " \n", + " \"precision\":precision,\n", + " \"recall\":recall,\n", + " \"accuracy\":score\n", + "}" ] }, { @@ -1051,18 +1137,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "clf_model_b = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1077,7 +1163,7 @@ "data": { "image/png": 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iuHc58FbEiZ0PKSZmALOBbwFU9ZSqlrXLF8BNIG0c/ZwHsolIZhHxxfpAUNUb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\nNskRpyU5E5/JNS9VPRlt6B7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+rB3SOKklxMr3gTHnd0tqLt++n6jtPcojwckV43gNBoPH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\n8AZBEBSZEN4gCIIi8x/FqW3KYadROgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1085,8 +1171,8 @@ } ], "source": [ - "clf.fit(mul_tfidf_train, mul_y_train)\n", - "pred = clf.predict(mul_tfidf_train)\n", + "clf_model_b.fit(mul_tfidf_train, mul_y_train)\n", + "pred = clf_model_b.predict(mul_tfidf_train)\n", "\n", "#print(pred[:20])\n", "\n", @@ -1098,18 +1184,18 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "#clf = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1124,7 +1210,7 @@ "data": { "image/png": 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QD0DRAnHsSIlMazovfpo0gVq1a1O9eg2/QzmMiOumFewRDfLSgt0v7uviTxG5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\n/P97IZNTlwywScUJNf8puLg3eCC708xGA0dwoZQ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1132,8 +1218,8 @@ } ], "source": [ - "clf.fit(mul_tfidf_train, mul_y_train)\n", - "pred = clf.predict(mul_tfidf_test)\n", + "#clf.fit(mul_tfidf_train, mul_y_train)\n", + "pred = clf_model_b.predict(mul_tfidf_test)\n", "score = metrics.accuracy_score(mul_y_test, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", "cm = metrics.confusion_matrix(mul_y_test, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n", @@ -1142,18 +1228,18 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "#clf_model_b = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1168,7 +1254,7 @@ "data": { "image/png": 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oCwCta8ZzJFmZsc7/BPvTz9MpVaoUW7dupXXLy6lWvTpNmzX3O6zjZLFEECci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\nYIuiKFrEvwEZTQgEsUeetgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1176,37 +1262,79 @@ } ], "source": [ - "clf.fit(mul_tfidf_train, mul_y_train)\n", - "pred = clf.predict(mul_tfidf_valid)\n", + "#clf_model_b.fit(mul_tfidf_train, mul_y_train)\n", + "pred = clf_model_b.predict(mul_tfidf_valid)\n", "score = metrics.accuracy_score(mul_y_valid, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", "cm = metrics.confusion_matrix(mul_y_valid, pred, labels=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])\n", "plot_confusion_matrix(cm, classes=['false', 'barely-true' , 'half-true' , 'mostly-true' , 'true'])" ] }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n", + "recall = cm[1,1]/(cm[1,1]+cm[1,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "configuration_2 = {\n", + " \"name\":\"configuration2\",\n", + " \n", + " \"model\":\"model_a\",\n", + " \"model-name\":\"multinomialNB\",\n", + "\n", + "\n", + " \"dataset\":\"X2\",\n", + " \"dataset-name\":\"liar\",\n", + " \"dataset-link\":\"unknown\",\n", + " \n", + " \"measurement-name\":\"measurement_config2\",\n", + " \n", + " \"execution-name\":\"execution_config2\",\n", + " \n", + " \"precision\":precision,\n", + " \"recall\":recall,\n", + " \"accuracy\":score\n", + "}" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Configuration 3 (!no train)\n", + "## Configuration 3\n", + "**In contrast to the discussed true false FAKE REAL bin class approch, I just want to see the expected horible results :)**\n", "* model a - test - dataset2 - [performance measures]\n", "* model b - test - dataset1 - [performance measures]" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "clf_tmp = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1232,19 +1360,19 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf.fit(bin_tfidf_train, bin_y_train)\n", - "pred = clf.predict(tmp_mul_tfidf_test)" + "clf_tmp.fit(bin_tfidf_train, bin_y_train)\n", + "pred = clf_tmp.predict(tmp_mul_tfidf_test)" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1254,7 +1382,7 @@ "1267\n", "['true' 'true' 'true' 'true' 'true' 'true' 'true' 'true' 'true' 'true'\n", " 'true' 'true' 'true' 'true' 'true' 'true' 'false' 'true' 'true' 'true'\n", - " 'true' 'true' 'true' 'false' 'false' 'true' 'false' 'true' 'true' 'false']\n" + " 'true' 'true' 'true' 'false' 'false' 'true' 'true' 'true' 'true' 'false']\n" ] } ], @@ -1271,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 48, "metadata": { "scrolled": true }, @@ -1280,15 +1408,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "accuracy: 0.169\n", + "accuracy: 0.170\n", "Confusion matrix, without normalization\n" ] }, { "data": { - "image/png": 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l70EdGXn7nzY5f9M1f6R9k13TpG5TXp0wntYtm9GyeVNuv21EuuVsgmsrmbsGdeCzO/vy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\nNdxSuKmhrEiqYqF0dLXIT7wrIS64M+F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BaCCpNXCGmV2QrKDMbA7QKuZ17A2r+ws5fmRhzws57jtgn5hN7xfYv5IwHWNh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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1304,11 +1432,13 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": {}, + "execution_count": 49, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "clf = MultinomialNB()\n", + "clf_tmp = MultinomialNB()\n", "#from sklearn.neural_network import MLPClassifier\n", "\n", "#clf = MLPClassifier(hidden_layer_sizes=(40,20))" @@ -1316,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1345,17 +1475,19 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, + "execution_count": 51, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "clf.fit(mul_tfidf_train, mul_y_train)\n", - "pred = clf.predict(tmp_bin_tfidf_test)" + "clf_tmp.fit(mul_tfidf_train, mul_y_train)\n", + "pred = clf_tmp.predict(tmp_bin_tfidf_test)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1379,7 +1511,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1394,7 +1526,7 @@ "data": { "image/png": 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VyaYgaXIpYYHpdeDLajz/InBgDOPauYL2Z4AmMfHDqcDXAGY2HrgEeE3S2Dh/\n0kzmjuNkhxDnuoqEaUnahpDSqyGwgaROwAlmNiBbQpnZZKBjwnXigtWdFfS/oqLzCvp9DWyRcOvd\ncu3zgT0refZJ4MmkgjuOUyMUF8i7dzpi3gL0AmYCmNmn+FZZx3FyREj2opRHPpDOIleRmX1XzuTO\navyn4zhOMgrEgE1Lwf4Q3QQmqZgQoO/pCh3HyQnSKrCTK4FTCW6CDYDpwBvxnuM4Tk7IEw9AStLJ\nRfAzcHgNyOI4jpMWBWLAphVFcA8V5CQws0prjDuO42QLQcG4CNLxFb9BCLR/ExhOyAW7MOkTjuM4\n2SKNTQZpVpW9X9LPialRY+rTqTFWfkxMIFXWdpGkiZK+krRXOqKm4yJYIfZT0sOEQHvHcZycoEpr\nFVaJBwhJrR4qd/+m8smiJG1GcJVuDjQH3pC0cao8JtWJdmgD+DZRx3FygoCSotRHKsxsGCFdaTr0\nBp4ws4VmNomQLnWbVA+l44P9jeU+2KIo0IVpCuU4jpNx0twK21TSqITru2PGu1ScIekYYBRwrpn9\nBrQAPkzoMyXeS0pSBRtrcXUiJLwGWGoV1ZVwHMepIcrKdqfBDDPrWsXh7wSuJhiVVwM3EEpkVTRj\nSl2Y1JCOyvQ5MyuNhytXx3Fyi0IUQaqjOpjZ9KjrlgL3sNwNMAVoldC1JfBjqvHS8cGOiKn7HMdx\nck6ZBZuNdIWSErPkHQiURRgMBg6XVEdSG0LFlRGpxktWk6usCOBOwImSviEkpxbBuHWl6zhOTsjE\nTi5JjxOS9jeVNAW4HOgmqTPh9X8ycDKAmX0eS1eNJyTmPz2dmnzJfLAjCMmnD1iJz+A4jpNhRFEG\nwrTM7IgKbt+XpP81wDVVmSOZglUc9JuqDOg4jpNNpMLJB5tMwa4j6ZzKGs3sxizI4ziOk5J8yfea\nimQKtphQubUwPonjOKsFYtXIpjXNzK6qMUkcx3HSpFCSvaT0wTqO4+QTYtWoaLB7jUnhOI6TLkp7\nq2zOqVTBmlm6SRAcx3FqDAHFha5gHcdx8pXCUK+uYB3HKUAKxIB1Bes4TmEh5C4Cx3GcbFEoi1yF\nEu2w2vLaq6+wxeabsHmH9lz/r4G5FmcFXLbq4bKtPErjyAdcweYxpaWlnH3m6bzw4lA+GTueQU88\nzhfjx+daLMBlqy4uWwaIYVqpjnzAFWweM3LECNq1a0+btm2pXbs2fQ47nJdefCHXYgEuW3Vx2Vae\nsjCtVEc+4Ao2j/nxx6m0bLk8iXqLFi2ZOnVqkidqDpeterhsmcFdBBlGUmtJRyZcd5P0UhXHuDjz\nkmWPiir05Murj8tWPVy2zCAPXyMoAAAgAElEQVSlPvKBglGwQGvgyFSdUlChglUg776LFi1aMmXK\nD8uup06dQvPmzXMo0XJcturhsq087iJgmcX5paR7JY2T9KikHpKGS5ogaRtJTSQ9L2mspA8lbRGf\n3VXSmHh8IqkBMBDYOd77a8I8RXG8dRKuJ0pqWk6egUDd+PyjUb4vJN0BjAZaSZqb0P8QSQ/E83Uk\nPSNpZDx2zNb3lkjXrbdm4sQJTJ40iUWLFjHoySfYt9f+NTF1Sly26uGyZQKl9V8+kO042PZAH+Ak\nYCTBAt0J2J9gTf4AfGJmB0jqDjwEdAbOI9S8GS6pPrAAuBA4z8x6QXARAJjZUkmPAH2B/wN6AJ+a\n2YxEQczsQklnmFnn+HxrYBPgWDM7Ld6r7HPcDNxkZu9J2gB4Fdi0fCdJJ8XPSqsNNqjSF1URJSUl\n3HTzbey3716UlpbSr/9xbLb55is9biZw2aqHy5YZMlST636gF/CzmXWM95oATxLemCcDh5rZbwrK\n4WZgH+APoL+ZjU45R7YqcUcF9rqZbRSvHwJeNbNHJbUFniUUFjvYzL6NfX4AOgKnEio6Pgo8a2ZT\nokItr2DPM7NekloBL5jZVpKeAB4xsz/5ZyXNNbP6CfK9bWZtKmk/BOhlZv0l/cyKJXrXATqY2ZzK\nPn+XLl1t+EejqvSdOc6qyI7bduXjj0dlzKTcuGNnu/Wp11P267l5s4/NrGtl7ZJ2AeYCDyUo2H8B\nv5rZQEkXAo3N7AJJ+wADCAp2W+BmM9s2lQzZ9jsuTDhfmnC9lGA9V/Slm5kNBE4A6gIfSuqQbBIz\n+wGYHq3gbYGhkooT3AyVJQ6fV36ohPM1Es6LgO3NrHM8WiRTro7jZJdMLHKZ2TCgfNbA3sCD8fxB\nlhd97U1QxGZmHwKNypX4rpBcL+wMI7zal1mkM8zsd0ntzOwzM7sOGAV0AOYADZKMdS/wCPCUmZXG\no0whXhb7LJZUK8kY0yVtGhe8Dky4/xpwRtlFLOvrOE6OyKIPdl0zmwYQ/2wW77cguDTLmBLvJSXX\nCvYKoKuksYRFrH7x/tlxYexTYD4wFBgLLJH0aeIiVwKDCTXE/ptkvruBsZIeraT9QuAl4C1gWsL9\nM8vklDQeOCWtT+c4TsapQhRBU0mjEo6TVnLa8qT0r2ZtkcvMJhP8qWXX/Stp613BswMqGbZ8lYV3\nEs47ERa3vkwi0wXABQm3OpZrfxp4uoLnZgCHVTau4zg1S5qLXDOS+WArYbqk9c1sWnQB/BzvTwFa\nJfRryYrrMhWSaws2I0Rn9DPARbmWxXGc7JNFF8Fglr9J9wNeSLh/TIyZ3w6YXeZKSMYqka4wLorl\nb+ofx3EyhoBMFJWV9DjQjeBKmAJcTtAjT0k6HvieEGYKMIQQQTCREKZ1bDpzrBIK1nGc1QiJogwE\nwprZEZU0/angq4V41tOrOocrWMdxCo782KeVGlewjuMUFMFFUBgq1hWs4zgFR4HoV1ewjuMUHvmS\nzCUVrmAdxyk43IJ1HMfJEq5gHcdxskAoCVMYGtYVrOM4hUUelYRJhStYx3EKDlewjuM4WSF/SsKk\nwhWs4zgFh1uwqzmffPE9jbc+I3XHHDD1vf/LtQiVstnZz+dahKT89+xdci1CpezcvmnqTjkg00Wp\nhG+VdRzHyRpJCpTmFa5gHccpOApEv7qCdRyn8CgQ/eoK1nGcAkPuInAcx8kKwl0EjuM4WaNA9Ksr\nWMdxCg93ETiO42SJTOlXSZOBOUApsMTMukpqAjwJtAYmA4ea2W/VGX+VKNvtOM7qhdI4qsBuZtbZ\nzLrG6wuBN81sI+DNeF0tXME6jlNQhEUupTxWgt7Ag/H8QeCA6g7kCtZxnMIipitMdQBNJY1KOE6q\nYDQDXpP0cUL7umY2DSD+2ay6oroPNg+oU7uEN+47m9q1SygpLua5Nz7hH/8Zwt1XHsXOXdoze+4C\nAE667GHGfj2VXt3+wmWn9mKpGUtKl/K365/m/THfZl3OAaeewGtDh9B0nWYMHzkGgOOPOZKJE74C\nYPbs2TRs2JD/ffBx1mUBaN64LrcetzXrNFwDM+PhYZO4982JbN6qIf86aivq1CqmtHQpFz76CZ9M\n/o2Dtm3FGT03AWDeglIueHQ046fMzopsN11yFiOGvU6jJk258/lhAMyZ/RvXnnsSP//4A82at+Ki\nG+6hQcNGAIwdMZy7r7uUJUuWsFbjJvzrgZrPyfD111/R/6gjll1PnvQtf7/sSk4fcFaNy5KKNO3T\nGQmv/ZWxo5n9KKkZ8LqkL1dWtkRcweYBCxctoedJtzBv/iJKSop46/5zeG34eAAu/r/nee6NMSv0\nf/ujr3jpnc8A6LhRcx657jg6H/SPrMt5RN9+nHDyaZx24nHL7t330GPLzi+96HzWWqth1uUoY8lS\n44pBY/ns+1msWaeE1y7dnWHjp3PpwVtww4tf8Na4n9i943pcesgWHPTv//H9jD848Pr/MfuPxXTv\nuB7/ProL+1z7VlZk63HA4ex35PHccPHyhD9P3XsrnbfbmUNPOJOn7r2FQffdynHnXMrc32dz+z8u\n5Oq7HqfZ+i2ZNfOXrMiUio033oT3R4wGoLS0lI3btmK//av9dpxdMrTIZWY/xj9/lvQcsA0wXdL6\nZjZN0vrAz9Ud310EecK8+YsAqFVSTElJMWaV5yAq6wuwZt06JOmaUXbYaWcaN25SYZuZ8fyzT3NQ\nn8NqRhjg59kL+Oz7WQDMW7iECdPmsF6juhhGgzWC7dCgXi1+mjUfgFHfzGT2H4sB+PjbmazfuG7W\nZPtL1+2XWadlfPj2K/ToHb6fHr0P44O3hgLwzpBn2aHHPjRbvyUAjdZeJ2typcs7b71Jmzbt2GDD\nDXMtSgWIIqU+Uo4irSmpQdk5sCcwDhgM9Ivd+gEvVFdSt2DzhKIi8f5jF9Cu1Trc9eQwRo77jhP7\n7MwVp+/HRSfuzTsjvuKSWwazaPESAPbfbQuuGrA/6zRpwEFn/ifH0sMHw99jnWbNaNd+o5zM32rt\nenRs1YjRk37lsic+5fGzd+ayPltQJLHfwLf/1P/Indrw1rifalTGWTN/ock66wLQZJ11mf3rDACm\nTv6GJUuWcEH/A5n/x1x69z2R3XsfWqOylefpQU/S57DDcypDZWQwXeG6wHNxQawEeMzMXpE0EnhK\n0vHA90Cf6k6wWilYSY2AI83sjlzLUp6lS43tDh9Iw/p1efLGE9ms3fpcdutgfprxO7VrlXD7pUdw\n7rE9uPbuVwAY/PZYBr89lh23asdlp+3LvqfcllP5nxn0BAf3yc0/yHp1irn31O257MkxzF2whH7d\n2nL5U5/y8uip7N+1JTf268KhN727rP+Om6zDETu1pvd17+RE3vKUlpYycfynXHvv0yxcuIBz++7L\nJp260LJ1u5zIs2jRIoa8/CJXXv3PnMyfFhnQsGb2LdCpgvszgd1XfobVz0XQCDit/E1JxTmQpUJm\nz53PsFET2HOHzfhpxu8ALFq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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1409,13 +1541,11 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected not these fancy results -.- " + ] }, { "cell_type": "markdown", @@ -1428,18 +1558,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "clf_model_c = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1454,7 +1584,7 @@ "data": { "image/png": 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GpJkfN9BZ5JOo9zcIfCCpY9pb7QjMSsunA4Xh0QWYkZbvskL507UdRBH186uH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\nMuZgNTPLmIPVzCxjDlYzs4z9P3Wjv5Jr3ijvAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1462,8 +1592,8 @@ } ], "source": [ - "clf.fit(concat_tfidf_train, concat_y_train)\n", - "pred = clf.predict(concat_tfidf_train)\n", + "clf_model_c.fit(concat_tfidf_train, concat_y_train)\n", + "pred = clf_model_c.predict(concat_tfidf_train)\n", "score = metrics.accuracy_score(concat_y_train, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", "cm = metrics.confusion_matrix(concat_y_train, pred, labels=['true', 'false'])\n", @@ -1472,18 +1602,18 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "clf = MultinomialNB()" + "#clf = MultinomialNB()" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1498,7 +1628,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1506,14 +1636,226 @@ } ], "source": [ - "clf.fit(concat_tfidf_train, concat_y_train)\n", - "pred = clf.predict(concat_tfidf_test)\n", + "#clf.fit(concat_tfidf_train, concat_y_train)\n", + "pred = clf_model_c.predict(concat_tfidf_test)\n", "score = metrics.accuracy_score(concat_y_test, pred)\n", "print(\"accuracy: %0.3f\" % score)\n", "cm = metrics.confusion_matrix(concat_y_test, pred, labels=['true', 'false'])\n", "plot_confusion_matrix(cm, classes=['true', 'false'])" ] }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "precision = cm[1,1]/(cm[1,1]+cm[0,1])\n", + "recall = cm[1,1]/(cm[1,1]+cm[1,0])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "configuration_4 = {\n", + " \"name\":\"configuration4\",\n", + " \n", + " \"model\":\"model_c\",\n", + " \"model-name\":\"multinomialNB\",\n", + "\n", + "\n", + " \"dataset\":\"X3\",\n", + " \"dataset-name\":\"concat\",\n", + " \"dataset-link\":\"unknown\",\n", + " \n", + " \"measurement-name\":\"measurement_config4\",\n", + " \n", + " \"execution-name\":\"execution_config4\",\n", + " \n", + " \"precision\":precision,\n", + " \"recall\":recall,\n", + " \"accuracy\":score\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exporting results as RDF data" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from rdflib import Namespace, Graph, Literal\n", + "from rdflib.namespace import FOAF, OWL, XSD, RDFS, DCTERMS, DOAP, DC, RDF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "prov = Namespace('http://www.w3.org/ns/prov#')\n", + "dcat = Namespace('http://www.w3.org/ns/dcat#')\n", + "mexalgo = Namespace('http://mex.aksw.org/mex-algo#')\n", + "mexperf = Namespace('http://mex.aksw.org/mex-perf#')\n", + "mexcore = Namespace('http://mex.aksw.org/mex-core#')\n", + "this = Namespace('http://mex.aksw.org/examples/')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "example_configuration = {\n", + " \"name\":\"experiment1\",\n", + " \n", + " \"model\":\"generic_model_1_name\",\n", + " \"model-name\":\"deepNN\",\n", + "\n", + "\n", + " \"dataset\":\"X1\",\n", + " \"dataset-name\":\"Liar\",\n", + " \"dataset-link\":\"google.com\",\n", + " \n", + " \"measurement-name\":\"generic_measurement_hub\",\n", + " \n", + " \"execution-name\":\"genereric_execution_name\",\n", + " \n", + " \"precision\":0.33,\n", + " \"recall\":0.73,\n", + " \"accuracy\":0.55\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "def experiment_root_graph(root_node_name):\n", + " g = Graph()\n", + " g.add((this[root_node_name],RDF.type, mexcore.Experiment))\n", + " g.add((this[root_node_name],RDF.type, mexcore.ApplicationContext))\n", + " g.add((this[root_node_name],RDFS.label, Literal('2719095')))\n", + " g.add((this[root_node_name],DCTERMS.date, Literal('2018-05-22',datatype=XSD.date)))\n", + " g.add((this[root_node_name],FOAF.givenName, Literal('Carsten')))\n", + " g.add((this[root_node_name],FOAF.mbox, Literal('carsten.draschner@gmail.com')))\n", + " return g ,this[root_node_name]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "g, r = experiment_root_graph(\"first_test\")\n", + "print(type(r))" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#extend a rdf graph with a configuration\n", + "def experiment_to_rdf(graph, experiment, root_node):\n", + " \n", + " g.add((this[experiment[\"name\"]],RDF.type,mexcore.ExperimentConfiguration))\n", + " g.add((this[experiment[\"name\"]],prov.used, this[experiment[\"model\"]]))\n", + " g.add((this[experiment[\"name\"]],prov.wasStartedBy, root_node))\n", + " \n", + " #dataset information\n", + " g.add((this[experiment[\"dataset\"]],RDF.type,mexcore.Dataset))\n", + " g.add((this.dataset2,RDFS.label,Literal(experiment[\"dataset-name\"])))\n", + " g.add((this.dataset2,DCTERMS.landingPage,Literal(experiment[\"dataset-link\"])))\n", + " \n", + " #model description\n", + " g.add((this[experiment[\"model\"]],RDF.type,mexalgo.Algorithm))\n", + " g.add((this[experiment[\"model\"]],RDFS.label,Literal(experiment[\"model-name\"])))\n", + " g.add((this[experiment[\"model\"]],DCTERMS.identifier,Literal(experiment[\"model-name\"])))\n", + " ###g.add((this.model1,mexalgo.hasHyperParameter,this.hyerparameter1))\n", + " \n", + " #execution\n", + " g.add((this[experiment[\"execution-name\"]],RDF.type,mexcore.ExecutionOverall))\n", + " g.add((this[experiment[\"execution-name\"]],prov.generated,this[experiment[\"measurement-name\"]]))\n", + " g.add((this[experiment[\"execution-name\"]],prov.used,this.test))\n", + " g.add((this[experiment[\"execution-name\"]],prov.used,this[experiment[\"model\"]]))\n", + " \n", + " #test\n", + " g.add((this.test,RDF.type,mexcore.Test))\n", + " g.add((this.test,RDFS.label,Literal('Test')))\n", + " \n", + " #evaluation information\n", + " g.add((this[experiment[\"measurement-name\"]],RDF.type,mexcore.PerformanceMeasure))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.precision,Literal(experiment[\"precision\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.recall,Literal(experiment[\"recall\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.accuracy,Literal(experiment[\"accuracy\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],prov.wasGeneratedBy,this[experiment[\"execution-name\"]]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_to_rdf(g,configuration_1,r)\n", + "experiment_to_rdf(g,configuration_2,r)\n", + "experiment_to_rdf(g,configuration_4,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "with open('task2_metadata.ttl','wb') as f:\n", + " f.write(g.serialize(format='turtle'))" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/Carsten_Solutions/Task 3 - Carsten Draschner.ipynb b/Carsten_Solutions/Task 3 - Carsten Draschner.ipynb new file mode 100644 index 0000000..d59ec14 --- /dev/null +++ b/Carsten_Solutions/Task 3 - Carsten Draschner.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " text sentiment\n", + "0 RT @NancyLeeGrahn: How did everyone feel about... Neutral\n", + "1 RT @ScottWalker: Didn't catch the full #GOPdeb... Positive\n", + "2 RT @TJMShow: No mention of Tamir Rice and the ... Neutral\n", + "3 RT @RobGeorge: That Carly Fiorina is trending ... Positive\n", + "4 RT @DanScavino: #GOPDebate w/ @realDonaldTrump... Positive\n", + "5 RT @GregAbbott_TX: @TedCruz: \"On my first day ... Positive\n", + "6 RT @warriorwoman91: I liked her and was happy ... Negative\n", + "7 Going on #MSNBC Live with @ThomasARoberts arou... Neutral\n", + "8 Deer in the headlights RT @lizzwinstead: Ben C... Negative\n", + "9 RT @NancyOsborne180: Last night's debate prove... Negative\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from keras.preprocessing.text import Tokenizer\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.utils.np_utils import to_categorical\n", + "import re\n", + "\n", + "'''\n", + "Task 3: playing with NN framwork/keras and basic sentiment analysis\n", + "- use the following model as a baseline and improve it!\n", + "- export your metadata (just basic hyperparameters and outcomes for test data!)\n", + "- test data = 0.3 (not in this example, change it!)\n", + "- random_state = 4222\n", + "- no need to cross-validation!\n", + "'''\n", + "\n", + "# parameters\n", + "max_fatures = 1000\n", + "embed_dim = 128\n", + "lstm_out = 196\n", + "dropout = 0.1\n", + "dropout_1d = 0.4\n", + "recurrent_dropout = 0.1\n", + "random_state = 4222\n", + "validation_size = 1000\n", + "batch_size = 16\n", + "epochs=2\n", + "verbose= 2\n", + "\n", + "df = pd.read_csv('/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/sentiment-analysis/dataset_sentiment.csv')\n", + "df = df[['text','sentiment']]\n", + "print(df[0:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df = df[df.sentiment != \"Neutral\"]\n", + "#replace all capital letters with its small character\n", + "df['text'] = df['text'].apply(lambda x: x.lower())\n", + "#removes all rt in messages, often occuring in front of twitter raw data\n", + "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n", + "#only accepts alphanumerical characters, erease all other characters\n", + "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]','',x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2236\n", + "8493\n" + ] + } + ], + "source": [ + "#evaluate distribution of positive and negative examples\n", + "print(len(df[df.sentiment == \"Positive\"]))\n", + "print(len(df[df.sentiment == \"Negative\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " text sentiment\n", + "1 scottwalker didnt catch the full gopdebate l... Positive\n", + "3 robgeorge that carly fiorina is trending ho... Positive\n", + "4 danscavino gopdebate w realdonaldtrump deliv... Positive\n", + "5 gregabbotttx tedcruz on my first day i will ... Positive\n", + "6 warriorwoman91 i liked her and was happy whe... Negative\n", + "8 deer in the headlights lizzwinstead ben cars... Negative\n", + "9 nancyosborne180 last nights debate proved it... Negative\n", + "10 jgreendc realdonaldtrump in all fairness billc... Negative\n", + "11 waynedupreeshow just woke up to tweet this o... Positive\n", + "12 me reading my familys comments about how great... Negative\n" + ] + } + ], + "source": [ + "print(df[0:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 363 122 1 722\n", + " 2 39 58 237 36 210 6 174 12 742]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 16 284 252 5 818 102 167\n", + " 26 135 6 1 172 12 2 233 723 17]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 309 23\n", + " 1 216 12 1 702 6 185 207 371 670]\n", + " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 127 17 53\n", + " 262 410 9 82 303 441 62 194 2 51]\n", + " [ 0 0 0 0 0 0 0 0 0 9 167 8 21 63 9 612 188 21\n", + " 189 4 34 1 562 19 819 2 44 743]]\n" + ] + } + ], + "source": [ + "#generates a tokenizer with fixed lenght\n", + "tok = Tokenizer(num_words=max_fatures, split=' ')\n", + "#train tokenizer\n", + "tok.fit_on_texts(df['text'].values)\n", + "#transforms each sentence to a sequence of intgers\n", + "X = tok.texts_to_sequences(df['text'].values)\n", + "\n", + "#print(X[:20])\n", + "\n", + "#for each of these sequences it transforms it to an array of same length by inserting 0 in front by standard configuration of parameters \n", + "X = pad_sequences(X)\n", + "print(X[:5])\n", + "\n", + "\n", + "#print(type(X))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding_5 (Embedding) (None, 28, 128) 128000 \n", + "_________________________________________________________________\n", + "spatial_dropout1d_5 (Spatial (None, 28, 128) 0 \n", + "_________________________________________________________________\n", + "lstm_5 (LSTM) (None, 196) 254800 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 2) 394 \n", + "=================================================================\n", + "Total params: 383,194\n", + "Trainable params: 383,194\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "#configuration of nn\n", + "nn = Sequential()\n", + "nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1]))\n", + "nn.add(SpatialDropout1D(dropout_1d))\n", + "nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout))\n", + "nn.add(Dense(2, activation='softmax'))\n", + "nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy'])\n", + "print(nn.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2\n", + " - 55s - loss: 0.4244 - acc: 0.8173\n", + "Epoch 2/2\n", + " - 53s - loss: 0.3386 - acc: 0.8565\n", + "score: 0.37\n", + "acc: 0.86\n" + ] + } + ], + "source": [ + "#get the teacher values\n", + "Y = pd.get_dummies(df['sentiment']).values\n", + "#gnerates X and y train & test data\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)\n", + "#trains the machine learning configuration with the declared parameters in the top\n", + "nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)\n", + "\n", + "#also generate validation set by cutting the last validation_size elements from test data\n", + "X_validate = X_test[-validation_size:]\n", + "Y_validate = Y_test[-validation_size:]\n", + "X_test = X_test[:-validation_size]\n", + "Y_test = Y_test[:-validation_size]\n", + "\n", + "#evaluates the score and the accuracy\n", + "score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)\n", + "print(\"score: %.2f\" % (score))\n", + "print(\"acc: %.2f\" % (accuracy))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pos_acc 48.78048780487805 %\n", + "neg_acc 94.46540880503144 %\n" + ] + } + ], + "source": [ + "#initialize counter for evaluating prediction\n", + "pos_cnt, neg_cnt, pos_ok, neg_ok, tp, fp, tn ,fn = 0, 0, 0, 0, 0, 0, 0, 0 \n", + "for x in range(len(X_validate)):\n", + " #predict for each element in validation set its true false probability\n", + " result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n", + " #print(result)\n", + " #check if highest prob for same class\n", + " if np.argmax(result) == np.argmax(Y_validate[x]):\n", + " #if high prob in first array element ---> classification as neg ---> count neg \n", + " if np.argmax(Y_validate[x]) == 0: neg_ok += 1\n", + " #else count as pos\n", + " else: pos_ok += 1\n", + " #count of teacher labels\n", + " if np.argmax(Y_validate[x]) == 0: neg_cnt += 1\n", + " else: pos_cnt += 1\n", + "\n", + "#print results\n", + "print(\"pos_acc\", pos_ok/pos_cnt*100, \"%\")\n", + "print(\"neg_acc\", neg_ok/neg_cnt*100, \"%\")\n", + "\n", + "pos_acc = pos_ok/pos_cnt\n", + "neg_acc = neg_ok/neg_cnt" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 5 700 8 8 12 218 8 5 290 14]]\n", + "[ 0.0937252 0.9062748]\n" + ] + } + ], + "source": [ + "#evaluate on different dataset\n", + "X2 = ['Jonas is nice and happy and in love and is looking for freedom']\n", + "X2 = tok.texts_to_sequences(X2)\n", + "X2 = pad_sequences(X2, maxlen=len(X[0]), dtype='int32', value=0)\n", + "print(X2)\n", + "print(nn.predict(X2, batch_size=1, verbose = 2)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "configuration_1 = {\n", + " \"name\":\"experiment1\",\n", + " \n", + " \"model\":\"generic_model_1_name\",\n", + " \"model-name\":\"deepNN\",\n", + "\n", + "\n", + " \"dataset\":\"X1\",\n", + " \"dataset-name\":\"dataset_sentiment.csv\",\n", + " \"dataset-link\":\"see_github\",\n", + " \n", + " \"measurement-name\":\"generic_measurement_hub\",\n", + " \n", + " \"execution-name\":\"genereric_execution_name\",\n", + " \n", + " \"precision\":-1,\n", + " \"recall\":-1, \n", + " \"pos_accn\":pos_acc,\n", + " \"neg_acc\":neg_acc,\n", + " \"accuracy\":score\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Export Data" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from rdflib import Namespace, Graph, Literal\n", + "from rdflib.namespace import FOAF, OWL, XSD, RDFS, DCTERMS, DOAP, DC, RDF" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "prov = Namespace('http://www.w3.org/ns/prov#')\n", + "dcat = Namespace('http://www.w3.org/ns/dcat#')\n", + "mexalgo = Namespace('http://mex.aksw.org/mex-algo#')\n", + "mexperf = Namespace('http://mex.aksw.org/mex-perf#')\n", + "mexcore = Namespace('http://mex.aksw.org/mex-core#')\n", + "this = Namespace('http://mex.aksw.org/examples/')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def experiment_root_graph(root_node_name):\n", + " g = Graph()\n", + " g.add((this[root_node_name],RDF.type, mexcore.Experiment))\n", + " g.add((this[root_node_name],RDF.type, mexcore.ApplicationContext))\n", + " g.add((this[root_node_name],RDFS.label, Literal('2719095')))\n", + " g.add((this[root_node_name],DCTERMS.date, Literal('2018-05-28',datatype=XSD.date)))\n", + " g.add((this[root_node_name],FOAF.givenName, Literal('Carsten')))\n", + " g.add((this[root_node_name],FOAF.mbox, Literal('carsten.draschner@gmail.com')))\n", + " return g ,this[root_node_name]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "g, r = experiment_root_graph(\"first_test\")\n", + "print(type(r))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#extend a rdf graph with a configuration\n", + "def experiment_to_rdf(graph, experiment, root_node):\n", + " \n", + " g.add((this[experiment[\"name\"]],RDF.type,mexcore.ExperimentConfiguration))\n", + " g.add((this[experiment[\"name\"]],prov.used, this[experiment[\"model\"]]))\n", + " g.add((this[experiment[\"name\"]],prov.wasStartedBy, root_node))\n", + " \n", + " #dataset information\n", + " g.add((this[experiment[\"dataset\"]],RDF.type,mexcore.Dataset))\n", + " g.add((this.dataset2,RDFS.label,Literal(experiment[\"dataset-name\"])))\n", + " g.add((this.dataset2,DCTERMS.landingPage,Literal(experiment[\"dataset-link\"])))\n", + " \n", + " #model description\n", + " g.add((this[experiment[\"model\"]],RDF.type,mexalgo.Algorithm))\n", + " g.add((this[experiment[\"model\"]],RDFS.label,Literal(experiment[\"model-name\"])))\n", + " g.add((this[experiment[\"model\"]],DCTERMS.identifier,Literal(experiment[\"model-name\"])))\n", + " ###g.add((this.model1,mexalgo.hasHyperParameter,this.hyerparameter1))\n", + " \n", + " #execution\n", + " g.add((this[experiment[\"execution-name\"]],RDF.type,mexcore.ExecutionOverall))\n", + " g.add((this[experiment[\"execution-name\"]],prov.generated,this[experiment[\"measurement-name\"]]))\n", + " g.add((this[experiment[\"execution-name\"]],prov.used,this.test))\n", + " g.add((this[experiment[\"execution-name\"]],prov.used,this[experiment[\"model\"]]))\n", + " \n", + " #test\n", + " g.add((this.test,RDF.type,mexcore.Test))\n", + " g.add((this.test,RDFS.label,Literal('Test')))\n", + " \n", + " #evaluation information\n", + " g.add((this[experiment[\"measurement-name\"]],RDF.type,mexcore.PerformanceMeasure))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.precision,Literal(experiment[\"precision\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.recall,Literal(experiment[\"recall\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],mexperf.accuracy,Literal(experiment[\"accuracy\"],datatype=XSD.float)))\n", + " g.add((this[experiment[\"measurement-name\"]],prov.wasGeneratedBy,this[experiment[\"execution-name\"]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_to_rdf(g,configuration_1,r)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "with open('task3_metadata.ttl','wb') as f:\n", + " f.write(g.serialize(format='turtle'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Project/Images/Naive_approach_test_image.png b/Project/Images/Naive_approach_test_image.png new file mode 100644 index 0000000000000000000000000000000000000000..0551fe311e1f771f0873624f7c2702151ccfde34 GIT binary patch literal 252624 zcmeFZWmuH$*FH)LC^d8n3`j_~G{Vpw3P=i4!caq}2*S`JNDU!Fsg%+U5+X6ch=L3$ 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