From f35e044c53b7f50d5703401dc2207c9a92a0bbd1 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Thu, 17 May 2018 10:38:34 +0200 Subject: [PATCH 01/15] Task2, 4 configurations done. Missing export so far --- Jonas_Solutions/Task_02_JonasWeinz.ipynb | 366 ++++++++++------------- README.md | 4 + 2 files changed, 168 insertions(+), 202 deletions(-) diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb index e450952..ad668d4 100644 --- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb +++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb @@ -13,7 +13,9 @@ "* Tutorial on Datacamp: https://www.datacamp.com/community/tutorials/scikit-learn-fake-news\n", "\n", "* liar dataset paper: https://www.cs.ucsb.edu/~william/papers/acl2017.pdf\n", - " * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip" + " * dataset: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip\n", + "\n", + "* Mex Vocabulary: http://jens-lehmann.org/files/2015/semantics_mex.pdf" ] }, { @@ -104,6 +106,9 @@ " print('Confusion matrix, without normalization')\n", "\n", " thresh = cm.max() / 2.\n", + " \n", + " pp(cm)\n", + " \n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", @@ -402,166 +407,65 @@ "metadata": {}, "outputs": [], "source": [ - "vectorizer_1 = CountVectorizer(stop_words='english')\n", + "vectorizer_1 = TfidfVectorizer(stop_words='english', max_df=0.7)\n", "vec_train_1 = vectorizer_1.fit_transform(X1)\n", "vec_test_1 = vectorizer_1.transform(Xt1)" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "#tfidf_vectorizer_1 = TfidfVectorizer(stop_words='english', max_df=0.7)\n", - "#tfidf_train_1 = tfidf_vectorizer_1.fit_transform(X1)\n", - "#tfidf_test_1 = tfidf_vectorizer_1.transform(Xt1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "#display(count_vectorizer.get_feature_names()[0:10])\n", - "#display(count_vectorizer.get_feature_names()[-10:])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#display(tfidf_vectorizer.get_feature_names()[:10])\n", - "#display(tfidf_vectorizer.get_feature_names()[-10:])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#count_df = pd.DataFrame(count_train.A, columns=count_vectorizer.get_feature_names())\n", - "#tfidf_df = pd.DataFrame(count_train.A, columns=tfidf_vectorizer.get_feature_names())\n", - "#diff = set(count_df.columns) - set(tfidf_df.columns)\n", - "#pp(count_df.equals(tfidf_df))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "#clf = MultinomialNB()\n", - "#clf.fit(tfidf_train_1, y1)\n", - "#pred = clf.predict(tfidf_test_1)\n", - "#score = metrics.accuracy_score(yt1, pred)\n", - "#pp(\"score: \" + str(score))\n", - "#cm = metrics.confusion_matrix(yt1, pred, labels=[\"FAKE\", \"REAL\"])\n", - "#plot_confusion_matrix(cm, classes=[\"FAKE\", \"REAL\"], title= \"TFIDF_Vecctorizer, Multinomial Naive Bayes\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'score: 0.9320143127762577'\n", - "Confusion matrix, without normalization\n", - "'score: 0.8838383838383839'\n", - "Confusion matrix, without normalization\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clf_a = MultinomialNB()\n", - "clf_a.fit(vec_train_1, y1)\n", - "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- train\", Xt=vec_train_1,yt=y1, clf=clf_a)\n", - "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* try to get most important features" + "* trying a Random Forest classifier " ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.9960008419280152'\n", + "Confusion matrix, without normalization\n", + "array([[2342, 3],\n", + " [ 16, 2390]])\n", + "'score: 0.8478535353535354'\n", + "Confusion matrix, without normalization\n", + "array([[737, 82],\n", + " [159, 606]])\n" + ] + }, { "data": { + "image/png": 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\n", "text/plain": [ - "'\\ndef most_informative_feature_for_binary_classification(vectorizer, classifier, n=100):\\n \"\"\"\\n See: https://stackoverflow.com/a/26980472\\n \\n Identify most important features if given a vectorizer and binary classifier. Set n to the number\\n of weighted features you would like to show. (Note: current implementation merely prints and does not \\n return top classes.)\\n \"\"\"\\n\\n class_labels = classifier.classes_\\n feature_names = vectorizer.get_feature_names()\\n topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]\\n topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]\\n \\n l = []\\n \\n for coef, feat in topn_class1:\\n l.append((class_labels[0], coef, feat))\\n\\n display(l)\\n\\n for coef, feat in reversed(topn_class2):\\n l.append((class_labels[1], coef, feat))\\n \\n display(l)\\n\\n\\nmost_informative_feature_for_binary_classification(tfidf_vectorizer_1, linear_clf, n=30)\\n'" + "" ] }, - "execution_count": 16, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "'''\n", - "def most_informative_feature_for_binary_classification(vectorizer, classifier, n=100):\n", - " \"\"\"\n", - " See: https://stackoverflow.com/a/26980472\n", - " \n", - " Identify most important features if given a vectorizer and binary classifier. Set n to the number\n", - " of weighted features you would like to show. (Note: current implementation merely prints and does not \n", - " return top classes.)\n", - " \"\"\"\n", - "\n", - " class_labels = classifier.classes_\n", - " feature_names = vectorizer.get_feature_names()\n", - " topn_class1 = sorted(zip(classifier.coef_[0], feature_names))[:n]\n", - " topn_class2 = sorted(zip(classifier.coef_[0], feature_names))[-n:]\n", - " \n", - " l = []\n", - " \n", - " for coef, feat in topn_class1:\n", - " l.append((class_labels[0], coef, feat))\n", - "\n", - " display(l)\n", - "\n", - " for coef, feat in reversed(topn_class2):\n", - " l.append((class_labels[1], coef, feat))\n", - " \n", - " display(l)\n", - "\n", - "\n", - "most_informative_feature_for_binary_classification(tfidf_vectorizer_1, linear_clf, n=30)\n", - "'''\n" + "from sklearn.ensemble import RandomForestClassifier as RFC\n", + "clf_a = RFC(criterion='entropy', random_state=4222)\n", + "max_size=10000\n", + "clf_a.fit(vec_train_1[:max_size], y1[:max_size])\n", + "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- train\", Xt=vec_train_1,yt=y1, clf=clf_a)\n", + "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)" ] }, { @@ -576,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1157,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1171,58 +1075,95 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "vectorizer_2 = CountVectorizer(stop_words='english', max_df=0.7)\n", + "vectorizer_2 = TfidfVectorizer(stop_words='english', max_df=0.7)\n", "vec_train_2 = vectorizer_2.fit_transform(X2)\n", "vec_test_2 = vectorizer_2.transform(Xt2)" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_b = MultinomialNB()\n", - "clf_b.fit(vec_train_2, y2)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'score: 0.8760555706891855'\n", + "Object `MLPClassifier` not found.\n" + ] + } + ], + "source": [ + "?MLPClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* trying a MLP as classifier " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "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=(100,), 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": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "clf_b = MLPClassifier(hidden_layer_sizes=(100,), random_state=4222)\n", + "clf_b.fit(vec_train_2, y2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.9997275946608554'\n", "Confusion matrix, without normalization\n", - "'score: 0.6105032822757112'\n", + "array([[1675, 1],\n", + " [ 0, 1995]])\n", + "'score: 0.5776805251641138'\n", "Confusion matrix, without normalization\n", - "'score: 0.6527777777777778'\n", - "Confusion matrix, without normalization\n" + "array([[109, 99],\n", + " [ 94, 155]])\n", + "'score: 0.6041666666666666'\n", + "Confusion matrix, without normalization\n", + "array([[ 92, 77],\n", + " [ 94, 169]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1265,22 +1206,24 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'score: 0.4617067833698031'\n", - "Confusion matrix, without normalization\n" + "'score: 0.5142231947483589'\n", + "Confusion matrix, without normalization\n", + "array([[ 7, 201],\n", + " [ 21, 228]])\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1300,22 +1243,24 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'score: 0.4962121212121212'\n", - "Confusion matrix, without normalization\n" + "'score: 0.5391414141414141'\n", + "Confusion matrix, without normalization\n", + "array([[274, 491],\n", + " [239, 580]])\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1343,7 +1288,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1378,14 +1323,14 @@ "\n", " print(df3['y'].value_counts())\n", " print('done')\n", - " return train_test_split(df3['claim'], df3['y'], test_size=0.25, random_state=4222)\n", + " return train_test_split(df3['claim'], df3['y'], test_size=0.3, random_state=4222)\n", " except Exception as e:\n", " print(e)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1402,7 +1347,7 @@ "6335\n", "-- liar liar\n", "Index(['y', 'claim'], dtype='object')\n", - "{'mostly-true', 'pants-fire', 'half-true', 'barely-true', 'false', 'true'} 10240\n", + "{'true', 'barely-true', 'half-true', 'false', 'pants-fire', 'mostly-true'} 10240\n", "1676\n", "1995\n", "{'true', 'false'} 3671\n", @@ -1419,56 +1364,73 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "vectorizer_3 = CountVectorizer(stop_words='english')\n", + "vectorizer_3 = TfidfVectorizer(stop_words='english', max_df=0.7)\n", "vec_train_3 = vectorizer_3.fit_transform(X3)\n", "vec_test_3 = vectorizer_3.transform(Xt3)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* using MLP again" + ] + }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" + "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": 29, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "clf_3 = MultinomialNB()\n", + "clf_3 = MLPClassifier(hidden_layer_sizes=(16,16), random_state=4222)\n", "clf_3.fit(vec_train_3, y3)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'score: 0.8030383795309168'\n", + "'score: 0.9997144488863506'\n", "Confusion matrix, without normalization\n", - "'score: 0.746203037569944'\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", + "image/png": 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\n", 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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/README.md b/README.md index cd98d00..6fb1680 100644 --- a/README.md +++ b/README.md @@ -39,4 +39,8 @@ Repository for the NLP-LAB * search for predefined Questions in Knowledge Graph +---- +## Links + +https://github.com/AKSW/NSpM From 444ea7d8761a67490a1e8335152a004e770dda45 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Thu, 17 May 2018 12:06:34 +0200 Subject: [PATCH 02/15] blubb --- Jonas_Solutions/Task_02_JonasWeinz.ipynb | 1090 ++-------------------- 1 file changed, 97 insertions(+), 993 deletions(-) diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb index ad668d4..31cd70f 100644 --- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb +++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb @@ -64,7 +64,7 @@ "from IPython.display import display, Markdown, Latex\n", "import collections\n", "import traceback\n", - "import os" + "import os\n" ] }, { @@ -133,6 +133,71 @@ " plot_confusion_matrix(cm, classes=labels, title=title)" ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nfrom rdflib import Graph, Literal, BNode, RDF, Namespace\\nfrom rdflib.namespace import FOAF, DC, XSD\\n\\nmexcore = Namespace('http://mex.aksw.org/mex-core#')\\nmexperf = Namespace('http://mex.aksw.org/mex-perf#')\\nmexalgo = Namespace('http://mex.aksw.org/mex-algo#')\\nprov = Namespace('http://www.w3.org/ns/prov#')\\n\\ndef create_mex_graph():\\n graph = Graph()\\n graph.bind(mexcore)\\n graph.bind(mexperf)\\n graph.bind(mexalgo)\\n graph.bind(prov)\\n graph.bind(FOAF)\\n graph.bind(DC)\\n graph.bind(XSD)\\n \\n return graph\\n\\ndef mex_performance(experiment, model, dataset, performance, phase='Train', graph=create_mex_graph()):\\n \\n p = BNode()\\n \\n\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''\n", + "from rdflib import Graph, Literal, BNode, RDF, Namespace\n", + "from rdflib.namespace import FOAF, DC, XSD\n", + "\n", + "mexcore = Namespace('http://mex.aksw.org/mex-core#')\n", + "mexperf = Namespace('http://mex.aksw.org/mex-perf#')\n", + "mexalgo = Namespace('http://mex.aksw.org/mex-algo#')\n", + "prov = Namespace('http://www.w3.org/ns/prov#')\n", + "\n", + "def create_mex_graph():\n", + " graph = Graph()\n", + " graph.bind(mexcore)\n", + " graph.bind(mexperf)\n", + " graph.bind(mexalgo)\n", + " graph.bind(prov)\n", + " graph.bind(FOAF)\n", + " graph.bind(DC)\n", + " graph.bind(XSD)\n", + " \n", + " return graph\n", + "\n", + "def mex_performance(experiment, model, dataset, performance, phase='Train', graph=create_mex_graph()):\n", + " \n", + " p = BNode()\n", + " \n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Namespace('http://xmlns.com/foaf/0.1/')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FOAF\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -144,70 +209,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "checking whether unzip is installed\n", - "================================================================================\n", - "UnZip 6.00 of 20 April 2009, by Debian. Original by Info-ZIP.\n", - "\n", - "Latest sources and executables are at ftp://ftp.info-zip.org/pub/infozip/ ;\n", - "see ftp://ftp.info-zip.org/pub/infozip/UnZip.html for other sites.\n", - "\n", - "Compiled with gcc 6.3.0 20170415 for Unix (Linux ELF).\n", - "\n", - "UnZip special compilation options:\n", - " ACORN_FTYPE_NFS\n", - " COPYRIGHT_CLEAN (PKZIP 0.9x unreducing method not supported)\n", - " SET_DIR_ATTRIB\n", - " SYMLINKS (symbolic links supported, if RTL and file system permit)\n", - " TIMESTAMP\n", - " UNIXBACKUP\n", - " USE_EF_UT_TIME\n", - " USE_UNSHRINK (PKZIP/Zip 1.x unshrinking method supported)\n", - " USE_DEFLATE64 (PKZIP 4.x Deflate64(tm) supported)\n", - " UNICODE_SUPPORT [wide-chars, char coding: UTF-8] (handle UTF-8 paths)\n", - " LARGE_FILE_SUPPORT (large files over 2 GiB supported)\n", - " ZIP64_SUPPORT (archives using Zip64 for large files supported)\n", - " USE_BZIP2 (PKZIP 4.6+, using bzip2 lib version 1.0.6, 6-Sept-2010)\n", - " VMS_TEXT_CONV\n", - " WILD_STOP_AT_DIR\n", - " [decryption, version 2.11 of 05 Jan 2007]\n", - "\n", - "UnZip and ZipInfo environment options:\n", - " UNZIP: [none]\n", - " UNZIPOPT: [none]\n", - " ZIPINFO: [none]\n", - " ZIPINFOOPT: [none]\n", - "================================================================================\n", - "successfully finished action: checking whether unzip is installed\n", - "================================================================================\n", - "================================================================================\n", - "downloading and unpacking https://www.cs.ucsb.edu/~william/data/liar_dataset.zip if not already existing\n", - "================================================================================\n", - "================================================================================\n", - "successfully finished action: downloading and unpacking https://www.cs.ucsb.edu/~william/data/liar_dataset.zip if not already existing\n", - "================================================================================\n", - "================================================================================\n", - "downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n", - "================================================================================\n", - "================================================================================\n", - "successfully finished action: downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n", - "================================================================================\n", - "================================================================================\n", - "downloading Helper script: script_dataset3.py\n", - "================================================================================\n", - "================================================================================\n", - "successfully finished action: downloading Helper script: script_dataset3.py\n", - "================================================================================\n" - ] - } - ], + "outputs": [], "source": [ "%%bash\n", "./Task_2_gen_data.sh" @@ -223,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -239,147 +243,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6335, 3)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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titletextlabel
Unnamed: 0
8476You Can Smell Hillary’s FearDaniel Greenfield, a Shillman Journalism Fello...FAKE
10294Watch The Exact Moment Paul Ryan Committed Pol...Google Pinterest Digg Linkedin Reddit Stumbleu...FAKE
3608Kerry to go to Paris in gesture of sympathyU.S. Secretary of State John F. Kerry said Mon...REAL
10142Bernie supporters on Twitter erupt in anger ag...— Kaydee King (@KaydeeKing) November 9, 2016 T...FAKE
875The Battle of New York: Why This Primary MattersIt's primary day in New York and front-runners...REAL
6903Tehran, USA\\nI’m not an immigrant, but my grandparents ...FAKE
7341Girl Horrified At What She Watches Boyfriend D...Share This Baylee Luciani (left), Screenshot o...FAKE
95‘Britain’s Schindler’ Dies at 106A Czech stockbroker who saved more than 650 Je...REAL
4869Fact check: Trump and Clinton at the 'commande...Hillary Clinton and Donald Trump made some ina...REAL
2909Iran reportedly makes new push for uranium con...Iranian negotiators reportedly have made a las...REAL
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" - ], - "text/plain": [ - " title \\\n", - "Unnamed: 0 \n", - "8476 You Can Smell Hillary’s Fear \n", - "10294 Watch The Exact Moment Paul Ryan Committed Pol... \n", - "3608 Kerry to go to Paris in gesture of sympathy \n", - "10142 Bernie supporters on Twitter erupt in anger ag... \n", - "875 The Battle of New York: Why This Primary Matters \n", - "6903 Tehran, USA \n", - "7341 Girl Horrified At What She Watches Boyfriend D... \n", - "95 ‘Britain’s Schindler’ Dies at 106 \n", - "4869 Fact check: Trump and Clinton at the 'commande... \n", - "2909 Iran reportedly makes new push for uranium con... \n", - "\n", - " text label \n", - "Unnamed: 0 \n", - "8476 Daniel Greenfield, a Shillman Journalism Fello... FAKE \n", - "10294 Google Pinterest Digg Linkedin Reddit Stumbleu... FAKE \n", - "3608 U.S. Secretary of State John F. Kerry said Mon... REAL \n", - "10142 — Kaydee King (@KaydeeKing) November 9, 2016 T... FAKE \n", - "875 It's primary day in New York and front-runners... REAL \n", - "6903 \\nI’m not an immigrant, but my grandparents ... FAKE \n", - "7341 Share This Baylee Luciani (left), Screenshot o... FAKE \n", - "95 A Czech stockbroker who saved more than 650 Je... REAL \n", - "4869 Hillary Clinton and Donald Trump made some ina... REAL \n", - "2909 Iranian negotiators reportedly have made a las... REAL " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(df_1.shape)\n", "display(df_1[:10])" @@ -394,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -403,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -421,44 +287,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'score: 0.9960008419280152'\n", - "Confusion matrix, without normalization\n", - "array([[2342, 3],\n", - " [ 16, 2390]])\n", - "'score: 0.8478535353535354'\n", - "Confusion matrix, without normalization\n", - "array([[737, 82],\n", - " [159, 606]])\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier as RFC\n", "clf_a = RFC(criterion='entropy', random_state=4222)\n", @@ -480,543 +311,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "----\n", - "#### Train Data:" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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`MLPClassifier` not found.\n" - ] - } - ], + "outputs": [], "source": [ "?MLPClassifier" ] @@ -1110,26 +399,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "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=(100,), 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": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", "clf_b = MLPClassifier(hidden_layer_sizes=(100,), random_state=4222)\n", @@ -1138,58 +410,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'score: 0.9997275946608554'\n", - "Confusion matrix, without normalization\n", - "array([[1675, 1],\n", - " [ 0, 1995]])\n", - "'score: 0.5776805251641138'\n", - "Confusion matrix, without normalization\n", - "array([[109, 99],\n", - " [ 94, 155]])\n", - "'score: 0.6041666666666666'\n", - "Confusion matrix, without normalization\n", - "array([[ 92, 77],\n", - " [ 94, 169]])\n" - ] - }, - { - "data": { - "image/png": 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\n", 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YZ+QgmvN/TnMXNTNiSBPrjhqUkqM/i5XUqAs8Gdi3g7o3AfYBXi0r3g+YkJdjgZ9Xs4O++n9rNLBKApTkgzDtLGtpXd61HVzWzWoJeHHGIh6fOo8XZixkcFOJJcta6hlqYSxa1srs+c3MWdBMa6RWYUsrvLUwjQsuXtZKc6szYJtqkl81CTAi7gTmdLDqx8A3Wfm/nYnA5ZHcB4yWtFGlffRVAjwP2EzSI5IekHSXpBuBpySNL2/iSvq6pDPy480k3Srp7/k1W/VRvHXz1oJlrLfGEADWW2MIby1YBqSjw21/MuuvOYS3FzXT4u9cnyhpxc9hg0ssXtq6vAxg1NAmH5Fvp7fGACVNBF6LiEfbrRoLTC17Pi2XdamvWmDfBraLiB0l7Q3clJ+/JGl8F6+bBBwXEc9Leg9wEfCh9htJOpbU7GXDd2xc49B7z+YbjGDN4YMY1CR2Gr8m02Yv5vW3ljBhwxGst+YQlja38twbCwEYPqTEZuuPAGDR0hZemLGonqEXyugRgyhJBMHbi9LpLsMHlxgxJB35XbyslUXLnABXUt3xuTGSHix7PikiJnVapTQC+A6p+1sT9eqC3h8RL3W1gaRRwO7Ar8uay0M72jb/0iYBbP1POzVMu2jKmws7LH/69QWrlM1f3MKjr87r7ZCsA3MWNK9StjCfD2gdq3KMb1ZE7NqNajcD3gk8muvfGHhI0m7Aa8AmZdtunMu6VK8EWP4Nb2blrviw/LME/CMiduyzqMxs9fXStcAR8Tiw/vLdSC8Du0bErDykdpKka4D3AHMjYnqlOvtqDHAesEYn694E1pe0rqShwAEAEfE28JKkQ2D5Ye4d+iRaM+uxNB9g5aViPdLVwL3AlpKmSTqmi81vBl4EpgCX0MFB1470SQswImZLuicf7FhESnpt65ZJOhO4n9RkfabspYcDP5d0GjAYuAZoP/hpZv1MLRqAEXFYhfXjyx4HcGJ399FnXeCI+EwX6y4ALuig/CU6OA/IzPq3/nKpWyU+D8/Maku1aQH2BSdAM6spAU0Ncp26E6CZ1Zy7wGZWTO4Cm1lRCbcAzaywqjvPrz9wAjSzmnML0MyKyWOAZlZUHgM0s0LzGKCZFVaDNACdAM2sxnppOqze4ARoZjWVxgDrHUV1nADNrMZ8HqCZFZi7wGZWTD4P0MyKyucBmlmheQzQzArLLUAzKyaPAZpZUQm5BWhmxdXU6GOAktbs6oX5xuVmZqtokAZgly3AJ4EgHdVu0/Y8gHG9GJeZNSgNhGuBI2KTvgzEzAaOWvSAJV0KHADMiIjtctlZwESgFZgBHBURrytl3J8A+wMLc/lDFeOsMpBDJX0nP95Y0i49eUNmVgylkiouVZgM7Nuu7IcRsX1E7Aj8HvhuLt8PmJCXY4GfVxVnpQ0kXQh8EPhcLloIXFxN5WZWPCIfCa7wr5KIuBOY066s/NjDSNJwHKRW4eWR3AeMlrRRpX1UcxR494jYWdLDOYA5koZU8TozK6gqu8BjJD1Y9nxSREyq9CJJ5wBHAHNJjTOAscDUss2m5bLpXcZZRZDLJJXImVbSuqT+t5nZqpTOA6y0ALMiYteypWLyA4iIU/MxiquAk1Yn1GoS4M+A3wDrSfoecDfwg9XZqZkNXCKdB1hpqYGrgH/Oj18Dyg/cbpzLulSxCxwRl0v6O/CRXHRIRDzRzUDNrEB66ywYSRMi4vn8dCLwTH58I3CSpGuA9wBzI6LL7i9UfyVIE7CM1A2u6sixmRVXLc4DlHQ1sDdprHAacDqwv6QtScNwrwDH5c1vJp0CM4V0oPbz1eyjYgKUdCrwGeAGUuv2V5Kuiojvd+vdmFkhqEaTIUTEYR0U/6KTbQM4sbv7qKYFeASwU0QshOVHYB4GnADNrENNjX4lSJnp7bYbRIVDy2ZWbA1/KZykH5PG/OYAT0q6LT/fB3igb8Izs0YjanMpXF/oqgXYdqT3SeCmsvL7ei8cM2t4GgC3xYyIDgcbzcwqafgucBtJmwHnANsAw9rKI2KLXozLzBpUI3WBqzmnbzLwS9L72g+4Dri2F2MyswZX5aVwdVdNAhwREbcBRMQLEXEaKRGama1CSqfBVFr6g2pOg1mSJ0N4QdJxpOvr1ujdsMyskfWT/FZRNQnwX0nzbn2JNBa4FnB0bwZlZo2tv3RxK6lmMoS/5YfzWDEpqplZpxok/3V5IvQNrJhtdRURcXCvRGRmDU2q2XRXva6rFuCFfRZFDY0Y0sSO40fXOwxrZ+13r9a8ldZgGr4LHBG392UgZjZwNMqcedXOB2hmVhUxAFqAZmY9NahBmoBVJ0BJQyNiSW8GY2aNL02I2hgtwGruC7ybpMeB5/PzHST9tNcjM7OGVVLlpT+opqF6AXAAMBsgIh5lxb04zcxW0TYtfldLf1BNF7gUEa+0a9K29FI8ZtbgBAzqLxmugmoS4FRJuwEhqQk4GXiud8Mys0bWIPmvqgR4PKkbPA54E/hTLjMzW4UkSg2SAau5FngGcGgfxGJmA0SD5L+qZoS+hA6uCY6IY3slIjNraAIG9ZfDvBVUcxT4T8DtebkHWB/w+YBm1qlaHAWWdKmkGZKeKCv7oaRnJD0m6QZJo8vWnSJpiqRnJX2smjgrJsCIuLZsuQw4GNilmsrNrICqOAewygbiZGDfdmV/BLaLiO1JB2NPAZC0DWmobtv8movyQdsu9eSClXcCG/TgdWZWEKriXyURcSfpvuTlZX+IiOb89D5g4/x4InBNRCyJiJeAKcBulfZRzRjgW6wYAyzlgL5dMXozK6Q0BtgnuzqaFTdoG8vK9yyflsu61GUCVDr7eQfSfUAAWiOi00lSzcyg6muBx0h6sOz5pIiYVGX9pwLNwFU9CG+5LhNgRISkmyNiu9XZiZkVRzfuCzwrInbtdv3SUaTLcz9c1iB7DdikbLONWdFw61Q1DdVHJO3U3SDNrKCqOALc0/MEJe0LfBP4REQsLFt1I3CopKGS3glMAO6vVF9X9wQZlAcbdwIekPQCsCC9PSIidu7ZWzCzgaxW5wFKuhrYm9RVngacTjrqOxT4Y+5m3xcRx0XEk5KuA54idY1PjIiKcxZ01QW+H9gZ+MRqvQszK5xaXAkSEYd1UPyLLrY/h3Tr3qp1lQCVK32hOxWaWdGJUhWnufQHXSXA9SR9tbOVEXF+L8RjZg0u3ROk3lFUp6sE2ASMggZJ5WbWP6hxrgXuKgFOj4gz+ywSMxsQBkoLsEHegpn1NwNhPsAP91kUZjagNEj+6zwBRsScztaZmXVGgqYGyYC+MbqZ1VxjpD8nQDOrsXQtcGOkQCdAM6u5xkh/ToBmVnOiNADOAzQz6zbRs6nm68EJ0MxqrsoJUevOCdDMaq4x0p8ToJnVmM8DNLNCcxfYzAqrMdKfE6CZ9YIGaQA6AZpZbQmPAZpZYQk1SCfYCdDMaq5BGoBOgGZWW+lKkMbIgE6AZlZbglKDXAvnBNiPNAma8h9OSyu0xMrrBjfB4ub6xFYkF59+OPvtuR0z58xj10POBeDUL+7P0Qfvzsy35gNw+oU3ctvdTzFuo3V45PrTeO6VGQDc//jLfOmca+oWe3/hMUDrFpGS39J8L/vBJWgNaMuBJUFEZ6+2Wrrid/dx8bX/x3+fdcRK5T+98i/85xW3r7L9i9Nm8d5Dz+ur8Pq9NB9gvaOoToM0VAc+KSW8Nq2x4o9ocAmaW+sTVxHd89ALzJm7sN5hNDRV8a9iHdKlkmZIeqKs7BBJT0pqlbRru+1PkTRF0rOSPlZNnE6A/UTEyv9rNpVSUiwptQLd+Ku/4w7dk/uvPYWLTz+c0WsMX14+fuy63Hv1t/jDf3+Z9++0WR0j7D9KUsWlCpOBfduVPQEcDNxZXihpG+BQYNv8moskNVWMs5ooekLSlyQ9LemqTtbvLen3vbX/RhOkcb8hTWlpaw0OcuuvX7jk13exzYFn8J5Dz+ONWW9z3lcPBuCNWW+zxX7f5X2H/YBv/eh6Jp97FGuMHFbnaOurrQtcaakkIu4E5rQrezoinu1g84nANRGxJCJeAqYAu1XaR2+2AE8APhoRh/fiPgaUlkhjgG3jgBHpj2loU1pgxU/rWzPmzKO1NYgILr3+HnbdblMAli5rZs7cBQA8/PRUXpw2iwmbrl/PUPuBajrAAhgj6cGy5djV2OlYYGrZ82m5rEu9chBE0sXAu4BbJF0JHAQMAxYBn2+fwSXtBfwkPw1gz4iYJ+kbwKeBocANEXF6b8TbH5UES1uhpWVF2dAmWNLS+Wus92w4Zk3emPU2ABM/tANPvTAdgDFrj2LO3AW0tgbjx67L5uPW46Vps+oZav2p6hOhZ0XErpU36z29kgAj4jhJ+wIfBJYCP4qIZkkfAc4F/rndS74OnBgR90gaBSyWtA8wgdSMFXCjpD1zs3hAGlLWunO3t34u+/5R7LHLBMaMHsWUW8/irItvZs9dJrD9lhsTEbwyfQ4nn301AB/YeXP+7fiPs6y5hdbW4ORzruGtt4t9AKVO1wK/BmxS9nzjXNalvjgNZi3gMkkTSK27wR1scw9wfh4vvD4ipuUEuA/wcN5mFCkhrpIAc9P5WIBNxo2r/TvoI0srtO7c+usbR54yeZWyy357b4fb/vb2R/jt7Y/0ckSNpw5nwdwI/ErS+cA7SLni/kov6oujwGcBf4mI7YADSV3hlUTEecAXgOHAPZK2Iv0Ovx8RO+Zl84j4RUc7iIhJEbFrROy63pj1eu+dmFl1VMVSqQrpauBeYEtJ0yQdI+mTkqYB7wNuknQbQEQ8CVwHPAXcSupRVmwy9FULsK0pelRHG0jaLCIeBx6X9G5gK+A24CxJV0XEfEljgWURMaMPYjaz1VCLK0Ei4rBOVt3QyfbnAOd0Zx99kQD/ndQFPg24qZNtviLpg0Ar8CRwS0QskbQ1cG+eXns+8FnACdCsn2uUK0F6LQFGxPj8cBawRdmq0/L6O4A78uOTO6njJ6w4OmxmjaLoCdDMiikN8TVGBnQCNLPaqv48wLpzAjSzmnMCNLOC8j1BzKzA3AI0s0Kq8jznfsEJ0MxqTg3SBHQCNLOaa5D85wRoZrXXIPnPCdDMaqyBBgGdAM2sptKU+I2RAZ0AzazmGiP9OQGaWW9okAzoBGhmNecrQcyssAo/H6CZFZgToJkVkecDNLPi8nyAZlZkToBmVlCeD9DMCswtQDMrpAa6FJhSvQMws4FHUsWlijoulTRD0hNlZetI+qOk5/PPtXO5JF0gaYqkxyTtXE2cToBmVnNS5aUKk4F925V9G7g9IiYAt+fnAPsBE/JyLPDzanbgBGhmNacqlkoi4k5gTrviicBl+fFlwEFl5ZdHch8wWtJGlfbhBGhmtVVF6281DpJsEBHT8+M3gA3y47HA1LLtpuWyLvkgiJnVlKj6niBjJD1Y9nxSREyqdj8REZKiu/GVcwI0s5qrsoE3KyJ27WbVb0raKCKm5y7ujFz+GrBJ2XYb57IuuQtsZjXXi13gG4Ej8+Mjgf8tKz8iHw1+LzC3rKvcKbcAzazmanFbTElXA3uTusrTgNOB84DrJB0DvAJ8Om9+M7A/MAVYCHy+mn04AZpZzdXiROiIOKyTVR/uYNsATuzuPpwAzaymVrOL26ecAM2s5jwZgpkVlluAZlZYToBmVlCeD9DMCipdCVLvKKrjBGhmNecEaGaF5S6wmRWTzwM0s6JqpCnxnQDNrOZqcS1wX3ACNLOaa5D85wRoZrXXIPnPCdDMekGDZEAnQDOrKQGlBukDK02jNXBImkmaKHEgGAPMqncQtoqB9rlsGhHr1aoySbeSfkeVzIqI9re97FMDLgEOJJIe7ME9E6yX+XMZOHxPEDMrLCdAMyssJ8D+rep7pFqf8ucyQHgM0MwKyy1AMyssJ0AzKywnQDMrLCdAsx5SuylP2j+3/s8J0KwHJCnyEURJ6wKEjyg2HB8FbgCSPgksAEoRcWu947EVJJ0MvA+YDvwfcEtELKtvVFYttwD7OUknAV8H1gF+I2mPOodkmaRDgEOA44F9gA84+TUWJ8B+SsmmwEeBDwFjSS2Mv0oaXNfgCqptjE9S2/dmLHAe8EngdeDUvH7DugRo3eYE2H8JmAlMA74L7AV8KiJagCPENHRTAAAGZklEQVQlbVHP4IqmfMwPaEtwLwLnAkdExMciYpmkrwHHlSVJ68c8H2A/JOkDwPYRcZGkEcAxETEsr/sM8AXg5nrGWBRtrb6yAx5fAQ6S9HHgBeBp4AFJuwBbAIcDn4uI1jqFbN3ggyD9SG41CDgG2AX4C/C/wJXAaOAJYHfg6Ih4vF5xFomkoRGxJD8+BvgX4JCImJqHIvYgfVZ7AYuAM/3ZNA4nwH5E0riIeDW3+g4hfbHui4hfSfoE0AI8HREv1jXQgpC0OWmM72sR8YqkrwJTgMXADsDRwEWk/6AWk75PC+sVr3Wfxyn6CUnvAO6StF/+Ev0PqcV3pKQjgJsj4iYnvz61lNTN/b6kjUjJ7wTgq8CrpIMeewJrRcQiJ7/G4wTYD0j6Julcsu8A50raJyIWRMQkYAiwPTCqnjEWUUS8CvyMdLDjR6Sj8J8C/jkirgXmARsDS+oWpK0WJ8A6k7Q/sDfwQERcBfwQOF/Sx3O3dxHwo4j4Rx3DLIR86tFK34mcBM8ntfj+C1gnIhZIOgH4AXBsREzv+2itFjwG2MckDQE2j4inJB0FfBuYEhEHlG3zKeBrpOT3lYh4rC7BFoykURExPz/+IrAm6eqbH0haC/gWMJ7UBd4ImOshicbmBNjH8sD6RaRLp8YBlwJfAS6LiAvKtlsLaI6IBXUJtGBya3tiRBwj6V+Bg4B/Ay4EHo+IwyWtAZwDjCC1/HyqS4PzeYB9LCKmSHoMOBb4VkRcIWkW8MV8ru1P83Zz6xpogeTJDL4EnCRpS2BXYL9c9gIwXNL/RMSnJJ0KDHfyGxicAOvjYuBR4KuS5kTEtZJmABdJmhURV9c5vqJZCjQDpwMBnALsRmoRvk/SbsAtkq6MiM+SDn7YAOAEWAcRMQWYIukfwDn55zDSF/G+ugZXQBExT9KfSZcc/kc+5++dwL15k61IB6euqVeM1jucAOsoIn4naRnwH6Tpro6JiJfqHFZRXQv8HbhQ0mzgFmAnSZeSusN7RcTLdYzPeoEPgvQDktYnXW46s96xFJ2knUnJ8DvA3aQZX2b7P6aByQnQrB1JOwB/Bk7JJ6PbAOUEaNYBSdsBiyLihXrHYr3HCdDMCsuXwplZYTkBmllhOQGaWWE5AZpZYTkBmllhOQEOUJJaJD0i6QlJv87T7Pe0rr0l/T4//oSkb3ex7eg8V15393GGpK9XW95um8l5CrFq9zVe0hPdjdEGHifAgWtRROwYEduRrjE+rnxlR5N/ViMiboyI87rYZDRp2nizfs8JsBjuAjbPLZ9nJV1Out/IJpL2kXSvpIdyS3EUgKR9JT0j6SHg4LaKJB0l6cL8eANJN0h6NC+7k24itFluff4wb/cNSQ9IekzS98rqOlXSc5LuBras9CYk/Uuu51FJv2nXqv2IpAdzfQfk7Zsk/bBs319c3V+kDSxOgAOcpEGki/nbbtU4AbgoIrYlTcBwGvCRiNgZeJA0Rdcw4BLgQNKd6TZcpeLkAuD/ImIHYGfgSdIM1y/k1uc3JO2T97kbsCOwi6Q9le6je2gu2x94dxVv5/qIeHfe39Ok24e2GZ/38XHg4vwejiHN2vzuXP+/5FlezADPBjOQDZf0SH58F/AL4B3AKxHRNuXWe4FtgHuU7v89hDQF1FbASxHxPICkK0kTuLb3IeAIgIhoAeZKWrvdNvvk5eH8fBQpIa4B3NB2JzVJN1bxnraTdDapmz0KuK1s3XV5ktLnJb2Y38M+wPZl44Nr5X0/V8W+rACcAAeuRRGxY3lBTnLlU+wL+GNEHNZuu5Vet5oEfD8i/qvdPr7Sg7omAwdFxKP5fip7l61rf01n5H2fHBHliRJJ43uwbxuA3AUutvuA9+f7lCBppKQtgGeA8ZI2y9sd1snrbweOz69tyvcxmUdq3bW5DTi6bGxxbJ7+607gIEnD8702Dqwi3jWA6ZIGA4e3W3eIpFKO+V3As3nfx+ftkbSFpJFV7McKwi3AAouImbkldbWkobn4tIh4TtKxwE2SFpK60Gt0UMWXgUmSjgFagOMj4l5J9+TTTG7J44BbA/fmFuh84LMR8ZCka0m3BpgBPFBFyP8G/A2YmX+Wx/QqcD/pTm7HRcRiSf9NGht8SGnnM0k3OzIDPBuMmRWYu8BmVlhOgGZWWE6AZlZYToBmVlhOgGZWWE6AZlZYToBmVlj/H1uPwZJZCrZuAAAAAElFTkSuQmCC\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- train\", Xt=vec_train_2, yt=y2, clf=clf_b)\n", "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- test\", Xt=vec_test_2, yt=yt2, clf=clf_b)\n", @@ -1206,30 +429,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'score: 0.5142231947483589'\n", - "Confusion matrix, without normalization\n", - "array([[ 7, 201],\n", - " [ 21, 228]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "yt2_c3 = yt2.copy()\n", "yt2_c3[yt2_c3 == \"true\"] = \"REAL\"\n", @@ -1243,30 +445,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'score: 0.5391414141414141'\n", - "Confusion matrix, without normalization\n", - "array([[274, 491],\n", - " [239, 580]])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "yt1_c3 = yt1.copy()\n", "yt1_c3[yt1_c3 == \"REAL\"] = \"true\"\n", @@ -1288,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1330,41 +511,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "processing datasets\n", - "ds1= data/fake_or_real_news.csv\n", - "ds2= data/train.tsv\n", - "-- fake news\n", - "Index(['y', 'claim'], dtype='object')\n", - "3171\n", - "3164\n", - "6335\n", - "-- liar liar\n", - "Index(['y', 'claim'], dtype='object')\n", - "{'true', 'barely-true', 'half-true', 'false', 'pants-fire', 'mostly-true'} 10240\n", - "1676\n", - "1995\n", - "{'true', 'false'} 3671\n", - "false 5159\n", - "true 4847\n", - "Name: y, dtype: int64\n", - "done\n" - ] - } - ], + "outputs": [], "source": [ "X3, Xt3, y3, yt3 = get_dataset3_split('data/fake_or_real_news.csv', 'data/train.tsv')" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1382,26 +538,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "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": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "clf_3 = MLPClassifier(hidden_layer_sizes=(16,16), random_state=4222)\n", "clf_3.fit(vec_train_3, y3)" @@ -1409,44 +548,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "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", 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MgO7duhDpv8Dqq3Zn5y0/zy0PPg/AkqXVLPjo45ateAciiV69shbhkiVLWLpkCZKorq7m3LPP4NLLf1Er/+qrr7789cKFCyv+VpU85HSNsS7vpS4y6euslF7futINrTddf/2bWztrHdttOoAJt45g/O9O5oe/eoDq6mUMWqcPc+Yv5IZzDua5357IyDMPdIsxZ9XV1Wy/zVYMXHdtdt9zL4Ztvz3XX3ct++1/IOuss85K+UeNvI7NNt6Q8845k1/9+poy1LiNKa0r3Rz3AzUjy0cD9xWkH5VGp3cAFqQu9yPA3pL6pEGXvVNag1olMErqLenkxnNasXGvvs02R1/HzifcwBnf2YXu3brQpXMnthqyDjf+3zh2/P4oFn28hNOP2KXcVW1XOnfuzNgJE6maNoPx4/7FM/94mnvu/gsnn/KDOvOfePIIXpk8lUsuu4LLL7uklWvb9uR0u87twHPAxpJmSDoWuBzYS9IUYM+0D9nyqG8AVcCNwMkAEfE+cDEwLm0XpbQGtdY1xt5kFR1ZmCipS0QsbaU6VLTJ0+fw0eJP2XyDtXl79ge8PfsDxr2a9QjufWoSP3FgbBG9e/dm1+G78fennuSNqVVsvslgABYtWsTmmwxm0mtVtfIf9s3D+dEpJ5Wjqm1GqYGvMQ2sK71HHXkDGFFPOTcDNzfl3K3Vlb4c2FDSREnjJP1D0v3AK5IGFd3Aebqkn6XXG0p6WNKE9J5N6im/Xfr8Or3p3Dn7EQ3svwYbD+zH9Jnzee/9j5gx6wOGrN8XgOHbfIHXps0uZ1XbldmzZzN//nwAFi9ezON/e4yth27DtBkzmVw1jclV0+jZs+fyoFg1Zcry9z40+kEGDx5Slnq3JS14jbFVtFaL8Wxgi4jYStJw4MG0/6akQQ287wbgxIiYIml7shbn7sWZ0jB/NtTffY18a56zW396CLtsPYh+a/Sk6q7TuPh3TzHvg0Vc9aN96dd7Ve654gheqprJgaffxk5fHMjpR+zCkqXVLIvgR1c9yNwFiwA47erR/O6/v0G3rp2Z9s48jv/5/5X5k7UfM999l+O+dzTV1dUsi2V845DD2He//evNf/3Ia3nyib/RtUtXevfpw40331pv3g6jwsefFNEio921T5IFv79GxBYpMF4QEbsVH0v7pwO9gCuB2cDkgqK6R8SmDZ2r02rrRvetj8v5E9hnNe+JC8tdBavHKl014bPcNlOse/8hMeCIqxvN9+av98v1vHkq132MCwteL6V2l75H+toJmB8RW7Varczssyv9PsY2q7U6+h8Cq9Vz7D1gbUl9JXUH9geIiA+ANyUdCssfEt+yVWprZs2WzcfY+NaWtUqLMSLmSno2DbIsJguGNceWSLoI+BfZjZevFbz1COB6SecDXYE/Ay+2Rp3NrPkqvMHYel3piPh2A8euAVa6KzYi3qSEB77NrG2p9K60n5U2s3zJLUYzs1oEdO5c2ZHRgdHMcueutJlZIXelzcxqE24xmpkVafv3KTbGgdHMcucWo5lZIV9jNDOrrT1cY2zbk6KZWUXK61lpST+S9LKkSZJOTWlrSnpM0pT0tU9Kl6RrJFVJeknS0GbXv7lvNDOrj9T41ngZ2gI4DhgGbAnsL2kw2fyuj0fEEODxtA+wDzAkbccD1ze3/g6MZpYv5bPmC7ApMDYiFqUlUP4OfB04CKiZDfhW4OD0+iDg95EZA/SuWVGwqRwYzSxX2TXGklqM/SSNL9iOLyrqZWCXNCVhT2BfsqVQ+6cVAAFmAv3T6wHAWwXvn5HSmsyDL2aWs5KvIc5paAbviHhV0hXAo2STW08EqovyhKTclyFwi9HMcpdTV5qIuCkitomIrwDzgNeB92q6yOnrrJT9bbIWZY31UlqTOTCaWb5K6EaXejePpLXT14Fk1xf/BNwPHJ2yHA3cl17fDxyVRqd3ABYUdLmbxF1pM8tVzvcx3i2pL7AEGBER8yVdDtwp6VhgOnBYyjua7DpkFbAI+G5zT+rAaGa5y+tZ6YjYpY60ucAedaQHMCKP8zowmlnuKv3JFwdGM8uXn5U2M6tNlD7q3FY5MJpZ7jq31/kYJa3e0Bsj4oP8q2Nm7UGFNxgbbDFOAoJs9L1GzX4AA1uwXmZWoaR2PPgSEevXd8zMrCEV3pMu7ckXSYdLOje9Xk/SNi1bLTOrZHnNx1gujQZGSdcCuwFHpqRFwKiWrJSZVS6RRqYb+deWlTIqvVNEDJX0AkBEvC+pWwvXy8wqWBtvEDaqlMC4RFInsgEX0nOLy1q0VmZWuZowe05bVUpgvA64G1hL0oVkD2xf2KK1MrOKJdrxfYw1IuL3kiYAe6akQyPi5ZatlplVsgpvMJb85Etnsml/As/haGaNqPSudCmj0ucBtwPrks2I+ydJ57R0xcysMpUySW1bj5ultP6OAraLiPMj4jyypQyPadFamVlF6yw1upVC0o/TmtIvS7pdUg9JG0gam9aPvqPmLhlJ3dN+VTo+qLn1LyUwvkvtLneXlGZmVqc81nyRNAD4IbBtRGxBdknvcOAK4NcRMZhsHZhj01uOBeal9F+nfM1Sb2CU9GtJVwHvA5Mk/VbSjcC/gTnNPaGZtW8iu4+xsa1EXYBVJHUBepI1ynYH7krHi9eVrllv+i5gDzXzYmdDgy81I8+TgAcL0sc050Rm1kGo5Ef++kkaX7B/Q0TcULMTEW9LuhL4D7CYbBnVCcD8iFiashWuHb18XemIWCppAdCXZjTkGppE4qamFmZmBiWPSje4rrSkPmStwA2A+cBfgK/lUsFGNHq7jqQNgUuBzYAeNekRsVEL1svMKlRNVzoHewJvRsRsAEn3AF8GekvqklqNhWtH16wrPSN1vdcA5jbnxKUMvtwC/I7s8+4D3Anc0ZyTmVnHkMfgC1kXegdJPdO1wj2AV4AngUNSnuJ1pWvWmz4EeCKtHNhkpQTGnhHxCEBETI2I88kCpJnZSqR8bteJiLFkgyjPkw36dgJuAM4CTpNURXYNseay301A35R+GnB2cz9DKU++fJImkZgq6USy5upqzT2hmbV/ed3AHREXABcUJb9Bdj91cd6PgUPzOG8pgfHHwKpk9xNdStZv/14eJzez9qnSHwksZRKJsenlh6yYrNbMrF4VHhcbXCXwXtIcjHWJiK+3SI3MrKJJatfTjl3barXI0dYbrcuzT3i6yLamz3anlLsK1orabVc6Ih5vzYqYWftR6XMTljofo5lZSUQ7bjGamTVXlwpvMpYcGCV1j4hPWrIyZlb5soloK7vFWMoM3sMk/RuYkva3lPQ/LV4zM6tYOU47VhalNHivAfYnPYwdES8Cu7VkpcysslX60galdKU7RcT0oqZxdQvVx8wqnIAubT3yNaKUwPiWpGFASOoM/AB4vWWrZWaVrMLjYkmB8SSy7vRA4D3gbynNzGwlkuhU4ZGxlGelZ5EtQGNmVpIKj4slzeB9I3U8Mx0Rx7dIjcysogno0taHnRtRyqj034DH0/YssDbg+xnNrF55jEpL2ljSxILtA0mnSlpT0mOSpqSvfVJ+SbomrSv9kqShza1/KV3pWssYSLoNeKa5JzSzdi6n+xQjYjKwFUAa+H0buJdsZu7HI+JySWen/bPIVhYYkrbtgevT1yZrzoM7GwD9m3MyM+sYVMK/JtoDmBoR06m9fnTxutK/j8wYskWz1mlO/Uu5xjiPFdcYOwHv8xnWUjCz9i27xph7sYcDt6fX/SPi3fR6JisaasvXlU5q1px+lyZqMDCmlbm2ZMXyhMuau+qWmXUcJT4r3U/S+IL9GyLihjrK6gYcCJxTfCwiQlLuManBwJhOOjoitsj7xGbWPjVhXek5EbFtCfn2AZ6PiPfS/nuS1omId1NXeVZKr1lXukbhmtNNUkqDd6KkrZtTuJl1QCWMSDfxPsdvsaIbDbXXjy5eV/qoNDq9A7CgoMvdJA2t+dIlIpYCWwPjJE0FFpL9QYiIaPZQuJm1X3nexyhpVWAv4ISC5MuBOyUdC0wHDkvpo4F9gSpgEfDd5p63oa70v4ChZH17M7OS5biu9EKgb1HaXLJR6uK8AYzI47wNBUalk03N40Rm1lGITk2/HadNaSgwriXptPoORsRVLVAfM6tw2Zov5a7FZ9NQYOwM9IIKD/1m1rpU+c9KNxQY342Ii1qtJmbWLrT3FmOFfzQzK5f2PB/jSqM+ZmalqPC4WH9gjIj3W7MiZtY+SNC5wiNjyetKm5mVqrLDogOjmeUse1a6skOjA6OZ5a6yw6IDo5nlTnRqx/cxmpk1mWje0gBtiQOjmeWuxIlq2ywHRjPLXWWHRQdGM8uZ72M0M6tDpXelK/0aqZm1QSphK6kcqbekuyS9JulVSTtKWlPSY5KmpK99Ul5JukZSlaSXJDV7lQEHRjPLXY5rvlwNPBwRm5CtWPoq2fLNj0fEEOBxViznvA8wJG3HA9c3t/4OjGaWK5FdY2xsa7QcaQ3gK8BNABHxaUTMBw4Cbk3ZbgUOTq8PAn4fmTFA77SKYJM5MJpZzlTSP9K60gXb8UUFbQDMBn4n6QVJv02LY/UvWP1vJtA/vR4AvFXw/hkprck8+GJmuSuxq9zYutJdyBbk+0FEjJV0NSu6zUC2AJakaHZF6+EWo5nlKnvyRY1uJZgBzIiIsWn/LrJA+V5NFzl9nZWOvw2sX/D+9VJakzkwmlm+BJ06Nb41JiJmAm9J2jgl7QG8AtwPHJ3SjgbuS6/vB45Ko9M7AAsKutxN4sBYRh9//DE77ziMYUO3ZOiWm3PxhRfUOn7aqT+kX+9ey/dv/N9RbLvVF9l+m63YfdedefWVV1q7yu3WqAuOYPrjP2f8X85dnvb1Pbdmwl3nsXDCNQzdbODy9C5dOnHjRUcy7s5zeeHu8zn9e3svP7bXTpvy4r3/zcv3XcDp392rVT9DW1LiNcZS/AD4o6SXgK2Ay4DLgb0kTQH2TPsAo4E3gCrgRuDk5tbfgbGMunfvzsOPPcG/nn+RseMn8ugjDzN2zBgAJowfz/x582rl/+a3vs34if9m7ISJnHb6mZx1Rr2r21oT3fbAGA4acV2ttElT3+Hwn9zIM8/XXlr9G3sOpXu3Lmx32GXsdMQVfP8bX2bgOmvSqZP4zdmHcdApI9n6G5dw6Ne2YZMvfK41P0abkM3H2PhWioiYGBHbRsSXIuLgiJgXEXMjYo+IGBIRe9asNpBGo0dExIYR8cWIGN/cz+DAWEaS6NUraxEuWbKEpUuWIInq6mrOPfsMLr38F7Xyr7766stfL1y4sOKfLmhLnn1+Ku8vWFQrbfKb7zFl+qyV8gZBzx7d6Ny5E6t078anS6r5cOHHbLfFIKa+NYdpb89lydJq/vLI8+w//Eut9RHalBxbjGXhUekyq66uZqdh2zB1ahUnnDSCYdtvz7XXXM1++x/IOuusfAvWqJHXcc3VV/Hpp5/y8KNPlKHGds/fXmD/4V/izccupWePbpx55T3M+2AR6669BjPeW9HKf/u9eQzbYlD5KlpGlT6Dd4u1GCX9MD3C88d6jg+X9NeWOn+l6Ny5M2MnTKRq2gzGj/sXz/zjae65+y+cfMoP6sx/4skjeGXyVC657Aouv+ySVq6tAWy3+SCqq5fxhb3PY9P9LuBHR+7OoAF9y12tNiPPrnS5tGRX+mRgr4g4ogXP0W707t2bXYfvxt+fepI3plax+SaD2XjwIBYtWsTmmwxeKf9h3zycB+7/vzLU1A7bZ1se/ecrLF26jNnzPuK5iW+wzWYDeWfWAtbr32d5vgH9+/D27AVlrGm5lHyDd5vVIoFR0ijgC8BDks6S9Fy6c/2fBUPvhfl3lTQxbS9IWi2lnyFpXHog/MKWqGs5zZ49m/nz5wOwePFiHv/bY2w9dBumzZjJ5KppTK6aRs+ePZn0WhUAVVOmLH/vQ6MfZPDgIWWpd0c3Y+b7DN8u+2/cs0c3hn1pEJOnvcf4SdMZPHAtPr9uX7p26cyhXx3Kg0+9VObalkEJz0m39Z52i1xjjIgTJX0N2A34FPhVRCyVtCfZcPs3it5yOjAiIp6V1Av4WNLeZA+DDyNrnd8v6SsR8XRL1LkcZr77Lsd972iqq6tZFsv4xiGHse9++9eb//qR1/LkE3+jjxKbAAAMGUlEQVSja5eu9O7ThxtvvrXevNY0t/78GHbZZgj9evei6uGLuXjUaOYtWMhVZx1Kvz69uOeaE3lp8tscOOI6Rt3xNDdc+B0m3HUeEtx23xhenvIOAD++4k4eGDmCzp3ErfeN4dU3Zpb5k7W+mmelK5kicn+aJitYmgZsC6wCXEMW5ALoGhGbSBoOnB4R+0s6G/gv4I/APRExQ9KVwCHA/FRkL+DnEXFTHec6nmw2DdYfOHCb16dOb5HPZM3XZ7tTyl0Fq8fHE6+b0MijeU2y6Re3jt/d+2Sj+XYc0ifX8+apNW7XuRh4MiK2AA4AehRniIjLge+TBdFnJW1C9ofn5xGxVdoG1xUU0/tvSPc6bbtWv7Va7pOYWWnympCxTFojMK7BiucVj6krg6QNI+LfEXEFMA7YBHgE+F7qWiNpgKS1W6G+ZvYZVfrgS2vcx/gL4FZJ5wMP1pPnVEm7AcuAScBDEfGJpE2B59KNzB8B32HFA+Nm1ka19dtxGtNigTEiBqWXc4CNCg6dn44/BTyVXtd5015EXE02g6+ZVRIHRjOzFbJLiJUdGR0YzSxfFXCfYmMcGM0sdw6MZma1tP1R58Z42jEzy11ejwRKmibp3+lx4fEpzetKm1llKeXe7ia2J3dLD3nUPCXjdaXNrPJIanT7DLyutJlVnhK70o2tKw3Z/AqPSppQcNzrSptZ5SmxPdjYutIAO0fE2+lx4MckvVZ40OtKm1llyPEiY0S8nb7OAu4lm4bQ60qbWWXJljZQo1uj5UirFkxavSqwN/AyrbCutLvSZpa7nO5i7A/cmwZqugB/ioiHJY0D7pR0LDAdOCzlHw3sS7au9CLgu809sQOjmeUvh8gYEW8AW9aRPhfYo470AEZ89jM7MJpZC6j0J18cGM0sd56P0cysmAOjmdkKno/RzKyY52M0M1uZA6OZWS2VPx+jA6OZ5c4tRjOzAs2Yb7HNcWA0s9x9xvkWy86B0cxyV+Fx0YHRzPJX4XHRgdHMcub7GM3MahO+xmhmtpLKDosOjGbWAiq8weilDcwsf3kunyqps6QXJP017W8gaaykKkl3SOqW0run/ap0fFBz6+/AaGa5y2ktrBo/Al4t2L8C+HVEDAbmAcem9GOBeSn91ylfszgwmlmuSllTutQGo6T1gP2A36Z9AbsDd6UstwIHp9cHpX3S8T3UzFEgB0Yzy51K+Af0kzS+YDu+jqJ+A5wJLEv7fYH5EbE07c8ABqTXA4C3ANLxBSl/k3nwxcxyV2I7bU5EbFt/GdofmBUREyQNz6lqJXFgNLPc5TQq/WXgQEn7Aj2A1YGrgd6SuqRW4XrA2yn/28D6wAxJXYA1gLnNObG70maWs1I60o1Hzog4JyLWi4hBwOHAExFxBPAkcEjKdjRwX3p9f9onHX8iLanaZA6MZpar7MmXfAZf6nEWcJqkKrJriDel9JuAvin9NODs5p7AXWkzy13eN3hHxFPAU+n1G8CwOvJ8DByax/kcGM0sd17awMyskGfXMTOrzUsbmJnVwdOOmZkVqfC46MBoZvmr8LjowGhmLaDCI6MDo5nlSkCnCu9Lq5lPzLRZkmYD08tdj5z0A+aUuxK2kvb2c/l8RKyVV2GSHib7HjVmTkR8La/z5qndBcb2RNL4hmYfsfLwz6X987PSZmZFHBjNzIo4MLZtN5S7AlYn/1zaOV9jNDMr4hajmVkRB0YzsyIOjGZmRRwYzZqpeM3i5q5hbG2PA6NZM0hSzUJLkvoCNHfhJWt7PCpdAST9F7AQ6BQRD5e7PraCpB8AOwLvAn8HHoqIJeWtlX1WbjG2cZJOAU4H1gTulrRLmatkiaRDyRZfOgnYG9jZQbF9cGBso5T5PLAXsDswgKxF8k9JXctauQ6q5hqipJrfmwHA5cB/Ae8A56XjnytLBS03Doxtl4DZwAzgp8CuwCERUQ0cLWmjclauoym8pgjUBL43gMuAoyLiqxGxRNJPgBMLgqdVIM/H2AZJ2hn4UkSMlNQTODYieqRj3wa+D4wuZx07ippWYsFAy6nAwZL2A6YCrwLjJG0DbAQcARwZEcvKVGXLgQdf2pDUyhBwLLAN8CRwH/AHoDfwMrAT8L2I+He56tmRSOoeEZ+k18cCxwGHRsRb6ZLGLmQ/q12BxcBF/tlUPgfGNkTSwIj4T2olHkr2CzcmIv4k6UCgGng1It4oa0U7CEmDya4h/iQipks6DagCPga2BL4HjCT7w/Ux2e/TonLV1/Lj6yBthKR1gX9I2if9ct1F1kI8WtJRwOiIeNBBsVV9StZd/rmkdciC4snAacB/yAZbvgKsERGLHRTbDwfGNkDSmWT3wp0LXCZp74hYGBE3AN2ALwG9ylnHjigi/gNcRzbI8iuyuwIOAb4REXcAHwLrAZ+UrZLWIhwYy0zSvsBwYFxE/BH4JXCVpP1S93kx8KuImF/GanYI6RapWr8TKTheRdZC/F9gzYhYKOlk4Arg+Ih4t/Vray3J1xhbmaRuwOCIeEXSMcDZQFVE7F+Q5xDgJ2RB8dSIeKksle1gJPWKiI/S6xOA1cmeNrpC0hrAWcAgsq70OsACX9ponxwYW1m6oD+S7BGygcDNwKnArRFxTUG+NYClEbGwLBXtYFLr/KCIOFbSj4GDgf8GrgX+HRFHSFoNuBToSdZS9C057ZTvY2xlEVEl6SXgeOCsiLhN0hzghHQP8f+kfAvKWtEOJE0C8UPgFEkbA9sC+6S0qcAqku6KiEMknQes4qDYvjkwlsco4EXgNEnvR8QdkmYBIyXNiYjby1y/juZTYClwARDAOcAwshbkjpKGAQ9J+kNEfIds0MXaMQfGMoiIKqBK0nzg0vS1B9kv6JiyVq4DiogPJT1B9ujllemexQ2A51KWTcgGxf5crjpa63JgLKOIeEDSEuBKsmnFjo2IN8tcrY7qDmACcK2kucBDwNaSbibrVu8aEdPKWD9rRR58aQMkrU32OO7sctelo5M0lCxIngs8QzaDzlz/wepYHBjNikjaEngCOCfdZG8djAOjWR0kbQEsjoip5a6LtT4HRjOzIn4k0MysiAOjmVkRB0YzsyIOjGZmRRwYzcyKODC2U5KqJU2U9LKkv6TlEppb1nBJf02vD5R0dgN5e6e5Cpt6jp9JOr3U9KI8t6Sp2ko91yBJLze1jtZxODC2X4sjYquI2ILsGewTCw/WNSlrKSLi/oi4vIEsvcmm/zerWA6MHcM/gMGppTRZ0u/J1pNZX9Lekp6T9HxqWfYCkPQ1Sa9Jeh74ek1Bko6RdG163V/SvZJeTNtOZItHbZhaq79M+c6QNE7SS5IuLCjrPEmvS3oG2LixDyHpuFTOi5LuLmoF7ylpfCpv/5S/s6RfFpz7hM/6jbSOwYGxnZPUhWwShJolPYcAIyNic7KJK84H9oyIocB4sqnQegA3AgeQrVT4uZUKzlwD/D0itgSGApPIZiSfmlqrZ0jaO51zGLAVsI2kryhbh/nwlLYvsF0JH+eeiNgune9VsmVmawxK59gPGJU+w7Fks2xvl8o/Ls2aY9Ygz67Tfq0iaWJ6/Q/gJmBdYHpE1ExttgOwGfCssnXlu5FNtbUJ8GZETAGQ9AeyiXWL7Q4cBRAR1cACSX2K8uydthfSfi+yQLkacG/NynqS7i/hM20h6RKy7nov4JGCY3emyWOnSHojfYa9gS8VXH9cI5379RLOZR2YA2P7tTgitipMSMGvcKkEAY9FxLeK8tV632ck4OcR8b9F5zi1GWXdAhwcES+m9XKGFxwrfrY10rl/EBGFARRJg5pxbutA3JXu2MYAX07r0CBpVUkbAa8BgyRtmPJ9q573Pw6clN7bOa1T8yFZa7DGI8D3Cq5dDkjTrD0NHCxplbSWygEl1Hc14F1JXYEjio4dKqlTqvMXgMnp3Cel/EjaSNKqJZzHOji3GDuwiJidWl63S+qeks+PiNclHQ88KGkRWVd8tTqK+BFwg6RjgWrgpIh4TtKz6XaYh9J1xk2B51KL9SPgOxHxvKQ7yJZ4mAWMK6HK/w2MBWanr4V1+g/wL7KV/U6MiI8l/Zbs2uPzyk4+m2yRK7MGeXYdM7Mi7kqbmRVxYDQzK+LAaGZWxIHRzKyIA6OZWREHRjOzIg6MZmZF/h+Swp3RaJ3qNAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- train\", Xt=vec_train_3, yt=y3, clf=clf_3)\n", "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- test\", Xt=vec_test_3, yt=yt3, clf=clf_3)" From 21ed9312355b1e4ddc7b7cee628fc7ed225c9906 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Thu, 17 May 2018 13:47:30 +0200 Subject: [PATCH 03/15] created Task 04 --- Jonas_Solutions/Task03_Instructions.py | 84 +++++++ Jonas_Solutions/Task_03.ipynb | 293 +++++++++++++++++++++++++ 2 files changed, 377 insertions(+) create mode 100644 Jonas_Solutions/Task03_Instructions.py create mode 100644 Jonas_Solutions/Task_03.ipynb diff --git a/Jonas_Solutions/Task03_Instructions.py b/Jonas_Solutions/Task03_Instructions.py new file mode 100644 index 0000000..17e8fe3 --- /dev/null +++ b/Jonas_Solutions/Task03_Instructions.py @@ -0,0 +1,84 @@ +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import CountVectorizer +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences +from keras.models import Sequential +from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D +from sklearn.model_selection import train_test_split +from keras.utils.np_utils import to_categorical +import re + +''' +Task 3: playing with NN framwork/keras and basic sentiment analysis +- use the following model as a baseline and improve it! +- export your metadata (just basic hyperparameters and outcomes for test data!) +- test data = 0.3 (not in this example, change it!) +- random_state = 4222 +- no need to cross-validation! +''' + +# parameters +max_fatures = 500 +embed_dim = 128 +lstm_out = 196 +dropout = 0.1 +dropout_1d = 0.4 +recurrent_dropout = 0.1 +random_state = 1324 +validation_size = 1000 +batch_size = 16 +epochs=2 +verbose= 2 + +df = pd.read_csv('dataset_sentiment.csv') +df = df[['text','sentiment']] +print(df[0:10]) + +df = df[df.sentiment != "Neutral"] +df['text'] = df['text'].apply(lambda x: x.lower()) +df['text'] = df['text'].apply(lambda x: x.replace('rt',' ')) +df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]','',x))) + +tok = Tokenizer(num_words=max_fatures, split=' ') +tok.fit_on_texts(df['text'].values) +X = tok.texts_to_sequences(df['text'].values) +X = pad_sequences(X) + +nn = Sequential() +nn.add(Embedding(max_fatures, embed_dim, input_length = X.shape[1])) +nn.add(SpatialDropout1D(dropout_1d)) +nn.add(LSTM(lstm_out, dropout=dropout, recurrent_dropout=recurrent_dropout)) +nn.add(Dense(2, activation='softmax')) +nn.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics = ['accuracy']) +print(nn.summary()) + +Y = pd.get_dummies(df['sentiment']).values +X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state) +nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose) + +X_validate = X_test[-validation_size:] +Y_validate = Y_test[-validation_size:] +X_test = X_test[:-validation_size] +Y_test = Y_test[:-validation_size] +score, accuracy = nn.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size) +print("score: %.2f" % (score)) +print("acc: %.2f" % (accuracy)) + +pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0 +for x in range(len(X_validate)): + result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0] + if np.argmax(result) == np.argmax(Y_validate[x]): + if np.argmax(Y_validate[x]) == 0: neg_ok += 1 + else: pos_ok += 1 + if np.argmax(Y_validate[x]) == 0: neg_cnt += 1 + else: pos_cnt += 1 + +print("pos_acc", pos_ok/pos_cnt*100, "%") +print("neg_acc", neg_ok/neg_cnt*100, "%") + +X2 = ['what are u going to say about that? the truth, wassock?!'] +X2 = tok.texts_to_sequences(X2) +X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0) +print(X2) +print(nn.predict(X2, batch_size=1, verbose = 2)[0]) diff --git a/Jonas_Solutions/Task_03.ipynb b/Jonas_Solutions/Task_03.ipynb new file mode 100644 index 0000000..49e0899 --- /dev/null +++ b/Jonas_Solutions/Task_03.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dataset already downloaded\n" + ] + } + ], + "source": [ + "%%bash\n", + "\n", + "if [ ! -e 'dataset_sentiment.csv' ]\n", + "then\n", + " echo \"downloading dataset\"\n", + " wget https://raw.githubusercontent.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/master/2018_SoSe/exercises/dataset_sentiment.csv\n", + "else\n", + " echo \"dataset already downloaded\"\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters\n", + "max_fatures = 500\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 = 1324\n", + "validation_size = 1000\n", + "batch_size = 16\n", + "epochs=2\n", + "verbose= 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": [ + "df = pd.read_csv('dataset_sentiment.csv')\n", + "df = df[['text','sentiment']]\n", + "print(df[0:10])\n", + "\n", + "df = df[df.sentiment != \"Neutral\"]\n", + "df['text'] = df['text'].apply(lambda x: x.lower())\n", + "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n", + "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "tok = Tokenizer(num_words=max_fatures, split=' ')\n", + "tok.fit_on_texts(df['text'].values)\n", + "X = tok.texts_to_sequences(df['text'].values)\n", + "X = pad_sequences(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding_1 (Embedding) (None, 26, 128) 64000 \n", + "_________________________________________________________________\n", + "spatial_dropout1d_1 (Spatial (None, 26, 128) 0 \n", + "_________________________________________________________________\n", + "lstm_1 (LSTM) (None, 196) 254800 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 2) 394 \n", + "=================================================================\n", + "Total params: 319,194\n", + "Trainable params: 319,194\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "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": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2\n", + " - 30s - loss: 0.4374 - acc: 0.8161\n", + "Epoch 2/2\n", + " - 30s - loss: 0.3614 - acc: 0.8487\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y = pd.get_dummies(df['sentiment']).values\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.30, random_state = random_state)\n", + "nn.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, verbose=verbose)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "score: 0.37\n", + "acc: 0.85\n" + ] + } + ], + "source": [ + "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", + "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": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pos_cnt, neg_cnt, pos_ok, neg_ok = 0, 0, 0, 0\n", + "for x in range(len(X_validate)):\n", + " result = nn.predict(X_validate[x].reshape(1,X_test.shape[1]),batch_size=1,verbose = 2)[0]\n", + " if np.argmax(result) == np.argmax(Y_validate[x]):\n", + " if np.argmax(Y_validate[x]) == 0: neg_ok += 1\n", + " else: pos_ok += 1\n", + " if np.argmax(Y_validate[x]) == 0: neg_cnt += 1\n", + " else: pos_cnt += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pos_acc 31.770833333333332 %\n", + "neg_acc 97.27722772277228 %\n" + ] + } + ], + "source": [ + "print(\"pos_acc\", pos_ok/pos_cnt*100, \"%\")\n", + "print(\"neg_acc\", neg_ok/neg_cnt*100, \"%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 37\n", + " 311 189 4 144 22 16 1 281]]\n", + "[0.93431044 0.06568963]\n" + ] + } + ], + "source": [ + "X2 = ['what are u going to say about that? the truth, wassock?!']\n", + "X2 = tok.texts_to_sequences(X2)\n", + "X2 = pad_sequences(X2, maxlen=26, dtype='int32', value=0)\n", + "print(X2)\n", + "print(nn.predict(X2, batch_size=1, verbose = 2)[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f819ae113e3bc31f12b2c3075834bc7d14937ac9 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Thu, 17 May 2018 18:58:11 +0200 Subject: [PATCH 04/15] whatsapp to csv converter --- Project/Tools/whatsapp2csv.sh | 85 +++++++++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100755 Project/Tools/whatsapp2csv.sh diff --git a/Project/Tools/whatsapp2csv.sh b/Project/Tools/whatsapp2csv.sh new file mode 100755 index 0000000..6583d4a --- /dev/null +++ b/Project/Tools/whatsapp2csv.sh @@ -0,0 +1,85 @@ +#!/usr/bin/env bash + +SCRIPT=`realpath $0` +SCRIPTPATH=`dirname $SCRIPT` + +# toolset:--------------------------------------------------------------------- + +command 2> >(while read line; do echo -e "\e[01;31m$line\e[0m" >&2; done) + +function lineprint { + printf '%*s\n' "${COLUMNS:-$(tput cols)}" '' | tr ' ' = +} + +function message { + lineprint + printf "$1\n" + lineprint +} + +function error_message { + lineprint + printf "$1\n" >&2 + lineprint +} + +current_action="IDLE" + +function confirm_action { + message "successfully finished action: $current_action" +} + +function set_action { + current_action="$1" + message "$1" +} + +function perform { + "$@" + local status=$? + if [ $status -ne 0 ] + then + error_message "$current_action failed!" + fi + return $status +} + +function perform_and_exit { + perform "$@" || exit 1 +} + +# ----------------------------------------------------------------------------- +# reading input + +INPUT=$1 +OUTPUT="$INPUT.csv" + +if [ $# -eq 0 ] +then + error_message "Error: no input file given. Usage: $0 " + exit 1 +fi + +set_action "processing File: ${INPUT}" + +if [ ! -e "$INPUT" ] +then + error_message "Error: file '$INPUT' not found" + exit 1 +fi + +perform_and_exit echo -e "date\ttime\tperson\tmessage" > "$OUTPUT" + +# doing the following things in the pipeline below: +# +# 1. merging multiline messages by replacing newlines with spaces if they are not starting with a number followed by '/' (not stating with a date) +# 2. delete all lines containing the keywords: "added, creates, end-to-end, Media ommited" (which are most probably system messages) +# 3. replace separators of whatsapp's txt-format with '\t' as separator for csv +# 4. delete all double-quotes. Because people are too stupid to type them in pairs and that breaks some csv-interpreters 🤦‍♂ + +perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'added|created|end-to-end|Media omitted' | sed 's/, /\t/1; s/- /\t/1; s/: /\t/1;' | tr -d '"' >> "$OUTPUT" + +confirm_action + +message "Wrote output to $OUTPUT" + From 44227edef515d8aea78a88bebf0e3a09c17f0d38 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Fri, 18 May 2018 15:38:36 +0200 Subject: [PATCH 05/15] emoji stuff --- Project/Tools/emoji tester.ipynb | 2735 ++++++++++++++++++++++++++++++ Project/Tools/emoji-data.txt | 714 ++++++++ Project/Tools/whatsapp2csv.sh | 2 +- 3 files changed, 3450 insertions(+), 1 deletion(-) create mode 100644 Project/Tools/emoji tester.ipynb create mode 100644 Project/Tools/emoji-data.txt diff --git a/Project/Tools/emoji tester.ipynb b/Project/Tools/emoji tester.ipynb new file mode 100644 index 0000000..1f3ad70 --- /dev/null +++ b/Project/Tools/emoji tester.ipynb @@ -0,0 +1,2735 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from IPython.display import clear_output, Markdown, Math" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* read table `test.csv` exported by `whatsapp2csv.sh`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimepersonmessage
010/18/1611:18Lena CartaHallo Jonas 😉
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...
210/18/1611:21Lena Carta😱🤕
310/18/1611:21Sonja Vorwerk🙈😂
410/18/1611:22Caner Ucar😂😂😂
510/18/1611:22Patrick (Arithmeum)hallo jeff
610/18/1611:22Caner UcarPatrick kein Handy am Empfang
710/18/1611:26Patrick (Arithmeum)halts maul und geh ma duschen
810/18/1611:34Simon (Arithmeum)Hab dich als Carsten abgespeichert, selber Schuld
910/18/1611:36Caner UcarPatrick ich hab geduscht mit Kristina und Sonja v
1010/18/1611:36Lena Cartaähm....
1110/18/1611:37Jonathan (Arithmeum)Caner benutzt das Damen-Bad
1210/18/1611:37Jonas WeinzLüg doch nicht, du tanzt die ganze Zeit aufm T...
1310/18/1611:37Jonathan (Arithmeum)bääh😱
1410/18/1611:40Patrick (Arithmeum)ja caner so ist das unter frauen..da geht man ...
1510/18/1612:02Thomas KoppelbergEeemmm was?
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht 😜
1710/18/1612:14Thomas KoppelbergIch wollte einiges, aber ich kann mich zurückh...
1810/18/1612:19Simon (Arithmeum)Genau 🙊
1910/18/1613:36Caner UcarSex
\n", + "
" + ], + "text/plain": [ + " date time person \\\n", + "0 10/18/16 11:18 Lena Carta \n", + "1 10/18/16 11:21 Jonas Weinz \n", + "2 10/18/16 11:21 Lena Carta \n", + "3 10/18/16 11:21 Sonja Vorwerk \n", + "4 10/18/16 11:22 Caner Ucar \n", + "5 10/18/16 11:22 Patrick (Arithmeum) \n", + "6 10/18/16 11:22 Caner Ucar \n", + "7 10/18/16 11:26 Patrick (Arithmeum) \n", + "8 10/18/16 11:34 Simon (Arithmeum) \n", + "9 10/18/16 11:36 Caner Ucar \n", + "10 10/18/16 11:36 Lena Carta \n", + "11 10/18/16 11:37 Jonathan (Arithmeum) \n", + "12 10/18/16 11:37 Jonas Weinz \n", + "13 10/18/16 11:37 Jonathan (Arithmeum) \n", + "14 10/18/16 11:40 Patrick (Arithmeum) \n", + "15 10/18/16 12:02 Thomas Koppelberg \n", + "16 10/18/16 12:12 Kristina Stellwag \n", + "17 10/18/16 12:14 Thomas Koppelberg \n", + "18 10/18/16 12:19 Simon (Arithmeum) \n", + "19 10/18/16 13:36 Caner Ucar \n", + "\n", + " message \n", + "0 Hallo Jonas 😉 \n", + "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... \n", + "2 😱🤕 \n", + "3 🙈😂 \n", + "4 😂😂😂 \n", + "5 hallo jeff \n", + "6 Patrick kein Handy am Empfang \n", + "7 halts maul und geh ma duschen \n", + "8 Hab dich als Carsten abgespeichert, selber Schuld \n", + "9 Patrick ich hab geduscht mit Kristina und Sonja v \n", + "10 ähm.... \n", + "11 Caner benutzt das Damen-Bad \n", + "12 Lüg doch nicht, du tanzt die ganze Zeit aufm T... \n", + "13 bääh😱 \n", + "14 ja caner so ist das unter frauen..da geht man ... \n", + "15 Eeemmm was? \n", + "16 Thomas, du wolltest gestern ja nicht 😜 \n", + "17 Ich wollte einiges, aber ich kann mich zurückh... \n", + "18 Genau 🙊 \n", + "19 Sex " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages = pd.read_csv('test.csv', delimiter='\\t')\n", + "messages[:20]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* read emoji-data (can be found here: https://www.unicode.org/Public/emoji/11.0/emoji-data.txt) and generate a table file out of it" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_blacklist = set([\n", + " 0x1F3FB,\n", + " 0x1F3FC,\n", + " 0x1F3FD,\n", + " 0x1F3FE,\n", + " 0x1F3FF\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unicodetype
228231A..231BEmoji_Presentation
22923E9..23ECEmoji_Presentation
23023F0Emoji_Presentation
23123F3Emoji_Presentation
23225FD..25FEEmoji_Presentation
2332614..2615Emoji_Presentation
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235267FEmoji_Presentation
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23826AA..26ABEmoji_Presentation
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24126CEEmoji_Presentation
24226D4Emoji_Presentation
24326EAEmoji_Presentation
24426F2..26F3Emoji_Presentation
24526F5Emoji_Presentation
24626FAEmoji_Presentation
24726FDEmoji_Presentation
2482705Emoji_Presentation
249270A..270BEmoji_Presentation
2502728Emoji_Presentation
251274CEmoji_Presentation
252274EEmoji_Presentation
2532753..2755Emoji_Presentation
2542757Emoji_Presentation
2552795..2797Emoji_Presentation
25627B0Emoji_Presentation
25727BFEmoji_Presentation
.........
3291F6F7..1F6F8Emoji_Presentation
3301F6F9Emoji_Presentation
3311F910..1F918Emoji_Presentation
3321F919..1F91EEmoji_Presentation
3331F91FEmoji_Presentation
3341F920..1F927Emoji_Presentation
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3391F93C..1F93EEmoji_Presentation
3401F940..1F945Emoji_Presentation
3411F947..1F94BEmoji_Presentation
3421F94CEmoji_Presentation
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3451F95F..1F96BEmoji_Presentation
3461F96C..1F970Emoji_Presentation
3471F973..1F976Emoji_Presentation
3481F97AEmoji_Presentation
3491F97C..1F97FEmoji_Presentation
3501F980..1F984Emoji_Presentation
3511F985..1F991Emoji_Presentation
3521F992..1F997Emoji_Presentation
3531F998..1F9A2Emoji_Presentation
3541F9B0..1F9B9Emoji_Presentation
3551F9C0Emoji_Presentation
3561F9C1..1F9C2Emoji_Presentation
3571F9D0..1F9E6Emoji_Presentation
3581F9E7..1F9FFEmoji_Presentation
\n", + "

131 rows × 2 columns

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" + ], + "text/plain": [ + " unicode type\n", + "228 231A..231B Emoji_Presentation\n", + "229 23E9..23EC Emoji_Presentation\n", + "230 23F0 Emoji_Presentation\n", + "231 23F3 Emoji_Presentation\n", + "232 25FD..25FE Emoji_Presentation\n", + "233 2614..2615 Emoji_Presentation\n", + "234 2648..2653 Emoji_Presentation\n", + "235 267F Emoji_Presentation\n", + "236 2693 Emoji_Presentation\n", + "237 26A1 Emoji_Presentation\n", + "238 26AA..26AB Emoji_Presentation\n", + "239 26BD..26BE Emoji_Presentation\n", + "240 26C4..26C5 Emoji_Presentation\n", + "241 26CE Emoji_Presentation\n", + "242 26D4 Emoji_Presentation\n", + "243 26EA Emoji_Presentation\n", + "244 26F2..26F3 Emoji_Presentation\n", + "245 26F5 Emoji_Presentation\n", + "246 26FA Emoji_Presentation\n", + "247 26FD Emoji_Presentation\n", + "248 2705 Emoji_Presentation\n", + "249 270A..270B Emoji_Presentation\n", + "250 2728 Emoji_Presentation\n", + "251 274C Emoji_Presentation\n", + "252 274E Emoji_Presentation\n", + "253 2753..2755 Emoji_Presentation\n", + "254 2757 Emoji_Presentation\n", + "255 2795..2797 Emoji_Presentation\n", + "256 27B0 Emoji_Presentation\n", + "257 27BF Emoji_Presentation\n", + ".. ... ...\n", + "329 1F6F7..1F6F8 Emoji_Presentation\n", + "330 1F6F9 Emoji_Presentation\n", + "331 1F910..1F918 Emoji_Presentation\n", + "332 1F919..1F91E Emoji_Presentation\n", + "333 1F91F Emoji_Presentation\n", + "334 1F920..1F927 Emoji_Presentation\n", + "335 1F928..1F92F Emoji_Presentation\n", + "336 1F930 Emoji_Presentation\n", + "337 1F931..1F932 Emoji_Presentation\n", + "338 1F933..1F93A Emoji_Presentation\n", + "339 1F93C..1F93E Emoji_Presentation\n", + "340 1F940..1F945 Emoji_Presentation\n", + "341 1F947..1F94B Emoji_Presentation\n", + "342 1F94C Emoji_Presentation\n", + "343 1F94D..1F94F Emoji_Presentation\n", + "344 1F950..1F95E Emoji_Presentation\n", + "345 1F95F..1F96B Emoji_Presentation\n", + "346 1F96C..1F970 Emoji_Presentation\n", + "347 1F973..1F976 Emoji_Presentation\n", + "348 1F97A Emoji_Presentation\n", + "349 1F97C..1F97F Emoji_Presentation\n", + "350 1F980..1F984 Emoji_Presentation\n", + "351 1F985..1F991 Emoji_Presentation\n", + "352 1F992..1F997 Emoji_Presentation\n", + "353 1F998..1F9A2 Emoji_Presentation\n", + "354 1F9B0..1F9B9 Emoji_Presentation\n", + "355 1F9C0 Emoji_Presentation\n", + "356 1F9C1..1F9C2 Emoji_Presentation\n", + "357 1F9D0..1F9E6 Emoji_Presentation\n", + "358 1F9E7..1F9FF Emoji_Presentation\n", + "\n", + "[131 rows x 2 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n", + "emoji_data['type'] = emoji_data['type'].str.strip()\n", + "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n", + "emoji_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* now build a set out of the unicode types" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ord(\"😀\") == int('0x1f600',16)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "228 231A..231B \n", + "229 23E9..23EC \n", + "230 23F0 \n", + "231 23F3 \n", + "232 25FD..25FE \n", + "Name: unicode, dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "emoji_codes = emoji_data['unicode']\n", + "emoji_codes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we have to iterate over the whole list and extract all given ranges:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_code_list = []\n", + "for entry in emoji_codes:\n", + " # testing whether we have an entry or a range:\n", + " if '.' in entry:\n", + " # range\n", + " a,b = entry.split(\"..\")\n", + " for i in range(int(a,16),int(b,16) +1):\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(i)\n", + " else:\n", + " # single entry\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(int(entry,16))\n", + "emoji_code_set = set(emoji_code_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True False\n" + ] + } + ], + "source": [ + "# simple test:\n", + "print(ord(\"😀\") in emoji_code_set, ord(\"a\") in emoji_code_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* expanding column and fill new emojis" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "messages[\"emojis\"] = None" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "for i in messages.index:\n", + " emoji_list = []\n", + " to_remove = []\n", + " m = messages.iloc[i]['message']\n", + " for c in str(m):\n", + " if ord(c) in emoji_code_set:\n", + " emoji_list.append(c)\n", + " elif ord(c) in emoji_blacklist:\n", + " to_remove.append(c)\n", + " \n", + " messages.loc[i,'emojis'] = emoji_list\n", + " #remove emiójis from message\n", + " for e in (emoji_list + to_remove):\n", + " m = m.replace(e,\"\")\n", + " messages.loc[i,'message'] = m\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimepersonmessageemojis
010/18/1611:18Lena CartaHallo Jonas[😉]
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...[🤔]
210/18/1611:21Lena Carta[😱, 🤕]
310/18/1611:21Sonja Vorwerk[🙈, 😂]
410/18/1611:22Caner Ucar[😂, 😂, 😂]
510/18/1611:22Patrick (Arithmeum)hallo jeff[]
610/18/1611:22Caner UcarPatrick kein Handy am Empfang[]
710/18/1611:26Patrick (Arithmeum)halts maul und geh ma duschen[]
810/18/1611:34Simon (Arithmeum)Hab dich als Carsten abgespeichert, selber Schuld[]
910/18/1611:36Caner UcarPatrick ich hab geduscht mit Kristina und Sonja v[]
1010/18/1611:36Lena Cartaähm....[]
1110/18/1611:37Jonathan (Arithmeum)Caner benutzt das Damen-Bad[]
1210/18/1611:37Jonas WeinzLüg doch nicht, du tanzt die ganze Zeit aufm T...[]
1310/18/1611:37Jonathan (Arithmeum)bääh[😱]
1410/18/1611:40Patrick (Arithmeum)ja caner so ist das unter frauen..da geht man ...[]
1510/18/1612:02Thomas KoppelbergEeemmm was?[]
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht[😜]
1710/18/1612:14Thomas KoppelbergIch wollte einiges, aber ich kann mich zurückh...[]
1810/18/1612:19Simon (Arithmeum)Genau[🙊]
1910/18/1613:36Caner UcarSex[]
\n", + "
" + ], + "text/plain": [ + " date time person \\\n", + "0 10/18/16 11:18 Lena Carta \n", + "1 10/18/16 11:21 Jonas Weinz \n", + "2 10/18/16 11:21 Lena Carta \n", + "3 10/18/16 11:21 Sonja Vorwerk \n", + "4 10/18/16 11:22 Caner Ucar \n", + "5 10/18/16 11:22 Patrick (Arithmeum) \n", + "6 10/18/16 11:22 Caner Ucar \n", + "7 10/18/16 11:26 Patrick (Arithmeum) \n", + "8 10/18/16 11:34 Simon (Arithmeum) \n", + "9 10/18/16 11:36 Caner Ucar \n", + "10 10/18/16 11:36 Lena Carta \n", + "11 10/18/16 11:37 Jonathan (Arithmeum) \n", + "12 10/18/16 11:37 Jonas Weinz \n", + "13 10/18/16 11:37 Jonathan (Arithmeum) \n", + "14 10/18/16 11:40 Patrick (Arithmeum) \n", + "15 10/18/16 12:02 Thomas Koppelberg \n", + "16 10/18/16 12:12 Kristina Stellwag \n", + "17 10/18/16 12:14 Thomas Koppelberg \n", + "18 10/18/16 12:19 Simon (Arithmeum) \n", + "19 10/18/16 13:36 Caner Ucar \n", + "\n", + " message emojis \n", + "0 Hallo Jonas [😉] \n", + "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... [🤔] \n", + "2 [😱, 🤕] \n", + "3 [🙈, 😂] \n", + "4 [😂, 😂, 😂] \n", + "5 hallo jeff [] \n", + "6 Patrick kein Handy am Empfang [] \n", + "7 halts maul und geh ma duschen [] \n", + "8 Hab dich als Carsten abgespeichert, selber Schuld [] \n", + "9 Patrick ich hab geduscht mit Kristina und Sonja v [] \n", + "10 ähm.... [] \n", + "11 Caner benutzt das Damen-Bad [] \n", + "12 Lüg doch nicht, du tanzt die ganze Zeit aufm T... [] \n", + "13 bääh [😱] \n", + "14 ja caner so ist das unter frauen..da geht man ... [] \n", + "15 Eeemmm was? [] \n", + "16 Thomas, du wolltest gestern ja nicht [😜] \n", + "17 Ich wollte einiges, aber ich kann mich zurückh... [] \n", + "18 Genau [🙊] \n", + "19 Sex [] " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages[:20]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* get a list only containing messaged with emojis" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n", + "emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datetimepersonmessageemojis
010/18/1611:18Lena CartaHallo Jonas[😉]
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...[🤔]
1310/18/1611:37Jonathan (Arithmeum)bääh[😱]
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht[😜]
1810/18/1612:19Simon (Arithmeum)Genau[🙊]
2910/18/1618:38Lena Cartawas du bist morgen nicht da ???[😱, 😯, 😔]
3110/18/1618:39Lena Cartaich dachte diese Woche komplett noch. Aber gut...[😇]
3210/18/1618:40Kristina StellwagNee das Semester hat doch angefangen[😜]
3310/18/1618:42Lena Cartaja hatte ich gerade vergessen . Dabei hatte ic...[😂, 😶]
4110/20/1612:50Steffi (Arithmeum)Klar[👹]
4410/20/1617:54Steffi (Arithmeum)Mein Zug lässt sich auch heute Zeit[😞]
4510/20/1618:08Steffi (Arithmeum)Wir fangen doch wahrscheinlich nicht vor halb ...[😳]
5110/20/1620:58Anna BorutzkyIst das dein Ernst?[🙄]
5210/20/1620:58Patrick (Arithmeum)Habs voll vercheckt sry ..gerade erst gemerkt[🙄]
7010/20/1622:59Jonathan (Arithmeum)Ich bitte darum Caner....[😍, 😍, 😍]
7810/25/1601:03Caner UcarHaaaaaalloooo[🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 😏, 🤗, 🤗, 😏]
7910/25/1601:04Michael (Arithmeum)Keine sorge, das hat bei dir nichts mit der St...[😉]
8810/25/1609:02Lena Cartaich wollte eigentlich schon da sein, aber ich ...[😇]
8910/25/1609:04Jonathan (Arithmeum)SIIIIEG...⚔[🎉]
9010/25/1610:00Lena Cartabesser spät als nie. ich bin immerhin auf dem ...[😁]
9310/25/1621:49Jonathan (Arithmeum)Ich nicht[😄]
9410/25/1621:50Lena Cartaich auch nicht[😳]
9510/25/1622:13Konstantin (Arithmeum)Wer hat den zuletzt was laminiert? lena und i...[🤔]
9610/25/1623:28Caner UcarIch auch nicht[🤔]
10210/27/1614:43Sonja Vorwerkhm müsste das am besten heute schon wissen...w...[🙈, 😄]
11510/28/1608:32Lena CartaGuten Morgen liebe Kollegen, könnte einer von ...[🙁]
11610/28/1609:48Lena CartaIst noch niemand im Büro?[😇]
11710/28/1609:50Caner Ucar10:10 ca.[🙌]
11810/28/1609:51Caner UcarWann hat das woki geöffnet[😄]
12110/28/1610:54Lena CartaDa hätte ich jetzt auch nicht gesucht[😝]
..................
86635/9/1817:42Jonas Weinzmehrere Bürostühle hintereinander?[🤔]
86655/9/1817:43Lena CartaWollte Kristina das nicht immer schonmal machen[😁]
86755/10/1814:06Lena KlöcknerErledigt[👍]
86765/10/1814:07Caner UcarDanke[😊]
86775/10/1814:23Anna ZensIch hatte es auch schon erledigt[😄]
86785/11/1808:16Lena CartaGute morgen, ich habe gesehen neben dem Buch l...[🙂]
86795/11/1808:25Anna ZensDie handschriftlichen sind die, von denen kein...[😄]
86805/11/1808:26Lena Cartaachso ich denke mal dann wird sich Ingo melden[😀, 😀]
86815/11/1813:18Freya (Arithmeum)Mein Kalender funktioniert nicht - könnte mir ...[🤔]
86825/12/1814:53Freya (Arithmeum)Könnte jemand für Montag Abend zwei Karten für...[😇]
86835/12/1815:05Anna BorutzkyUnd eine Freikarte für Sonja bitte![😀]
86895/12/1815:16Lena CartaDanke Wieso kommen solche Fragen immer am Woc...[😀]
86925/12/1815:17Anna BorutzkyMurphy‘s Gesetz?[😉]
86935/12/1815:17Caner UcarCool[😍]
86955/12/1815:23Jonas Weinz...und die kommt auf Annas Tisch[😉]
86965/12/1815:23Freya (Arithmeum)Vielen, vielen Dank ☺ Sowas passiert immer am ...[🙈]
86975/12/1815:23Lena CartaDas war nicht auf dich bezogen .[😀]
86985/12/1815:24Freya (Arithmeum)Na gut[😄]
86995/12/1815:24Lena CartaEigentlich ist es halbwegs entspannt heute Ic...[😀, 😛]
87005/12/1815:25Freya (Arithmeum)Es gibt keine doofen Fragen[😋]
87025/12/1815:25Lena Cartahttps://www.youtube.com/watch?v=uPHi5xn_q5c[😛]
87085/12/1817:56Freya (Arithmeum)Ich möchte arbeiten und habe mega Bock drauf! ...[😇]
87195/12/1820:09Lena Cartabin leider verabredet .... mit Thilo[😬, 🤗]
87255/13/1817:24Thomas Koppelberg♥[😍]
87265/14/1817:00Sonja SchäferKann mir jemand noch eine zweite Freikarte res...[😊]
87395/14/1822:40Jonas WeinzHab Ingo beim vorbeilaufenden nicht gesehen, h...[💁]
87445/15/1817:16Caner Ucar‍♀[🤷]
87555/17/1810:43Lena Cartasuper perfekt dann ist beiden geholfen[🙂, 😎]
87595/17/1810:43Lena Cartaob das so sinnvoll ist?[😀]
87635/17/1810:45Lena Cartaich dachte es ging konkret um den Termin[😀]
\n", + "

2455 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " date time person \\\n", + "0 10/18/16 11:18 Lena Carta \n", + "1 10/18/16 11:21 Jonas Weinz \n", + "13 10/18/16 11:37 Jonathan (Arithmeum) \n", + "16 10/18/16 12:12 Kristina Stellwag \n", + "18 10/18/16 12:19 Simon (Arithmeum) \n", + "29 10/18/16 18:38 Lena Carta \n", + "31 10/18/16 18:39 Lena Carta \n", + "32 10/18/16 18:40 Kristina Stellwag \n", + "33 10/18/16 18:42 Lena Carta \n", + "41 10/20/16 12:50 Steffi (Arithmeum) \n", + "44 10/20/16 17:54 Steffi (Arithmeum) \n", + "45 10/20/16 18:08 Steffi (Arithmeum) \n", + "51 10/20/16 20:58 Anna Borutzky \n", + "52 10/20/16 20:58 Patrick (Arithmeum) \n", + "70 10/20/16 22:59 Jonathan (Arithmeum) \n", + "78 10/25/16 01:03 Caner Ucar \n", + "79 10/25/16 01:04 Michael (Arithmeum) \n", + "88 10/25/16 09:02 Lena Carta \n", + "89 10/25/16 09:04 Jonathan (Arithmeum) \n", + "90 10/25/16 10:00 Lena Carta \n", + "93 10/25/16 21:49 Jonathan (Arithmeum) \n", + "94 10/25/16 21:50 Lena Carta \n", + "95 10/25/16 22:13 Konstantin (Arithmeum) \n", + "96 10/25/16 23:28 Caner Ucar \n", + "102 10/27/16 14:43 Sonja Vorwerk \n", + "115 10/28/16 08:32 Lena Carta \n", + "116 10/28/16 09:48 Lena Carta \n", + "117 10/28/16 09:50 Caner Ucar \n", + "118 10/28/16 09:51 Caner Ucar \n", + "121 10/28/16 10:54 Lena Carta \n", + "... ... ... ... \n", + "8663 5/9/18 17:42 Jonas Weinz \n", + "8665 5/9/18 17:43 Lena Carta \n", + "8675 5/10/18 14:06 Lena Klöckner \n", + "8676 5/10/18 14:07 Caner Ucar \n", + "8677 5/10/18 14:23 Anna Zens \n", + "8678 5/11/18 08:16 Lena Carta \n", + "8679 5/11/18 08:25 Anna Zens \n", + "8680 5/11/18 08:26 Lena Carta \n", + "8681 5/11/18 13:18 Freya (Arithmeum) \n", + "8682 5/12/18 14:53 Freya (Arithmeum) \n", + "8683 5/12/18 15:05 Anna Borutzky \n", + "8689 5/12/18 15:16 Lena Carta \n", + "8692 5/12/18 15:17 Anna Borutzky \n", + "8693 5/12/18 15:17 Caner Ucar \n", + "8695 5/12/18 15:23 Jonas Weinz \n", + "8696 5/12/18 15:23 Freya (Arithmeum) \n", + "8697 5/12/18 15:23 Lena Carta \n", + "8698 5/12/18 15:24 Freya (Arithmeum) \n", + "8699 5/12/18 15:24 Lena Carta \n", + "8700 5/12/18 15:25 Freya (Arithmeum) \n", + "8702 5/12/18 15:25 Lena Carta \n", + "8708 5/12/18 17:56 Freya (Arithmeum) \n", + "8719 5/12/18 20:09 Lena Carta \n", + "8725 5/13/18 17:24 Thomas Koppelberg \n", + "8726 5/14/18 17:00 Sonja Schäfer \n", + "8739 5/14/18 22:40 Jonas Weinz \n", + "8744 5/15/18 17:16 Caner Ucar \n", + "8755 5/17/18 10:43 Lena Carta \n", + "8759 5/17/18 10:43 Lena Carta \n", + "8763 5/17/18 10:45 Lena Carta \n", + "\n", + " message \\\n", + "0 Hallo Jonas \n", + "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... \n", + "13 bääh \n", + "16 Thomas, du wolltest gestern ja nicht \n", + "18 Genau \n", + "29 was du bist morgen nicht da ??? \n", + "31 ich dachte diese Woche komplett noch. Aber gut... \n", + "32 Nee das Semester hat doch angefangen \n", + "33 ja hatte ich gerade vergessen . Dabei hatte ic... \n", + "41 Klar \n", + "44 Mein Zug lässt sich auch heute Zeit \n", + "45 Wir fangen doch wahrscheinlich nicht vor halb ... \n", + "51 Ist das dein Ernst? \n", + "52 Habs voll vercheckt sry ..gerade erst gemerkt \n", + "70 Ich bitte darum Caner.... \n", + "78 Haaaaaalloooo \n", + "79 Keine sorge, das hat bei dir nichts mit der St... \n", + "88 ich wollte eigentlich schon da sein, aber ich ... \n", + "89 SIIIIEG...⚔ \n", + "90 besser spät als nie. ich bin immerhin auf dem ... \n", + "93 Ich nicht \n", + "94 ich auch nicht \n", + "95 Wer hat den zuletzt was laminiert? lena und i... \n", + "96 Ich auch nicht \n", + "102 hm müsste das am besten heute schon wissen...w... \n", + "115 Guten Morgen liebe Kollegen, könnte einer von ... \n", + "116 Ist noch niemand im Büro? \n", + "117 10:10 ca. \n", + "118 Wann hat das woki geöffnet \n", + "121 Da hätte ich jetzt auch nicht gesucht \n", + "... ... \n", + "8663 mehrere Bürostühle hintereinander? \n", + "8665 Wollte Kristina das nicht immer schonmal machen \n", + "8675 Erledigt \n", + "8676 Danke \n", + "8677 Ich hatte es auch schon erledigt \n", + "8678 Gute morgen, ich habe gesehen neben dem Buch l... \n", + "8679 Die handschriftlichen sind die, von denen kein... \n", + "8680 achso ich denke mal dann wird sich Ingo melden \n", + "8681 Mein Kalender funktioniert nicht - könnte mir ... \n", + "8682 Könnte jemand für Montag Abend zwei Karten für... \n", + "8683 Und eine Freikarte für Sonja bitte! \n", + "8689 Danke Wieso kommen solche Fragen immer am Woc... \n", + "8692 Murphy‘s Gesetz? \n", + "8693 Cool \n", + "8695 ...und die kommt auf Annas Tisch \n", + "8696 Vielen, vielen Dank ☺ Sowas passiert immer am ... \n", + "8697 Das war nicht auf dich bezogen . \n", + "8698 Na gut \n", + "8699 Eigentlich ist es halbwegs entspannt heute Ic... \n", + "8700 Es gibt keine doofen Fragen \n", + "8702 https://www.youtube.com/watch?v=uPHi5xn_q5c \n", + "8708 Ich möchte arbeiten und habe mega Bock drauf! ... \n", + "8719 bin leider verabredet .... mit Thilo \n", + "8725 ♥ \n", + "8726 Kann mir jemand noch eine zweite Freikarte res... \n", + "8739 Hab Ingo beim vorbeilaufenden nicht gesehen, h... \n", + "8744 ‍♀ \n", + "8755 super perfekt dann ist beiden geholfen \n", + "8759 ob das so sinnvoll ist? \n", + "8763 ich dachte es ging konkret um den Termin \n", + "\n", + " emojis \n", + "0 [😉] \n", + "1 [🤔] \n", + "13 [😱] \n", + "16 [😜] \n", + "18 [🙊] \n", + "29 [😱, 😯, 😔] \n", + "31 [😇] \n", + "32 [😜] \n", + "33 [😂, 😶] \n", + "41 [👹] \n", + "44 [😞] \n", + "45 [😳] \n", + "51 [🙄] \n", + "52 [🙄] \n", + "70 [😍, 😍, 😍] \n", + "78 [🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 😏, 🤗, 🤗, 😏] \n", + "79 [😉] \n", + "88 [😇] \n", + "89 [🎉] \n", + "90 [😁] \n", + "93 [😄] \n", + "94 [😳] \n", + "95 [🤔] \n", + "96 [🤔] \n", + "102 [🙈, 😄] \n", + "115 [🙁] \n", + "116 [😇] \n", + "117 [🙌] \n", + "118 [😄] \n", + "121 [😝] \n", + "... ... \n", + "8663 [🤔] \n", + "8665 [😁] \n", + "8675 [👍] \n", + "8676 [😊] \n", + "8677 [😄] \n", + "8678 [🙂] \n", + "8679 [😄] \n", + "8680 [😀, 😀] \n", + "8681 [🤔] \n", + "8682 [😇] \n", + "8683 [😀] \n", + "8689 [😀] \n", + "8692 [😉] \n", + "8693 [😍] \n", + "8695 [😉] \n", + "8696 [🙈] \n", + "8697 [😀] \n", + "8698 [😄] \n", + "8699 [😀, 😛] \n", + "8700 [😋] \n", + "8702 [😛] \n", + "8708 [😇] \n", + "8719 [😬, 🤗] \n", + "8725 [😍] \n", + "8726 [😊] \n", + "8739 [💁] \n", + "8744 [🤷] \n", + "8755 [🙂, 😎] \n", + "8759 [😀] \n", + "8763 [😀] \n", + "\n", + "[2455 rows x 5 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(emoji_messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## learning part" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import itertools\n", + "import sklearn.utils as sku\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "labels=[e[-1] for e in emoji_messages['emojis']]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['😉', '🤔', '😱', '😜', '🙊', '😔', '😇', '😜', '😶', '👹']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "vectorizer = TfidfVectorizer(stop_words='english')\n", + "vec_train = vectorizer.fit_transform(X1)\n", + "vec_test = vectorizer.transform(Xt1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonas/.local/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "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=(100,), 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=None,\n", + " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", + " verbose=False, warm_start=False)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier as RFC\n", + "from sklearn.neural_network import MLPClassifier as MLP\n", + "from sklearn.naive_bayes import MultinomialNB as MNB\n", + "#clf_a = RFC(criterion='entropy', random_state=4222)\n", + "clf_a = MLP()\n", + "clf_a.fit(vec_train, y1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "pred = clf_a.predict(vectorizer.transform(Xt1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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messagepredtrainedemojis
8663mehrere Bürostühle hintereinander?🙈🤔[🤔]
1862Ich will auch Essen😉😞[😞]
39212x orange🙂🙂[🙂]
2222Man soll auch keine Lernsachen da lassen😀😀[😀]
761Wir können auch gerne wieder bei mir zusammen ...😇😉[😉]
3600Hab den Bus verpasst, den ich eigentlich nehme...😟👸[👸]
1794Klaro, gerne😎🙂[🙂]
861Sehr gut, Jan! Wenn sich noch jemand melden wü...😘😇[😇]
1469Jaaa aber mein Mitbewohner ist messy😬🙊[😱, 😔, 🌚, 🙊]
6254Hallöchen Es hat ein Herr angerufen, der gern...💁🙂[🙂]
7968Iwo🤦😂[😂]
8102Keine Ahnung wer da haariger ist😅🧐[🧐]
6835Danke😊😊[😊]
7135Wurde bestimmt von Big Bang geklaut🤷😂[😂, 😂, 😂]
4556Ich kann leider nicht, aber arbeiten da nicht ...😂😀[😀]
6705Ist von heute, aber halt 3x in englisch😇🙄[🙄]
2058..🤦😅[😅]
2072Oder fang endlich mal wieder an, was fürs Stud...😄😈[😈]
756Ich hab vom letzten Jahr noch ein Trauma😂😆[😆]
5966Ist Freitag schon Wochenende?😍🤔[🤔]
7007Wenn wir die Klimaanlage im Museum ausschalten...🤔🤪[🤪]
6968Ein Vorbild für uns alle🤔🤩[🤩]
5665Kein Problem. Du kannst mich eintragen.🙂🙂[🙂]
2641Jonas, du bist gelb markiert! Du kannst keine ...😉🙃[🙃]
1981Da bin ich mir sehr sicher!😘😄[😄]
3334Das wäre auf jeden Fall hilfreich! Danke😍🙂[🙂]
4447Ist das Schild denn auch laminiert?!🤔😏[😏]
288da bin ich mal gespannt wie das aussieht, wen...😂👍[👍]
4265Aber es ist doch gebrochen...🤔😢[😢]
7405Partey🤦🤪[🤪]
...............
4910Vllt gilt das Schild auch nur für schwarze Hun...😂😱[😱]
2047Ich erprobe mich morgen mit Freya an Addieren ...🙂😄[😄]
2650Ich kann leider nicht😕😕[😕]
299Das schafft meine Handykamera aber nicht😂😉[😉]
2499also ihr lieben ich arbeite gerade im arithmeu...😁😅[😅]
4783danke😊💸[💸, 💸, 💸]
4040Herrlich🤦😅[🤗, 😅]
7553babbage, meine Autokerrektur ‍♂🤔🤦[🤦]
6232Bin erst um 9.15Uhr da.....👍🔪[💣, 🔪]
3514Dankeschön! Dann trage ich dich ein😘😊[😊]
3422Schnitzel😅😍[😍]
2294Club discreto?😂😳[😳, 😳, 😳]
8218auch da bin ich etwas neidisch🙄😅[😅]
4463Möchte jemand was vom Café Roller?🤧😂[😂]
8032Frohe Ostern an Euch alle...😊🐥[🐰, 🐣, 🐥]
4505okay, ihr geht eigentlich ganz verantwortungsv...😊💁[💁]
4810Meine Meinung: Wenn das Personal da ist, dann ...😂🤷[🤷]
5762Ein shk ist aber billiger👵🙄[🙄]
4247Alle die morgen auf der Arbeit sind: nachher k...🙈😂[😂]
2501ich glaube das wäre eine stilvolle und romanti...😍😊[😊]
5278Ich kann das machen💁✋[✋]
5747In der Küche im Kühlschrank steht eine grüne T...😉🙂[😉, 🙂]
7022Dann halt nicht ‍♂🤷🤷[🤷]
1736Ich hab absolut keine Ahnung davon😂😞[😞]
5909Wir bekommen es bestimmt zu zweit hin, aber we...🙈😉[😉]
5890Pass bloß auf, wenn du die isst mutierst du be...😄😱[😱]
566Das sind 5 Maß pro Abend🙄😋[😋]
6044Sind sehr lecker😀👌[👌]
1472Ich werde die alle drillen das sage ich dir, a...🤔😇[😇]
5702Oh man, das wird ein Tag😀😄[😄]
\n", + "

246 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " message pred trained \\\n", + "8663 mehrere Bürostühle hintereinander? 🙈 🤔 \n", + "1862 Ich will auch Essen 😉 😞 \n", + "3921 2x orange 🙂 🙂 \n", + "2222 Man soll auch keine Lernsachen da lassen 😀 😀 \n", + "761 Wir können auch gerne wieder bei mir zusammen ... 😇 😉 \n", + "3600 Hab den Bus verpasst, den ich eigentlich nehme... 😟 👸 \n", + "1794 Klaro, gerne 😎 🙂 \n", + "861 Sehr gut, Jan! Wenn sich noch jemand melden wü... 😘 😇 \n", + "1469 Jaaa aber mein Mitbewohner ist messy 😬 🙊 \n", + "6254 Hallöchen Es hat ein Herr angerufen, der gern... 💁 🙂 \n", + "7968 Iwo 🤦 😂 \n", + "8102 Keine Ahnung wer da haariger ist 😅 🧐 \n", + "6835 Danke 😊 😊 \n", + "7135 Wurde bestimmt von Big Bang geklaut 🤷 😂 \n", + "4556 Ich kann leider nicht, aber arbeiten da nicht ... 😂 😀 \n", + "6705 Ist von heute, aber halt 3x in englisch 😇 🙄 \n", + "2058 .. 🤦 😅 \n", + "2072 Oder fang endlich mal wieder an, was fürs Stud... 😄 😈 \n", + "756 Ich hab vom letzten Jahr noch ein Trauma 😂 😆 \n", + "5966 Ist Freitag schon Wochenende? 😍 🤔 \n", + "7007 Wenn wir die Klimaanlage im Museum ausschalten... 🤔 🤪 \n", + "6968 Ein Vorbild für uns alle 🤔 🤩 \n", + "5665 Kein Problem. Du kannst mich eintragen. 🙂 🙂 \n", + "2641 Jonas, du bist gelb markiert! Du kannst keine ... 😉 🙃 \n", + "1981 Da bin ich mir sehr sicher! 😘 😄 \n", + "3334 Das wäre auf jeden Fall hilfreich! Danke 😍 🙂 \n", + "4447 Ist das Schild denn auch laminiert?! 🤔 😏 \n", + "288 da bin ich mal gespannt wie das aussieht, wen... 😂 👍 \n", + "4265 Aber es ist doch gebrochen... 🤔 😢 \n", + "7405 Partey 🤦 🤪 \n", + "... ... ... ... \n", + "4910 Vllt gilt das Schild auch nur für schwarze Hun... 😂 😱 \n", + "2047 Ich erprobe mich morgen mit Freya an Addieren ... 🙂 😄 \n", + "2650 Ich kann leider nicht 😕 😕 \n", + "299 Das schafft meine Handykamera aber nicht 😂 😉 \n", + "2499 also ihr lieben ich arbeite gerade im arithmeu... 😁 😅 \n", + "4783 danke 😊 💸 \n", + "4040 Herrlich 🤦 😅 \n", + "7553 babbage, meine Autokerrektur ‍♂ 🤔 🤦 \n", + "6232 Bin erst um 9.15Uhr da..... 👍 🔪 \n", + "3514 Dankeschön! Dann trage ich dich ein 😘 😊 \n", + "3422 Schnitzel 😅 😍 \n", + "2294 Club discreto? 😂 😳 \n", + "8218 auch da bin ich etwas neidisch 🙄 😅 \n", + "4463 Möchte jemand was vom Café Roller? 🤧 😂 \n", + "8032 Frohe Ostern an Euch alle... 😊 🐥 \n", + "4505 okay, ihr geht eigentlich ganz verantwortungsv... 😊 💁 \n", + "4810 Meine Meinung: Wenn das Personal da ist, dann ... 😂 🤷 \n", + "5762 Ein shk ist aber billiger 👵 🙄 \n", + "4247 Alle die morgen auf der Arbeit sind: nachher k... 🙈 😂 \n", + "2501 ich glaube das wäre eine stilvolle und romanti... 😍 😊 \n", + "5278 Ich kann das machen 💁 ✋ \n", + "5747 In der Küche im Kühlschrank steht eine grüne T... 😉 🙂 \n", + "7022 Dann halt nicht ‍♂ 🤷 🤷 \n", + "1736 Ich hab absolut keine Ahnung davon 😂 😞 \n", + "5909 Wir bekommen es bestimmt zu zweit hin, aber we... 🙈 😉 \n", + "5890 Pass bloß auf, wenn du die isst mutierst du be... 😄 😱 \n", + "566 Das sind 5 Maß pro Abend 🙄 😋 \n", + "6044 Sind sehr lecker 😀 👌 \n", + "1472 Ich werde die alle drillen das sage ich dir, a... 🤔 😇 \n", + "5702 Oh man, das wird ein Tag 😀 😄 \n", + "\n", + " emojis \n", + "8663 [🤔] \n", + "1862 [😞] \n", + "3921 [🙂] \n", + "2222 [😀] \n", + "761 [😉] \n", + "3600 [👸] \n", + "1794 [🙂] \n", + "861 [😇] \n", + "1469 [😱, 😔, 🌚, 🙊] \n", + "6254 [🙂] \n", + "7968 [😂] \n", + "8102 [🧐] \n", + "6835 [😊] \n", + "7135 [😂, 😂, 😂] \n", + "4556 [😀] \n", + "6705 [🙄] \n", + "2058 [😅] \n", + "2072 [😈] \n", + "756 [😆] \n", + "5966 [🤔] \n", + "7007 [🤪] \n", + "6968 [🤩] \n", + "5665 [🙂] \n", + "2641 [🙃] \n", + "1981 [😄] \n", + "3334 [🙂] \n", + "4447 [😏] \n", + "288 [👍] \n", + "4265 [😢] \n", + "7405 [🤪] \n", + "... ... \n", + "4910 [😱] \n", + "2047 [😄] \n", + "2650 [😕] \n", + "299 [😉] \n", + "2499 [😅] \n", + "4783 [💸, 💸, 💸] \n", + "4040 [🤗, 😅] \n", + "7553 [🤦] \n", + "6232 [💣, 🔪] \n", + "3514 [😊] \n", + "3422 [😍] \n", + "2294 [😳, 😳, 😳] \n", + "8218 [😅] \n", + "4463 [😂] \n", + "8032 [🐰, 🐣, 🐥] \n", + "4505 [💁] \n", + "4810 [🤷] \n", + "5762 [🙄] \n", + "4247 [😂] \n", + "2501 [😊] \n", + "5278 [✋] \n", + "5747 [😉, 🙂] \n", + "7022 [🤷] \n", + "1736 [😞] \n", + "5909 [😉] \n", + "5890 [😱] \n", + "566 [😋] \n", + "6044 [👌] \n", + "1472 [😇] \n", + "5702 [😄] \n", + "\n", + "[246 rows x 4 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "testlist = pd.DataFrame({'message': Xt1, 'pred': pred, 'trained': yt1})\n", + "testlist = pd.merge(testlist, emoji_messages['emojis'].to_frame(), left_index=True, right_index=True)\n", + "testlist.to_csv('export.csv')\n", + "testlist" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'🙄'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(clf_a.predict(vectorizer.transform([\"Boah Caner\"]))[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ἷ\n" + ] + } + ], + "source": [ + "print(chr(0x1F3F))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<1x3931 sparse matrix of type ''\n", + "\twith 2 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vec_train[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b212fa64d5e457d89ad798983002a1c", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type Text.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "Text(value='')" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f1d5e13f70a24e80acc95372e3f3db0d", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(Button(description='get smiley', icon='check', style=ButtonStyle(), tooltip='Click me'), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets as widgets \n", + "\n", + "out = widgets.Output()\n", + "\n", + "t = widgets.Text()\n", + "b = widgets.Button(\n", + " description='get smiley',\n", + " disabled=False,\n", + " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", + " tooltip='Click me',\n", + " icon='check'\n", + ")\n", + "\n", + "\n", + "\n", + "def handle_submit(sender):\n", + " with out:\n", + " clear_output()\n", + " with out:\n", + " display(Markdown(\"# \" + str(clf_a.predict(vectorizer.transform([t.value]))[0])))\n", + "\n", + "b.on_click(handle_submit)\n", + " \n", + "display(t)\n", + "display(widgets.VBox([b, out])) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Project/Tools/emoji-data.txt b/Project/Tools/emoji-data.txt new file mode 100644 index 0000000..6e66455 --- /dev/null +++ b/Project/Tools/emoji-data.txt @@ -0,0 +1,714 @@ +# emoji-data.txt +# Date: 2018-02-07, 07:55:18 GMT +# © 2018 Unicode®, Inc. +# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries. +# For terms of use, see http://www.unicode.org/terms_of_use.html +# +# Emoji Data for UTS #51 +# Version: 11.0 +# +# For documentation and usage, see http://www.unicode.org/reports/tr51 +# +# Format: +# ; # +# Note: there is no guarantee as to the structure of whitespace or comments +# +# Characters and sequences are listed in code point order. Users should be shown a more natural order. +# See the CLDR collation order for Emoji. + + +# ================================================ + +# All omitted code points have Emoji=No +# @missing: 0000..10FFFF ; Emoji ; No + +0023 ; Emoji # 1.1 [1] (#️) number sign +002A ; Emoji # 1.1 [1] (*️) asterisk +0030..0039 ; Emoji # 1.1 [10] (0️..9️) digit zero..digit nine +00A9 ; Emoji # 1.1 [1] (©️) copyright +00AE ; Emoji # 1.1 [1] (®️) registered +203C ; Emoji # 1.1 [1] (‼️) double exclamation mark +2049 ; Emoji # 3.0 [1] (⁉️) exclamation question mark +2122 ; Emoji # 1.1 [1] (™️) trade mark +2139 ; Emoji # 3.0 [1] (ℹ️) information +2194..2199 ; Emoji # 1.1 [6] (↔️..↙️) left-right arrow..down-left arrow +21A9..21AA ; Emoji # 1.1 [2] (↩️..↪️) right arrow curving left..left arrow curving right +231A..231B ; Emoji # 1.1 [2] (⌚..⌛) watch..hourglass done +2328 ; Emoji # 1.1 [1] (⌨️) keyboard +23CF ; Emoji # 4.0 [1] (⏏️) eject button +23E9..23F3 ; Emoji # 6.0 [11] (⏩..⏳) fast-forward button..hourglass not done +23F8..23FA ; Emoji # 7.0 [3] (⏸️..⏺️) pause button..record button +24C2 ; Emoji # 1.1 [1] (Ⓜ️) circled M +25AA..25AB ; Emoji # 1.1 [2] (▪️..▫️) black small square..white small square +25B6 ; Emoji # 1.1 [1] (▶️) play button +25C0 ; Emoji # 1.1 [1] (◀️) reverse button +25FB..25FE ; Emoji # 3.2 [4] (◻️..◾) white medium square..black medium-small square +2600..2604 ; Emoji # 1.1 [5] (☀️..☄️) sun..comet +260E ; Emoji # 1.1 [1] (☎️) telephone +2611 ; Emoji # 1.1 [1] (☑️) ballot box with check +2614..2615 ; Emoji # 4.0 [2] (☔..☕) umbrella with rain drops..hot beverage +2618 ; Emoji # 4.1 [1] (☘️) shamrock +261D ; Emoji # 1.1 [1] (☝️) index pointing up +2620 ; Emoji # 1.1 [1] (☠️) skull and crossbones +2622..2623 ; Emoji # 1.1 [2] (☢️..☣️) radioactive..biohazard +2626 ; Emoji # 1.1 [1] (☦️) orthodox cross +262A ; Emoji # 1.1 [1] (☪️) star and crescent +262E..262F ; Emoji # 1.1 [2] (☮️..☯️) peace symbol..yin yang +2638..263A ; Emoji # 1.1 [3] (☸️..☺️) wheel of dharma..smiling face +2640 ; Emoji # 1.1 [1] (♀️) female sign +2642 ; Emoji # 1.1 [1] (♂️) male sign +2648..2653 ; Emoji # 1.1 [12] (♈..♓) Aries..Pisces +265F..2660 ; Emoji # 1.1 [2] (♟️..♠️) chess pawn..spade suit +2663 ; Emoji # 1.1 [1] (♣️) club suit +2665..2666 ; Emoji # 1.1 [2] (♥️..♦️) heart suit..diamond suit +2668 ; Emoji # 1.1 [1] (♨️) hot springs +267B ; Emoji # 3.2 [1] (♻️) recycling symbol +267E..267F ; Emoji # 4.1 [2] (♾️..♿) infinity..wheelchair symbol +2692..2697 ; Emoji # 4.1 [6] (⚒️..⚗️) hammer and pick..alembic +2699 ; Emoji # 4.1 [1] (⚙️) gear +269B..269C ; Emoji # 4.1 [2] (⚛️..⚜️) atom symbol..fleur-de-lis +26A0..26A1 ; Emoji # 4.0 [2] (⚠️..⚡) warning..high voltage +26AA..26AB ; Emoji # 4.1 [2] (⚪..⚫) white circle..black circle +26B0..26B1 ; Emoji # 4.1 [2] (⚰️..⚱️) coffin..funeral urn +26BD..26BE ; Emoji # 5.2 [2] (⚽..⚾) soccer ball..baseball +26C4..26C5 ; Emoji # 5.2 [2] (⛄..⛅) snowman without snow..sun behind cloud +26C8 ; Emoji # 5.2 [1] (⛈️) cloud with lightning and rain +26CE ; Emoji # 6.0 [1] (⛎) Ophiuchus +26CF ; Emoji # 5.2 [1] (⛏️) pick +26D1 ; Emoji # 5.2 [1] (⛑️) rescue worker’s helmet +26D3..26D4 ; Emoji # 5.2 [2] (⛓️..⛔) chains..no entry +26E9..26EA ; Emoji # 5.2 [2] (⛩️..⛪) shinto shrine..church +26F0..26F5 ; Emoji # 5.2 [6] (⛰️..⛵) mountain..sailboat +26F7..26FA ; Emoji # 5.2 [4] (⛷️..⛺) skier..tent +26FD ; Emoji # 5.2 [1] (⛽) fuel pump +2702 ; Emoji # 1.1 [1] (✂️) scissors +2705 ; Emoji # 6.0 [1] (✅) white heavy check mark +2708..2709 ; Emoji # 1.1 [2] (✈️..✉️) airplane..envelope +270A..270B ; Emoji # 6.0 [2] (✊..✋) raised fist..raised hand +270C..270D ; Emoji # 1.1 [2] (✌️..✍️) victory hand..writing hand +270F ; Emoji # 1.1 [1] (✏️) pencil +2712 ; Emoji # 1.1 [1] (✒️) black nib +2714 ; Emoji # 1.1 [1] (✔️) heavy check mark +2716 ; Emoji # 1.1 [1] (✖️) heavy multiplication x +271D ; Emoji # 1.1 [1] (✝️) latin cross +2721 ; Emoji # 1.1 [1] (✡️) star of David +2728 ; Emoji # 6.0 [1] (✨) sparkles +2733..2734 ; Emoji # 1.1 [2] (✳️..✴️) eight-spoked asterisk..eight-pointed star +2744 ; Emoji # 1.1 [1] (❄️) snowflake +2747 ; Emoji # 1.1 [1] (❇️) sparkle +274C ; Emoji # 6.0 [1] (❌) cross mark +274E ; Emoji # 6.0 [1] (❎) cross mark button +2753..2755 ; Emoji # 6.0 [3] (❓..❕) question mark..white exclamation mark +2757 ; Emoji # 5.2 [1] (❗) exclamation mark +2763..2764 ; Emoji # 1.1 [2] (❣️..❤️) heavy heart exclamation..red heart +2795..2797 ; Emoji # 6.0 [3] (➕..➗) heavy plus sign..heavy division sign +27A1 ; Emoji # 1.1 [1] (➡️) right arrow +27B0 ; Emoji # 6.0 [1] (➰) curly loop +27BF ; Emoji # 6.0 [1] (➿) double curly loop +2934..2935 ; Emoji # 3.2 [2] (⤴️..⤵️) right arrow curving up..right arrow curving down +2B05..2B07 ; Emoji # 4.0 [3] (⬅️..⬇️) left arrow..down arrow +2B1B..2B1C ; Emoji # 5.1 [2] (⬛..⬜) black large square..white large square +2B50 ; Emoji # 5.1 [1] (⭐) star +2B55 ; Emoji # 5.2 [1] (⭕) heavy large circle +3030 ; Emoji # 1.1 [1] (〰️) wavy dash +303D ; Emoji # 3.2 [1] (〽️) part alternation mark +3297 ; Emoji # 1.1 [1] (㊗️) Japanese “congratulations” button +3299 ; Emoji # 1.1 [1] (㊙️) Japanese “secret” button +1F004 ; Emoji # 5.1 [1] (🀄) mahjong red dragon +1F0CF ; Emoji # 6.0 [1] (🃏) joker +1F170..1F171 ; Emoji # 6.0 [2] (🅰️..🅱️) A button (blood type)..B button (blood type) +1F17E ; Emoji # 6.0 [1] (🅾️) O button (blood type) +1F17F ; Emoji # 5.2 [1] (🅿️) P button +1F18E ; Emoji # 6.0 [1] (🆎) AB button (blood type) +1F191..1F19A ; Emoji # 6.0 [10] (🆑..🆚) CL button..VS button +1F1E6..1F1FF ; Emoji # 6.0 [26] (🇦..🇿) regional indicator symbol letter a..regional indicator symbol letter z +1F201..1F202 ; Emoji # 6.0 [2] (🈁..🈂️) Japanese “here” button..Japanese “service charge” button +1F21A ; Emoji # 5.2 [1] (🈚) Japanese “free of charge” button +1F22F ; Emoji # 5.2 [1] (🈯) Japanese “reserved” button +1F232..1F23A ; Emoji # 6.0 [9] (🈲..🈺) Japanese “prohibited” button..Japanese “open for business” button +1F250..1F251 ; Emoji # 6.0 [2] (🉐..🉑) Japanese “bargain” button..Japanese “acceptable” button +1F300..1F320 ; Emoji # 6.0 [33] (🌀..🌠) cyclone..shooting star +1F321 ; Emoji # 7.0 [1] (🌡️) thermometer +1F324..1F32C ; Emoji # 7.0 [9] (🌤️..🌬️) sun behind small cloud..wind face +1F32D..1F32F ; Emoji # 8.0 [3] (🌭..🌯) hot dog..burrito +1F330..1F335 ; Emoji # 6.0 [6] (🌰..🌵) chestnut..cactus +1F336 ; Emoji # 7.0 [1] (🌶️) hot pepper +1F337..1F37C ; Emoji # 6.0 [70] (🌷..🍼) tulip..baby bottle +1F37D ; Emoji # 7.0 [1] (🍽️) fork and knife with plate +1F37E..1F37F ; Emoji # 8.0 [2] (🍾..🍿) bottle with popping cork..popcorn +1F380..1F393 ; Emoji # 6.0 [20] (🎀..🎓) ribbon..graduation cap +1F396..1F397 ; Emoji # 7.0 [2] (🎖️..🎗️) military medal..reminder ribbon +1F399..1F39B ; Emoji # 7.0 [3] (🎙️..🎛️) studio microphone..control knobs +1F39E..1F39F ; Emoji # 7.0 [2] (🎞️..🎟️) film frames..admission tickets +1F3A0..1F3C4 ; Emoji # 6.0 [37] (🎠..🏄) carousel horse..person surfing +1F3C5 ; Emoji # 7.0 [1] (🏅) sports medal +1F3C6..1F3CA ; Emoji # 6.0 [5] (🏆..🏊) trophy..person swimming +1F3CB..1F3CE ; Emoji # 7.0 [4] (🏋️..🏎️) person lifting weights..racing car +1F3CF..1F3D3 ; Emoji # 8.0 [5] (🏏..🏓) cricket game..ping pong +1F3D4..1F3DF ; Emoji # 7.0 [12] (🏔️..🏟️) snow-capped mountain..stadium +1F3E0..1F3F0 ; Emoji # 6.0 [17] (🏠..🏰) house..castle +1F3F3..1F3F5 ; Emoji # 7.0 [3] (🏳️..🏵️) white flag..rosette +1F3F7 ; Emoji # 7.0 [1] (🏷️) label +1F3F8..1F3FF ; Emoji # 8.0 [8] (🏸..🏿) badminton..dark skin tone +1F400..1F43E ; Emoji # 6.0 [63] (🐀..🐾) rat..paw prints +1F43F ; Emoji # 7.0 [1] (🐿️) chipmunk +1F440 ; Emoji # 6.0 [1] (👀) eyes +1F441 ; Emoji # 7.0 [1] (👁️) eye +1F442..1F4F7 ; Emoji # 6.0[182] (👂..📷) ear..camera +1F4F8 ; Emoji # 7.0 [1] (📸) camera with flash +1F4F9..1F4FC ; Emoji # 6.0 [4] (📹..📼) video camera..videocassette +1F4FD ; Emoji # 7.0 [1] (📽️) film projector +1F4FF ; Emoji # 8.0 [1] (📿) prayer beads +1F500..1F53D ; Emoji # 6.0 [62] (🔀..🔽) shuffle tracks button..downwards button +1F549..1F54A ; Emoji # 7.0 [2] (🕉️..🕊️) om..dove +1F54B..1F54E ; Emoji # 8.0 [4] (🕋..🕎) kaaba..menorah +1F550..1F567 ; Emoji # 6.0 [24] (🕐..🕧) one o’clock..twelve-thirty +1F56F..1F570 ; Emoji # 7.0 [2] (🕯️..🕰️) candle..mantelpiece clock +1F573..1F579 ; Emoji # 7.0 [7] (🕳️..🕹️) hole..joystick +1F57A ; Emoji # 9.0 [1] (🕺) man dancing +1F587 ; Emoji # 7.0 [1] (🖇️) linked paperclips +1F58A..1F58D ; Emoji # 7.0 [4] (🖊️..🖍️) pen..crayon +1F590 ; Emoji # 7.0 [1] (🖐️) hand with fingers splayed +1F595..1F596 ; Emoji # 7.0 [2] (🖕..🖖) middle finger..vulcan salute +1F5A4 ; Emoji # 9.0 [1] (🖤) black heart +1F5A5 ; Emoji # 7.0 [1] (🖥️) desktop computer +1F5A8 ; Emoji # 7.0 [1] (🖨️) printer +1F5B1..1F5B2 ; Emoji # 7.0 [2] (🖱️..🖲️) computer mouse..trackball +1F5BC ; Emoji # 7.0 [1] (🖼️) framed picture +1F5C2..1F5C4 ; Emoji # 7.0 [3] (🗂️..🗄️) card index dividers..file cabinet +1F5D1..1F5D3 ; Emoji # 7.0 [3] (🗑️..🗓️) wastebasket..spiral calendar +1F5DC..1F5DE ; Emoji # 7.0 [3] (🗜️..🗞️) clamp..rolled-up newspaper +1F5E1 ; Emoji # 7.0 [1] (🗡️) dagger +1F5E3 ; Emoji # 7.0 [1] (🗣️) speaking head +1F5E8 ; Emoji # 7.0 [1] (🗨️) left speech bubble +1F5EF ; Emoji # 7.0 [1] (🗯️) right anger bubble +1F5F3 ; Emoji # 7.0 [1] (🗳️) ballot box with ballot +1F5FA ; Emoji # 7.0 [1] (🗺️) world map +1F5FB..1F5FF ; Emoji # 6.0 [5] (🗻..🗿) mount fuji..moai +1F600 ; Emoji # 6.1 [1] (😀) grinning face +1F601..1F610 ; Emoji # 6.0 [16] (😁..😐) beaming face with smiling eyes..neutral face +1F611 ; Emoji # 6.1 [1] (😑) expressionless face +1F612..1F614 ; Emoji # 6.0 [3] (😒..😔) unamused face..pensive face +1F615 ; Emoji # 6.1 [1] (😕) confused face +1F616 ; Emoji # 6.0 [1] (😖) confounded face +1F617 ; Emoji # 6.1 [1] (😗) kissing face +1F618 ; Emoji # 6.0 [1] (😘) face blowing a kiss +1F619 ; Emoji # 6.1 [1] (😙) kissing face with smiling eyes +1F61A ; Emoji # 6.0 [1] (😚) kissing face with closed eyes +1F61B ; Emoji # 6.1 [1] (😛) face with tongue +1F61C..1F61E ; Emoji # 6.0 [3] (😜..😞) winking face with tongue..disappointed face +1F61F ; Emoji # 6.1 [1] (😟) worried face +1F620..1F625 ; Emoji # 6.0 [6] (😠..😥) angry face..sad but relieved face +1F626..1F627 ; Emoji # 6.1 [2] (😦..😧) frowning face with open mouth..anguished face +1F628..1F62B ; Emoji # 6.0 [4] (😨..😫) fearful face..tired face +1F62C ; Emoji # 6.1 [1] (😬) grimacing face +1F62D ; Emoji # 6.0 [1] (😭) loudly crying face +1F62E..1F62F ; Emoji # 6.1 [2] (😮..😯) face with open mouth..hushed face +1F630..1F633 ; Emoji # 6.0 [4] (😰..😳) anxious face with sweat..flushed face +1F634 ; Emoji # 6.1 [1] (😴) sleeping face +1F635..1F640 ; Emoji # 6.0 [12] (😵..🙀) dizzy face..weary cat face +1F641..1F642 ; Emoji # 7.0 [2] (🙁..🙂) slightly frowning face..slightly smiling face +1F643..1F644 ; Emoji # 8.0 [2] (🙃..🙄) upside-down face..face with rolling eyes +1F645..1F64F ; Emoji # 6.0 [11] (🙅..🙏) person gesturing NO..folded hands +1F680..1F6C5 ; Emoji # 6.0 [70] (🚀..🛅) rocket..left luggage +1F6CB..1F6CF ; Emoji # 7.0 [5] (🛋️..🛏️) couch and lamp..bed +1F6D0 ; Emoji # 8.0 [1] (🛐) place of worship +1F6D1..1F6D2 ; Emoji # 9.0 [2] (🛑..🛒) stop sign..shopping cart +1F6E0..1F6E5 ; Emoji # 7.0 [6] (🛠️..🛥️) hammer and wrench..motor boat +1F6E9 ; Emoji # 7.0 [1] (🛩️) small airplane +1F6EB..1F6EC ; Emoji # 7.0 [2] (🛫..🛬) airplane departure..airplane arrival +1F6F0 ; Emoji # 7.0 [1] (🛰️) satellite +1F6F3 ; Emoji # 7.0 [1] (🛳️) passenger ship +1F6F4..1F6F6 ; Emoji # 9.0 [3] (🛴..🛶) kick scooter..canoe +1F6F7..1F6F8 ; Emoji # 10.0 [2] (🛷..🛸) sled..flying saucer +1F6F9 ; Emoji # 11.0 [1] (🛹) skateboard +1F910..1F918 ; Emoji # 8.0 [9] (🤐..🤘) zipper-mouth face..sign of the horns +1F919..1F91E ; Emoji # 9.0 [6] (🤙..🤞) call me hand..crossed fingers +1F91F ; Emoji # 10.0 [1] (🤟) love-you gesture +1F920..1F927 ; Emoji # 9.0 [8] (🤠..🤧) cowboy hat face..sneezing face +1F928..1F92F ; Emoji # 10.0 [8] (🤨..🤯) face with raised eyebrow..exploding head +1F930 ; Emoji # 9.0 [1] (🤰) pregnant woman +1F931..1F932 ; Emoji # 10.0 [2] (🤱..🤲) breast-feeding..palms up together +1F933..1F93A ; Emoji # 9.0 [8] (🤳..🤺) selfie..person fencing +1F93C..1F93E ; Emoji # 9.0 [3] (🤼..🤾) people wrestling..person playing handball +1F940..1F945 ; Emoji # 9.0 [6] (🥀..🥅) wilted flower..goal net +1F947..1F94B ; Emoji # 9.0 [5] (🥇..🥋) 1st place medal..martial arts uniform +1F94C ; Emoji # 10.0 [1] (🥌) curling stone +1F94D..1F94F ; Emoji # 11.0 [3] (🥍..🥏) lacrosse..flying disc +1F950..1F95E ; Emoji # 9.0 [15] (🥐..🥞) croissant..pancakes +1F95F..1F96B ; Emoji # 10.0 [13] (🥟..🥫) dumpling..canned food +1F96C..1F970 ; Emoji # 11.0 [5] (🥬..🥰) leafy green..smiling face with 3 hearts +1F973..1F976 ; Emoji # 11.0 [4] (🥳..🥶) partying face..cold face +1F97A ; Emoji # 11.0 [1] (🥺) pleading face +1F97C..1F97F ; Emoji # 11.0 [4] (🥼..🥿) lab coat..woman’s flat shoe +1F980..1F984 ; Emoji # 8.0 [5] (🦀..🦄) crab..unicorn face +1F985..1F991 ; Emoji # 9.0 [13] (🦅..🦑) eagle..squid +1F992..1F997 ; Emoji # 10.0 [6] (🦒..🦗) giraffe..cricket +1F998..1F9A2 ; Emoji # 11.0 [11] (🦘..🦢) kangaroo..swan +1F9B0..1F9B9 ; Emoji # 11.0 [10] (🦰..🦹) red-haired..supervillain +1F9C0 ; Emoji # 8.0 [1] (🧀) cheese wedge +1F9C1..1F9C2 ; Emoji # 11.0 [2] (🧁..🧂) cupcake..salt +1F9D0..1F9E6 ; Emoji # 10.0 [23] (🧐..🧦) face with monocle..socks +1F9E7..1F9FF ; Emoji # 11.0 [25] (🧧..🧿) red envelope..nazar amulet + +# Total elements: 1250 + +# ================================================ + +# All omitted code points have Emoji_Presentation=No +# @missing: 0000..10FFFF ; Emoji_Presentation ; No + +231A..231B ; Emoji_Presentation # 1.1 [2] (⌚..⌛) watch..hourglass done +23E9..23EC ; Emoji_Presentation # 6.0 [4] (⏩..⏬) fast-forward button..fast down button +23F0 ; Emoji_Presentation # 6.0 [1] (⏰) alarm clock +23F3 ; Emoji_Presentation # 6.0 [1] (⏳) hourglass not done +25FD..25FE ; Emoji_Presentation # 3.2 [2] (◽..◾) white medium-small square..black medium-small square +2614..2615 ; Emoji_Presentation # 4.0 [2] (☔..☕) umbrella with rain drops..hot beverage +2648..2653 ; Emoji_Presentation # 1.1 [12] (♈..♓) Aries..Pisces +267F ; Emoji_Presentation # 4.1 [1] (♿) wheelchair symbol +2693 ; Emoji_Presentation # 4.1 [1] (⚓) anchor +26A1 ; Emoji_Presentation # 4.0 [1] (⚡) high voltage +26AA..26AB ; Emoji_Presentation # 4.1 [2] (⚪..⚫) white circle..black circle +26BD..26BE ; Emoji_Presentation # 5.2 [2] (⚽..⚾) soccer ball..baseball +26C4..26C5 ; Emoji_Presentation # 5.2 [2] (⛄..⛅) snowman without snow..sun behind cloud +26CE ; Emoji_Presentation # 6.0 [1] (⛎) Ophiuchus +26D4 ; Emoji_Presentation # 5.2 [1] (⛔) no entry +26EA ; Emoji_Presentation # 5.2 [1] (⛪) church +26F2..26F3 ; Emoji_Presentation # 5.2 [2] (⛲..⛳) fountain..flag in hole +26F5 ; Emoji_Presentation # 5.2 [1] (⛵) sailboat +26FA ; Emoji_Presentation # 5.2 [1] (⛺) tent +26FD ; Emoji_Presentation # 5.2 [1] (⛽) fuel pump +2705 ; Emoji_Presentation # 6.0 [1] (✅) white heavy check mark +270A..270B ; Emoji_Presentation # 6.0 [2] (✊..✋) raised fist..raised hand +2728 ; Emoji_Presentation # 6.0 [1] (✨) sparkles +274C ; Emoji_Presentation # 6.0 [1] (❌) cross mark +274E ; Emoji_Presentation # 6.0 [1] (❎) cross mark button +2753..2755 ; Emoji_Presentation # 6.0 [3] (❓..❕) question mark..white exclamation mark +2757 ; Emoji_Presentation # 5.2 [1] (❗) exclamation mark +2795..2797 ; Emoji_Presentation # 6.0 [3] (➕..➗) heavy plus sign..heavy division sign +27B0 ; Emoji_Presentation # 6.0 [1] (➰) curly loop +27BF ; Emoji_Presentation # 6.0 [1] (➿) double curly loop +2B1B..2B1C ; Emoji_Presentation # 5.1 [2] (⬛..⬜) black large square..white large square +2B50 ; Emoji_Presentation # 5.1 [1] (⭐) star +2B55 ; Emoji_Presentation # 5.2 [1] (⭕) heavy large circle +1F004 ; Emoji_Presentation # 5.1 [1] (🀄) mahjong red dragon +1F0CF ; Emoji_Presentation # 6.0 [1] (🃏) joker +1F18E ; Emoji_Presentation # 6.0 [1] (🆎) AB button (blood type) +1F191..1F19A ; Emoji_Presentation # 6.0 [10] (🆑..🆚) CL button..VS button +1F1E6..1F1FF ; Emoji_Presentation # 6.0 [26] (🇦..🇿) regional indicator symbol letter a..regional indicator symbol letter z +1F201 ; Emoji_Presentation # 6.0 [1] (🈁) Japanese “here” button +1F21A ; Emoji_Presentation # 5.2 [1] (🈚) Japanese “free of charge” button +1F22F ; Emoji_Presentation # 5.2 [1] (🈯) Japanese “reserved” button +1F232..1F236 ; Emoji_Presentation # 6.0 [5] (🈲..🈶) Japanese “prohibited” button..Japanese “not free of charge” button +1F238..1F23A ; Emoji_Presentation # 6.0 [3] (🈸..🈺) Japanese “application” button..Japanese “open for business” button +1F250..1F251 ; Emoji_Presentation # 6.0 [2] (🉐..🉑) Japanese “bargain” button..Japanese “acceptable” button +1F300..1F320 ; Emoji_Presentation # 6.0 [33] (🌀..🌠) cyclone..shooting star +1F32D..1F32F ; Emoji_Presentation # 8.0 [3] (🌭..🌯) hot dog..burrito +1F330..1F335 ; Emoji_Presentation # 6.0 [6] (🌰..🌵) chestnut..cactus +1F337..1F37C ; Emoji_Presentation # 6.0 [70] (🌷..🍼) tulip..baby bottle +1F37E..1F37F ; Emoji_Presentation # 8.0 [2] (🍾..🍿) bottle with popping cork..popcorn +1F380..1F393 ; Emoji_Presentation # 6.0 [20] (🎀..🎓) ribbon..graduation cap +1F3A0..1F3C4 ; Emoji_Presentation # 6.0 [37] (🎠..🏄) carousel horse..person surfing +1F3C5 ; Emoji_Presentation # 7.0 [1] (🏅) sports medal +1F3C6..1F3CA ; Emoji_Presentation # 6.0 [5] (🏆..🏊) trophy..person swimming +1F3CF..1F3D3 ; Emoji_Presentation # 8.0 [5] (🏏..🏓) cricket game..ping pong +1F3E0..1F3F0 ; Emoji_Presentation # 6.0 [17] (🏠..🏰) house..castle +1F3F4 ; Emoji_Presentation # 7.0 [1] (🏴) black flag +1F3F8..1F3FF ; Emoji_Presentation # 8.0 [8] (🏸..🏿) badminton..dark skin tone +1F400..1F43E ; Emoji_Presentation # 6.0 [63] (🐀..🐾) rat..paw prints +1F440 ; Emoji_Presentation # 6.0 [1] (👀) eyes +1F442..1F4F7 ; Emoji_Presentation # 6.0[182] (👂..📷) ear..camera +1F4F8 ; Emoji_Presentation # 7.0 [1] (📸) camera with flash +1F4F9..1F4FC ; Emoji_Presentation # 6.0 [4] (📹..📼) video camera..videocassette +1F4FF ; Emoji_Presentation # 8.0 [1] (📿) prayer beads +1F500..1F53D ; Emoji_Presentation # 6.0 [62] (🔀..🔽) shuffle tracks button..downwards button +1F54B..1F54E ; Emoji_Presentation # 8.0 [4] (🕋..🕎) kaaba..menorah +1F550..1F567 ; Emoji_Presentation # 6.0 [24] (🕐..🕧) one o’clock..twelve-thirty +1F57A ; Emoji_Presentation # 9.0 [1] (🕺) man dancing +1F595..1F596 ; Emoji_Presentation # 7.0 [2] (🖕..🖖) middle finger..vulcan salute +1F5A4 ; Emoji_Presentation # 9.0 [1] (🖤) black heart +1F5FB..1F5FF ; Emoji_Presentation # 6.0 [5] (🗻..🗿) mount fuji..moai +1F600 ; Emoji_Presentation # 6.1 [1] (😀) grinning face +1F601..1F610 ; Emoji_Presentation # 6.0 [16] (😁..😐) beaming face with smiling eyes..neutral face +1F611 ; Emoji_Presentation # 6.1 [1] (😑) expressionless face +1F612..1F614 ; Emoji_Presentation # 6.0 [3] (😒..😔) unamused face..pensive face +1F615 ; Emoji_Presentation # 6.1 [1] (😕) confused face +1F616 ; Emoji_Presentation # 6.0 [1] (😖) confounded face +1F617 ; Emoji_Presentation # 6.1 [1] (😗) kissing face +1F618 ; Emoji_Presentation # 6.0 [1] (😘) face blowing a kiss +1F619 ; Emoji_Presentation # 6.1 [1] (😙) kissing face with smiling eyes +1F61A ; Emoji_Presentation # 6.0 [1] (😚) kissing face with closed eyes +1F61B ; Emoji_Presentation # 6.1 [1] (😛) face with tongue +1F61C..1F61E ; Emoji_Presentation # 6.0 [3] (😜..😞) winking face with tongue..disappointed face +1F61F ; Emoji_Presentation # 6.1 [1] (😟) worried face +1F620..1F625 ; Emoji_Presentation # 6.0 [6] (😠..😥) angry face..sad but relieved face +1F626..1F627 ; Emoji_Presentation # 6.1 [2] (😦..😧) frowning face with open mouth..anguished face +1F628..1F62B ; Emoji_Presentation # 6.0 [4] (😨..😫) fearful face..tired face +1F62C ; Emoji_Presentation # 6.1 [1] (😬) grimacing face +1F62D ; Emoji_Presentation # 6.0 [1] (😭) loudly crying face +1F62E..1F62F ; Emoji_Presentation # 6.1 [2] (😮..😯) face with open mouth..hushed face +1F630..1F633 ; Emoji_Presentation # 6.0 [4] (😰..😳) anxious face with sweat..flushed face +1F634 ; Emoji_Presentation # 6.1 [1] (😴) sleeping face +1F635..1F640 ; Emoji_Presentation # 6.0 [12] (😵..🙀) dizzy face..weary cat face +1F641..1F642 ; Emoji_Presentation # 7.0 [2] (🙁..🙂) slightly frowning face..slightly smiling face +1F643..1F644 ; Emoji_Presentation # 8.0 [2] (🙃..🙄) upside-down face..face with rolling eyes +1F645..1F64F ; Emoji_Presentation # 6.0 [11] (🙅..🙏) person gesturing NO..folded hands +1F680..1F6C5 ; Emoji_Presentation # 6.0 [70] (🚀..🛅) rocket..left luggage +1F6CC ; Emoji_Presentation # 7.0 [1] (🛌) person in bed +1F6D0 ; Emoji_Presentation # 8.0 [1] (🛐) place of worship +1F6D1..1F6D2 ; Emoji_Presentation # 9.0 [2] (🛑..🛒) stop sign..shopping cart +1F6EB..1F6EC ; Emoji_Presentation # 7.0 [2] (🛫..🛬) airplane departure..airplane arrival +1F6F4..1F6F6 ; Emoji_Presentation # 9.0 [3] (🛴..🛶) kick scooter..canoe +1F6F7..1F6F8 ; Emoji_Presentation # 10.0 [2] (🛷..🛸) sled..flying saucer +1F6F9 ; Emoji_Presentation # 11.0 [1] (🛹) skateboard +1F910..1F918 ; Emoji_Presentation # 8.0 [9] (🤐..🤘) zipper-mouth face..sign of the horns +1F919..1F91E ; Emoji_Presentation # 9.0 [6] (🤙..🤞) call me hand..crossed fingers +1F91F ; Emoji_Presentation # 10.0 [1] (🤟) love-you gesture +1F920..1F927 ; Emoji_Presentation # 9.0 [8] (🤠..🤧) cowboy hat face..sneezing face +1F928..1F92F ; Emoji_Presentation # 10.0 [8] (🤨..🤯) face with raised eyebrow..exploding head +1F930 ; Emoji_Presentation # 9.0 [1] (🤰) pregnant woman +1F931..1F932 ; Emoji_Presentation # 10.0 [2] (🤱..🤲) breast-feeding..palms up together +1F933..1F93A ; Emoji_Presentation # 9.0 [8] (🤳..🤺) selfie..person fencing +1F93C..1F93E ; Emoji_Presentation # 9.0 [3] (🤼..🤾) people wrestling..person playing handball +1F940..1F945 ; Emoji_Presentation # 9.0 [6] (🥀..🥅) wilted flower..goal net +1F947..1F94B ; Emoji_Presentation # 9.0 [5] (🥇..🥋) 1st place medal..martial arts uniform +1F94C ; Emoji_Presentation # 10.0 [1] (🥌) curling stone +1F94D..1F94F ; Emoji_Presentation # 11.0 [3] (🥍..🥏) lacrosse..flying disc +1F950..1F95E ; Emoji_Presentation # 9.0 [15] (🥐..🥞) croissant..pancakes +1F95F..1F96B ; Emoji_Presentation # 10.0 [13] (🥟..🥫) dumpling..canned food +1F96C..1F970 ; Emoji_Presentation # 11.0 [5] (🥬..🥰) leafy green..smiling face with 3 hearts +1F973..1F976 ; Emoji_Presentation # 11.0 [4] (🥳..🥶) partying face..cold face +1F97A ; Emoji_Presentation # 11.0 [1] (🥺) pleading face +1F97C..1F97F ; Emoji_Presentation # 11.0 [4] (🥼..🥿) lab coat..woman’s flat shoe +1F980..1F984 ; Emoji_Presentation # 8.0 [5] (🦀..🦄) crab..unicorn face +1F985..1F991 ; Emoji_Presentation # 9.0 [13] (🦅..🦑) eagle..squid +1F992..1F997 ; Emoji_Presentation # 10.0 [6] (🦒..🦗) giraffe..cricket +1F998..1F9A2 ; Emoji_Presentation # 11.0 [11] (🦘..🦢) kangaroo..swan +1F9B0..1F9B9 ; Emoji_Presentation # 11.0 [10] (🦰..🦹) red-haired..supervillain +1F9C0 ; Emoji_Presentation # 8.0 [1] (🧀) cheese wedge +1F9C1..1F9C2 ; Emoji_Presentation # 11.0 [2] (🧁..🧂) cupcake..salt +1F9D0..1F9E6 ; Emoji_Presentation # 10.0 [23] (🧐..🧦) face with monocle..socks +1F9E7..1F9FF ; Emoji_Presentation # 11.0 [25] (🧧..🧿) red envelope..nazar amulet + +# Total elements: 1032 + +# ================================================ + +# All omitted code points have Emoji_Modifier=No +# @missing: 0000..10FFFF ; Emoji_Modifier ; No + +1F3FB..1F3FF ; Emoji_Modifier # 8.0 [5] (🏻..🏿) light skin tone..dark skin tone + +# Total elements: 5 + +# ================================================ + +# All omitted code points have Emoji_Modifier_Base=No +# @missing: 0000..10FFFF ; Emoji_Modifier_Base ; No + +261D ; Emoji_Modifier_Base # 1.1 [1] (☝️) index pointing up +26F9 ; Emoji_Modifier_Base # 5.2 [1] (⛹️) person bouncing ball +270A..270B ; Emoji_Modifier_Base # 6.0 [2] (✊..✋) raised fist..raised hand +270C..270D ; Emoji_Modifier_Base # 1.1 [2] (✌️..✍️) victory hand..writing hand +1F385 ; Emoji_Modifier_Base # 6.0 [1] (🎅) Santa Claus +1F3C2..1F3C4 ; Emoji_Modifier_Base # 6.0 [3] (🏂..🏄) snowboarder..person surfing +1F3C7 ; Emoji_Modifier_Base # 6.0 [1] (🏇) horse racing +1F3CA ; Emoji_Modifier_Base # 6.0 [1] (🏊) person swimming +1F3CB..1F3CC ; Emoji_Modifier_Base # 7.0 [2] (🏋️..🏌️) person lifting weights..person golfing +1F442..1F443 ; Emoji_Modifier_Base # 6.0 [2] (👂..👃) ear..nose +1F446..1F450 ; Emoji_Modifier_Base # 6.0 [11] (👆..👐) backhand index pointing up..open hands +1F466..1F469 ; Emoji_Modifier_Base # 6.0 [4] (👦..👩) boy..woman +1F46E ; Emoji_Modifier_Base # 6.0 [1] (👮) police officer +1F470..1F478 ; Emoji_Modifier_Base # 6.0 [9] (👰..👸) bride with veil..princess +1F47C ; Emoji_Modifier_Base # 6.0 [1] (👼) baby angel +1F481..1F483 ; Emoji_Modifier_Base # 6.0 [3] (💁..💃) person tipping hand..woman dancing +1F485..1F487 ; Emoji_Modifier_Base # 6.0 [3] (💅..💇) nail polish..person getting haircut +1F4AA ; Emoji_Modifier_Base # 6.0 [1] (💪) flexed biceps +1F574..1F575 ; Emoji_Modifier_Base # 7.0 [2] (🕴️..🕵️) man in suit levitating..detective +1F57A ; Emoji_Modifier_Base # 9.0 [1] (🕺) man dancing +1F590 ; Emoji_Modifier_Base # 7.0 [1] (🖐️) hand with fingers splayed +1F595..1F596 ; Emoji_Modifier_Base # 7.0 [2] (🖕..🖖) middle finger..vulcan salute +1F645..1F647 ; Emoji_Modifier_Base # 6.0 [3] (🙅..🙇) person gesturing NO..person bowing +1F64B..1F64F ; Emoji_Modifier_Base # 6.0 [5] (🙋..🙏) person raising hand..folded hands +1F6A3 ; Emoji_Modifier_Base # 6.0 [1] (🚣) person rowing boat +1F6B4..1F6B6 ; Emoji_Modifier_Base # 6.0 [3] (🚴..🚶) person biking..person walking +1F6C0 ; Emoji_Modifier_Base # 6.0 [1] (🛀) person taking bath +1F6CC ; Emoji_Modifier_Base # 7.0 [1] (🛌) person in bed +1F918 ; Emoji_Modifier_Base # 8.0 [1] (🤘) sign of the horns +1F919..1F91C ; Emoji_Modifier_Base # 9.0 [4] (🤙..🤜) call me hand..right-facing fist +1F91E ; Emoji_Modifier_Base # 9.0 [1] (🤞) crossed fingers +1F91F ; Emoji_Modifier_Base # 10.0 [1] (🤟) love-you gesture +1F926 ; Emoji_Modifier_Base # 9.0 [1] (🤦) person facepalming +1F930 ; Emoji_Modifier_Base # 9.0 [1] (🤰) pregnant woman +1F931..1F932 ; Emoji_Modifier_Base # 10.0 [2] (🤱..🤲) breast-feeding..palms up together +1F933..1F939 ; Emoji_Modifier_Base # 9.0 [7] (🤳..🤹) selfie..person juggling +1F93D..1F93E ; Emoji_Modifier_Base # 9.0 [2] (🤽..🤾) person playing water polo..person playing handball +1F9B5..1F9B6 ; Emoji_Modifier_Base # 11.0 [2] (🦵..🦶) leg..foot +1F9B8..1F9B9 ; Emoji_Modifier_Base # 11.0 [2] (🦸..🦹) superhero..supervillain +1F9D1..1F9DD ; Emoji_Modifier_Base # 10.0 [13] (🧑..🧝) adult..elf + +# Total elements: 106 + +# ================================================ + +# All omitted code points have Emoji_Component=No +# @missing: 0000..10FFFF ; Emoji_Component ; No + +0023 ; Emoji_Component # 1.1 [1] (#️) number sign +002A ; Emoji_Component # 1.1 [1] (*️) asterisk +0030..0039 ; Emoji_Component # 1.1 [10] (0️..9️) digit zero..digit nine +200D ; Emoji_Component # 1.1 [1] (‍) zero width joiner +20E3 ; Emoji_Component # 3.0 [1] (⃣) combining enclosing keycap +FE0F ; Emoji_Component # 3.2 [1] () VARIATION SELECTOR-16 +1F1E6..1F1FF ; Emoji_Component # 6.0 [26] (🇦..🇿) regional indicator symbol letter a..regional indicator symbol letter z +1F3FB..1F3FF ; Emoji_Component # 8.0 [5] (🏻..🏿) light skin tone..dark skin tone +1F9B0..1F9B3 ; Emoji_Component # 11.0 [4] (🦰..🦳) red-haired..white-haired +E0020..E007F ; Emoji_Component # 3.1 [96] (󠀠..󠁿) tag space..cancel tag + +# Total elements: 146 + +# ================================================ + +# All omitted code points have Extended_Pictographic=No +# @missing: 0000..10FFFF ; Extended_Pictographic ; No + +00A9 ; Extended_Pictographic# 1.1 [1] (©️) copyright +00AE ; Extended_Pictographic# 1.1 [1] (®️) registered +203C ; Extended_Pictographic# 1.1 [1] (‼️) double exclamation mark +2049 ; Extended_Pictographic# 3.0 [1] (⁉️) exclamation question mark +2122 ; Extended_Pictographic# 1.1 [1] (™️) trade mark +2139 ; Extended_Pictographic# 3.0 [1] (ℹ️) information +2194..2199 ; Extended_Pictographic# 1.1 [6] (↔️..↙️) left-right arrow..down-left arrow +21A9..21AA ; Extended_Pictographic# 1.1 [2] (↩️..↪️) right arrow curving left..left arrow curving right +231A..231B ; Extended_Pictographic# 1.1 [2] (⌚..⌛) watch..hourglass done +2328 ; Extended_Pictographic# 1.1 [1] (⌨️) keyboard +2388 ; Extended_Pictographic# 3.0 [1] (⎈️) HELM SYMBOL +23CF ; Extended_Pictographic# 4.0 [1] (⏏️) eject button +23E9..23F3 ; Extended_Pictographic# 6.0 [11] (⏩..⏳) fast-forward button..hourglass not done +23F8..23FA ; Extended_Pictographic# 7.0 [3] (⏸️..⏺️) pause button..record button +24C2 ; Extended_Pictographic# 1.1 [1] (Ⓜ️) circled M +25AA..25AB ; Extended_Pictographic# 1.1 [2] (▪️..▫️) black small square..white small square +25B6 ; Extended_Pictographic# 1.1 [1] (▶️) play button +25C0 ; Extended_Pictographic# 1.1 [1] (◀️) reverse button +25FB..25FE ; Extended_Pictographic# 3.2 [4] (◻️..◾) white medium square..black medium-small square +2600..2605 ; Extended_Pictographic# 1.1 [6] (☀️..★️) sun..BLACK STAR +2607..2612 ; Extended_Pictographic# 1.1 [12] (☇️..☒️) LIGHTNING..BALLOT BOX WITH X +2614..2615 ; Extended_Pictographic# 4.0 [2] (☔..☕) umbrella with rain drops..hot beverage +2616..2617 ; Extended_Pictographic# 3.2 [2] (☖️..☗️) WHITE SHOGI PIECE..BLACK SHOGI PIECE +2618 ; Extended_Pictographic# 4.1 [1] (☘️) shamrock +2619 ; Extended_Pictographic# 3.0 [1] (☙️) REVERSED ROTATED FLORAL HEART BULLET +261A..266F ; Extended_Pictographic# 1.1 [86] (☚️..♯️) BLACK LEFT POINTING INDEX..MUSIC SHARP SIGN +2670..2671 ; Extended_Pictographic# 3.0 [2] (♰️..♱️) WEST SYRIAC CROSS..EAST SYRIAC CROSS +2672..267D ; Extended_Pictographic# 3.2 [12] (♲️..♽️) UNIVERSAL RECYCLING SYMBOL..PARTIALLY-RECYCLED PAPER SYMBOL +267E..267F ; Extended_Pictographic# 4.1 [2] (♾️..♿) infinity..wheelchair symbol +2680..2685 ; Extended_Pictographic# 3.2 [6] (⚀️..⚅️) DIE FACE-1..DIE FACE-6 +2690..2691 ; Extended_Pictographic# 4.0 [2] (⚐️..⚑️) WHITE FLAG..BLACK FLAG +2692..269C ; Extended_Pictographic# 4.1 [11] (⚒️..⚜️) hammer and pick..fleur-de-lis +269D ; Extended_Pictographic# 5.1 [1] (⚝️) OUTLINED WHITE STAR +269E..269F ; Extended_Pictographic# 5.2 [2] (⚞️..⚟️) THREE LINES CONVERGING RIGHT..THREE LINES CONVERGING LEFT +26A0..26A1 ; Extended_Pictographic# 4.0 [2] (⚠️..⚡) warning..high voltage +26A2..26B1 ; Extended_Pictographic# 4.1 [16] (⚢️..⚱️) DOUBLED FEMALE SIGN..funeral urn +26B2 ; Extended_Pictographic# 5.0 [1] (⚲️) NEUTER +26B3..26BC ; Extended_Pictographic# 5.1 [10] (⚳️..⚼️) CERES..SESQUIQUADRATE +26BD..26BF ; Extended_Pictographic# 5.2 [3] (⚽..⚿️) soccer ball..SQUARED KEY +26C0..26C3 ; Extended_Pictographic# 5.1 [4] (⛀️..⛃️) WHITE DRAUGHTS MAN..BLACK DRAUGHTS KING +26C4..26CD ; Extended_Pictographic# 5.2 [10] (⛄..⛍️) snowman without snow..DISABLED CAR +26CE ; Extended_Pictographic# 6.0 [1] (⛎) Ophiuchus +26CF..26E1 ; Extended_Pictographic# 5.2 [19] (⛏️..⛡️) pick..RESTRICTED LEFT ENTRY-2 +26E2 ; Extended_Pictographic# 6.0 [1] (⛢️) ASTRONOMICAL SYMBOL FOR URANUS +26E3 ; Extended_Pictographic# 5.2 [1] (⛣️) HEAVY CIRCLE WITH STROKE AND TWO DOTS ABOVE +26E4..26E7 ; Extended_Pictographic# 6.0 [4] (⛤️..⛧️) PENTAGRAM..INVERTED PENTAGRAM +26E8..26FF ; Extended_Pictographic# 5.2 [24] (⛨️..⛿️) BLACK CROSS ON SHIELD..WHITE FLAG WITH HORIZONTAL MIDDLE BLACK STRIPE +2700 ; Extended_Pictographic# 7.0 [1] (✀️) BLACK SAFETY SCISSORS +2701..2704 ; Extended_Pictographic# 1.1 [4] (✁️..✄️) UPPER BLADE SCISSORS..WHITE SCISSORS +2705 ; Extended_Pictographic# 6.0 [1] (✅) white heavy check mark +2708..2709 ; Extended_Pictographic# 1.1 [2] (✈️..✉️) airplane..envelope +270A..270B ; Extended_Pictographic# 6.0 [2] (✊..✋) raised fist..raised hand +270C..2712 ; Extended_Pictographic# 1.1 [7] (✌️..✒️) victory hand..black nib +2714 ; Extended_Pictographic# 1.1 [1] (✔️) heavy check mark +2716 ; Extended_Pictographic# 1.1 [1] (✖️) heavy multiplication x +271D ; Extended_Pictographic# 1.1 [1] (✝️) latin cross +2721 ; Extended_Pictographic# 1.1 [1] (✡️) star of David +2728 ; Extended_Pictographic# 6.0 [1] (✨) sparkles +2733..2734 ; Extended_Pictographic# 1.1 [2] (✳️..✴️) eight-spoked asterisk..eight-pointed star +2744 ; Extended_Pictographic# 1.1 [1] (❄️) snowflake +2747 ; Extended_Pictographic# 1.1 [1] (❇️) sparkle +274C ; Extended_Pictographic# 6.0 [1] (❌) cross mark +274E ; Extended_Pictographic# 6.0 [1] (❎) cross mark button +2753..2755 ; Extended_Pictographic# 6.0 [3] (❓..❕) question mark..white exclamation mark +2757 ; Extended_Pictographic# 5.2 [1] (❗) exclamation mark +2763..2767 ; Extended_Pictographic# 1.1 [5] (❣️..❧️) heavy heart exclamation..ROTATED FLORAL HEART BULLET +2795..2797 ; Extended_Pictographic# 6.0 [3] (➕..➗) heavy plus sign..heavy division sign +27A1 ; Extended_Pictographic# 1.1 [1] (➡️) right arrow +27B0 ; Extended_Pictographic# 6.0 [1] (➰) curly loop +27BF ; Extended_Pictographic# 6.0 [1] (➿) double curly loop +2934..2935 ; Extended_Pictographic# 3.2 [2] (⤴️..⤵️) right arrow curving up..right arrow curving down +2B05..2B07 ; Extended_Pictographic# 4.0 [3] (⬅️..⬇️) left arrow..down arrow +2B1B..2B1C ; Extended_Pictographic# 5.1 [2] (⬛..⬜) black large square..white large square +2B50 ; Extended_Pictographic# 5.1 [1] (⭐) star +2B55 ; Extended_Pictographic# 5.2 [1] (⭕) heavy large circle +3030 ; Extended_Pictographic# 1.1 [1] (〰️) wavy dash +303D ; Extended_Pictographic# 3.2 [1] (〽️) part alternation mark +3297 ; Extended_Pictographic# 1.1 [1] (㊗️) Japanese “congratulations” button +3299 ; Extended_Pictographic# 1.1 [1] (㊙️) Japanese “secret” button +1F000..1F02B ; Extended_Pictographic# 5.1 [44] (🀀️..🀫️) MAHJONG TILE EAST WIND..MAHJONG TILE BACK +1F02C..1F02F ; Extended_Pictographic# NA [4] (🀬️..🀯️) .. +1F030..1F093 ; Extended_Pictographic# 5.1[100] (🀰️..🂓️) DOMINO TILE HORIZONTAL BACK..DOMINO TILE VERTICAL-06-06 +1F094..1F09F ; Extended_Pictographic# NA [12] (🂔️..🂟️) .. +1F0A0..1F0AE ; Extended_Pictographic# 6.0 [15] (🂠️..🂮️) PLAYING CARD BACK..PLAYING CARD KING OF SPADES +1F0AF..1F0B0 ; Extended_Pictographic# NA [2] (🂯️..🂰️) .. +1F0B1..1F0BE ; Extended_Pictographic# 6.0 [14] (🂱️..🂾️) PLAYING CARD ACE OF HEARTS..PLAYING CARD KING OF HEARTS +1F0BF ; Extended_Pictographic# 7.0 [1] (🂿️) PLAYING CARD RED JOKER +1F0C0 ; Extended_Pictographic# NA [1] (🃀️) +1F0C1..1F0CF ; Extended_Pictographic# 6.0 [15] (🃁️..🃏) PLAYING CARD ACE OF DIAMONDS..joker +1F0D0 ; Extended_Pictographic# NA [1] (🃐️) +1F0D1..1F0DF ; Extended_Pictographic# 6.0 [15] (🃑️..🃟️) PLAYING CARD ACE OF CLUBS..PLAYING CARD WHITE JOKER +1F0E0..1F0F5 ; Extended_Pictographic# 7.0 [22] (🃠️..🃵️) PLAYING CARD FOOL..PLAYING CARD TRUMP-21 +1F0F6..1F0FF ; Extended_Pictographic# NA [10] (🃶️..🃿️) .. +1F10D..1F10F ; Extended_Pictographic# NA [3] (🄍️..🄏️) .. +1F12F ; Extended_Pictographic# 11.0 [1] (🄯️) COPYLEFT SYMBOL +1F16C..1F16F ; Extended_Pictographic# NA [4] (🅬️..🅯️) .. +1F170..1F171 ; Extended_Pictographic# 6.0 [2] (🅰️..🅱️) A button (blood type)..B button (blood type) +1F17E ; Extended_Pictographic# 6.0 [1] (🅾️) O button (blood type) +1F17F ; Extended_Pictographic# 5.2 [1] (🅿️) P button +1F18E ; Extended_Pictographic# 6.0 [1] (🆎) AB button (blood type) +1F191..1F19A ; Extended_Pictographic# 6.0 [10] (🆑..🆚) CL button..VS button +1F1AD..1F1E5 ; Extended_Pictographic# NA [57] (🆭️..🇥️) .. +1F201..1F202 ; Extended_Pictographic# 6.0 [2] (🈁..🈂️) Japanese “here” button..Japanese “service charge” button +1F203..1F20F ; Extended_Pictographic# NA [13] (🈃️..🈏️) .. +1F21A ; Extended_Pictographic# 5.2 [1] (🈚) Japanese “free of charge” button +1F22F ; Extended_Pictographic# 5.2 [1] (🈯) Japanese “reserved” button +1F232..1F23A ; Extended_Pictographic# 6.0 [9] (🈲..🈺) Japanese “prohibited” button..Japanese “open for business” button +1F23C..1F23F ; Extended_Pictographic# NA [4] (🈼️..🈿️) .. +1F249..1F24F ; Extended_Pictographic# NA [7] (🉉️..🉏️) .. +1F250..1F251 ; Extended_Pictographic# 6.0 [2] (🉐..🉑) Japanese “bargain” button..Japanese “acceptable” button +1F252..1F25F ; Extended_Pictographic# NA [14] (🉒️..🉟️) .. +1F260..1F265 ; Extended_Pictographic# 10.0 [6] (🉠️..🉥️) ROUNDED SYMBOL FOR FU..ROUNDED SYMBOL FOR CAI +1F266..1F2FF ; Extended_Pictographic# NA[154] (🉦️..🋿️) .. +1F300..1F320 ; Extended_Pictographic# 6.0 [33] (🌀..🌠) cyclone..shooting star +1F321..1F32C ; Extended_Pictographic# 7.0 [12] (🌡️..🌬️) thermometer..wind face +1F32D..1F32F ; Extended_Pictographic# 8.0 [3] (🌭..🌯) hot dog..burrito +1F330..1F335 ; Extended_Pictographic# 6.0 [6] (🌰..🌵) chestnut..cactus +1F336 ; Extended_Pictographic# 7.0 [1] (🌶️) hot pepper +1F337..1F37C ; Extended_Pictographic# 6.0 [70] (🌷..🍼) tulip..baby bottle +1F37D ; Extended_Pictographic# 7.0 [1] (🍽️) fork and knife with plate +1F37E..1F37F ; Extended_Pictographic# 8.0 [2] (🍾..🍿) bottle with popping cork..popcorn +1F380..1F393 ; Extended_Pictographic# 6.0 [20] (🎀..🎓) ribbon..graduation cap +1F394..1F39F ; Extended_Pictographic# 7.0 [12] (🎔️..🎟️) HEART WITH TIP ON THE LEFT..admission tickets +1F3A0..1F3C4 ; Extended_Pictographic# 6.0 [37] (🎠..🏄) carousel horse..person surfing +1F3C5 ; Extended_Pictographic# 7.0 [1] (🏅) sports medal +1F3C6..1F3CA ; Extended_Pictographic# 6.0 [5] (🏆..🏊) trophy..person swimming +1F3CB..1F3CE ; Extended_Pictographic# 7.0 [4] (🏋️..🏎️) person lifting weights..racing car +1F3CF..1F3D3 ; Extended_Pictographic# 8.0 [5] (🏏..🏓) cricket game..ping pong +1F3D4..1F3DF ; Extended_Pictographic# 7.0 [12] (🏔️..🏟️) snow-capped mountain..stadium +1F3E0..1F3F0 ; Extended_Pictographic# 6.0 [17] (🏠..🏰) house..castle +1F3F1..1F3F7 ; Extended_Pictographic# 7.0 [7] (🏱️..🏷️) WHITE PENNANT..label +1F3F8..1F3FA ; Extended_Pictographic# 8.0 [3] (🏸..🏺) badminton..amphora +1F400..1F43E ; Extended_Pictographic# 6.0 [63] (🐀..🐾) rat..paw prints +1F43F ; Extended_Pictographic# 7.0 [1] (🐿️) chipmunk +1F440 ; Extended_Pictographic# 6.0 [1] (👀) eyes +1F441 ; Extended_Pictographic# 7.0 [1] (👁️) eye +1F442..1F4F7 ; Extended_Pictographic# 6.0[182] (👂..📷) ear..camera +1F4F8 ; Extended_Pictographic# 7.0 [1] (📸) camera with flash +1F4F9..1F4FC ; Extended_Pictographic# 6.0 [4] (📹..📼) video camera..videocassette +1F4FD..1F4FE ; Extended_Pictographic# 7.0 [2] (📽️..📾️) film projector..PORTABLE STEREO +1F4FF ; Extended_Pictographic# 8.0 [1] (📿) prayer beads +1F500..1F53D ; Extended_Pictographic# 6.0 [62] (🔀..🔽) shuffle tracks button..downwards button +1F546..1F54A ; Extended_Pictographic# 7.0 [5] (🕆️..🕊️) WHITE LATIN CROSS..dove +1F54B..1F54F ; Extended_Pictographic# 8.0 [5] (🕋..🕏️) kaaba..BOWL OF HYGIEIA +1F550..1F567 ; Extended_Pictographic# 6.0 [24] (🕐..🕧) one o’clock..twelve-thirty +1F568..1F579 ; Extended_Pictographic# 7.0 [18] (🕨️..🕹️) RIGHT SPEAKER..joystick +1F57A ; Extended_Pictographic# 9.0 [1] (🕺) man dancing +1F57B..1F5A3 ; Extended_Pictographic# 7.0 [41] (🕻️..🖣️) LEFT HAND TELEPHONE RECEIVER..BLACK DOWN POINTING BACKHAND INDEX +1F5A4 ; Extended_Pictographic# 9.0 [1] (🖤) black heart +1F5A5..1F5FA ; Extended_Pictographic# 7.0 [86] (🖥️..🗺️) desktop computer..world map +1F5FB..1F5FF ; Extended_Pictographic# 6.0 [5] (🗻..🗿) mount fuji..moai +1F600 ; Extended_Pictographic# 6.1 [1] (😀) grinning face +1F601..1F610 ; Extended_Pictographic# 6.0 [16] (😁..😐) beaming face with smiling eyes..neutral face +1F611 ; Extended_Pictographic# 6.1 [1] (😑) expressionless face +1F612..1F614 ; Extended_Pictographic# 6.0 [3] (😒..😔) unamused face..pensive face +1F615 ; Extended_Pictographic# 6.1 [1] (😕) confused face +1F616 ; Extended_Pictographic# 6.0 [1] (😖) confounded face +1F617 ; Extended_Pictographic# 6.1 [1] (😗) kissing face +1F618 ; Extended_Pictographic# 6.0 [1] (😘) face blowing a kiss +1F619 ; Extended_Pictographic# 6.1 [1] (😙) kissing face with smiling eyes +1F61A ; Extended_Pictographic# 6.0 [1] (😚) kissing face with closed eyes +1F61B ; Extended_Pictographic# 6.1 [1] (😛) face with tongue +1F61C..1F61E ; Extended_Pictographic# 6.0 [3] (😜..😞) winking face with tongue..disappointed face +1F61F ; Extended_Pictographic# 6.1 [1] (😟) worried face +1F620..1F625 ; Extended_Pictographic# 6.0 [6] (😠..😥) angry face..sad but relieved face +1F626..1F627 ; Extended_Pictographic# 6.1 [2] (😦..😧) frowning face with open mouth..anguished face +1F628..1F62B ; Extended_Pictographic# 6.0 [4] (😨..😫) fearful face..tired face +1F62C ; Extended_Pictographic# 6.1 [1] (😬) grimacing face +1F62D ; Extended_Pictographic# 6.0 [1] (😭) loudly crying face +1F62E..1F62F ; Extended_Pictographic# 6.1 [2] (😮..😯) face with open mouth..hushed face +1F630..1F633 ; Extended_Pictographic# 6.0 [4] (😰..😳) anxious face with sweat..flushed face +1F634 ; Extended_Pictographic# 6.1 [1] (😴) sleeping face +1F635..1F640 ; Extended_Pictographic# 6.0 [12] (😵..🙀) dizzy face..weary cat face +1F641..1F642 ; Extended_Pictographic# 7.0 [2] (🙁..🙂) slightly frowning face..slightly smiling face +1F643..1F644 ; Extended_Pictographic# 8.0 [2] (🙃..🙄) upside-down face..face with rolling eyes +1F645..1F64F ; Extended_Pictographic# 6.0 [11] (🙅..🙏) person gesturing NO..folded hands +1F680..1F6C5 ; Extended_Pictographic# 6.0 [70] (🚀..🛅) rocket..left luggage +1F6C6..1F6CF ; Extended_Pictographic# 7.0 [10] (🛆️..🛏️) TRIANGLE WITH ROUNDED CORNERS..bed +1F6D0 ; Extended_Pictographic# 8.0 [1] (🛐) place of worship +1F6D1..1F6D2 ; Extended_Pictographic# 9.0 [2] (🛑..🛒) stop sign..shopping cart +1F6D3..1F6D4 ; Extended_Pictographic# 10.0 [2] (🛓️..🛔️) STUPA..PAGODA +1F6D5..1F6DF ; Extended_Pictographic# NA [11] (🛕️..🛟️) .. +1F6E0..1F6EC ; Extended_Pictographic# 7.0 [13] (🛠️..🛬) hammer and wrench..airplane arrival +1F6ED..1F6EF ; Extended_Pictographic# NA [3] (🛭️..🛯️) .. +1F6F0..1F6F3 ; Extended_Pictographic# 7.0 [4] (🛰️..🛳️) satellite..passenger ship +1F6F4..1F6F6 ; Extended_Pictographic# 9.0 [3] (🛴..🛶) kick scooter..canoe +1F6F7..1F6F8 ; Extended_Pictographic# 10.0 [2] (🛷..🛸) sled..flying saucer +1F6F9 ; Extended_Pictographic# 11.0 [1] (🛹) skateboard +1F6FA..1F6FF ; Extended_Pictographic# NA [6] (🛺️..🛿️) .. +1F774..1F77F ; Extended_Pictographic# NA [12] (🝴️..🝿️) .. +1F7D5..1F7D8 ; Extended_Pictographic# 11.0 [4] (🟕️..🟘️) CIRCLED TRIANGLE..NEGATIVE CIRCLED SQUARE +1F7D9..1F7FF ; Extended_Pictographic# NA [39] (🟙️..🟿️) .. +1F80C..1F80F ; Extended_Pictographic# NA [4] (🠌️..🠏️) .. +1F848..1F84F ; Extended_Pictographic# NA [8] (🡈️..🡏️) .. +1F85A..1F85F ; Extended_Pictographic# NA [6] (🡚️..🡟️) .. +1F888..1F88F ; Extended_Pictographic# NA [8] (🢈️..🢏️) .. +1F8AE..1F8FF ; Extended_Pictographic# NA [82] (🢮️..🣿️) .. +1F90C..1F90F ; Extended_Pictographic# NA [4] (🤌️..🤏️) .. +1F910..1F918 ; Extended_Pictographic# 8.0 [9] (🤐..🤘) zipper-mouth face..sign of the horns +1F919..1F91E ; Extended_Pictographic# 9.0 [6] (🤙..🤞) call me hand..crossed fingers +1F91F ; Extended_Pictographic# 10.0 [1] (🤟) love-you gesture +1F920..1F927 ; Extended_Pictographic# 9.0 [8] (🤠..🤧) cowboy hat face..sneezing face +1F928..1F92F ; Extended_Pictographic# 10.0 [8] (🤨..🤯) face with raised eyebrow..exploding head +1F930 ; Extended_Pictographic# 9.0 [1] (🤰) pregnant woman +1F931..1F932 ; Extended_Pictographic# 10.0 [2] (🤱..🤲) breast-feeding..palms up together +1F933..1F93A ; Extended_Pictographic# 9.0 [8] (🤳..🤺) selfie..person fencing +1F93C..1F93E ; Extended_Pictographic# 9.0 [3] (🤼..🤾) people wrestling..person playing handball +1F93F ; Extended_Pictographic# NA [1] (🤿️) +1F940..1F945 ; Extended_Pictographic# 9.0 [6] (🥀..🥅) wilted flower..goal net +1F947..1F94B ; Extended_Pictographic# 9.0 [5] (🥇..🥋) 1st place medal..martial arts uniform +1F94C ; Extended_Pictographic# 10.0 [1] (🥌) curling stone +1F94D..1F94F ; Extended_Pictographic# 11.0 [3] (🥍..🥏) lacrosse..flying disc +1F950..1F95E ; Extended_Pictographic# 9.0 [15] (🥐..🥞) croissant..pancakes +1F95F..1F96B ; Extended_Pictographic# 10.0 [13] (🥟..🥫) dumpling..canned food +1F96C..1F970 ; Extended_Pictographic# 11.0 [5] (🥬..🥰) leafy green..smiling face with 3 hearts +1F971..1F972 ; Extended_Pictographic# NA [2] (🥱️..🥲️) .. +1F973..1F976 ; Extended_Pictographic# 11.0 [4] (🥳..🥶) partying face..cold face +1F977..1F979 ; Extended_Pictographic# NA [3] (🥷️..🥹️) .. +1F97A ; Extended_Pictographic# 11.0 [1] (🥺) pleading face +1F97B ; Extended_Pictographic# NA [1] (🥻️) +1F97C..1F97F ; Extended_Pictographic# 11.0 [4] (🥼..🥿) lab coat..woman’s flat shoe +1F980..1F984 ; Extended_Pictographic# 8.0 [5] (🦀..🦄) crab..unicorn face +1F985..1F991 ; Extended_Pictographic# 9.0 [13] (🦅..🦑) eagle..squid +1F992..1F997 ; Extended_Pictographic# 10.0 [6] (🦒..🦗) giraffe..cricket +1F998..1F9A2 ; Extended_Pictographic# 11.0 [11] (🦘..🦢) kangaroo..swan +1F9A3..1F9AF ; Extended_Pictographic# NA [13] (🦣️..🦯️) .. +1F9B0..1F9B9 ; Extended_Pictographic# 11.0 [10] (🦰..🦹) red-haired..supervillain +1F9BA..1F9BF ; Extended_Pictographic# NA [6] (🦺️..🦿️) .. +1F9C0 ; Extended_Pictographic# 8.0 [1] (🧀) cheese wedge +1F9C1..1F9C2 ; Extended_Pictographic# 11.0 [2] (🧁..🧂) cupcake..salt +1F9C3..1F9CF ; Extended_Pictographic# NA [13] (🧃️..🧏️) .. +1F9D0..1F9E6 ; Extended_Pictographic# 10.0 [23] (🧐..🧦) face with monocle..socks +1F9E7..1F9FF ; Extended_Pictographic# 11.0 [25] (🧧..🧿) red envelope..nazar amulet +1FA00..1FA5F ; Extended_Pictographic# NA [96] (🨀️..🩟️) .. +1FA60..1FA6D ; Extended_Pictographic# 11.0 [14] (🩠️..🩭️) XIANGQI RED GENERAL..XIANGQI BLACK SOLDIER +1FA6E..1FFFD ; Extended_Pictographic# NA[1424] (🩮️..🿽️) .. + +# Total elements: 3793 + +#EOF diff --git a/Project/Tools/whatsapp2csv.sh b/Project/Tools/whatsapp2csv.sh index 6583d4a..ad59d5d 100755 --- a/Project/Tools/whatsapp2csv.sh +++ b/Project/Tools/whatsapp2csv.sh @@ -77,7 +77,7 @@ perform_and_exit echo -e "date\ttime\tperson\tmessage" > "$OUTPUT" # 3. replace separators of whatsapp's txt-format with '\t' as separator for csv # 4. delete all double-quotes. Because people are too stupid to type them in pairs and that breaks some csv-interpreters 🤦‍♂ -perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'added|created|end-to-end|Media omitted' | sed 's/, /\t/1; s/- /\t/1; s/: /\t/1;' | tr -d '"' >> "$OUTPUT" +perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'left|added|created|end-to-end|Media omitted' | sed 's/, /\t/1; s/- /\t/1; s/: /\t/1;' | tr -d '"' >> "$OUTPUT" confirm_action From ef5422bb0c8ac594d02299f0c80eef4977903c80 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Sat, 19 May 2018 20:43:06 +0200 Subject: [PATCH 06/15] more emoji stuff --- Project/Tools/emoji tester.ipynb | 2590 +++--------------------------- Project/Tools/whatsapp2csv.sh | 2 +- 2 files changed, 180 insertions(+), 2412 deletions(-) diff --git a/Project/Tools/emoji tester.ipynb b/Project/Tools/emoji tester.ipynb index 1f3ad70..23f3462 100644 --- a/Project/Tools/emoji tester.ipynb +++ b/Project/Tools/emoji tester.ipynb @@ -7,7 +7,45 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from IPython.display import clear_output, Markdown, Math" + "from IPython.display import clear_output, Markdown, Math\n", + "import ipywidgets as widgets\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## file input stuff:\n", + "\n", + "* replace `test.txt` with your whatsapp log file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "processing File: test.txt\n", + "================================================================================\n", + "================================================================================\n", + "successfully finished action: processing File: test.txt\n", + "================================================================================\n", + "================================================================================\n", + "Wrote output to test.txt.csv\n", + "================================================================================\n" + ] + } + ], + "source": [ + "%%bash\n", + "./whatsapp2csv.sh test.txt" ] }, { @@ -19,235 +57,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimepersonmessage
010/18/1611:18Lena CartaHallo Jonas 😉
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...
210/18/1611:21Lena Carta😱🤕
310/18/1611:21Sonja Vorwerk🙈😂
410/18/1611:22Caner Ucar😂😂😂
510/18/1611:22Patrick (Arithmeum)hallo jeff
610/18/1611:22Caner UcarPatrick kein Handy am Empfang
710/18/1611:26Patrick (Arithmeum)halts maul und geh ma duschen
810/18/1611:34Simon (Arithmeum)Hab dich als Carsten abgespeichert, selber Schuld
910/18/1611:36Caner UcarPatrick ich hab geduscht mit Kristina und Sonja v
1010/18/1611:36Lena Cartaähm....
1110/18/1611:37Jonathan (Arithmeum)Caner benutzt das Damen-Bad
1210/18/1611:37Jonas WeinzLüg doch nicht, du tanzt die ganze Zeit aufm T...
1310/18/1611:37Jonathan (Arithmeum)bääh😱
1410/18/1611:40Patrick (Arithmeum)ja caner so ist das unter frauen..da geht man ...
1510/18/1612:02Thomas KoppelbergEeemmm was?
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht 😜
1710/18/1612:14Thomas KoppelbergIch wollte einiges, aber ich kann mich zurückh...
1810/18/1612:19Simon (Arithmeum)Genau 🙊
1910/18/1613:36Caner UcarSex
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" - ], - "text/plain": [ - " date time person \\\n", - "0 10/18/16 11:18 Lena Carta \n", - "1 10/18/16 11:21 Jonas Weinz \n", - "2 10/18/16 11:21 Lena Carta \n", - "3 10/18/16 11:21 Sonja Vorwerk \n", - "4 10/18/16 11:22 Caner Ucar \n", - "5 10/18/16 11:22 Patrick (Arithmeum) \n", - "6 10/18/16 11:22 Caner Ucar \n", - "7 10/18/16 11:26 Patrick (Arithmeum) \n", - "8 10/18/16 11:34 Simon (Arithmeum) \n", - "9 10/18/16 11:36 Caner Ucar \n", - "10 10/18/16 11:36 Lena Carta \n", - "11 10/18/16 11:37 Jonathan (Arithmeum) \n", - "12 10/18/16 11:37 Jonas Weinz \n", - "13 10/18/16 11:37 Jonathan (Arithmeum) \n", - "14 10/18/16 11:40 Patrick (Arithmeum) \n", - "15 10/18/16 12:02 Thomas Koppelberg \n", - "16 10/18/16 12:12 Kristina Stellwag \n", - "17 10/18/16 12:14 Thomas Koppelberg \n", - "18 10/18/16 12:19 Simon (Arithmeum) \n", - "19 10/18/16 13:36 Caner Ucar \n", - "\n", - " message \n", - "0 Hallo Jonas 😉 \n", - "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... \n", - "2 😱🤕 \n", - "3 🙈😂 \n", - "4 😂😂😂 \n", - "5 hallo jeff \n", - "6 Patrick kein Handy am Empfang \n", - "7 halts maul und geh ma duschen \n", - "8 Hab dich als Carsten abgespeichert, selber Schuld \n", - "9 Patrick ich hab geduscht mit Kristina und Sonja v \n", - "10 ähm.... \n", - "11 Caner benutzt das Damen-Bad \n", - "12 Lüg doch nicht, du tanzt die ganze Zeit aufm T... \n", - "13 bääh😱 \n", - "14 ja caner so ist das unter frauen..da geht man ... \n", - "15 Eeemmm was? \n", - "16 Thomas, du wolltest gestern ja nicht 😜 \n", - "17 Ich wollte einiges, aber ich kann mich zurückh... \n", - "18 Genau 🙊 \n", - "19 Sex " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "messages = pd.read_csv('test.csv', delimiter='\\t')\n", - "messages[:20]" + "messages = pd.read_csv('test.txt.csv', delimiter='\\t')\n", + "messages.head()" ] }, { @@ -259,7 +74,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "found existing emoji specification\n" + ] + } + ], + "source": [ + "%%bash\n", + "if [ ! -e emoji-data.txt ]\n", + "then\n", + " echo \"downloading emoji specification\"\n", + " wget https://www.unicode.org/Public/emoji/11.0/emoji-data.txt\n", + "else\n", + " echo \"found existing emoji specification\"\n", + "fi\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -268,428 +107,21 @@ " 0x1F3FC,\n", " 0x1F3FD,\n", " 0x1F3FE,\n", - " 0x1F3FF\n", + " 0x1F3FF,\n", + " 0x2642,\n", + " 0x2640\n", "])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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unicodetype
228231A..231BEmoji_Presentation
22923E9..23ECEmoji_Presentation
23023F0Emoji_Presentation
23123F3Emoji_Presentation
23225FD..25FEEmoji_Presentation
2332614..2615Emoji_Presentation
2342648..2653Emoji_Presentation
235267FEmoji_Presentation
2362693Emoji_Presentation
23726A1Emoji_Presentation
23826AA..26ABEmoji_Presentation
23926BD..26BEEmoji_Presentation
24026C4..26C5Emoji_Presentation
24126CEEmoji_Presentation
24226D4Emoji_Presentation
24326EAEmoji_Presentation
24426F2..26F3Emoji_Presentation
24526F5Emoji_Presentation
24626FAEmoji_Presentation
24726FDEmoji_Presentation
2482705Emoji_Presentation
249270A..270BEmoji_Presentation
2502728Emoji_Presentation
251274CEmoji_Presentation
252274EEmoji_Presentation
2532753..2755Emoji_Presentation
2542757Emoji_Presentation
2552795..2797Emoji_Presentation
25627B0Emoji_Presentation
25727BFEmoji_Presentation
.........
3291F6F7..1F6F8Emoji_Presentation
3301F6F9Emoji_Presentation
3311F910..1F918Emoji_Presentation
3321F919..1F91EEmoji_Presentation
3331F91FEmoji_Presentation
3341F920..1F927Emoji_Presentation
3351F928..1F92FEmoji_Presentation
3361F930Emoji_Presentation
3371F931..1F932Emoji_Presentation
3381F933..1F93AEmoji_Presentation
3391F93C..1F93EEmoji_Presentation
3401F940..1F945Emoji_Presentation
3411F947..1F94BEmoji_Presentation
3421F94CEmoji_Presentation
3431F94D..1F94FEmoji_Presentation
3441F950..1F95EEmoji_Presentation
3451F95F..1F96BEmoji_Presentation
3461F96C..1F970Emoji_Presentation
3471F973..1F976Emoji_Presentation
3481F97AEmoji_Presentation
3491F97C..1F97FEmoji_Presentation
3501F980..1F984Emoji_Presentation
3511F985..1F991Emoji_Presentation
3521F992..1F997Emoji_Presentation
3531F998..1F9A2Emoji_Presentation
3541F9B0..1F9B9Emoji_Presentation
3551F9C0Emoji_Presentation
3561F9C1..1F9C2Emoji_Presentation
3571F9D0..1F9E6Emoji_Presentation
3581F9E7..1F9FFEmoji_Presentation
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131 rows × 2 columns

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" - ], - "text/plain": [ - " unicode type\n", - "228 231A..231B Emoji_Presentation\n", - "229 23E9..23EC Emoji_Presentation\n", - "230 23F0 Emoji_Presentation\n", - "231 23F3 Emoji_Presentation\n", - "232 25FD..25FE Emoji_Presentation\n", - "233 2614..2615 Emoji_Presentation\n", - "234 2648..2653 Emoji_Presentation\n", - "235 267F Emoji_Presentation\n", - "236 2693 Emoji_Presentation\n", - "237 26A1 Emoji_Presentation\n", - "238 26AA..26AB Emoji_Presentation\n", - "239 26BD..26BE Emoji_Presentation\n", - "240 26C4..26C5 Emoji_Presentation\n", - "241 26CE Emoji_Presentation\n", - "242 26D4 Emoji_Presentation\n", - "243 26EA Emoji_Presentation\n", - "244 26F2..26F3 Emoji_Presentation\n", - "245 26F5 Emoji_Presentation\n", - "246 26FA Emoji_Presentation\n", - "247 26FD Emoji_Presentation\n", - "248 2705 Emoji_Presentation\n", - "249 270A..270B Emoji_Presentation\n", - "250 2728 Emoji_Presentation\n", - "251 274C Emoji_Presentation\n", - "252 274E Emoji_Presentation\n", - "253 2753..2755 Emoji_Presentation\n", - "254 2757 Emoji_Presentation\n", - "255 2795..2797 Emoji_Presentation\n", - "256 27B0 Emoji_Presentation\n", - "257 27BF Emoji_Presentation\n", - ".. ... ...\n", - "329 1F6F7..1F6F8 Emoji_Presentation\n", - "330 1F6F9 Emoji_Presentation\n", - "331 1F910..1F918 Emoji_Presentation\n", - "332 1F919..1F91E Emoji_Presentation\n", - "333 1F91F Emoji_Presentation\n", - "334 1F920..1F927 Emoji_Presentation\n", - "335 1F928..1F92F Emoji_Presentation\n", - "336 1F930 Emoji_Presentation\n", - "337 1F931..1F932 Emoji_Presentation\n", - "338 1F933..1F93A Emoji_Presentation\n", - "339 1F93C..1F93E Emoji_Presentation\n", - "340 1F940..1F945 Emoji_Presentation\n", - "341 1F947..1F94B Emoji_Presentation\n", - "342 1F94C Emoji_Presentation\n", - "343 1F94D..1F94F Emoji_Presentation\n", - "344 1F950..1F95E Emoji_Presentation\n", - "345 1F95F..1F96B Emoji_Presentation\n", - "346 1F96C..1F970 Emoji_Presentation\n", - "347 1F973..1F976 Emoji_Presentation\n", - "348 1F97A Emoji_Presentation\n", - "349 1F97C..1F97F Emoji_Presentation\n", - "350 1F980..1F984 Emoji_Presentation\n", - "351 1F985..1F991 Emoji_Presentation\n", - "352 1F992..1F997 Emoji_Presentation\n", - "353 1F998..1F9A2 Emoji_Presentation\n", - "354 1F9B0..1F9B9 Emoji_Presentation\n", - "355 1F9C0 Emoji_Presentation\n", - "356 1F9C1..1F9C2 Emoji_Presentation\n", - "357 1F9D0..1F9E6 Emoji_Presentation\n", - "358 1F9E7..1F9FF Emoji_Presentation\n", - "\n", - "[131 rows x 2 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n", "emoji_data['type'] = emoji_data['type'].str.strip()\n", - "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n", - "emoji_data" + "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]" ] }, { @@ -701,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -710,7 +142,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -721,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -735,7 +167,7 @@ "Name: unicode, dtype: object" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -754,9 +186,52 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**used Emojis:**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'🀄\\U0001f9f1\\U0001f9f2\\U0001f9f3\\U0001f9f4\\U0001f9f5\\U0001f9f6🃏🤐🤑🤒🤓🤔🤕🤖🤗🤘🤙🤚🤛🤜🤝🤞\\U0001f91f🤠🤡🤢🤣🤤🤥🤦🤧\\U0001f928\\U0001f929\\U0001f92a\\U0001f92b\\U0001f92c\\U0001f92d\\U0001f92e\\U0001f92f🤰\\U0001f931\\U0001f932🤳🤴🤵🤶🤷🤸🤹🤺🤼🤽🤾🥀🥁🥂🥃🥄🥅🥇🥈🥉🥊🥋\\U0001f94c\\U0001f94d\\U0001f94e\\U0001f94f🥐🥑🥒🥓🥔🥕🥖🥗🥘🥙🥚🥛🥜🥝🥞\\U0001f95f\\U0001f960\\U0001f961\\U0001f962\\U0001f963\\U0001f964\\U0001f965\\U0001f966\\U0001f967\\U0001f968\\U0001f969\\U0001f96a\\U0001f96b\\U0001f96c\\U0001f96d\\U0001f96e\\U0001f96f\\U0001f970\\U0001f9ec\\U0001f9ed\\U0001f973\\U0001f974\\U0001f975\\U0001f976\\U0001f9ee\\U0001f97a\\U0001f9ef\\U0001f97c\\U0001f97d\\U0001f97e\\U0001f97f🦀🦁🦂🦃🦄🦅🦆🦇🦈🦉🦊🦋🦌🦍🆎🦎🦏🆑🆒🆓🆔🆕🆖🆗🆘🆙🆚\\U0001f992\\U0001f993\\U0001f994\\U0001f995\\U0001f996\\U0001f997\\U0001f998\\U0001f999\\U0001f99a\\U0001f99b\\U0001f99c\\U0001f99d\\U0001f99e\\U0001f99f\\U0001f9a0\\U0001f9a1\\U0001f9a2\\U0001f9f8\\U0001f9b0\\U0001f9b1\\U0001f9b2\\U0001f9b3\\U0001f9b4\\U0001f9b5\\U0001f9b6\\U0001f9b7\\U0001f9b8\\U0001f9b9🧀\\U0001f9c1\\U0001f9c2\\U0001f9d0\\U0001f9d1\\U0001f9d2\\U0001f9d3\\U0001f9d4\\U0001f9d5\\U0001f9d6\\U0001f9d7\\U0001f9d8\\U0001f9d9\\U0001f9da\\U0001f9db\\U0001f9dc\\U0001f9dd\\U0001f9de\\U0001f9df\\U0001f9e0\\U0001f9e1\\U0001f9e2\\U0001f9e3\\U0001f9e4\\U0001f9e5🇦🇧🇨🇩🇪🇫🇬🇭🇮🇯🇰🇱🇲🇳🇴🇵🇶🇷🇸🇹🇺🇻🇼🇽🇾🇿\\U0001f9f7🈁\\U0001f9f9\\U0001f9fa\\U0001f9fb\\U0001f9fc\\U0001f9fd\\U0001f9fe\\U0001f9ff🈚🈯🈲🈳🈴🈵🈶🈸🈹🈺🉐🉑🌀🌁🌂🌃🌄🌅🌆🌇🌈🌉🌊🌋🌌🌍🌎🌏🌐🌑🌒🌓🌔🌕🌖🌗🌘🌙⌚⌛⬛⬜🌚🌛🌜🌝🌞🌟🌠🌭🌮🌯🌰🌱🌲🌳🌴🌵🌷🌸🌹🌺🌻🌼🌽🌾🌿🍀🍁🍂🍃🍄🍅🍆🍇🍈🍉🍊🍋🍌🍍🍎🍏⭐🍐🍑🍒🍓⭕🍔🍕🍖🍗🍘🍙🍚🍛🍜🍟🍝🍞🍠🍡🍤🍢🍣🍥🍦🍩🍧🍨🍪🍫🍮🍬🍭🍯🍰🍳🍱🍲🍴🍵🍸🍶🍷🍹🍺🍻🍼🍾🍿🎀🎁🎂🎃🎅🎄🎆🎈🎉🎊🎋🎌🎍🎇🎏🎐🎎🎑🎒🎓🎠🎡🎢🎣🎤🎥🎦🎧🎨🎩🎪🎫🎬🎭🎮🎯🎰🎱🎲🎳🎴🎵🎶🎷🎸🎹🎺🎻🎼🎽🎾🎿🏀🏁🏂🏃🏄🏅🏆🏇🏈🏉🏊🏏🏐🏑🏒🏓🏠🏡🏢🏣🏤🏥🏦🏧🏨🏩🏪🏫🏬🏭🏮🏯🏰⏩⏪⏫🏴⏬⏰⏳🏸🏹🏺🐀🐁🐂🐃🐄🐅🐆🐇🐈🐉🐊🐋🐌🐍🐎🐏🐐🐑🐒🐓🐔🐕🐖🐗🐘🐙🐚🐛🐜🐝🐞🐟🐠🐡🐢🐣🐤🐥🐦🐧🐨🐩🐪🐫🐬🐭🐮🐯🐰🐱🐲🐳🐴🐵🐶🐷🐸🐹🐺🐻🐼🐽🐾👀👂👃👄👅👆👇👈👉👊👋👌👍👎👏👐👑👒👓👔👕👖👗👘👙👚👛👜👝👞👟👠👡👢👣👤👥👦👧👨👩👪👫👬👭👮👯👰👱👲👳👴👵👶👷👸👹👺👻👼👽👾👿💀💁💂💃💄💅💆💇💈💉💊💋💌💍💎💏💐💑💒💓💔💕💖💗💘💙💚💛💜💝💞💟💠💡💢💣💤💥💦💧💨💩💪💫💬💭💮💯💰💱💲💳💴💵💶💷💸💹💺💻💼💽💾💿📀📁📂📃📄📅📆📇📈📉📊📋📌📍📎📏📐📑📒📓📔📕📖📗📘📙📚📛📜📝📞📟📠📡📢📣📤📥📦📧📨📩📪📫📬📭📮📯📰📱📲📳📴📵📶📷📸📹📺📻📼📿🔀🔁🔂🔃🔄🔅🔆🔇🔈🔉🔊🔋🔌🔍🔎🔏🔐🔑🔒🔓🔔🔕🔖🔗🔘🔙🔚🔛🔜🔝🔞🔟🔠🔡🔢🔣🔤🔥🔦🔧🔨🔩🔪🔫🔬🔭🔮🔯🔰🔱🔲🔳🔴🔵🔶🔷🔸🔹🔺🔻🔼🔽🕋🕌🕍🕎🕐🕑🕒🕓🕔🕕🕖🕗🕘🕙🕚🕛🕜🕝🕞🕟🕠🕡🕢🕣🕤🕥🕦🕧🕺🖕🖖🖤🗻🗼◽◾🗽🗾🗿😀😁😂😃😄😅😆😇😈😉😊😋😌😍😎😏😐😑☔☕😒😓😔😕😖😗😘😙😚😛😜😝😞😟😠😡😢😣😤😥😦😧😨😩😪😫😬😭😮😯😰😱😲😳😴😵😶😷😸😹😺😻😼😽😾😿🙀🙁🙂🙃♈♉♊♋♌♍♎♏♐♑♒♓🙋🙌🙍🙎🙏♿🚀🚁🚂🚃🚄🚅🚆🚇🚈🚉🚊🚋🚌🚍🚎🚏🚐🚑🚒⚓🚓🚔🚕🚖🚗🚘🚙🚚🚛🚜🚝🚞🚟⚡🚠🚡🚢🚣🚤🚥🚦🚧⚪⚫🚨🚩🚪🚫🚬🚭🚮🚯🚰🚱🚲🚳🚴🚵🚶🚷🚸⚽⚾🚹🚺🚻🚼🚽⛄⛅🚾🚿🛀🛁🛂🛃🛄🛅⛎🛌🛐🛑🛒⛔⛪🛫🛬⛲⛳🛴⛵🛵🛶\\U0001f6f7\\U0001f6f8⛺\\U0001f6f9⛽✅\\U0001f9e6🙄✊✋🙅🙆🙇🙈\\U0001f9e7🙉🙊✨\\U0001f9e8❌❎\\U0001f9e9❓❔❕❗\\U0001f9ea\\U0001f9eb➕➖➗🦐🦑➰➿\\U0001f9f0'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**blacklisted Emojis:**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'♀♂🏻🏼🏽🏾🏿'" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "emoji_code_list = []\n", "for entry in emoji_codes:\n", @@ -771,12 +246,16 @@ " # single entry\n", " if i not in emoji_blacklist:\n", " emoji_code_list.append(int(entry,16))\n", - "emoji_code_set = set(emoji_code_list)" + "emoji_code_set = set(emoji_code_list)\n", + "display(Markdown(\"**used Emojis:**\"))\n", + "display(\"\".join([chr(x) for x in emoji_code_set]))\n", + "display(Markdown(\"**blacklisted Emojis:**\"))\n", + "display(\"\".join([chr(x) for x in emoji_blacklist]))" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -801,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -810,276 +289,30 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "for i in messages.index:\n", " emoji_list = []\n", - " to_remove = []\n", " m = messages.iloc[i]['message']\n", + " m_new = \"\"\n", " for c in str(m):\n", " if ord(c) in emoji_code_set:\n", " emoji_list.append(c)\n", - " elif ord(c) in emoji_blacklist:\n", - " to_remove.append(c)\n", + " elif ord(c) not in emoji_blacklist:\n", + " m_new += c\n", " \n", - " messages.loc[i,'emojis'] = emoji_list\n", + " messages.loc[i,'emojis'] = set(emoji_list)\n", " #remove emiójis from message\n", - " for e in (emoji_list + to_remove):\n", - " m = m.replace(e,\"\")\n", - " messages.loc[i,'message'] = m\n" + " messages.loc[i,'message'] = m_new\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimepersonmessageemojis
010/18/1611:18Lena CartaHallo Jonas[😉]
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...[🤔]
210/18/1611:21Lena Carta[😱, 🤕]
310/18/1611:21Sonja Vorwerk[🙈, 😂]
410/18/1611:22Caner Ucar[😂, 😂, 😂]
510/18/1611:22Patrick (Arithmeum)hallo jeff[]
610/18/1611:22Caner UcarPatrick kein Handy am Empfang[]
710/18/1611:26Patrick (Arithmeum)halts maul und geh ma duschen[]
810/18/1611:34Simon (Arithmeum)Hab dich als Carsten abgespeichert, selber Schuld[]
910/18/1611:36Caner UcarPatrick ich hab geduscht mit Kristina und Sonja v[]
1010/18/1611:36Lena Cartaähm....[]
1110/18/1611:37Jonathan (Arithmeum)Caner benutzt das Damen-Bad[]
1210/18/1611:37Jonas WeinzLüg doch nicht, du tanzt die ganze Zeit aufm T...[]
1310/18/1611:37Jonathan (Arithmeum)bääh[😱]
1410/18/1611:40Patrick (Arithmeum)ja caner so ist das unter frauen..da geht man ...[]
1510/18/1612:02Thomas KoppelbergEeemmm was?[]
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht[😜]
1710/18/1612:14Thomas KoppelbergIch wollte einiges, aber ich kann mich zurückh...[]
1810/18/1612:19Simon (Arithmeum)Genau[🙊]
1910/18/1613:36Caner UcarSex[]
\n", - "
" - ], - "text/plain": [ - " date time person \\\n", - "0 10/18/16 11:18 Lena Carta \n", - "1 10/18/16 11:21 Jonas Weinz \n", - "2 10/18/16 11:21 Lena Carta \n", - "3 10/18/16 11:21 Sonja Vorwerk \n", - "4 10/18/16 11:22 Caner Ucar \n", - "5 10/18/16 11:22 Patrick (Arithmeum) \n", - "6 10/18/16 11:22 Caner Ucar \n", - "7 10/18/16 11:26 Patrick (Arithmeum) \n", - "8 10/18/16 11:34 Simon (Arithmeum) \n", - "9 10/18/16 11:36 Caner Ucar \n", - "10 10/18/16 11:36 Lena Carta \n", - "11 10/18/16 11:37 Jonathan (Arithmeum) \n", - "12 10/18/16 11:37 Jonas Weinz \n", - "13 10/18/16 11:37 Jonathan (Arithmeum) \n", - "14 10/18/16 11:40 Patrick (Arithmeum) \n", - "15 10/18/16 12:02 Thomas Koppelberg \n", - "16 10/18/16 12:12 Kristina Stellwag \n", - "17 10/18/16 12:14 Thomas Koppelberg \n", - "18 10/18/16 12:19 Simon (Arithmeum) \n", - "19 10/18/16 13:36 Caner Ucar \n", - "\n", - " message emojis \n", - "0 Hallo Jonas [😉] \n", - "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... [🤔] \n", - "2 [😱, 🤕] \n", - "3 [🙈, 😂] \n", - "4 [😂, 😂, 😂] \n", - "5 hallo jeff [] \n", - "6 Patrick kein Handy am Empfang [] \n", - "7 halts maul und geh ma duschen [] \n", - "8 Hab dich als Carsten abgespeichert, selber Schuld [] \n", - "9 Patrick ich hab geduscht mit Kristina und Sonja v [] \n", - "10 ähm.... [] \n", - "11 Caner benutzt das Damen-Bad [] \n", - "12 Lüg doch nicht, du tanzt die ganze Zeit aufm T... [] \n", - "13 bääh [😱] \n", - "14 ja caner so ist das unter frauen..da geht man ... [] \n", - "15 Eeemmm was? [] \n", - "16 Thomas, du wolltest gestern ja nicht [😜] \n", - "17 Ich wollte einiges, aber ich kann mich zurückh... [] \n", - "18 Genau [🙊] \n", - "19 Sex [] " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "messages[:20]" ] @@ -1093,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1103,728 +336,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimepersonmessageemojis
010/18/1611:18Lena CartaHallo Jonas[😉]
110/18/1611:21Jonas WeinzMoin moin ;) Vllt. bin ich aber gar nicht Jona...[🤔]
1310/18/1611:37Jonathan (Arithmeum)bääh[😱]
1610/18/1612:12Kristina StellwagThomas, du wolltest gestern ja nicht[😜]
1810/18/1612:19Simon (Arithmeum)Genau[🙊]
2910/18/1618:38Lena Cartawas du bist morgen nicht da ???[😱, 😯, 😔]
3110/18/1618:39Lena Cartaich dachte diese Woche komplett noch. Aber gut...[😇]
3210/18/1618:40Kristina StellwagNee das Semester hat doch angefangen[😜]
3310/18/1618:42Lena Cartaja hatte ich gerade vergessen . Dabei hatte ic...[😂, 😶]
4110/20/1612:50Steffi (Arithmeum)Klar[👹]
4410/20/1617:54Steffi (Arithmeum)Mein Zug lässt sich auch heute Zeit[😞]
4510/20/1618:08Steffi (Arithmeum)Wir fangen doch wahrscheinlich nicht vor halb ...[😳]
5110/20/1620:58Anna BorutzkyIst das dein Ernst?[🙄]
5210/20/1620:58Patrick (Arithmeum)Habs voll vercheckt sry ..gerade erst gemerkt[🙄]
7010/20/1622:59Jonathan (Arithmeum)Ich bitte darum Caner....[😍, 😍, 😍]
7810/25/1601:03Caner UcarHaaaaaalloooo[🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 😏, 🤗, 🤗, 😏]
7910/25/1601:04Michael (Arithmeum)Keine sorge, das hat bei dir nichts mit der St...[😉]
8810/25/1609:02Lena Cartaich wollte eigentlich schon da sein, aber ich ...[😇]
8910/25/1609:04Jonathan (Arithmeum)SIIIIEG...⚔[🎉]
9010/25/1610:00Lena Cartabesser spät als nie. ich bin immerhin auf dem ...[😁]
9310/25/1621:49Jonathan (Arithmeum)Ich nicht[😄]
9410/25/1621:50Lena Cartaich auch nicht[😳]
9510/25/1622:13Konstantin (Arithmeum)Wer hat den zuletzt was laminiert? lena und i...[🤔]
9610/25/1623:28Caner UcarIch auch nicht[🤔]
10210/27/1614:43Sonja Vorwerkhm müsste das am besten heute schon wissen...w...[🙈, 😄]
11510/28/1608:32Lena CartaGuten Morgen liebe Kollegen, könnte einer von ...[🙁]
11610/28/1609:48Lena CartaIst noch niemand im Büro?[😇]
11710/28/1609:50Caner Ucar10:10 ca.[🙌]
11810/28/1609:51Caner UcarWann hat das woki geöffnet[😄]
12110/28/1610:54Lena CartaDa hätte ich jetzt auch nicht gesucht[😝]
..................
86635/9/1817:42Jonas Weinzmehrere Bürostühle hintereinander?[🤔]
86655/9/1817:43Lena CartaWollte Kristina das nicht immer schonmal machen[😁]
86755/10/1814:06Lena KlöcknerErledigt[👍]
86765/10/1814:07Caner UcarDanke[😊]
86775/10/1814:23Anna ZensIch hatte es auch schon erledigt[😄]
86785/11/1808:16Lena CartaGute morgen, ich habe gesehen neben dem Buch l...[🙂]
86795/11/1808:25Anna ZensDie handschriftlichen sind die, von denen kein...[😄]
86805/11/1808:26Lena Cartaachso ich denke mal dann wird sich Ingo melden[😀, 😀]
86815/11/1813:18Freya (Arithmeum)Mein Kalender funktioniert nicht - könnte mir ...[🤔]
86825/12/1814:53Freya (Arithmeum)Könnte jemand für Montag Abend zwei Karten für...[😇]
86835/12/1815:05Anna BorutzkyUnd eine Freikarte für Sonja bitte![😀]
86895/12/1815:16Lena CartaDanke Wieso kommen solche Fragen immer am Woc...[😀]
86925/12/1815:17Anna BorutzkyMurphy‘s Gesetz?[😉]
86935/12/1815:17Caner UcarCool[😍]
86955/12/1815:23Jonas Weinz...und die kommt auf Annas Tisch[😉]
86965/12/1815:23Freya (Arithmeum)Vielen, vielen Dank ☺ Sowas passiert immer am ...[🙈]
86975/12/1815:23Lena CartaDas war nicht auf dich bezogen .[😀]
86985/12/1815:24Freya (Arithmeum)Na gut[😄]
86995/12/1815:24Lena CartaEigentlich ist es halbwegs entspannt heute Ic...[😀, 😛]
87005/12/1815:25Freya (Arithmeum)Es gibt keine doofen Fragen[😋]
87025/12/1815:25Lena Cartahttps://www.youtube.com/watch?v=uPHi5xn_q5c[😛]
87085/12/1817:56Freya (Arithmeum)Ich möchte arbeiten und habe mega Bock drauf! ...[😇]
87195/12/1820:09Lena Cartabin leider verabredet .... mit Thilo[😬, 🤗]
87255/13/1817:24Thomas Koppelberg♥[😍]
87265/14/1817:00Sonja SchäferKann mir jemand noch eine zweite Freikarte res...[😊]
87395/14/1822:40Jonas WeinzHab Ingo beim vorbeilaufenden nicht gesehen, h...[💁]
87445/15/1817:16Caner Ucar‍♀[🤷]
87555/17/1810:43Lena Cartasuper perfekt dann ist beiden geholfen[🙂, 😎]
87595/17/1810:43Lena Cartaob das so sinnvoll ist?[😀]
87635/17/1810:45Lena Cartaich dachte es ging konkret um den Termin[😀]
\n", - "

2455 rows × 5 columns

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" - ], - "text/plain": [ - " date time person \\\n", - "0 10/18/16 11:18 Lena Carta \n", - "1 10/18/16 11:21 Jonas Weinz \n", - "13 10/18/16 11:37 Jonathan (Arithmeum) \n", - "16 10/18/16 12:12 Kristina Stellwag \n", - "18 10/18/16 12:19 Simon (Arithmeum) \n", - "29 10/18/16 18:38 Lena Carta \n", - "31 10/18/16 18:39 Lena Carta \n", - "32 10/18/16 18:40 Kristina Stellwag \n", - "33 10/18/16 18:42 Lena Carta \n", - "41 10/20/16 12:50 Steffi (Arithmeum) \n", - "44 10/20/16 17:54 Steffi (Arithmeum) \n", - "45 10/20/16 18:08 Steffi (Arithmeum) \n", - "51 10/20/16 20:58 Anna Borutzky \n", - "52 10/20/16 20:58 Patrick (Arithmeum) \n", - "70 10/20/16 22:59 Jonathan (Arithmeum) \n", - "78 10/25/16 01:03 Caner Ucar \n", - "79 10/25/16 01:04 Michael (Arithmeum) \n", - "88 10/25/16 09:02 Lena Carta \n", - "89 10/25/16 09:04 Jonathan (Arithmeum) \n", - "90 10/25/16 10:00 Lena Carta \n", - "93 10/25/16 21:49 Jonathan (Arithmeum) \n", - "94 10/25/16 21:50 Lena Carta \n", - "95 10/25/16 22:13 Konstantin (Arithmeum) \n", - "96 10/25/16 23:28 Caner Ucar \n", - "102 10/27/16 14:43 Sonja Vorwerk \n", - "115 10/28/16 08:32 Lena Carta \n", - "116 10/28/16 09:48 Lena Carta \n", - "117 10/28/16 09:50 Caner Ucar \n", - "118 10/28/16 09:51 Caner Ucar \n", - "121 10/28/16 10:54 Lena Carta \n", - "... ... ... ... \n", - "8663 5/9/18 17:42 Jonas Weinz \n", - "8665 5/9/18 17:43 Lena Carta \n", - "8675 5/10/18 14:06 Lena Klöckner \n", - "8676 5/10/18 14:07 Caner Ucar \n", - "8677 5/10/18 14:23 Anna Zens \n", - "8678 5/11/18 08:16 Lena Carta \n", - "8679 5/11/18 08:25 Anna Zens \n", - "8680 5/11/18 08:26 Lena Carta \n", - "8681 5/11/18 13:18 Freya (Arithmeum) \n", - "8682 5/12/18 14:53 Freya (Arithmeum) \n", - "8683 5/12/18 15:05 Anna Borutzky \n", - "8689 5/12/18 15:16 Lena Carta \n", - "8692 5/12/18 15:17 Anna Borutzky \n", - "8693 5/12/18 15:17 Caner Ucar \n", - "8695 5/12/18 15:23 Jonas Weinz \n", - "8696 5/12/18 15:23 Freya (Arithmeum) \n", - "8697 5/12/18 15:23 Lena Carta \n", - "8698 5/12/18 15:24 Freya (Arithmeum) \n", - "8699 5/12/18 15:24 Lena Carta \n", - "8700 5/12/18 15:25 Freya (Arithmeum) \n", - "8702 5/12/18 15:25 Lena Carta \n", - "8708 5/12/18 17:56 Freya (Arithmeum) \n", - "8719 5/12/18 20:09 Lena Carta \n", - "8725 5/13/18 17:24 Thomas Koppelberg \n", - "8726 5/14/18 17:00 Sonja Schäfer \n", - "8739 5/14/18 22:40 Jonas Weinz \n", - "8744 5/15/18 17:16 Caner Ucar \n", - "8755 5/17/18 10:43 Lena Carta \n", - "8759 5/17/18 10:43 Lena Carta \n", - "8763 5/17/18 10:45 Lena Carta \n", - "\n", - " message \\\n", - "0 Hallo Jonas \n", - "1 Moin moin ;) Vllt. bin ich aber gar nicht Jona... \n", - "13 bääh \n", - "16 Thomas, du wolltest gestern ja nicht \n", - "18 Genau \n", - "29 was du bist morgen nicht da ??? \n", - "31 ich dachte diese Woche komplett noch. Aber gut... \n", - "32 Nee das Semester hat doch angefangen \n", - "33 ja hatte ich gerade vergessen . Dabei hatte ic... \n", - "41 Klar \n", - "44 Mein Zug lässt sich auch heute Zeit \n", - "45 Wir fangen doch wahrscheinlich nicht vor halb ... \n", - "51 Ist das dein Ernst? \n", - "52 Habs voll vercheckt sry ..gerade erst gemerkt \n", - "70 Ich bitte darum Caner.... \n", - "78 Haaaaaalloooo \n", - "79 Keine sorge, das hat bei dir nichts mit der St... \n", - "88 ich wollte eigentlich schon da sein, aber ich ... \n", - "89 SIIIIEG...⚔ \n", - "90 besser spät als nie. ich bin immerhin auf dem ... \n", - "93 Ich nicht \n", - "94 ich auch nicht \n", - "95 Wer hat den zuletzt was laminiert? lena und i... \n", - "96 Ich auch nicht \n", - "102 hm müsste das am besten heute schon wissen...w... \n", - "115 Guten Morgen liebe Kollegen, könnte einer von ... \n", - "116 Ist noch niemand im Büro? \n", - "117 10:10 ca. \n", - "118 Wann hat das woki geöffnet \n", - "121 Da hätte ich jetzt auch nicht gesucht \n", - "... ... \n", - "8663 mehrere Bürostühle hintereinander? \n", - "8665 Wollte Kristina das nicht immer schonmal machen \n", - "8675 Erledigt \n", - "8676 Danke \n", - "8677 Ich hatte es auch schon erledigt \n", - "8678 Gute morgen, ich habe gesehen neben dem Buch l... \n", - "8679 Die handschriftlichen sind die, von denen kein... \n", - "8680 achso ich denke mal dann wird sich Ingo melden \n", - "8681 Mein Kalender funktioniert nicht - könnte mir ... \n", - "8682 Könnte jemand für Montag Abend zwei Karten für... \n", - "8683 Und eine Freikarte für Sonja bitte! \n", - "8689 Danke Wieso kommen solche Fragen immer am Woc... \n", - "8692 Murphy‘s Gesetz? \n", - "8693 Cool \n", - "8695 ...und die kommt auf Annas Tisch \n", - "8696 Vielen, vielen Dank ☺ Sowas passiert immer am ... \n", - "8697 Das war nicht auf dich bezogen . \n", - "8698 Na gut \n", - "8699 Eigentlich ist es halbwegs entspannt heute Ic... \n", - "8700 Es gibt keine doofen Fragen \n", - "8702 https://www.youtube.com/watch?v=uPHi5xn_q5c \n", - "8708 Ich möchte arbeiten und habe mega Bock drauf! ... \n", - "8719 bin leider verabredet .... mit Thilo \n", - "8725 ♥ \n", - "8726 Kann mir jemand noch eine zweite Freikarte res... \n", - "8739 Hab Ingo beim vorbeilaufenden nicht gesehen, h... \n", - "8744 ‍♀ \n", - "8755 super perfekt dann ist beiden geholfen \n", - "8759 ob das so sinnvoll ist? \n", - "8763 ich dachte es ging konkret um den Termin \n", - "\n", - " emojis \n", - "0 [😉] \n", - "1 [🤔] \n", - "13 [😱] \n", - "16 [😜] \n", - "18 [🙊] \n", - "29 [😱, 😯, 😔] \n", - "31 [😇] \n", - "32 [😜] \n", - "33 [😂, 😶] \n", - "41 [👹] \n", - "44 [😞] \n", - "45 [😳] \n", - "51 [🙄] \n", - "52 [🙄] \n", - "70 [😍, 😍, 😍] \n", - "78 [🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 🤗, 😏, 🤗, 🤗, 😏] \n", - "79 [😉] \n", - "88 [😇] \n", - "89 [🎉] \n", - "90 [😁] \n", - "93 [😄] \n", - "94 [😳] \n", - "95 [🤔] \n", - "96 [🤔] \n", - "102 [🙈, 😄] \n", - "115 [🙁] \n", - "116 [😇] \n", - "117 [🙌] \n", - "118 [😄] \n", - "121 [😝] \n", - "... ... \n", - "8663 [🤔] \n", - "8665 [😁] \n", - "8675 [👍] \n", - "8676 [😊] \n", - "8677 [😄] \n", - "8678 [🙂] \n", - "8679 [😄] \n", - "8680 [😀, 😀] \n", - "8681 [🤔] \n", - "8682 [😇] \n", - "8683 [😀] \n", - "8689 [😀] \n", - "8692 [😉] \n", - "8693 [😍] \n", - "8695 [😉] \n", - "8696 [🙈] \n", - "8697 [😀] \n", - "8698 [😄] \n", - "8699 [😀, 😛] \n", - "8700 [😋] \n", - "8702 [😛] \n", - "8708 [😇] \n", - "8719 [😬, 🤗] \n", - "8725 [😍] \n", - "8726 [😊] \n", - "8739 [💁] \n", - "8744 [🤷] \n", - "8755 [🙂, 😎] \n", - "8759 [😀] \n", - "8763 [😀] \n", - "\n", - "[2455 rows x 5 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(emoji_messages)" ] @@ -1839,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1847,36 +361,8 @@ "import itertools\n", "import sklearn.utils as sku\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "labels=[e[-1] for e in emoji_messages['emojis']]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['😉', '🤔', '😱', '😜', '🙊', '😔', '😇', '😜', '😶', '👹']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "labels[:10]" + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import MultiLabelBinarizer" ] }, { @@ -1884,13 +370,31 @@ "execution_count": 17, "metadata": {}, "outputs": [], + "source": [ + "mlb = MultiLabelBinarizer()\n", + "\n", + "labels=mlb.fit_transform(emoji_messages['emojis'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], "source": [ "X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1901,782 +405,46 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jonas/.local/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " % self.max_iter, ConvergenceWarning)\n" - ] - }, - { - "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=(100,), 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=None,\n", - " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", - " verbose=False, warm_start=False)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier as RFC\n", "from sklearn.neural_network import MLPClassifier as MLP\n", "from sklearn.naive_bayes import MultinomialNB as MNB\n", - "#clf_a = RFC(criterion='entropy', random_state=4222)\n", - "clf_a = MLP()\n", + "from sklearn.multiclass import OneVsRestClassifier as OVRC\n", + "#clf_a = OVRC(RFC(criterion='entropy', random_state=4222))\n", + "clf_a = OVRC(MLP(hidden_layer_sizes=(10,)))\n", + "#clf_a = OVRC(MNB())\n", "clf_a.fit(vec_train, y1)" ] }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "pred = clf_a.predict(vectorizer.transform(Xt1))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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messagepredtrainedemojis
8663mehrere Bürostühle hintereinander?🙈🤔[🤔]
1862Ich will auch Essen😉😞[😞]
39212x orange🙂🙂[🙂]
2222Man soll auch keine Lernsachen da lassen😀😀[😀]
761Wir können auch gerne wieder bei mir zusammen ...😇😉[😉]
3600Hab den Bus verpasst, den ich eigentlich nehme...😟👸[👸]
1794Klaro, gerne😎🙂[🙂]
861Sehr gut, Jan! Wenn sich noch jemand melden wü...😘😇[😇]
1469Jaaa aber mein Mitbewohner ist messy😬🙊[😱, 😔, 🌚, 🙊]
6254Hallöchen Es hat ein Herr angerufen, der gern...💁🙂[🙂]
7968Iwo🤦😂[😂]
8102Keine Ahnung wer da haariger ist😅🧐[🧐]
6835Danke😊😊[😊]
7135Wurde bestimmt von Big Bang geklaut🤷😂[😂, 😂, 😂]
4556Ich kann leider nicht, aber arbeiten da nicht ...😂😀[😀]
6705Ist von heute, aber halt 3x in englisch😇🙄[🙄]
2058..🤦😅[😅]
2072Oder fang endlich mal wieder an, was fürs Stud...😄😈[😈]
756Ich hab vom letzten Jahr noch ein Trauma😂😆[😆]
5966Ist Freitag schon Wochenende?😍🤔[🤔]
7007Wenn wir die Klimaanlage im Museum ausschalten...🤔🤪[🤪]
6968Ein Vorbild für uns alle🤔🤩[🤩]
5665Kein Problem. Du kannst mich eintragen.🙂🙂[🙂]
2641Jonas, du bist gelb markiert! Du kannst keine ...😉🙃[🙃]
1981Da bin ich mir sehr sicher!😘😄[😄]
3334Das wäre auf jeden Fall hilfreich! Danke😍🙂[🙂]
4447Ist das Schild denn auch laminiert?!🤔😏[😏]
288da bin ich mal gespannt wie das aussieht, wen...😂👍[👍]
4265Aber es ist doch gebrochen...🤔😢[😢]
7405Partey🤦🤪[🤪]
...............
4910Vllt gilt das Schild auch nur für schwarze Hun...😂😱[😱]
2047Ich erprobe mich morgen mit Freya an Addieren ...🙂😄[😄]
2650Ich kann leider nicht😕😕[😕]
299Das schafft meine Handykamera aber nicht😂😉[😉]
2499also ihr lieben ich arbeite gerade im arithmeu...😁😅[😅]
4783danke😊💸[💸, 💸, 💸]
4040Herrlich🤦😅[🤗, 😅]
7553babbage, meine Autokerrektur ‍♂🤔🤦[🤦]
6232Bin erst um 9.15Uhr da.....👍🔪[💣, 🔪]
3514Dankeschön! Dann trage ich dich ein😘😊[😊]
3422Schnitzel😅😍[😍]
2294Club discreto?😂😳[😳, 😳, 😳]
8218auch da bin ich etwas neidisch🙄😅[😅]
4463Möchte jemand was vom Café Roller?🤧😂[😂]
8032Frohe Ostern an Euch alle...😊🐥[🐰, 🐣, 🐥]
4505okay, ihr geht eigentlich ganz verantwortungsv...😊💁[💁]
4810Meine Meinung: Wenn das Personal da ist, dann ...😂🤷[🤷]
5762Ein shk ist aber billiger👵🙄[🙄]
4247Alle die morgen auf der Arbeit sind: nachher k...🙈😂[😂]
2501ich glaube das wäre eine stilvolle und romanti...😍😊[😊]
5278Ich kann das machen💁✋[✋]
5747In der Küche im Kühlschrank steht eine grüne T...😉🙂[😉, 🙂]
7022Dann halt nicht ‍♂🤷🤷[🤷]
1736Ich hab absolut keine Ahnung davon😂😞[😞]
5909Wir bekommen es bestimmt zu zweit hin, aber we...🙈😉[😉]
5890Pass bloß auf, wenn du die isst mutierst du be...😄😱[😱]
566Das sind 5 Maß pro Abend🙄😋[😋]
6044Sind sehr lecker😀👌[👌]
1472Ich werde die alle drillen das sage ich dir, a...🤔😇[😇]
5702Oh man, das wird ein Tag😀😄[😄]
\n", - "

246 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " message pred trained \\\n", - "8663 mehrere Bürostühle hintereinander? 🙈 🤔 \n", - "1862 Ich will auch Essen 😉 😞 \n", - "3921 2x orange 🙂 🙂 \n", - "2222 Man soll auch keine Lernsachen da lassen 😀 😀 \n", - "761 Wir können auch gerne wieder bei mir zusammen ... 😇 😉 \n", - "3600 Hab den Bus verpasst, den ich eigentlich nehme... 😟 👸 \n", - "1794 Klaro, gerne 😎 🙂 \n", - "861 Sehr gut, Jan! Wenn sich noch jemand melden wü... 😘 😇 \n", - "1469 Jaaa aber mein Mitbewohner ist messy 😬 🙊 \n", - "6254 Hallöchen Es hat ein Herr angerufen, der gern... 💁 🙂 \n", - "7968 Iwo 🤦 😂 \n", - "8102 Keine Ahnung wer da haariger ist 😅 🧐 \n", - "6835 Danke 😊 😊 \n", - "7135 Wurde bestimmt von Big Bang geklaut 🤷 😂 \n", - "4556 Ich kann leider nicht, aber arbeiten da nicht ... 😂 😀 \n", - "6705 Ist von heute, aber halt 3x in englisch 😇 🙄 \n", - "2058 .. 🤦 😅 \n", - "2072 Oder fang endlich mal wieder an, was fürs Stud... 😄 😈 \n", - "756 Ich hab vom letzten Jahr noch ein Trauma 😂 😆 \n", - "5966 Ist Freitag schon Wochenende? 😍 🤔 \n", - "7007 Wenn wir die Klimaanlage im Museum ausschalten... 🤔 🤪 \n", - "6968 Ein Vorbild für uns alle 🤔 🤩 \n", - "5665 Kein Problem. Du kannst mich eintragen. 🙂 🙂 \n", - "2641 Jonas, du bist gelb markiert! Du kannst keine ... 😉 🙃 \n", - "1981 Da bin ich mir sehr sicher! 😘 😄 \n", - "3334 Das wäre auf jeden Fall hilfreich! Danke 😍 🙂 \n", - "4447 Ist das Schild denn auch laminiert?! 🤔 😏 \n", - "288 da bin ich mal gespannt wie das aussieht, wen... 😂 👍 \n", - "4265 Aber es ist doch gebrochen... 🤔 😢 \n", - "7405 Partey 🤦 🤪 \n", - "... ... ... ... \n", - "4910 Vllt gilt das Schild auch nur für schwarze Hun... 😂 😱 \n", - "2047 Ich erprobe mich morgen mit Freya an Addieren ... 🙂 😄 \n", - "2650 Ich kann leider nicht 😕 😕 \n", - "299 Das schafft meine Handykamera aber nicht 😂 😉 \n", - "2499 also ihr lieben ich arbeite gerade im arithmeu... 😁 😅 \n", - "4783 danke 😊 💸 \n", - "4040 Herrlich 🤦 😅 \n", - "7553 babbage, meine Autokerrektur ‍♂ 🤔 🤦 \n", - "6232 Bin erst um 9.15Uhr da..... 👍 🔪 \n", - "3514 Dankeschön! Dann trage ich dich ein 😘 😊 \n", - "3422 Schnitzel 😅 😍 \n", - "2294 Club discreto? 😂 😳 \n", - "8218 auch da bin ich etwas neidisch 🙄 😅 \n", - "4463 Möchte jemand was vom Café Roller? 🤧 😂 \n", - "8032 Frohe Ostern an Euch alle... 😊 🐥 \n", - "4505 okay, ihr geht eigentlich ganz verantwortungsv... 😊 💁 \n", - "4810 Meine Meinung: Wenn das Personal da ist, dann ... 😂 🤷 \n", - "5762 Ein shk ist aber billiger 👵 🙄 \n", - "4247 Alle die morgen auf der Arbeit sind: nachher k... 🙈 😂 \n", - "2501 ich glaube das wäre eine stilvolle und romanti... 😍 😊 \n", - "5278 Ich kann das machen 💁 ✋ \n", - "5747 In der Küche im Kühlschrank steht eine grüne T... 😉 🙂 \n", - "7022 Dann halt nicht ‍♂ 🤷 🤷 \n", - "1736 Ich hab absolut keine Ahnung davon 😂 😞 \n", - "5909 Wir bekommen es bestimmt zu zweit hin, aber we... 🙈 😉 \n", - "5890 Pass bloß auf, wenn du die isst mutierst du be... 😄 😱 \n", - "566 Das sind 5 Maß pro Abend 🙄 😋 \n", - "6044 Sind sehr lecker 😀 👌 \n", - "1472 Ich werde die alle drillen das sage ich dir, a... 🤔 😇 \n", - "5702 Oh man, das wird ein Tag 😀 😄 \n", - "\n", - " emojis \n", - "8663 [🤔] \n", - "1862 [😞] \n", - "3921 [🙂] \n", - "2222 [😀] \n", - "761 [😉] \n", - "3600 [👸] \n", - "1794 [🙂] \n", - "861 [😇] \n", - "1469 [😱, 😔, 🌚, 🙊] \n", - "6254 [🙂] \n", - "7968 [😂] \n", - "8102 [🧐] \n", - "6835 [😊] \n", - "7135 [😂, 😂, 😂] \n", - "4556 [😀] \n", - "6705 [🙄] \n", - "2058 [😅] \n", - "2072 [😈] \n", - "756 [😆] \n", - "5966 [🤔] \n", - "7007 [🤪] \n", - "6968 [🤩] \n", - "5665 [🙂] \n", - "2641 [🙃] \n", - "1981 [😄] \n", - "3334 [🙂] \n", - "4447 [😏] \n", - "288 [👍] \n", - "4265 [😢] \n", - "7405 [🤪] \n", - "... ... \n", - "4910 [😱] \n", - "2047 [😄] \n", - "2650 [😕] \n", - "299 [😉] \n", - "2499 [😅] \n", - "4783 [💸, 💸, 💸] \n", - "4040 [🤗, 😅] \n", - "7553 [🤦] \n", - "6232 [💣, 🔪] \n", - "3514 [😊] \n", - "3422 [😍] \n", - "2294 [😳, 😳, 😳] \n", - "8218 [😅] \n", - "4463 [😂] \n", - "8032 [🐰, 🐣, 🐥] \n", - "4505 [💁] \n", - "4810 [🤷] \n", - "5762 [🙄] \n", - "4247 [😂] \n", - "2501 [😊] \n", - "5278 [✋] \n", - "5747 [😉, 🙂] \n", - "7022 [🤷] \n", - "1736 [😞] \n", - "5909 [😉] \n", - "5890 [😱] \n", - "566 [😋] \n", - "6044 [👌] \n", - "1472 [😇] \n", - "5702 [😄] \n", - "\n", - "[246 rows x 4 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "testlist = pd.DataFrame({'message': Xt1, 'pred': pred, 'trained': yt1})\n", - "testlist = pd.merge(testlist, emoji_messages['emojis'].to_frame(), left_index=True, right_index=True)\n", - "testlist.to_csv('export.csv')\n", - "testlist" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'🙄'" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(clf_a.predict(vectorizer.transform([\"Boah Caner\"]))[0])" - ] - }, { "cell_type": "code", "execution_count": 23, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ἷ\n" - ] - } - ], + "outputs": [], "source": [ - "print(chr(0x1F3F))" + "pred = clf_a.predict(vectorizer.transform(Xt1))" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<1x3931 sparse matrix of type ''\n", - "\twith 2 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "vec_train[0]" + "testlist = pd.DataFrame({'message': Xt1, 'pred': mlb.inverse_transform(pred), 'teacher': mlb.inverse_transform(yt1)})\n", + "testlist.to_csv('export.csv')\n", + "testlist" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0b212fa64d5e457d89ad798983002a1c", - "version_major": 2, - "version_minor": 0 - }, - "text/html": [ - "

Failed to display Jupyter Widget of type Text.

\n", - "

\n", - " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", - " that the widgets JavaScript is still loading. If this message persists, it\n", - " likely means that the widgets JavaScript library is either not installed or\n", - " not enabled. See the Jupyter\n", - " Widgets Documentation for setup instructions.\n", - "

\n", - "

\n", - " If you're reading this message in another frontend (for example, a static\n", - " rendering on GitHub or NBViewer),\n", - " it may mean that your frontend doesn't currently support widgets.\n", - "

\n" - ], - "text/plain": [ - "Text(value='')" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f1d5e13f70a24e80acc95372e3f3db0d", - "version_major": 2, - "version_minor": 0 - }, - "text/html": [ - "

Failed to display Jupyter Widget of type VBox.

\n", - "

\n", - " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", - " that the widgets JavaScript is still loading. If this message persists, it\n", - " likely means that the widgets JavaScript library is either not installed or\n", - " not enabled. See the Jupyter\n", - " Widgets Documentation for setup instructions.\n", - "

\n", - "

\n", - " If you're reading this message in another frontend (for example, a static\n", - " rendering on GitHub or NBViewer),\n", - " it may mean that your frontend doesn't currently support widgets.\n", - "

\n" - ], - "text/plain": [ - "VBox(children=(Button(description='get smiley', icon='check', style=ButtonStyle(), tooltip='Click me'), Output()))" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "import ipywidgets as widgets \n", "\n", "out = widgets.Output()\n", "\n", @@ -2695,7 +463,7 @@ " with out:\n", " clear_output()\n", " with out:\n", - " display(Markdown(\"# \" + str(clf_a.predict(vectorizer.transform([t.value]))[0])))\n", + " display(Markdown(\"# \" + str(mlb.inverse_transform(clf_a.predict(vectorizer.transform([t.value])))[0])))\n", "\n", "b.on_click(handle_submit)\n", " \n", diff --git a/Project/Tools/whatsapp2csv.sh b/Project/Tools/whatsapp2csv.sh index ad59d5d..178f170 100755 --- a/Project/Tools/whatsapp2csv.sh +++ b/Project/Tools/whatsapp2csv.sh @@ -77,7 +77,7 @@ perform_and_exit echo -e "date\ttime\tperson\tmessage" > "$OUTPUT" # 3. replace separators of whatsapp's txt-format with '\t' as separator for csv # 4. delete all double-quotes. Because people are too stupid to type them in pairs and that breaks some csv-interpreters 🤦‍♂ -perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'left|added|created|end-to-end|Media omitted' | sed 's/, /\t/1; s/- /\t/1; s/: /\t/1;' | tr -d '"' >> "$OUTPUT" +perform_and_exit sed ':a;N;/\n[0-9]\{1,\}\+\//!s/\n/ /;ta;P;D' "$INPUT" | grep -v -E 'left|added|created|end-to-end|Media omitted|import java|> "$OUTPUT" confirm_action From 4b31d44b877aa34fd45a483e3990e4fab5a72f93 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Sun, 20 May 2018 10:38:14 +0200 Subject: [PATCH 07/15] blubb --- Project/Tools/emoji tester old.ipynb | 423 ++++++++++++++++ Project/Tools/emoji tester.ipynb | 716 ++++++++++++--------------- 2 files changed, 737 insertions(+), 402 deletions(-) create mode 100644 Project/Tools/emoji tester old.ipynb diff --git a/Project/Tools/emoji tester old.ipynb b/Project/Tools/emoji tester old.ipynb new file mode 100644 index 0000000..054473e --- /dev/null +++ b/Project/Tools/emoji tester old.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from IPython.display import clear_output, Markdown, Math\n", + "import ipywidgets as widgets\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## file input stuff:\n", + "\n", + "* replace `test.txt` with yout whatsapp log file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "./whatsapp2csv.sh test.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* read table `test.csv` exported by `whatsapp2csv.sh`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "messages = pd.read_csv('test.txt.csv', delimiter='\\t')\n", + "messages.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* read emoji-data (can be found here: https://www.unicode.org/Public/emoji/11.0/emoji-data.txt) and generate a table file out of it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash\n", + "if [ ! -e emoji-data.txt ]\n", + "then\n", + " echo \"downloading emoji specification\"\n", + " wget https://www.unicode.org/Public/emoji/11.0/emoji-data.txt\n", + "else\n", + " echo \"found existing emoji specification\"\n", + "fi\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_blacklist = set([\n", + " 0x1F3FB,\n", + " 0x1F3FC,\n", + " 0x1F3FD,\n", + " 0x1F3FE,\n", + " 0x1F3FF\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n", + "emoji_data['type'] = emoji_data['type'].str.strip()\n", + "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n", + "emoji_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* now build a set out of the unicode types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ord(\"😀\") == int('0x1f600',16)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_codes = emoji_data['unicode']\n", + "emoji_codes.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* we have to iterate over the whole list and extract all given ranges:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_code_list = []\n", + "for entry in emoji_codes:\n", + " # testing whether we have an entry or a range:\n", + " if '.' in entry:\n", + " # range\n", + " a,b = entry.split(\"..\")\n", + " for i in range(int(a,16),int(b,16) +1):\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(i)\n", + " else:\n", + " # single entry\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(int(entry,16))\n", + "emoji_code_set = set(emoji_code_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# simple test:\n", + "print(ord(\"😀\") in emoji_code_set, ord(\"a\") in emoji_code_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* expanding column and fill new emojis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "messages[\"emojis\"] = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in messages.index:\n", + " emoji_list = []\n", + " to_remove = []\n", + " m = messages.iloc[i]['message']\n", + " for c in str(m):\n", + " if ord(c) in emoji_code_set:\n", + " emoji_list.append(c)\n", + " elif ord(c) in emoji_blacklist:\n", + " to_remove.append(c)\n", + " \n", + " messages.loc[i,'emojis'] = emoji_list\n", + " #remove emiójis from message\n", + " for e in (emoji_list + to_remove):\n", + " m = m.replace(e,\"\")\n", + " messages.loc[i,'message'] = m\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "messages[:20]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* get a list only containing messaged with emojis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n", + "emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(emoji_messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## learning part" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import itertools\n", + "import sklearn.utils as sku\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "labels=[e[-1] for e in emoji_messages['emojis']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "labels[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vectorizer = TfidfVectorizer(stop_words='english')\n", + "vec_train = vectorizer.fit_transform(X1)\n", + "vec_test = vectorizer.transform(Xt1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier as RFC\n", + "from sklearn.neural_network import MLPClassifier as MLP\n", + "from sklearn.naive_bayes import MultinomialNB as MNB\n", + "#clf_a = RFC(criterion='entropy', random_state=4222)\n", + "clf_a = MLP()\n", + "clf_a.fit(vec_train, y1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pred = clf_a.predict(vectorizer.transform(Xt1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "testlist = pd.DataFrame({'message': Xt1, 'pred': pred, 'trained': yt1})\n", + "testlist = pd.merge(testlist, emoji_messages['emojis'].to_frame(), left_index=True, right_index=True)\n", + "testlist.to_csv('export.csv')\n", + "testlist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "display(clf_a.predict(vectorizer.transform([\"Boah Caner\"]))[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(chr(0x1F3F))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vec_train[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "out = widgets.Output()\n", + "\n", + "t = widgets.Text()\n", + "b = widgets.Button(\n", + " description='get smiley',\n", + " disabled=False,\n", + " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", + " tooltip='Click me',\n", + " icon='check'\n", + ")\n", + "\n", + "\n", + "\n", + "def handle_submit(sender):\n", + " with out:\n", + " clear_output()\n", + " with out:\n", + " display(Markdown(\"# \" + str(clf_a.predict(vectorizer.transform([t.value]))[0])))\n", + "\n", + "b.on_click(handle_submit)\n", + " \n", + "display(t)\n", + "display(widgets.VBox([b, out])) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Project/Tools/emoji tester.ipynb b/Project/Tools/emoji tester.ipynb index 23f3462..f9b59db 100644 --- a/Project/Tools/emoji tester.ipynb +++ b/Project/Tools/emoji tester.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -12,47 +12,45 @@ "import os" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----\n", - "## file input stuff:\n", - "\n", - "* replace `test.txt` with your whatsapp log file" - ] - }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "processing File: test.txt\n", - "================================================================================\n", - "================================================================================\n", - "successfully finished action: processing File: test.txt\n", - "================================================================================\n", - "================================================================================\n", - "Wrote output to test.txt.csv\n", - "================================================================================\n" - ] - } - ], + "outputs": [], "source": [ - "%%bash\n", - "./whatsapp2csv.sh test.txt" + "def create_widgets(t_text, b_text, out, additional_widgets=[]):\n", + " texts = []\n", + " for t in t_text:\n", + " texts.append(widgets.Text(t))\n", + " \n", + " button = widgets.Button(\n", + " description=b_text,\n", + " disabled=False,\n", + " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", + " tooltip=b_text,\n", + " icon='check'\n", + " )\n", + " display(widgets.VBox([widgets.HBox(texts + additional_widgets + [button]), out]))\n", + " return texts + [button]\n", + "\n", + "out_convert = widgets.Output()\n", + "out_build = widgets.Output()\n", + "out_train = widgets.Output()\n", + "out_save = widgets.Output()\n", + "out_read = widgets.Output()\n", + "out_test = widgets.Output()\n", + "\n", + "def mp(msg):\n", + " display(Markdown(msg))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* read table `test.csv` exported by `whatsapp2csv.sh`" + "# Emoji Tester\n", + "\n", + "just run all cells at first. Then select on of the actions below." ] }, { @@ -61,30 +59,63 @@ "metadata": {}, "outputs": [], "source": [ - "messages = pd.read_csv('test.txt.csv', delimiter='\\t')\n", - "messages.head()" + "mp(\"## converting plain whatsapp export to csv\")\n", + "t_convert, b_convert = create_widgets([\"test.txt\"], \"convert whatsapp file to csv\", out_convert)\n", + "mp(\"## read csv and build database\")\n", + "single_label = widgets.Checkbox(value=False, description='using only last emoji', disable=False)\n", + "t_build, b_build = create_widgets([\"test.txt.csv\"], \"read\", out_build, [single_label])\n", + "mp(\"## Train\")\n", + "d = widgets.Dropdown(options=['DecisionTree', 'MLP', 'RandomForest'], value='MLP', description='Learning Method', disabled=False)\n", + "ova = widgets.Checkbox(value=False, description='Using one vs all (very slow, only with multi-label!)', disabled=False)\n", + "b_train = button = widgets.Button(description=\"train\", disabled=False, button_style='', tooltip=\"train\",icon='check')\n", + "display(widgets.VBox([widgets.HBox([d,ova,b_train]), out_train]))\n", + "mp(\"## save trained classifier\")\n", + "t_save_c, t_save_m, t_save_v, b_save = create_widgets([\"clf.pkl\", \"mlb.pkl\", \"vectorizer.pkl\"], \"save classifier\", out_save)\n", + "mp(\"## import trained classifier\")\n", + "t_read_c, t_read_m, t_read_v, b_read = create_widgets([\"clf.pkl\", \"mlb.pkl\", \"vectorizer.pkl\"], \"import classifier\", out_read)\n", + "mp(\"## predict emoji on custom text\")\n", + "b_prop = widgets.Checkbox(value=False, description='Show probabilities (only on trees)', disabled=False)\n", + "t_test, b_test = create_widgets([\"\"], \"get emoji\", out_test,[b_prop])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "* read emoji-data (can be found here: https://www.unicode.org/Public/emoji/11.0/emoji-data.txt) and generate a table file out of it" + "----\n", + "## Code Section:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "found existing emoji specification\n" - ] - } - ], + "outputs": [], + "source": [ + "def convert(b):\n", + " with out_convert:\n", + " clear_output()\n", + " with out_convert:\n", + " mp(\"**converting \" + t_convert.value + \"…**\")\n", + " import subprocess\n", + " print(str(subprocess.check_output([\"./whatsapp2csv.sh\", t_convert.value])).strip())\n", + " mp(\"**done**\")\n", + "\n", + "b_convert.on_click(convert)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* download emoji specification if not already existing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%%bash\n", "if [ ! -e emoji-data.txt ]\n", @@ -96,9 +127,16 @@ "fi\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* stuff for creating emoji database" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,365 +148,239 @@ " 0x1F3FF,\n", " 0x2642,\n", " 0x2640\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n", - "emoji_data['type'] = emoji_data['type'].str.strip()\n", - "emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* now build a set out of the unicode types" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ord(\"😀\") == int('0x1f600',16)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "228 231A..231B \n", - "229 23E9..23EC \n", - "230 23F0 \n", - "231 23F3 \n", - "232 25FD..25FE \n", - "Name: unicode, dtype: object" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "emoji_codes = emoji_data['unicode']\n", - "emoji_codes.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* we have to iterate over the whole list and extract all given ranges:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "**used Emojis:**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'🀄\\U0001f9f1\\U0001f9f2\\U0001f9f3\\U0001f9f4\\U0001f9f5\\U0001f9f6🃏🤐🤑🤒🤓🤔🤕🤖🤗🤘🤙🤚🤛🤜🤝🤞\\U0001f91f🤠🤡🤢🤣🤤🤥🤦🤧\\U0001f928\\U0001f929\\U0001f92a\\U0001f92b\\U0001f92c\\U0001f92d\\U0001f92e\\U0001f92f🤰\\U0001f931\\U0001f932🤳🤴🤵🤶🤷🤸🤹🤺🤼🤽🤾🥀🥁🥂🥃🥄🥅🥇🥈🥉🥊🥋\\U0001f94c\\U0001f94d\\U0001f94e\\U0001f94f🥐🥑🥒🥓🥔🥕🥖🥗🥘🥙🥚🥛🥜🥝🥞\\U0001f95f\\U0001f960\\U0001f961\\U0001f962\\U0001f963\\U0001f964\\U0001f965\\U0001f966\\U0001f967\\U0001f968\\U0001f969\\U0001f96a\\U0001f96b\\U0001f96c\\U0001f96d\\U0001f96e\\U0001f96f\\U0001f970\\U0001f9ec\\U0001f9ed\\U0001f973\\U0001f974\\U0001f975\\U0001f976\\U0001f9ee\\U0001f97a\\U0001f9ef\\U0001f97c\\U0001f97d\\U0001f97e\\U0001f97f🦀🦁🦂🦃🦄🦅🦆🦇🦈🦉🦊🦋🦌🦍🆎🦎🦏🆑🆒🆓🆔🆕🆖🆗🆘🆙🆚\\U0001f992\\U0001f993\\U0001f994\\U0001f995\\U0001f996\\U0001f997\\U0001f998\\U0001f999\\U0001f99a\\U0001f99b\\U0001f99c\\U0001f99d\\U0001f99e\\U0001f99f\\U0001f9a0\\U0001f9a1\\U0001f9a2\\U0001f9f8\\U0001f9b0\\U0001f9b1\\U0001f9b2\\U0001f9b3\\U0001f9b4\\U0001f9b5\\U0001f9b6\\U0001f9b7\\U0001f9b8\\U0001f9b9🧀\\U0001f9c1\\U0001f9c2\\U0001f9d0\\U0001f9d1\\U0001f9d2\\U0001f9d3\\U0001f9d4\\U0001f9d5\\U0001f9d6\\U0001f9d7\\U0001f9d8\\U0001f9d9\\U0001f9da\\U0001f9db\\U0001f9dc\\U0001f9dd\\U0001f9de\\U0001f9df\\U0001f9e0\\U0001f9e1\\U0001f9e2\\U0001f9e3\\U0001f9e4\\U0001f9e5🇦🇧🇨🇩🇪🇫🇬🇭🇮🇯🇰🇱🇲🇳🇴🇵🇶🇷🇸🇹🇺🇻🇼🇽🇾🇿\\U0001f9f7🈁\\U0001f9f9\\U0001f9fa\\U0001f9fb\\U0001f9fc\\U0001f9fd\\U0001f9fe\\U0001f9ff🈚🈯🈲🈳🈴🈵🈶🈸🈹🈺🉐🉑🌀🌁🌂🌃🌄🌅🌆🌇🌈🌉🌊🌋🌌🌍🌎🌏🌐🌑🌒🌓🌔🌕🌖🌗🌘🌙⌚⌛⬛⬜🌚🌛🌜🌝🌞🌟🌠🌭🌮🌯🌰🌱🌲🌳🌴🌵🌷🌸🌹🌺🌻🌼🌽🌾🌿🍀🍁🍂🍃🍄🍅🍆🍇🍈🍉🍊🍋🍌🍍🍎🍏⭐🍐🍑🍒🍓⭕🍔🍕🍖🍗🍘🍙🍚🍛🍜🍟🍝🍞🍠🍡🍤🍢🍣🍥🍦🍩🍧🍨🍪🍫🍮🍬🍭🍯🍰🍳🍱🍲🍴🍵🍸🍶🍷🍹🍺🍻🍼🍾🍿🎀🎁🎂🎃🎅🎄🎆🎈🎉🎊🎋🎌🎍🎇🎏🎐🎎🎑🎒🎓🎠🎡🎢🎣🎤🎥🎦🎧🎨🎩🎪🎫🎬🎭🎮🎯🎰🎱🎲🎳🎴🎵🎶🎷🎸🎹🎺🎻🎼🎽🎾🎿🏀🏁🏂🏃🏄🏅🏆🏇🏈🏉🏊🏏🏐🏑🏒🏓🏠🏡🏢🏣🏤🏥🏦🏧🏨🏩🏪🏫🏬🏭🏮🏯🏰⏩⏪⏫🏴⏬⏰⏳🏸🏹🏺🐀🐁🐂🐃🐄🐅🐆🐇🐈🐉🐊🐋🐌🐍🐎🐏🐐🐑🐒🐓🐔🐕🐖🐗🐘🐙🐚🐛🐜🐝🐞🐟🐠🐡🐢🐣🐤🐥🐦🐧🐨🐩🐪🐫🐬🐭🐮🐯🐰🐱🐲🐳🐴🐵🐶🐷🐸🐹🐺🐻🐼🐽🐾👀👂👃👄👅👆👇👈👉👊👋👌👍👎👏👐👑👒👓👔👕👖👗👘👙👚👛👜👝👞👟👠👡👢👣👤👥👦👧👨👩👪👫👬👭👮👯👰👱👲👳👴👵👶👷👸👹👺👻👼👽👾👿💀💁💂💃💄💅💆💇💈💉💊💋💌💍💎💏💐💑💒💓💔💕💖💗💘💙💚💛💜💝💞💟💠💡💢💣💤💥💦💧💨💩💪💫💬💭💮💯💰💱💲💳💴💵💶💷💸💹💺💻💼💽💾💿📀📁📂📃📄📅📆📇📈📉📊📋📌📍📎📏📐📑📒📓📔📕📖📗📘📙📚📛📜📝📞📟📠📡📢📣📤📥📦📧📨📩📪📫📬📭📮📯📰📱📲📳📴📵📶📷📸📹📺📻📼📿🔀🔁🔂🔃🔄🔅🔆🔇🔈🔉🔊🔋🔌🔍🔎🔏🔐🔑🔒🔓🔔🔕🔖🔗🔘🔙🔚🔛🔜🔝🔞🔟🔠🔡🔢🔣🔤🔥🔦🔧🔨🔩🔪🔫🔬🔭🔮🔯🔰🔱🔲🔳🔴🔵🔶🔷🔸🔹🔺🔻🔼🔽🕋🕌🕍🕎🕐🕑🕒🕓🕔🕕🕖🕗🕘🕙🕚🕛🕜🕝🕞🕟🕠🕡🕢🕣🕤🕥🕦🕧🕺🖕🖖🖤🗻🗼◽◾🗽🗾🗿😀😁😂😃😄😅😆😇😈😉😊😋😌😍😎😏😐😑☔☕😒😓😔😕😖😗😘😙😚😛😜😝😞😟😠😡😢😣😤😥😦😧😨😩😪😫😬😭😮😯😰😱😲😳😴😵😶😷😸😹😺😻😼😽😾😿🙀🙁🙂🙃♈♉♊♋♌♍♎♏♐♑♒♓🙋🙌🙍🙎🙏♿🚀🚁🚂🚃🚄🚅🚆🚇🚈🚉🚊🚋🚌🚍🚎🚏🚐🚑🚒⚓🚓🚔🚕🚖🚗🚘🚙🚚🚛🚜🚝🚞🚟⚡🚠🚡🚢🚣🚤🚥🚦🚧⚪⚫🚨🚩🚪🚫🚬🚭🚮🚯🚰🚱🚲🚳🚴🚵🚶🚷🚸⚽⚾🚹🚺🚻🚼🚽⛄⛅🚾🚿🛀🛁🛂🛃🛄🛅⛎🛌🛐🛑🛒⛔⛪🛫🛬⛲⛳🛴⛵🛵🛶\\U0001f6f7\\U0001f6f8⛺\\U0001f6f9⛽✅\\U0001f9e6🙄✊✋🙅🙆🙇🙈\\U0001f9e7🙉🙊✨\\U0001f9e8❌❎\\U0001f9e9❓❔❕❗\\U0001f9ea\\U0001f9eb➕➖➗🦐🦑➰➿\\U0001f9f0'" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "**blacklisted Emojis:**" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'♀♂🏻🏼🏽🏾🏿'" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "emoji_code_list = []\n", - "for entry in emoji_codes:\n", - " # testing whether we have an entry or a range:\n", - " if '.' in entry:\n", - " # range\n", - " a,b = entry.split(\"..\")\n", - " for i in range(int(a,16),int(b,16) +1):\n", - " if i not in emoji_blacklist:\n", - " emoji_code_list.append(i)\n", - " else:\n", - " # single entry\n", - " if i not in emoji_blacklist:\n", - " emoji_code_list.append(int(entry,16))\n", - "emoji_code_set = set(emoji_code_list)\n", - "display(Markdown(\"**used Emojis:**\"))\n", - "display(\"\".join([chr(x) for x in emoji_code_set]))\n", - "display(Markdown(\"**blacklisted Emojis:**\"))\n", - "display(\"\".join([chr(x) for x in emoji_blacklist]))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True False\n" - ] - } - ], - "source": [ - "# simple test:\n", - "print(ord(\"😀\") in emoji_code_set, ord(\"a\") in emoji_code_set)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* expanding column and fill new emojis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "messages[\"emojis\"] = None" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "for i in messages.index:\n", - " emoji_list = []\n", - " m = messages.iloc[i]['message']\n", - " m_new = \"\"\n", - " for c in str(m):\n", - " if ord(c) in emoji_code_set:\n", - " emoji_list.append(c)\n", - " elif ord(c) not in emoji_blacklist:\n", - " m_new += c\n", - " \n", - " messages.loc[i,'emojis'] = set(emoji_list)\n", - " #remove emiójis from message\n", - " messages.loc[i,'message'] = m_new\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "messages[:20]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* get a list only containing messaged with emojis" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n", - "emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(emoji_messages)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----\n", - "## learning part" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import itertools\n", - "import sklearn.utils as sku\n", - "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import MultiLabelBinarizer" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "mlb = MultiLabelBinarizer()\n", + "])\n", "\n", - "labels=mlb.fit_transform(emoji_messages['emojis'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "vectorizer = TfidfVectorizer(stop_words='english')\n", - "vec_train = vectorizer.fit_transform(X1)\n", - "vec_test = vectorizer.transform(Xt1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestClassifier as RFC\n", - "from sklearn.neural_network import MLPClassifier as MLP\n", - "from sklearn.naive_bayes import MultinomialNB as MNB\n", - "from sklearn.multiclass import OneVsRestClassifier as OVRC\n", - "#clf_a = OVRC(RFC(criterion='entropy', random_state=4222))\n", - "clf_a = OVRC(MLP(hidden_layer_sizes=(10,)))\n", - "#clf_a = OVRC(MNB())\n", - "clf_a.fit(vec_train, y1)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "pred = clf_a.predict(vectorizer.transform(Xt1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "testlist = pd.DataFrame({'message': Xt1, 'pred': mlb.inverse_transform(pred), 'teacher': mlb.inverse_transform(yt1)})\n", - "testlist.to_csv('export.csv')\n", - "testlist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "emoji_code_set = None\n", "\n", - "out = widgets.Output()\n", - "\n", - "t = widgets.Text()\n", - "b = widgets.Button(\n", - " description='get smiley',\n", - " disabled=False,\n", - " button_style='', # 'success', 'info', 'warning', 'danger' or ''\n", - " tooltip='Click me',\n", - " icon='check'\n", - ")\n", - "\n", - "\n", - "\n", - "def handle_submit(sender):\n", - " with out:\n", - " clear_output()\n", - " with out:\n", - " display(Markdown(\"# \" + str(mlb.inverse_transform(clf_a.predict(vectorizer.transform([t.value])))[0])))\n", - "\n", - "b.on_click(handle_submit)\n", + "def create_emoji_set():\n", + " global emoji_code_set\n", " \n", - "display(t)\n", - "display(widgets.VBox([b, out])) " + " emoji_data = pd.read_csv('emoji-data.txt', delimiter=';', comment='#', names=[\"unicode\",\"type\"])\n", + " emoji_data['type'] = emoji_data['type'].str.strip()\n", + " emoji_data = emoji_data[emoji_data['type'] == \"Emoji_Presentation\"]\n", + " \n", + " emoji_codes = emoji_data['unicode']\n", + " emoji_codes.head()\n", + " \n", + " emoji_code_list = []\n", + " for entry in emoji_codes:\n", + " # testing whether we have an entry or a range:\n", + " if '.' in entry:\n", + " # range\n", + " a,b = entry.split(\"..\")\n", + " for i in range(int(a,16),int(b,16) +1):\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(i)\n", + " else:\n", + " # single entry\n", + " if i not in emoji_blacklist:\n", + " emoji_code_list.append(int(entry,16))\n", + " emoji_code_set = set(emoji_code_list)\n", + " display(Markdown(\"**imported Emojis** (without modifier):\\n>\" + \"\".join([chr(x) for x in emoji_code_set])))\n", + " display(Markdown(\"**blacklisted Emojis:**\\n>\" + \"\".join([chr(x) for x in emoji_blacklist])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* stuff for reading whatsapp messages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "messages = None\n", + "vectorizer = None\n", + "clf_a = None\n", + "mlb = None\n", + "\n", + "emoji_messages=None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def read_message_and_build_db(filename):\n", + " global messages\n", + " global emoji_messages\n", + " global vectorizer\n", + " global clf_a\n", + " global mlb\n", + " \n", + " messages = pd.read_csv(filename, delimiter='\\t')\n", + " mp(\"**filter messages and creating labels. This can take a while...**\")\n", + " messages[\"emojis\"] = None\n", + " \n", + " msg_batchsize = 1000\n", + " msg_counter = 0\n", + " \n", + " for i in messages.index:\n", + " \n", + " msg_counter+=1\n", + " if msg_counter >= msg_batchsize:\n", + " print(str(100 * i / messages.shape[0]) + \"%\")\n", + " msg_counter=0\n", + " \n", + " emoji_list = []\n", + " m = messages.iloc[i]['message']\n", + " m_new = \"\"\n", + " for c in str(m):\n", + " if ord(c) in emoji_code_set:\n", + " emoji_list.append(c)\n", + " elif ord(c) not in emoji_blacklist:\n", + " m_new += c\n", + " # if single label: only use last found emoji\n", + " messages.loc[i,'emojis'] = set(emoji_list) if (not single_label.value) or len(emoji_list)==0 else set(emoji_list[-1])\n", + " #remove emiójis from message\n", + " messages.loc[i,'message'] = m_new\n", + " \n", + " emoji_messages = messages[[True if len(e) > 0 else False for e in messages['emojis']]]\n", + " emoji_messages = emoji_messages[emoji_messages['message'] != \"\"]\n", + " \n", + " mp(\"**Done**\")\n", + " \n", + " display(emoji_messages)\n", + "\n", + "def train(b):\n", + " global messages\n", + " global emoji_messages\n", + " global vectorizer\n", + " global clf_a\n", + " global mlb\n", + " with out_train:\n", + " clear_output()\n", + " # train part:\n", + " import numpy as np\n", + " import itertools\n", + " import sklearn.utils as sku\n", + " from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", + " from sklearn.model_selection import train_test_split\n", + " from sklearn.preprocessing import MultiLabelBinarizer\n", + "\n", + " mlb = MultiLabelBinarizer() if not single_label.value else None\n", + " \n", + " if not mlb:\n", + " l = [list(e)[-1] for e in emoji_messages['emojis']]\n", + " \n", + " labels=mlb.fit_transform(emoji_messages['emojis']) if mlb else l\n", + " \n", + " if mlb:\n", + " display(Markdown(\"**emojis contained in Dataset:**\\n >\" + \"\".join(mlb.classes_ )))\n", + " else:\n", + " display(Markdown(\"**emojis contained in Dataset:**\\n >\" + \"\".join(set(l))))\n", + "\n", + " X1, Xt1, y1, yt1 = train_test_split(emoji_messages['message'], labels, test_size=0.1, random_state=4222)\n", + "\n", + " vectorizer = TfidfVectorizer(stop_words='english')\n", + " vec_train = vectorizer.fit_transform(X1)\n", + " vec_test = vectorizer.transform(Xt1)\n", + "\n", + " mp(\"**train classifier. This can take a very long time… Grab a coffe! 😀**\")\n", + "\n", + " from sklearn.ensemble import RandomForestClassifier as RFC\n", + " from sklearn.neural_network import MLPClassifier as MLP\n", + " #from sklearn.naive_bayes import MultinomialNB as MNB\n", + " from sklearn.tree import DecisionTreeClassifier as DTC\n", + " from sklearn.multiclass import OneVsRestClassifier as OVRC\n", + " clf_a = None\n", + " if (d.value == \"DecisionTree\"):\n", + " clf_a = DTC()\n", + " elif d.value == \"MLP\":\n", + " clf_a = MLP(hidden_layer_sizes=(64,))\n", + " elif d.value == \"RandomForest\":\n", + " RFC(criterion='entropy', random_state=4222)\n", + "\n", + " if ova.value:\n", + " clf_a=OVRC(clf_a)\n", + "\n", + " display(clf_a)\n", + " clf_a.fit(vec_train, y1)\n", + "\n", + " mp(\"**training done**\")\n", + "\n", + " pred = clf_a.predict(vectorizer.transform(Xt1))\n", + "\n", + " testlist = pd.DataFrame({'message': Xt1, 'pred': mlb.inverse_transform(pred) if mlb else pred, 'teacher': mlb.inverse_transform(yt1) if mlb else yt1})\n", + " testlist.to_csv('export.csv')\n", + " display(testlist)\n", + " \n", + "def build_db(b):\n", + " with out_build:\n", + " clear_output()\n", + " create_emoji_set()\n", + " read_message_and_build_db(t_build.value)\n", + "b_build.on_click(build_db)\n", + "b_train.on_click(train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.externals import joblib\n", + "def write_to_file(b):\n", + " global vectorizer\n", + " global clf_a\n", + " global mlb\n", + " \n", + " with out_save:\n", + " clear_output()\n", + " mp(\"**write to file...**\")\n", + " joblib.dump(clf_a, t_save_c.value)\n", + " if mlb:\n", + " joblib.dump(mlb, t_save_m.value) \n", + " joblib.dump(vectorizer, t_save_v.value)\n", + " mp(\"**done**\")\n", + "b_save.on_click(write_to_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def read_from_file(b):\n", + " global vectorizer\n", + " global clf_a\n", + " global mlb\n", + " \n", + " with out_read:\n", + " clear_output()\n", + " mp(\"**read from file…**\")\n", + " clf_a = joblib.load(t_read_c.value)\n", + " if t_read_m.value != \"\":\n", + " mlb = joblib.load(t_read_m.value)\n", + " vectorizer = joblib.load(t_read_v.value)\n", + " mp(\"**done**\")\n", + "b_read.on_click(read_from_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(b):\n", + " with out_test:\n", + " clear_output()\n", + " v = mlb.inverse_transform(clf_a.predict(vectorizer.transform([t_test.value])))[0] if mlb else clf_a.predict(vectorizer.transform([t_test.value]))[0]\n", + " mp(\"**prediction:**\\n# \" + (\"\".join(v) if len(v)>0 else \" \"))\n", + " if b_prop.value:\n", + " pred = clf_a.predict_proba(vectorizer.transform([t_test.value]))\n", + " print(mlb.inverse_transform(pred))\n", + "\n", + "b_test.on_click(predict)" ] }, { From 1eb68b1bbc93930297bc77761e7a70b2051e1ca5 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 21 May 2018 22:46:36 +0200 Subject: [PATCH 08/15] TWITTER! yeah! --- Project/Tools/emoji tester.ipynb | 289 +++++++- Project/Tools/emoji-list.txt | 1027 +++++++++++++++++++++++++++++ Project/Tools/twitter2messages.sh | 70 ++ 3 files changed, 1372 insertions(+), 14 deletions(-) create mode 100644 Project/Tools/emoji-list.txt create mode 100755 Project/Tools/twitter2messages.sh diff --git a/Project/Tools/emoji tester.ipynb b/Project/Tools/emoji tester.ipynb index f9b59db..e47d039 100644 --- a/Project/Tools/emoji tester.ipynb +++ b/Project/Tools/emoji tester.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -55,9 +55,256 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "## converting plain whatsapp export to csv" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c94a8d3b3724ad08359817b2086cf85", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Text(value='test.txt'), Button(description='convert whatsapp file to csv', icon='check', style=ButtonStyle(), tooltip='convert whatsapp file to csv'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## read csv and build database" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ee22e300097e49f1ac24b11662f7dc69", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Text(value='test.txt.csv'), Checkbox(value=False, description='using only last emoji'), Button(description='read', icon='check', style=ButtonStyle(), tooltip='read'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## Train" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de06136ee346492d80ef39d304cbc31c", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Dropdown(description='Learning Method', index=1, options=('DecisionTree', 'MLP', 'RandomForest'), value='MLP'), Checkbox(value=False, description='Using one vs all (very slow, only with multi-label!)'), Button(description='train', icon='check', style=ButtonStyle(), tooltip='train'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## save trained classifier" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b046f3ada7ec4ba7a17965d718552d21", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Text(value='clf.pkl'), Text(value='mlb.pkl'), Text(value='vectorizer.pkl'), Button(description='save classifier', icon='check', style=ButtonStyle(), tooltip='save classifier'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## import trained classifier" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac4054e75d8a4214ad040b1b21d6c925", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Text(value='clf.pkl'), Text(value='mlb.pkl'), Text(value='vectorizer.pkl'), Button(description='import classifier', icon='check', style=ButtonStyle(), tooltip='import classifier'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## predict emoji on custom text" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "db631acbe9c94cac907efaf501a69c6a", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "

Failed to display Jupyter Widget of type VBox.

\n", + "

\n", + " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", + " that the widgets JavaScript is still loading. If this message persists, it\n", + " likely means that the widgets JavaScript library is either not installed or\n", + " not enabled. See the Jupyter\n", + " Widgets Documentation for setup instructions.\n", + "

\n", + "

\n", + " If you're reading this message in another frontend (for example, a static\n", + " rendering on GitHub or NBViewer),\n", + " it may mean that your frontend doesn't currently support widgets.\n", + "

\n" + ], + "text/plain": [ + "VBox(children=(HBox(children=(Text(value=''), Checkbox(value=False, description='Show probabilities (only on trees)'), Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='get emoji'))), Output()))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mp(\"## converting plain whatsapp export to csv\")\n", "t_convert, b_convert = create_widgets([\"test.txt\"], \"convert whatsapp file to csv\", out_convert)\n", @@ -88,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -113,9 +360,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "found existing emoji specification\n" + ] + } + ], "source": [ "%%bash\n", "if [ ! -e emoji-data.txt ]\n", @@ -136,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +432,11 @@ " emoji_code_list.append(int(entry,16))\n", " emoji_code_set = set(emoji_code_list)\n", " display(Markdown(\"**imported Emojis** (without modifier):\\n>\" + \"\".join([chr(x) for x in emoji_code_set])))\n", - " display(Markdown(\"**blacklisted Emojis:**\\n>\" + \"\".join([chr(x) for x in emoji_blacklist])))" + " display(Markdown(\"**blacklisted Emojis:**\\n>\" + \"\".join([chr(x) for x in emoji_blacklist])))\n", + " f = open('emoji-list.txt', 'w')\n", + " for e in emoji_code_set:\n", + " f.write(chr(e) + \"\\n\")\n", + " f.close()" ] }, { @@ -189,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -203,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -360,6 +619,8 @@ " clf_a = joblib.load(t_read_c.value)\n", " if t_read_m.value != \"\":\n", " mlb = joblib.load(t_read_m.value)\n", + " else:\n", + " mlb = None\n", " vectorizer = joblib.load(t_read_v.value)\n", " mp(\"**done**\")\n", "b_read.on_click(read_from_file)" @@ -367,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ diff --git a/Project/Tools/emoji-list.txt b/Project/Tools/emoji-list.txt new file mode 100644 index 0000000..9bc456f --- /dev/null +++ b/Project/Tools/emoji-list.txt @@ -0,0 +1,1027 @@ +🀄 +🧱 +🧲 +🧳 +🧴 +🧵 +🧶 +🃏 +🤐 +🤑 +🤒 +🤓 +🤔 +🤕 +🤖 +🤗 +🤘 +🤙 +🤚 +🤛 +🤜 +🤝 +🤞 +🤟 +🤠 +🤡 +🤢 +🤣 +🤤 +🤥 +🤦 +🤧 +🤨 +🤩 +🤪 +🤫 +🤬 +🤭 +🤮 +🤯 +🤰 +🤱 +🤲 +🤳 +🤴 +🤵 +🤶 +🤷 +🤸 +🤹 +🤺 +🤼 +🤽 +🤾 +🥀 +🥁 +🥂 +🥃 +🥄 +🥅 +🥇 +🥈 +🥉 +🥊 +🥋 +🥌 +🥍 +🥎 +🥏 +🥐 +🥑 +🥒 +🥓 +🥔 +🥕 +🥖 +🥗 +🥘 +🥙 +🥚 +🥛 +🥜 +🥝 +🥞 +🥟 +🥠 +🥡 +🥢 +🥣 +🥤 +🥥 +🥦 +🥧 +🥨 +🥩 +🥪 +🥫 +🥬 +🥭 +🥮 +🥯 +🥰 +🧬 +🧭 +🥳 +🥴 +🥵 +🥶 +🧮 +🥺 +🧯 +🥼 +🥽 +🥾 +🥿 +🦀 +🦁 +🦂 +🦃 +🦄 +🦅 +🦆 +🦇 +🦈 +🦉 +🦊 +🦋 +🦌 +🦍 +🆎 +🦎 +🦏 +🆑 +🆒 +🆓 +🆔 +🆕 +🆖 +🆗 +🆘 +🆙 +🆚 +🦒 +🦓 +🦔 +🦕 +🦖 +🦗 +🦘 +🦙 +🦚 +🦛 +🦜 +🦝 +🦞 +🦟 +🦠 +🦡 +🦢 +🧸 +🦰 +🦱 +🦲 +🦳 +🦴 +🦵 +🦶 +🦷 +🦸 +🦹 +🧀 +🧁 +🧂 +🧐 +🧑 +🧒 +🧓 +🧔 +🧕 +🧖 +🧗 +🧘 +🧙 +🧚 +🧛 +🧜 +🧝 +🧞 +🧟 +🧠 +🧡 +🧢 +🧣 +🧤 +🧥 +🇦 +🇧 +🇨 +🇩 +🇪 +🇫 +🇬 +🇭 +🇮 +🇯 +🇰 +🇱 +🇲 +🇳 +🇴 +🇵 +🇶 +🇷 +🇸 +🇹 +🇺 +🇻 +🇼 +🇽 +🇾 +🇿 +🧷 +🈁 +🧹 +🧺 +🧻 +🧼 +🧽 +🧾 +🧿 +🈚 +🈯 +🈲 +🈳 +🈴 +🈵 +🈶 +🈸 +🈹 +🈺 +🉐 +🉑 +🌀 +🌁 +🌂 +🌃 +🌄 +🌅 +🌆 +🌇 +🌈 +🌉 +🌊 +🌋 +🌌 +🌍 +🌎 +🌏 +🌐 +🌑 +🌒 +🌓 +🌔 +🌕 +🌖 +🌗 +🌘 +🌙 +⌚ +⌛ +⬛ +⬜ +🌚 +🌛 +🌜 +🌝 +🌞 +🌟 +🌠 +🌭 +🌮 +🌯 +🌰 +🌱 +🌲 +🌳 +🌴 +🌵 +🌷 +🌸 +🌹 +🌺 +🌻 +🌼 +🌽 +🌾 +🌿 +🍀 +🍁 +🍂 +🍃 +🍄 +🍅 +🍆 +🍇 +🍈 +🍉 +🍊 +🍋 +🍌 +🍍 +🍎 +🍏 +⭐ +🍐 +🍑 +🍒 +🍓 +⭕ +🍔 +🍕 +🍖 +🍗 +🍘 +🍙 +🍚 +🍛 +🍜 +🍟 +🍝 +🍞 +🍠 +🍡 +🍤 +🍢 +🍣 +🍥 +🍦 +🍩 +🍧 +🍨 +🍪 +🍫 +🍮 +🍬 +🍭 +🍯 +🍰 +🍳 +🍱 +🍲 +🍴 +🍵 +🍸 +🍶 +🍷 +🍹 +🍺 +🍻 +🍼 +🍾 +🍿 +🎀 +🎁 +🎂 +🎃 +🎅 +🎄 +🎆 +🎈 +🎉 +🎊 +🎋 +🎌 +🎍 +🎇 +🎏 +🎐 +🎎 +🎑 +🎒 +🎓 +🎠 +🎡 +🎢 +🎣 +🎤 +🎥 +🎦 +🎧 +🎨 +🎩 +🎪 +🎫 +🎬 +🎭 +🎮 +🎯 +🎰 +🎱 +🎲 +🎳 +🎴 +🎵 +🎶 +🎷 +🎸 +🎹 +🎺 +🎻 +🎼 +🎽 +🎾 +🎿 +🏀 +🏁 +🏂 +🏃 +🏄 +🏅 +🏆 +🏇 +🏈 +🏉 +🏊 +🏏 +🏐 +🏑 +🏒 +🏓 +🏠 +🏡 +🏢 +🏣 +🏤 +🏥 +🏦 +🏧 +🏨 +🏩 +🏪 +🏫 +🏬 +🏭 +🏮 +🏯 +🏰 +⏩ +⏪ +⏫ +🏴 +⏬ +⏰ +⏳ +🏸 +🏹 +🏺 +🐀 +🐁 +🐂 +🐃 +🐄 +🐅 +🐆 +🐇 +🐈 +🐉 +🐊 +🐋 +🐌 +🐍 +🐎 +🐏 +🐐 +🐑 +🐒 +🐓 +🐔 +🐕 +🐖 +🐗 +🐘 +🐙 +🐚 +🐛 +🐜 +🐝 +🐞 +🐟 +🐠 +🐡 +🐢 +🐣 +🐤 +🐥 +🐦 +🐧 +🐨 +🐩 +🐪 +🐫 +🐬 +🐭 +🐮 +🐯 +🐰 +🐱 +🐲 +🐳 +🐴 +🐵 +🐶 +🐷 +🐸 +🐹 +🐺 +🐻 +🐼 +🐽 +🐾 +👀 +👂 +👃 +👄 +👅 +👆 +👇 +👈 +👉 +👊 +👋 +👌 +👍 +👎 +👏 +👐 +👑 +👒 +👓 +👔 +👕 +👖 +👗 +👘 +👙 +👚 +👛 +👜 +👝 +👞 +👟 +👠 +👡 +👢 +👣 +👤 +👥 +👦 +👧 +👨 +👩 +👪 +👫 +👬 +👭 +👮 +👯 +👰 +👱 +👲 +👳 +👴 +👵 +👶 +👷 +👸 +👹 +👺 +👻 +👼 +👽 +👾 +👿 +💀 +💁 +💂 +💃 +💄 +💅 +💆 +💇 +💈 +💉 +💊 +💋 +💌 +💍 +💎 +💏 +💐 +💑 +💒 +💓 +💔 +💕 +💖 +💗 +💘 +💙 +💚 +💛 +💜 +💝 +💞 +💟 +💠 +💡 +💢 +💣 +💤 +💥 +💦 +💧 +💨 +💩 +💪 +💫 +💬 +💭 +💮 +💯 +💰 +💱 +💲 +💳 +💴 +💵 +💶 +💷 +💸 +💹 +💺 +💻 +💼 +💽 +💾 +💿 +📀 +📁 +📂 +📃 +📄 +📅 +📆 +📇 +📈 +📉 +📊 +📋 +📌 +📍 +📎 +📏 +📐 +📑 +📒 +📓 +📔 +📕 +📖 +📗 +📘 +📙 +📚 +📛 +📜 +📝 +📞 +📟 +📠 +📡 +📢 +📣 +📤 +📥 +📦 +📧 +📨 +📩 +📪 +📫 +📬 +📭 +📮 +📯 +📰 +📱 +📲 +📳 +📴 +📵 +📶 +📷 +📸 +📹 +📺 +📻 +📼 +📿 +🔀 +🔁 +🔂 +🔃 +🔄 +🔅 +🔆 +🔇 +🔈 +🔉 +🔊 +🔋 +🔌 +🔍 +🔎 +🔏 +🔐 +🔑 +🔒 +🔓 +🔔 +🔕 +🔖 +🔗 +🔘 +🔙 +🔚 +🔛 +🔜 +🔝 +🔞 +🔟 +🔠 +🔡 +🔢 +🔣 +🔤 +🔥 +🔦 +🔧 +🔨 +🔩 +🔪 +🔫 +🔬 +🔭 +🔮 +🔯 +🔰 +🔱 +🔲 +🔳 +🔴 +🔵 +🔶 +🔷 +🔸 +🔹 +🔺 +🔻 +🔼 +🔽 +🕋 +🕌 +🕍 +🕎 +🕐 +🕑 +🕒 +🕓 +🕔 +🕕 +🕖 +🕗 +🕘 +🕙 +🕚 +🕛 +🕜 +🕝 +🕞 +🕟 +🕠 +🕡 +🕢 +🕣 +🕤 +🕥 +🕦 +🕧 +🕺 +🖕 +🖖 +🖤 +🗻 +🗼 +◽ +◾ +🗽 +🗾 +🗿 +😀 +😁 +😂 +😃 +😄 +😅 +😆 +😇 +😈 +😉 +😊 +😋 +😌 +😍 +😎 +😏 +😐 +😑 +☔ +☕ +😒 +😓 +😔 +😕 +😖 +😗 +😘 +😙 +😚 +😛 +😜 +😝 +😞 +😟 +😠 +😡 +😢 +😣 +😤 +😥 +😦 +😧 +😨 +😩 +😪 +😫 +😬 +😭 +😮 +😯 +😰 +😱 +😲 +😳 +😴 +😵 +😶 +😷 +😸 +😹 +😺 +😻 +😼 +😽 +😾 +😿 +🙀 +🙁 +🙂 +🙃 +♈ +♉ +♊ +♋ +♌ +♍ +♎ +♏ +♐ +♑ +♒ +♓ +🙋 +🙌 +🙍 +🙎 +🙏 +♿ +🚀 +🚁 +🚂 +🚃 +🚄 +🚅 +🚆 +🚇 +🚈 +🚉 +🚊 +🚋 +🚌 +🚍 +🚎 +🚏 +🚐 +🚑 +🚒 +⚓ +🚓 +🚔 +🚕 +🚖 +🚗 +🚘 +🚙 +🚚 +🚛 +🚜 +🚝 +🚞 +🚟 +⚡ +🚠 +🚡 +🚢 +🚣 +🚤 +🚥 +🚦 +🚧 +⚪ +⚫ +🚨 +🚩 +🚪 +🚫 +🚬 +🚭 +🚮 +🚯 +🚰 +🚱 +🚲 +🚳 +🚴 +🚵 +🚶 +🚷 +🚸 +⚽ +⚾ +🚹 +🚺 +🚻 +🚼 +🚽 +⛄ +⛅ +🚾 +🚿 +🛀 +🛁 +🛂 +🛃 +🛄 +🛅 +⛎ +🛌 +🛐 +🛑 +🛒 +⛔ +⛪ +🛫 +🛬 +⛲ +⛳ +🛴 +⛵ +🛵 +🛶 +🛷 +🛸 +⛺ +🛹 +⛽ +✅ +🧦 +🙄 +✊ +✋ +🙅 +🙆 +🙇 +🙈 +🧧 +🙉 +🙊 +✨ +🧨 +❌ +❎ +🧩 +❓ +❔ +❕ +❗ +🧪 +🧫 +➕ +➖ +➗ +🦐 +🦑 +➰ +➿ +🧰 diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh new file mode 100755 index 0000000..9bb20a3 --- /dev/null +++ b/Project/Tools/twitter2messages.sh @@ -0,0 +1,70 @@ +#!/usr/bin/env bash + +SCRIPT=`realpath $0` +SCRIPTPATH=`dirname $SCRIPT` + +# toolset:--------------------------------------------------------------------- + +command 2> >(while read line; do echo -e "\e[01;31m$line\e[0m" >&2; done) + +function lineprint { + printf '%*s\n' "${COLUMNS:-$(tput cols)}" '' | tr ' ' = +} + +function message { + lineprint + printf "$1\n" + lineprint +} + +function error_message { + lineprint + printf "$1\n" >&2 + lineprint +} + +current_action="IDLE" + +function confirm_action { + message "successfully finished action: $current_action" +} + +function set_action { + current_action="$1" + message "$1" +} + +function perform { + "$@" + local status=$? + if [ $status -ne 0 ] + then + error_message "$current_action failed!" + fi + return $status +} + +function perform_and_exit { + perform "$@" || exit 1 +} + +# ----------------------------------------------------------------------------- + +INPUT=$1 +OUTPUT=$2 +if [ $# -ne 2 ] +then + error_message "Error: no input file given. Usage: $0 " + exit 1 +fi + +set_action "processing all files in $INPUT and write to $OUTPUT" + +perform_and_exit export elist=\"`head -c -1 "$SCRIPTPATH/emoji-list.txt" | tr '\n' ',' | sed 's/,/\",\"/g'`\" +perform_and_exit echo "filter by emoji list:" +perform_and_exit echo $elist | tr -d '"' | tr -d ',' + +perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" +# ↑ such obvious, much selfexplaining 💁😈 + +confirm_action From c71e761bab5ea6c17c257b9816f64ad127544d43 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 21 May 2018 23:42:55 +0200 Subject: [PATCH 09/15] faster twitter parsing --- Project/Tools/twitter2messages.sh | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh index 9bb20a3..d5538ef 100755 --- a/Project/Tools/twitter2messages.sh +++ b/Project/Tools/twitter2messages.sh @@ -64,7 +64,9 @@ perform_and_exit export elist=\"`head -c -1 "$SCRIPTPATH/emoji-list.txt" | tr '\ perform_and_exit echo "filter by emoji list:" perform_and_exit echo $elist | tr -d '"' | tr -d ',' -perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" +#perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" +perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" + # ↑ such obvious, much selfexplaining 💁😈 confirm_action From 952aa6b676581fa0d8a399b5ebe324f1ac68261a Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 21 May 2018 23:50:07 +0200 Subject: [PATCH 10/15] fixing twitter parsing --- Project/Tools/twitter2messages.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh index d5538ef..3d22b0e 100755 --- a/Project/Tools/twitter2messages.sh +++ b/Project/Tools/twitter2messages.sh @@ -65,7 +65,7 @@ perform_and_exit echo "filter by emoji list:" perform_and_exit echo $elist | tr -d '"' | tr -d ',' #perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" -perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" +perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" # ↑ such obvious, much selfexplaining 💁😈 From d5d1c34aa4d6188bff41cf2ba8bd968850c03de4 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 22 May 2018 16:40:46 +0200 Subject: [PATCH 11/15] Task 2 complete --- Jonas_Solutions/Task_02_JonasWeinz.ipynb | 1218 ++++++++++++++++++++-- Jonas_Solutions/output.ttl | 94 ++ 2 files changed, 1253 insertions(+), 59 deletions(-) create mode 100644 Jonas_Solutions/output.ttl diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb index 31cd70f..b3825f2 100644 --- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb +++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb @@ -130,12 +130,13 @@ " score = metrics.accuracy_score(yt, pred)\n", " pp(\"score: \" + str(score))\n", " cm = metrics.confusion_matrix(yt, pred, labels=labels)\n", - " plot_confusion_matrix(cm, classes=labels, title=title)" + " plot_confusion_matrix(cm, classes=labels, title=title)\n", + " return cm" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -144,7 +145,7 @@ "\"\\nfrom rdflib import Graph, Literal, BNode, RDF, Namespace\\nfrom rdflib.namespace import FOAF, DC, XSD\\n\\nmexcore = Namespace('http://mex.aksw.org/mex-core#')\\nmexperf = Namespace('http://mex.aksw.org/mex-perf#')\\nmexalgo = Namespace('http://mex.aksw.org/mex-algo#')\\nprov = Namespace('http://www.w3.org/ns/prov#')\\n\\ndef create_mex_graph():\\n graph = Graph()\\n graph.bind(mexcore)\\n graph.bind(mexperf)\\n graph.bind(mexalgo)\\n graph.bind(prov)\\n graph.bind(FOAF)\\n graph.bind(DC)\\n graph.bind(XSD)\\n \\n return graph\\n\\ndef mex_performance(experiment, model, dataset, performance, phase='Train', graph=create_mex_graph()):\\n \\n p = BNode()\\n \\n\"" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -178,26 +179,6 @@ "'''" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Namespace('http://xmlns.com/foaf/0.1/')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "FOAF\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -209,9 +190,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================================================================================\n", + "checking whether unzip is installed\n", + "================================================================================\n", + "UnZip 6.00 of 20 April 2009, by Debian. Original by Info-ZIP.\n", + "\n", + "Latest sources and executables are at ftp://ftp.info-zip.org/pub/infozip/ ;\n", + "see ftp://ftp.info-zip.org/pub/infozip/UnZip.html for other sites.\n", + "\n", + "Compiled with gcc 6.3.0 20170415 for Unix (Linux ELF).\n", + "\n", + "UnZip special compilation options:\n", + " ACORN_FTYPE_NFS\n", + " COPYRIGHT_CLEAN (PKZIP 0.9x unreducing method not supported)\n", + " SET_DIR_ATTRIB\n", + " SYMLINKS (symbolic links supported, if RTL and file system permit)\n", + " TIMESTAMP\n", + " UNIXBACKUP\n", + " USE_EF_UT_TIME\n", + " USE_UNSHRINK (PKZIP/Zip 1.x unshrinking method supported)\n", + " USE_DEFLATE64 (PKZIP 4.x Deflate64(tm) supported)\n", + " UNICODE_SUPPORT [wide-chars, char coding: UTF-8] (handle UTF-8 paths)\n", + " LARGE_FILE_SUPPORT (large files over 2 GiB supported)\n", + " ZIP64_SUPPORT (archives using Zip64 for large files supported)\n", + " USE_BZIP2 (PKZIP 4.6+, using bzip2 lib version 1.0.6, 6-Sept-2010)\n", + " VMS_TEXT_CONV\n", + " WILD_STOP_AT_DIR\n", + " [decryption, version 2.11 of 05 Jan 2007]\n", + "\n", + "UnZip and ZipInfo environment options:\n", + " UNZIP: [none]\n", + " UNZIPOPT: [none]\n", + " ZIPINFO: [none]\n", + " ZIPINFOOPT: [none]\n", + "================================================================================\n", + "successfully finished action: checking whether unzip is installed\n", + "================================================================================\n", + "================================================================================\n", + "downloading and unpacking https://www.cs.ucsb.edu/~william/data/liar_dataset.zip if not already existing\n", + "================================================================================\n", + "================================================================================\n", + "successfully finished action: downloading and unpacking https://www.cs.ucsb.edu/~william/data/liar_dataset.zip if not already existing\n", + "================================================================================\n", + "================================================================================\n", + "downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n", + "================================================================================\n", + "================================================================================\n", + "successfully finished action: downloading and unpacking https://raw.githubusercontent.com/GeorgeMcIntire/fake_real_news_dataset/master/fake_or_real_news.csv.zip if not already existing\n", + "================================================================================\n", + "================================================================================\n", + "downloading Helper script: script_dataset3.py\n", + "================================================================================\n", + "================================================================================\n", + "successfully finished action: downloading Helper script: script_dataset3.py\n", + "================================================================================\n" + ] + } + ], "source": [ "%%bash\n", "./Task_2_gen_data.sh" @@ -227,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -243,9 +285,147 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(6335, 3)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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titletextlabel
Unnamed: 0
8476You Can Smell Hillary’s FearDaniel Greenfield, a Shillman Journalism Fello...FAKE
10294Watch The Exact Moment Paul Ryan Committed Pol...Google Pinterest Digg Linkedin Reddit Stumbleu...FAKE
3608Kerry to go to Paris in gesture of sympathyU.S. Secretary of State John F. Kerry said Mon...REAL
10142Bernie supporters on Twitter erupt in anger ag...— Kaydee King (@KaydeeKing) November 9, 2016 T...FAKE
875The Battle of New York: Why This Primary MattersIt's primary day in New York and front-runners...REAL
6903Tehran, USA\\nI’m not an immigrant, but my grandparents ...FAKE
7341Girl Horrified At What She Watches Boyfriend D...Share This Baylee Luciani (left), Screenshot o...FAKE
95‘Britain’s Schindler’ Dies at 106A Czech stockbroker who saved more than 650 Je...REAL
4869Fact check: Trump and Clinton at the 'commande...Hillary Clinton and Donald Trump made some ina...REAL
2909Iran reportedly makes new push for uranium con...Iranian negotiators reportedly have made a las...REAL
\n", + "
" + ], + "text/plain": [ + " title \\\n", + "Unnamed: 0 \n", + "8476 You Can Smell Hillary’s Fear \n", + "10294 Watch The Exact Moment Paul Ryan Committed Pol... \n", + "3608 Kerry to go to Paris in gesture of sympathy \n", + "10142 Bernie supporters on Twitter erupt in anger ag... \n", + "875 The Battle of New York: Why This Primary Matters \n", + "6903 Tehran, USA \n", + "7341 Girl Horrified At What She Watches Boyfriend D... \n", + "95 ‘Britain’s Schindler’ Dies at 106 \n", + "4869 Fact check: Trump and Clinton at the 'commande... \n", + "2909 Iran reportedly makes new push for uranium con... \n", + "\n", + " text label \n", + "Unnamed: 0 \n", + "8476 Daniel Greenfield, a Shillman Journalism Fello... FAKE \n", + "10294 Google Pinterest Digg Linkedin Reddit Stumbleu... FAKE \n", + "3608 U.S. Secretary of State John F. Kerry said Mon... REAL \n", + "10142 — Kaydee King (@KaydeeKing) November 9, 2016 T... FAKE \n", + "875 It's primary day in New York and front-runners... REAL \n", + "6903 \\nI’m not an immigrant, but my grandparents ... FAKE \n", + "7341 Share This Baylee Luciani (left), Screenshot o... FAKE \n", + "95 A Czech stockbroker who saved more than 650 Je... REAL \n", + "4869 Hillary Clinton and Donald Trump made some ina... REAL \n", + "2909 Iranian negotiators reportedly have made a las... REAL " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(df_1.shape)\n", "display(df_1[:10])" @@ -260,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -287,16 +467,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.9960008419280152'\n", + "Confusion matrix, without normalization\n", + "array([[2342, 3],\n", + " [ 16, 2390]])\n", + "'score: 0.8478535353535354'\n", + "Confusion matrix, without normalization\n", + "array([[737, 82],\n", + " [159, 606]])\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.ensemble import RandomForestClassifier as RFC\n", "clf_a = RFC(criterion='entropy', random_state=4222)\n", "max_size=10000\n", "clf_a.fit(vec_train_1[:max_size], y1[:max_size])\n", "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- train\", Xt=vec_train_1,yt=y1, clf=clf_a)\n", - "test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)" + "cm_1 = test_classifier(labels=[\"FAKE\",\"REAL\"], title=\"Configuration 1, model a -- test\", Xt=vec_test_1,yt=yt1, clf=clf_a)" ] }, { @@ -311,9 +526,543 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "----\n", + "#### Train Data:" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + 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idlabelstatementsubjectsspeakerjobstateparty#barely_true#false#half_true#mostly_true#pants_on_firecontext
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66861.jsontrueSays Tennessee is providing millions of dollar...education,state-budgetandy-berkeLawyer and state senatorTennesseedemocrat00000a letter to state Senate education committee c...
71122.jsonfalseThe health care reform plan would set limits s...health-careclub-growthNaNNaNnone45420a TV ad
813138.jsontrueSays Donald Trump started his career back in 1...candidates-biography,diversity,housinghillary-clintonPresidential candidateNew Yorkdemocrat402969767the first presidential debate
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" + ], + "text/plain": [ + " id label statement \\\n", + "2 7891.json false Says Having organizations parading as being so... \n", + "5 9416.json false Says when armed civilians stop mass shootings ... \n", + "6 6861.json true Says Tennessee is providing millions of dollar... \n", + "7 1122.json false The health care reform plan would set limits s... \n", + "8 13138.json true Says Donald Trump started his career back in 1... \n", + "\n", + " subjects speaker \\\n", + "2 campaign-finance,congress,taxes earl-blumenauer \n", + "5 guns jim-rubens \n", + "6 education,state-budget andy-berke \n", + "7 health-care club-growth \n", + "8 candidates-biography,diversity,housing hillary-clinton \n", + "\n", + " job state party #barely_true #false \\\n", + "2 U.S. representative Oregon democrat 0 1 \n", + "5 Small business owner New Hampshire republican 1 1 \n", + "6 Lawyer and state senator Tennessee democrat 0 0 \n", + "7 NaN NaN none 4 5 \n", + "8 Presidential candidate New York democrat 40 29 \n", + "\n", + " #half_true #mostly_true #pants_on_fire \\\n", + "2 1 1 0 \n", + "5 0 1 0 \n", + "6 0 0 0 \n", + "7 4 2 0 \n", + "8 69 76 7 \n", + "\n", + " context \n", + "2 a U.S. Ways and Means hearing \n", + "5 in an interview at gun shop in Hudson, N.H. \n", + "6 a letter to state Senate education committee c... \n", + "7 a TV ad \n", + "8 the first presidential debate " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "names = [\n", " \"id\",\n", @@ -358,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -372,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -383,9 +1132,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Object `MLPClassifier` not found.\n" + ] + } + ], "source": [ "?MLPClassifier" ] @@ -399,9 +1156,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "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=(100,), 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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "clf_b = MLPClassifier(hidden_layer_sizes=(100,), random_state=4222)\n", @@ -410,12 +1184,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.9997275946608554'\n", + "Confusion matrix, without normalization\n", + "array([[1675, 1],\n", + " [ 0, 1995]])\n", + "'score: 0.5776805251641138'\n", + "Confusion matrix, without normalization\n", + "array([[109, 99],\n", + " [ 94, 155]])\n", + "'score: 0.6041666666666666'\n", + "Confusion matrix, without normalization\n", + "array([[ 92, 77],\n", + " [ 94, 169]])\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 92, 77],\n", + " [ 94, 169]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- train\", Xt=vec_train_2, yt=y2, clf=clf_b)\n", - "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- test\", Xt=vec_test_2, yt=yt2, clf=clf_b)\n", + "cm_2 = test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- test\", Xt=vec_test_2, yt=yt2, clf=clf_b)\n", "test_classifier(labels=[\"true\", \"false\"], title=\"configuration 2 -- valid\", Xt=vectorizer_2.transform(Xv2), yt=yv2, clf=clf_b)" ] }, @@ -429,15 +1263,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.5142231947483589'\n", + "Confusion matrix, without normalization\n", + "array([[ 7, 201],\n", + " [ 21, 228]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "yt2_c3 = yt2.copy()\n", "yt2_c3[yt2_c3 == \"true\"] = \"REAL\"\n", "yt2_c3[yt2_c3 == \"false\"] = \"FAKE\"\n", "\n", - "test_classifier(labels=[\"REAL\", \"FAKE\"], \n", + "cm_3b=test_classifier(labels=[\"REAL\", \"FAKE\"], \n", " title=\"configuration 3: model a) → dataset 2\",\n", " Xt=vectorizer_1.transform(Xt2),\n", " yt=yt2_c3, clf=clf_a)" @@ -445,15 +1300,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'score: 0.5391414141414141'\n", + "Confusion matrix, without normalization\n", + "array([[274, 491],\n", + " [239, 580]])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "yt1_c3 = yt1.copy()\n", "yt1_c3[yt1_c3 == \"REAL\"] = \"true\"\n", "yt1_c3[yt1_c3 == \"FAKE\"] = \"false\"\n", "\n", - "test_classifier(labels=[\"true\", \"false\"], \n", + "cm_3a=test_classifier(labels=[\"true\", \"false\"], \n", " title=\"configuration 3: model b) → dataset 1\",\n", " Xt=vectorizer_2.transform(Xt1),\n", " yt=yt1_c3, clf=clf_b)" @@ -469,7 +1345,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -511,16 +1387,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "processing datasets\n", + "ds1= data/fake_or_real_news.csv\n", + "ds2= data/train.tsv\n", + "-- fake news\n", + "Index(['y', 'claim'], dtype='object')\n", + "3171\n", + "3164\n", + "6335\n", + "-- liar liar\n", + "Index(['y', 'claim'], dtype='object')\n", + "{'half-true', 'false', 'pants-fire', 'mostly-true', 'true', 'barely-true'} 10240\n", + "1676\n", + "1995\n", + "{'false', 'true'} 3671\n", + "false 5159\n", + "true 4847\n", + "Name: y, dtype: int64\n", + "done\n" + ] + } + ], "source": [ "X3, Xt3, y3, yt3 = get_dataset3_split('data/fake_or_real_news.csv', 'data/train.tsv')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -538,9 +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)" @@ -548,12 +1466,194 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, + "metadata": {}, + "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", 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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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### exporting" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ - "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- train\", Xt=vec_train_3, yt=y3, clf=clf_3)\n", - "test_classifier(labels=[\"true\",\"false\"], title=\"Configuration 4 -- test\", Xt=vec_test_3, yt=yt3, clf=clf_3)" + "def recall(cm):\n", + " return cm[0,0] / (cm[0,0] + cm[0,1])\n", + "def precision(cm):\n", + " return cm[0,0] / (cm[0,0] + cm[1,0])\n", + "\n", + "def accuracy(cm):\n", + " return (cm[0,0] + cm[1,1]) / np.sum(cm)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from rdflib import Namespace, Graph, Literal\n", + "from rdflib.namespace import DCTERMS, RDF" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "nsp = {\n", + " \"this\": Namespace(\"http://mex.aksw.org/examples/\"),\n", + " \"xsd\": Namespace(\"http://www.w3.org/2001/XMLSchema#\"),\n", + " \"rdfs\": Namespace(\"http://www.w3.org/2000/01/rdf-schema#\"),\n", + " \"mexcore\": Namespace(\"http://mex.aksw.org/mex-core#\"),\n", + " \"mexperf\": Namespace(\"http://mex.aksw.org/mex-perf#\"),\n", + " \"mexalgo\": Namespace(\"http://mex.aksw.org/mex-algo#\"),\n", + " \"prov\": Namespace(\"http://www.w3.org/ns/prov#\"),\n", + " \"dct\": DCTERMS,\n", + " \"rdf\": RDF\n", + "}\n", + "g = Graph()\n", + "\n", + "for i in nsp.items():\n", + " g.bind(i[0],i[1])\n", + "\n", + "g.add((nsp[\"this\"].jonas_weinz_task_2, nsp[\"rdf\"].type, nsp[\"mexcore\"].Experiment))\n", + "\n", + "g.add((nsp[\"this\"].ds1, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", + "g.add((nsp[\"this\"].ds1, nsp[\"rdfs\"].label, Literal(\"Dataset01\")))\n", + "\n", + "g.add((nsp[\"this\"].ds2, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", + "g.add((nsp[\"this\"].ds2, nsp[\"rdfs\"].label, Literal(\"Dataset02\")))\n", + "\n", + "g.add((nsp[\"this\"].ds3, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", + "g.add((nsp[\"this\"].ds3, nsp[\"rdfs\"].label, Literal(\"Dataset03\")))\n", + "\n", + "g.add((nsp[\"this\"].modela,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", + "g.add((nsp[\"this\"].modela,nsp[\"rdfs\"].label,Literal('model a')))\n", + "g.add((nsp[\"this\"].modela,nsp[\"dct\"].identifier,Literal('RandomForestClassifier')))\n", + "\n", + "g.add((nsp[\"this\"].modelb,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", + "g.add((nsp[\"this\"].modelb,nsp[\"rdfs\"].label,Literal('model b')))\n", + "g.add((nsp[\"this\"].modelb,nsp[\"dct\"].identifier,Literal('MLPClassifier')))\n", + "\n", + "g.add((nsp[\"this\"].modelc,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", + "g.add((nsp[\"this\"].modelc,nsp[\"rdfs\"].label,Literal('model c')))\n", + "g.add((nsp[\"this\"].modelc,nsp[\"dct\"].identifier,Literal('MLPClassifier')))\n", + "\n", + "g.add((nsp[\"this\"].conf1,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", + "g.add((nsp[\"this\"].conf1,nsp[\"rdfs\"].label,Literal('configuration 1')))\n", + "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].used, nsp[\"this\"].modela))\n", + "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].used, nsp[\"this\"].ds1))\n", + "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", + "\n", + "g.add((nsp[\"this\"].conf2,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", + "g.add((nsp[\"this\"].conf2,nsp[\"rdfs\"].label,Literal('configuration 2')))\n", + "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].used, nsp[\"this\"].modelb))\n", + "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].used, nsp[\"this\"].ds2))\n", + "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", + "\n", + "g.add((nsp[\"this\"].conf3a,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", + "g.add((nsp[\"this\"].conf3a,nsp[\"rdfs\"].label,Literal('configuration 3a')))\n", + "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].used, nsp[\"this\"].modelb))\n", + "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].used, nsp[\"this\"].ds1))\n", + "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", + "\n", + "g.add((nsp[\"this\"].conf3b,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", + "g.add((nsp[\"this\"].conf3b,nsp[\"rdfs\"].label,Literal('configuration 3b')))\n", + "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].used, nsp[\"this\"].modela))\n", + "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].used, nsp[\"this\"].ds2))\n", + "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", + " \n", + "g.add((nsp[\"this\"].conf4,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", + "g.add((nsp[\"this\"].conf4,nsp[\"rdfs\"].label,Literal('configuration 4')))\n", + "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].used, nsp[\"this\"].modelc))\n", + "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].used, nsp[\"this\"].ds3))\n", + "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", + "\n", + "g.add((nsp[\"this\"].measure1,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", + "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].precision,Literal(str(precision(cm_1)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].recall,Literal(str(recall(cm_1)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_1)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure1,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf1))\n", + "\n", + "g.add((nsp[\"this\"].measure2,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", + "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].precision,Literal(str(precision(cm_2)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].recall,Literal(str(recall(cm_2)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_2)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure2,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf2))\n", + "\n", + "g.add((nsp[\"this\"].measure3a,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", + "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].precision,Literal(str(precision(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].recall,Literal(str(recall(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3a,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf3a))\n", + "\n", + "g.add((nsp[\"this\"].measure3b,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", + "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].precision,Literal(str(precision(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].recall,Literal(str(recall(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure3b,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf3b))\n", + "\n", + "g.add((nsp[\"this\"].measure4,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", + "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].precision,Literal(str(precision(cm_4)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].recall,Literal(str(recall(cm_4)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_4)),datatype=nsp[\"xsd\"].float)))\n", + "g.add((nsp[\"this\"].measure4,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf4))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "g.serialize(destination='output.ttl', format='turtle')" ] }, { diff --git a/Jonas_Solutions/output.ttl b/Jonas_Solutions/output.ttl new file mode 100644 index 0000000..104e95b --- /dev/null +++ b/Jonas_Solutions/output.ttl @@ -0,0 +1,94 @@ +@prefix dct: . +@prefix mexalgo: . +@prefix mexcore: . +@prefix mexperf: . +@prefix prov: . +@prefix rdf: . +@prefix rdfs: . +@prefix this: . +@prefix xml: . +@prefix xsd: . + +this:measure1 a mexcore:PerformanceMeasure ; + mexperf:accuracy "0.8478535353535354"^^xsd:float ; + mexperf:precision "0.8225446428571429"^^xsd:float ; + mexperf:recall "0.8998778998778999"^^xsd:float ; + prov:wasGeneratedBy this:conf1 . + +this:measure2 a mexcore:PerformanceMeasure ; + mexperf:accuracy "0.5776805251641138"^^xsd:float ; + mexperf:precision "0.5369458128078818"^^xsd:float ; + mexperf:recall "0.5240384615384616"^^xsd:float ; + prov:wasGeneratedBy this:conf2 . + +this:measure3a a mexcore:PerformanceMeasure ; + mexperf:accuracy "0.5391414141414141"^^xsd:float ; + mexperf:precision "0.5341130604288499"^^xsd:float ; + mexperf:recall "0.3581699346405229"^^xsd:float ; + prov:wasGeneratedBy this:conf3a . + +this:measure3b a mexcore:PerformanceMeasure ; + mexperf:accuracy "0.5142231947483589"^^xsd:float ; + mexperf:precision "0.25"^^xsd:float ; + mexperf:recall "0.03365384615384615"^^xsd:float ; + prov:wasGeneratedBy this:conf3b . + +this:measure4 a mexcore:PerformanceMeasure ; + mexperf:accuracy "0.7714856762158561"^^xsd:float ; + mexperf:precision "0.7680865449628127"^^xsd:float ; + mexperf:recall "0.7680865449628127"^^xsd:float ; + prov:wasGeneratedBy this:conf4 . + +this:conf1 a mexcore:ExperimentConfiguration ; + rdfs:label "configuration 1" ; + prov:used this:ds1, + this:modela ; + prov:wasStartedBy this:jonas_weinz_task_2 . + +this:conf2 a mexcore:ExperimentConfiguration ; + rdfs:label "configuration 2" ; + prov:used this:ds2, + this:modelb ; + prov:wasStartedBy this:jonas_weinz_task_2 . + +this:conf3a a mexcore:ExperimentConfiguration ; + rdfs:label "configuration 3a" ; + prov:used this:ds1, + this:modelb ; + prov:wasStartedBy this:jonas_weinz_task_2 . + +this:conf3b a mexcore:ExperimentConfiguration ; + rdfs:label "configuration 3b" ; + prov:used this:ds2, + this:modela ; + prov:wasStartedBy this:jonas_weinz_task_2 . + +this:conf4 a mexcore:ExperimentConfiguration ; + rdfs:label "configuration 4" ; + prov:used this:ds3, + this:modelc ; + prov:wasStartedBy this:jonas_weinz_task_2 . + +this:ds3 a mexcore:dataset ; + rdfs:label "Dataset03" . + +this:modelc a mexalgo:Algorithm ; + rdfs:label "model c" ; + dct:identifier "MLPClassifier" . + +this:ds1 a mexcore:dataset ; + rdfs:label "Dataset01" . + +this:ds2 a mexcore:dataset ; + rdfs:label "Dataset02" . + +this:modela a mexalgo:Algorithm ; + rdfs:label "model a" ; + dct:identifier "RandomForestClassifier" . + +this:modelb a mexalgo:Algorithm ; + rdfs:label "model b" ; + dct:identifier "MLPClassifier" . + +this:jonas_weinz_task_2 a mexcore:Experiment . + From 8c4b167057bbdc0d9cf017bd14e98774870055d7 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 22 May 2018 20:17:24 +0200 Subject: [PATCH 12/15] stream language detector --- Project/Tools/stream_language_detector.py | 19 +++++++++++++++++++ Project/Tools/twitter2messages.sh | 2 +- 2 files changed, 20 insertions(+), 1 deletion(-) create mode 100755 Project/Tools/stream_language_detector.py diff --git a/Project/Tools/stream_language_detector.py b/Project/Tools/stream_language_detector.py new file mode 100755 index 0000000..23666d5 --- /dev/null +++ b/Project/Tools/stream_language_detector.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python3 +from langdetect import detect +import fileinput as fi +import sys + +# just a little script to detect languages of lines starting +# with the json keyword 'text' and writing the language as json value to stdout. +# other lines are just passing through so that this script can be used in a shell pipeline + +for line in fi.input(): + s = str.split(line.lstrip(' '), ":") + if s[0] == '"text"': + try: + sys.stdout.write(' "lang": "' + detect(s[1]) + '"\n') + except Exception: + sys.stdout.write(' "lang": "NaN"\n') + sys.stdout.write(line) + + diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh index 3d22b0e..bf80470 100755 --- a/Project/Tools/twitter2messages.sh +++ b/Project/Tools/twitter2messages.sh @@ -65,7 +65,7 @@ perform_and_exit echo "filter by emoji list:" perform_and_exit echo $elist | tr -d '"' | tr -d ',' #perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" -perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" +perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" # ↑ such obvious, much selfexplaining 💁😈 From c9e182b5fa13afb04f1781e69cb3f89c018a247e Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Wed, 23 May 2018 19:34:38 +0200 Subject: [PATCH 13/15] save reply also --- Project/Tools/twitter2messages.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project/Tools/twitter2messages.sh b/Project/Tools/twitter2messages.sh index bf80470..0920e0a 100755 --- a/Project/Tools/twitter2messages.sh +++ b/Project/Tools/twitter2messages.sh @@ -65,7 +65,7 @@ perform_and_exit echo "filter by emoji list:" perform_and_exit echo $elist | tr -d '"' | tr -d ',' #perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.name, text: .text} | select(.text != null) | [select(.text | contains($elist))] | select(any)| unique_by(.id) | .[]" | tee /dev/tty > "$OUTPUT" -perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 1 -B 4 | tee /dev/tty > "$OUTPUT" +perform_and_exit find ./ -type f -name '*.bz2' -exec bzip2 -dc "{}" \; | jq ". | {id: .id, datetime: .created_at, person: .user.id, text: .text, lang: .lang, reply_to: .in_reply_to_status_id} | select(.text != null)" | grep --no-group-separator -Ff "$SCRIPTPATH/emoji-list.txt" -A 3 -B 4 | tee /dev/tty > "$OUTPUT" # ↑ such obvious, much selfexplaining 💁😈 From 7e1af5ac2c675e0edba8e0587a44ccd23da48172 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 28 May 2018 14:55:43 +0200 Subject: [PATCH 14/15] changed language detector streamer --- Project/Tools/stream_language_detector.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Project/Tools/stream_language_detector.py b/Project/Tools/stream_language_detector.py index 23666d5..08d4a2e 100755 --- a/Project/Tools/stream_language_detector.py +++ b/Project/Tools/stream_language_detector.py @@ -8,10 +8,9 @@ import sys # other lines are just passing through so that this script can be used in a shell pipeline for line in fi.input(): - s = str.split(line.lstrip(' '), ":") - if s[0] == '"text"': + if line.startswith(' "text"'): try: - sys.stdout.write(' "lang": "' + detect(s[1]) + '"\n') + sys.stdout.write(' "lang": "' + detect(line[10:]) + '"\n') except Exception: sys.stdout.write(' "lang": "NaN"\n') sys.stdout.write(line) From c055f72af75999ba751e106581e03af6f9d2c500 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 28 May 2018 19:13:57 +0200 Subject: [PATCH 15/15] simple improvements on tasks --- Jonas_Solutions/Task_02_JonasWeinz.ipynb | 250 ++++++++--------------- Jonas_Solutions/Task_03.ipynb | 67 ++++-- Jonas_Solutions/output.ttl | 52 ++--- 3 files changed, 156 insertions(+), 213 deletions(-) diff --git a/Jonas_Solutions/Task_02_JonasWeinz.ipynb b/Jonas_Solutions/Task_02_JonasWeinz.ipynb index b3825f2..52e963d 100644 --- a/Jonas_Solutions/Task_02_JonasWeinz.ipynb +++ b/Jonas_Solutions/Task_02_JonasWeinz.ipynb @@ -488,7 +488,7 @@ "data": { "image/png": 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\n", 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\n", 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\n", 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WfdSpVcHy5cu55k9X8+H99l+t/YpfT2P/A7p7emNxSSq71IP+Gk+NAY4BzittlNQSEe7SJAtb21jYmv04JowaRNvKLAWOHtbCyCHNPDv/1VqGV2g3XH8d27xlW9Zdd91VbW1tbfzh91dx+1331TCy+lNPCa6c/kqAZwEbS3oAWAG8CrwMbC5pD+BPEbEVgKQTgJERcaqkjYEfAWsDS4FPRsSj/RRzv2tugpXt0NIkRg1t4Zl5rYwY0sy4EYN49qVWfPK3dq64fNrrhr9//ctNTN1sczbYYIMaRVW/GmUOsL8S4FeArSJiG0m7Adek909LmtLD5y4AjoqIJyS9jawH+a7OG0k6EjgSYP2Jk3IOvf9MHDuUZokgeHFhdrnLumsNRsrOCkN2IuTFV5bXONJiWbJkCX+96UbOPe8nq7V3NSdoSWN0AGt2FvjuiHi6pw0kjQR2Aq4s6U4P6WrbiLiALFmy1dbbNmxHqash7lNzW2sQiZUaMWIEs16c/7r2n154Uf8H0yA8BO7ZkpLXbax+MmZo+rMJWBAR2/RbVGa25hroOsD+GqgvAkZ1s+5FYB1J4yUNAfYGiIhXgKcl7Q+gzNb9Eq2Z9VlWD7D8Ug/6pQcYEfMl3S7pEaCVLOl1rFsh6XTgbmAWUHqS42Dgx5JOBgYBvwYe7I+YzazvGqQD2H9D4Ig4qId15wDndNH+NLBnNeMys/w1yhDY91WZWb7kHqCZFZSA5ubGyIBOgGaWOw+BzayYPAQ2s6ISjdMDbIwb9sysgeRzHaCkCyXNSZfPdV73BUkhaUJ6L0nnSJoh6SFJ21YSqROgmeUup3JYF9HFZXCSJgF7AM+WNO8FbJqWI4EfV3IAJ0Azy1eaAyy3lBMRtwAvdbHqB8CXYLUCSfsAl0TmTmCMpPXKHcNzgGaWq17MAU6QdG/J+wtSYZPu9y3tA8yKiAc7HWMi8FzJ+5mpbXZP+3MCNLPcVXiv77yI2K7SfUoaDnyVbPibCydAM8tdlU4CbwxsBHT0/jYA7pe0A1kdgdJioBukth55DtDM8qXqPBMkIh6OiHUiYkpETCEb5m4bES8AVwOHpLPBOwILI6LH4S84AZpZzrI5wDU/CSJpGnAHsJmkmZKO6GHza4GngBnAT8meQVSWh8BmlrN86v1FRI/PG0i9wI7XARzb22M4AZpZ7hrlThAnQDPLl+8FNrOiaqR7gZ0AzSx39fLMj3KcAM0sd+4BmlkxeQ7QzIpK9O1C51pwAjSz3DU3+hygpLV6+mB6cLmZ2es0SAewxx7gdLJ6W6VfpeN9AJOrGJeZNShpAJwEiYhJ3a0zM+tJg4yAKyuGIOkASV9NrzeQ9NbqhmVmjSyPZ4L0h7IJUNK5wO7Ax1PTUuD8agZlZo1LpDPBZf6rB5WcBd4pIraV9E+AiHhJ0uAqx2VmDaxOOnhlVZIAV0hqIj2ARNJ4oL2qUZlZ4+pjwdNaqCQB/gj4LbC2pNOAjwCnVTUqM2tYYgBcB9ghIi6RdB/wntS0f0S87kHFZmYdGqQDWPGdIM3ACrJhsMvom1mPGmUIXMlZ4JOAacD6ZE9a+pWkE6sdmJk1pkqeB1Iv+bGSHuAhwFsiYimApDOBfwLfqmZgZta4muslw5VRSQKc3Wm7Fso8bd3Miq1RhsA9FUP4Admc30vAdEk3pPd7APf0T3hm1mjEwLgOsONM73TgmpL2O6sXjpk1PNXPrW7l9FQM4ef9GYiZDRwNPwTuIGlj4ExgC2BoR3tETK1iXGbWoBppCFzJNX0XAb8g+157AVcAl1cxJjNrcEq3w/W01INKEuDwiLgBICKejIiTyRKhmdnrSNllMOWWelDJZTDLUjGEJyUdBcwCRlU3LDNrZHWS38qqJAF+HhgBfIZsLnA0cHg1gzKzxlYvQ9xyKimGcFd6uYjXiqKamXWrQfJfjxdC/45UA7ArEfHhqkRkZg1N0oAoh3Vuv0WRowhYsbLbvG01Mnb7T9c6BOtHDT8Ejoi/9GcgZjZwNErNvErrAZqZVUQMgB6gmVlftTRIF7DiMCUNqWYgZjYwZAVP1/xOEEkXSpoj6ZGStu9KelTSQ5J+J2lMyboTJc2Q9Jik91YSayUVoXeQ9DDwRHq/taT/q2TnZlZMTSq/VOAiYM9ObTcCW0XEm4HHgRMBJG0BHABsmT5znqTmsnFWEMQ5wN7AfICIeJDsQelmZl3KoyR+RNxCVo+0tO3PEdGW3t5J9pgOgH2AX0fEsoh4GpgB7FDuGJXMATZFxDOduqwrK/icmRWQgJbKToJMkHRvyfsLIuKCXhzqcF4rzDKR1WuVzkxtPaokAT4naQcgUpfyOLKup5lZlyo8CTwvIrbr2/51EtAG/LIvn+9QSQI8mmwYPBl4EbgptZmZvY4kmqp4GYykw8im5d4dER13PcwCJpVstkFq61El9wLPIZtcNDOrSLXyn6Q9gS8Bu3Y8qTK5muyRvWeTPcJ3U+DucvurpCL0T+ninuCIOLLSoM2sOAS05HAvsKRpwG5kc4UzgVPIzvoOAW5M5yXujIijImK6pCuAf5ENjY+NiLLnKioZAt9U8noo8CHgud58ETMrljx6gBFxYBfN3T6rKCLOJCvZV7FKhsCrlb+XdClwW28OYmYFUvl1fjXXl1vhNgLWzTsQMxs4RGNkwErmAF/mtTnAJrILE79SzaDMrHFlc4C1jqIyPSZAZbOMW/Pa6eT2ktPOZmZdapRqMD3m6ZTsro2IlWlx8jOzHnU8FziHe4GrrpKO6gOS3lL1SMxsYKjgPuB66SD29EyQlnTT8VuAeyQ9CSwhS/AREdv2U4xm1kDyug6wP/Q0B3g3sC3wwX6KxcwGiHrp4ZXTUwIUQEQ82U+xmNmAIJoGwGUwa0s6vruVEXF2FeIxswaXPROk1lFUpqcE2AyMhAZJ5WZWHzQw5gBnR8Tp/RaJmQ0IA6UH2CBfwczqTTXrAeappwT47n6LwswGlAbJf90nwIh4qbt1ZmbdkaC5QTKgH4xuZrlrjPTnBGhmOcvuBW6MFOgEaGa5a4z05wRoZrkTTQPgOkAzs14TlZWZqgdOgGaWu0YpiOoEaGa5a4z05wRoZjnzdYBmVmgeAptZYTVG+nMCNLMqaJAOoBOgmeVLeA7QzApLqEEGwU6AZpa7BukAOgGaWb6yO0EaIwM6AZpZvgRNDXIvnBNgHRk/chDjRmR/JS8taWP+4hWr1k0YOYj1xgzhX88vZmV7rSIshvNPOZi9dtmKuS8tYrv9v7mq/egDduVTH9mZle3B9bc+wkk//AODWpo59+QD2XaLybRHOyd857fcet8TNYy+PngO0HplSEsT40a0MGNOKxGw0YShLGptY/nKYFCzGDm0meVtznz94dI/3sn5l/+dn51xyKq2XbbblL13+3/s8NGzWL6ijbXHjgTg8A+/A4DtP/JN1h47kt+fewzv/Nh3iYiaxF4PsnqAtY6iMg3SUR34hgwSS5e30/F7s2TZStYalv37tN7oIbywcHkNoyuW2+9/kpcWLl2t7cj9d+Z7v7iR5SvaAJj78mIANn/jG7j5nsdWtS1c1Mpbt5jcvwHXIVXwXz1wAqwTy1a0M2JwM81N2Rm0UUNbGNQiRg1tZsXKdl5d4d5fLW2y4Tq84y0bc8slJ/Dnn312VZJ7+PFZ7L3r/6O5uYkN1x/PW7aYxAZvGFvjaGuvSSq71IOqJUBJn5H0b0m/7Gb9bpL+VK3jN5plbcHcRcvZaMIwNpowjNYV7TQh1llrMC++4t5frbU0NzFu9Ah2OeR7fPUHv+ey7xwOwMV/uINZLy7g9l9+ie9+8b+588GnWVnwSdqOIXC5pex+pAslzZH0SEnbOEk3Snoi/Tk2tUvSOZJmSHpI0raVxFrNOcBjgPdExMwqHmNAeXlpGy8vzYZY6641mLb2YK1hzWy67nAABjWLTdYZzpNzWmlrL+4cUy3MenEBv//LAwDcO/0Z2tuDCWNHMu/lxXzp+1et2u5vFx3PE8/OqVWYdSK3Ie5FwLnAJSVtXwH+EhFnSfpKev9lYC9g07S8Dfhx+rNHVekBSjofeCNwnaQvS7pD0j8l/UPSZl1sv6ukB9LyT0mjUvsXJd2TMvpp1Yi1njSnfxYHNYu1hrXw8pIV/Hv2Uh57IVtWrAxmzFnq5FcDf7z5IXbdfioAm0xeh8GDWpj38mKGDR3E8KGDAXjX2zanbWU7jz71Qi1DrT1l0zjllnIi4hag8+N59wEuTq8vBvYtab8kMncCYyStV+4YVekBRsRRkvYEdgeWA9+PiDZJ7wG+Cfx3p4+cABwbEbdLGgm8KmkPsmy+A1mv+mpJu6QfyoC04fihNDeJiOD5BctwnquNi791GDu/dVMmjBnJjOvP4Izzr+Xi39/BT049mHuv/CrLV6zkE1+/FIC1x47ij+cdS3t78PzcBRxx8sVl9j7wVfle4HUjYnZ6/QKwbno9EXiuZLuZqW02PeiPy2BGAxdL2hQIYFAX29wOnJ3mC6+KiJkpAe4B/DNtM5IsIb4uAUo6EjgSYL2Jk/L/Bv3kqbmtPa5/7IWlPa63fBx64kVdth9+8iWva3t29kts/aEzqhxR46kw/U2QdG/J+wsi4oJKjxERIWmNugn9kQDPAP4WER+SNAW4ufMGaTx/DfA+4HZJ7yX7GX4rIn5S7gDph3YBwJZv3tb9JrNaqywDzouI7Xq55xclrRcRs9MQt2PCdRZQ2vvZILX1qD8ugxldEshhXW0gaeOIeDgivg3cA2wO3AAcnobESJooaZ1+iNfM1lAVrwO8Gjg0vT4U+ENJ+yHpbPCOwMKSoXK3+qMH+B2yIfDJwDXdbPM5SbsD7cB04LqIWCbpTcAdqbz2YuBjvJbxzaxO5XEniKRpwG5kQ+WZwCnAWcAVko4AngE+kja/lmwEOQNYCvxPJceoWgKMiCnp5Txgasmqk9P6m0nD4Yg4rpt9/BD4YbViNLMqySEBRsSB3ax6dxfbBnBsb4/he4HNLFfCxRDMrKgqvM6vHjgBmlnunADNrKDqp9pLOU6AZpY79wDNrJCEH4xuZgWmBukCOgGaWe4aJP85AZpZ/hok/zkBmlnOGmgS0AnQzHKVlcRvjAzoBGhmuWuM9OcEaGbV0CAZ0AnQzHLnO0HMrLDyqAfYH5wAzSx/ToBmVkSuB2hmxeV6gGZWZE6AZlZQrgdoZgXmHqCZFVID3QrsBGhm+XM9QDMrrAbJf06AZpa/Bsl/ToBmljNfB2hmRSU8B2hmBdYY6c8J0MyqoEE6gE6AZpY/D4HNrLAaI/05AZpZzuSzwGZWZC6GYGaF5R6gmRWWE6CZFVTj1ANsqnUAZjawZHeClF8q2pf0eUnTJT0iaZqkoZI2knSXpBmSLpc0uK+xOgGaWe7ySICSJgKfAbaLiK2AZuAA4NvADyJiE+Bl4Ii+xukEaGa5UwX/VagFGCapBRgOzAbeBfwmrb8Y2LevcToBmlm+Kuj9pR7gBEn3lixHlu4mImYB3wOeJUt8C4H7gAUR0ZY2mwlM7GuoPgliZrnqRUn8eRGxXbf7kcYC+wAbAQuAK4E91zzC1zgBmlnucroX+D3A0xExN+3zKuAdwBhJLakXuAEwq68H8BDYzHKX01ngZ4EdJQ1XllHfDfwL+BuwX9rmUOAPfY3TCdDMcqcKlnIi4i6ykx33Aw+T5asLgC8Dx0uaAYwHft7XOD0ENrP85XQddEScApzSqfkpYIc89u8EaGa5EtDUIPfCKSJqHUOuJM0Fnql1HDmZAMyrdRD2OgPt72XDiFg7r51Jup7sZ1TOvIjI9axubw24BDiQSLq3p8sErDb89zJw+CSImRWWE6CZFZYTYH27oNYBWJf89zJAeA7QzArLPUAzKywnQDMrLCdAMyssJ0CzPlKnkied31v9cwI06wNJinQGUdJ4gPAZxYbjs8ANQNKHgCVAU0RcX+t47DWSjgPeTlax+O/AdRGxorZRWaXcA6xzkj4NnACMA34raecah2SJpP2B/YGjgT2Adzr5NRYnwDqlzIbAf5E9BGYiWQ/jH5IG1TS4guqY45PU8XszETgL+BDwPHBSWv+GmgRoveYEWL8EzCV76MvXgV2B/SJiJXCopKm1DK5oSuf8gI4E9xTwTeCQiHixQdzGAAAGV0lEQVRvRKyQ9AXgqJIkaXXM9QDrkKR3Am+OiPMkDQeOiIihad1BwCeAa2sZY1F09PpKTnh8DthX0vuBJ4F/A/dIeiswFTgY+HhEtNcoZOsFnwSpI6nXILIHPb+V7NkHfwAuA8YAjwA7AYdHxMO1irNIJA2JiGXp9RHAJ4H9I+K5NBWxM9nf1a5AK3C6/24ahxNgHZE0OSKeTb2+/cl+se6MiF9J+iCwEvh3RDxV00ALQtImZHN8X4iIZyQdD8wAXgW2Bg4HziP7B+pVst+npbWK13rP8xR1QtL6wK2S9kq/RL8h6/EdKukQ4NqIuMbJr18tJxvmfkvSemTJ7xjgeLInlp0E7AKMjohWJ7/G4wRYByR9iexasq8C35S0R0QsiYgLgMHAm4GRtYyxiCLiWeBHZCc7vk92Fn4/4L8j4nJgEdlzaZfVLEhbI06ANSbpfcBuwD0R8Uvgu8DZkt6fhr2twPcjYkENwyyEdOnRar8TKQmeTdbj+wkwLiKWSDoG+DZwZETM7v9oLQ+eA+xnkgYDm0TEvyQdBnwFmBERe5dssx/wBbLk97mIeKgmwRaMpJERsTi9/hSwFtndN9+WNJrsebRTyIbA6wELPSXR2JwA+1maWD+P7NapycCFwOeAiyPinJLtRgNtEbGkJoEWTOpt7xMRR0j6PLAv8DXgXODhiDhY0ijgTGA4Wc/Pl7o0OF8H2M8iYoakh4AjgS9HxKWS5gGfStfa/l/abmFNAy2QVMzgM8CnJW0GbAfsldqeBIZJ+k1E7CfpJGCYk9/A4ARYG+cDDwLHS3opIi6XNAc4T9K8iJhW4/iKZjnQBpwCBHAisANZj/DtknYArpN0WUR8jOzkhw0AToA1EBEzgBmSFgBnpj+Hkv0i3lnT4AooIhZJ+ivZLYffS9f8bQTckTbZnOzk1K9rFaNVhxNgDUXEHyWtAL5HVu7qiIh4usZhFdXlwH3AuZLmA9cBb5F0IdlweNeI+E8N47Mq8EmQOiBpHbLbTefWOpaik7QtWTL8KnAbWcWX+f6HaWByAjTrRNLWwF+BE9PF6DZAOQGadUHSVkBrRDxZ61isepwAzaywfCucmRWWE6CZFZYToJkVlhOgmRWWE6CZFZYT4AAlaaWkByQ9IunKVGa/r/vaTdKf0usPSvpKD9uOSbXyenuMUyWdUGl7p20uSiXEKj3WFEmP9DZGG3icAAeu1ojYJiK2IrvH+KjSlV0V/6xERFwdEWf1sMkYsrLxZnXPCbAYbgU2ST2fxyRdQva8kUmS9pB0h6T7U09xJICkPSU9Kul+4MMdO5J0mKRz0+t1Jf1O0oNp2YnsIUIbp97nd9N2X5R0j6SHJJ1Wsq+TJD0u6TZgs3JfQtIn034elPTbTr3a90i6N+1v77R9s6Tvlhz7U2v6g7SBxQlwgJPUQnYzf8ejGjcFzouILckKMJwMvCcitgXuJSvRNRT4KfABsifTveF1O86cA/w9IrYGtgWmk1W4fjL1Pr8oaY90zB2AbYC3StpF2XN0D0ht7wO2r+DrXBUR26fj/Zvs8aEdpqRjvB84P32HI8iqNm+f9v/JVOXFDHA1mIFsmKQH0utbgZ8D6wPPRERHya0dgS2A25U9/3swWQmozYGnI+IJAEmXkRVw7exdwCEAEbESWChpbKdt9kjLP9P7kWQJcRTwu44nqUm6uoLvtJWkb5ANs0cCN5SsuyIVKX1C0lPpO+wBvLlkfnB0OvbjFRzLCsAJcOBqjYhtShtSkistsS/gxog4sNN2q31uDQn4VkT8pNMxPteHfV0E7BsRD6bnqexWsq7zPZ2Rjn1cRJQmSiRN6cOxbQDyELjY7gTekZ5TgqQRkqYCjwJTJG2ctjuwm8//BTg6fbY5PcdkEVnvrsMNwOElc4sTU/mvW4B9JQ1Lz9r4QAXxjgJmSxoEHNxp3f6SmlLMbwQeS8c+Om2PpKmSRlRwHCsI9wALLCLmpp7UNElDUvPJEfG4pCOBayQtJRtCj+piF58FLpB0BLASODoi7pB0e7rM5Lo0D/gm4I7UA10MfCwi7pd0OdmjAeYA91QQ8teAu4C56c/SmJ4F7iZ7kttREfGqpJ+RzQ3er+zgc8kedmQGuBqMmRWYh8BmVlhOgGZWWE6AZlZYToBmVlhOgGZWWE6AZlZYToBmVlj/H8G36opvkp33AAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1404,10 +1404,10 @@ "6335\n", "-- liar liar\n", "Index(['y', 'claim'], dtype='object')\n", - "{'half-true', 'false', 'pants-fire', 'mostly-true', 'true', 'barely-true'} 10240\n", + "{'pants-fire', 'false', 'mostly-true', 'true', 'barely-true', 'half-true'} 10240\n", "1676\n", "1995\n", - "{'false', 'true'} 3671\n", + "{'true', 'false'} 3671\n", "false 5159\n", "true 4847\n", "Name: y, dtype: int64\n", @@ -1439,26 +1439,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "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" - } - ], + "outputs": [], "source": [ "clf_3 = MLPClassifier(hidden_layer_sizes=(16,16), random_state=4222)\n", "clf_3.fit(vec_train_3, y3)" @@ -1466,44 +1449,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "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", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "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)" @@ -1518,7 +1466,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1533,7 +1481,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1543,117 +1491,89 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "nsp = {\n", - " \"this\": Namespace(\"http://mex.aksw.org/examples/\"),\n", - " \"xsd\": Namespace(\"http://www.w3.org/2001/XMLSchema#\"),\n", - " \"rdfs\": Namespace(\"http://www.w3.org/2000/01/rdf-schema#\"),\n", - " \"mexcore\": Namespace(\"http://mex.aksw.org/mex-core#\"),\n", - " \"mexperf\": Namespace(\"http://mex.aksw.org/mex-perf#\"),\n", - " \"mexalgo\": Namespace(\"http://mex.aksw.org/mex-algo#\"),\n", - " \"prov\": Namespace(\"http://www.w3.org/ns/prov#\"),\n", - " \"dct\": DCTERMS,\n", - " \"rdf\": RDF\n", - "}\n", - "g = Graph()\n", + "class mexcore_graph(object):\n", + " def __init__(self, name_exp):\n", + " self.nsp = {\n", + " \"this\": Namespace(\"http://mex.aksw.org/examples/\"),\n", + " \"xsd\": Namespace(\"http://www.w3.org/2001/XMLSchema#\"),\n", + " \"rdfs\": Namespace(\"http://www.w3.org/2000/01/rdf-schema#\"),\n", + " \"mexcore\": Namespace(\"http://mex.aksw.org/mex-core#\"),\n", + " \"mexperf\": Namespace(\"http://mex.aksw.org/mex-perf#\"),\n", + " \"mexalgo\": Namespace(\"http://mex.aksw.org/mex-algo#\"),\n", + " \"prov\": Namespace(\"http://www.w3.org/ns/prov#\"),\n", + " \"dct\": DCTERMS,\n", + " \"rdf\": RDF\n", + " }\n", + " self.g = Graph()\n", "\n", - "for i in nsp.items():\n", - " g.bind(i[0],i[1])\n", + " for i in nsp.items():\n", + " self.g.bind(i[0],i[1])\n", + " \n", + " self.exp = self.nsp[\"this\"][name_exp]\n", + " self.g.add((self.nsp[\"this\"][name_exp], self.nsp[\"rdf\"].type, self.nsp[\"mexcore\"].Experiment))\n", + " \n", + " def add_dataset(self,dset_name):\n", + " self.g.add((self.nsp[\"this\"][dset_name], self.nsp[\"rdf\"].type, self.nsp[\"mexcore\"].dataset))\n", + " self.g.add((self.nsp[\"this\"][dset_name], self.nsp[\"rdfs\"].label, Literal(dset_name)))\n", + " \n", + " \n", + " def add_datasets(self, dset_names):\n", + " for dset_name in dset_names:\n", + " self.add_dataset(dset_name)\n", + " \n", + " def add_model(self, model_name, literal):\n", + " self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"rdf\"].type,self.nsp[\"mexalgo\"].Algorithm))\n", + " self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"rdfs\"].label,Literal(model_name)))\n", + " self.g.add((self.nsp[\"this\"][model_name],self.nsp[\"dct\"].identifier,Literal(literal)))\n", + " \n", + " def add_configuration(self, conf_name, used_model, used_dset):\n", + " self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"rdf\"].type,self.nsp[\"mexcore\"].ExperimentConfiguration))\n", + " self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"rdfs\"].label,Literal(conf_name)))\n", + " self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].used, self.nsp[\"this\"][used_model]))\n", + " self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].used, self.nsp[\"this\"][used_dset]))\n", + " self.g.add((self.nsp[\"this\"][conf_name],self.nsp[\"prov\"].wasStartedBy, self.exp))\n", + " \n", + " def add_measurement(self, meas_name, used_conf, confusion_matrix):\n", + " self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"rdf\"].type,self.nsp[\"mexcore\"].PerformanceMeasure))\n", + " self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].precision,Literal(str(precision(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n", + " self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].recall,Literal(str(recall(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n", + " self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"mexperf\"].accuracy,Literal(str(accuracy(confusion_matrix)),datatype=self.nsp[\"xsd\"].float)))\n", + " self.g.add((self.nsp[\"this\"][meas_name],self.nsp[\"prov\"].wasGeneratedBy,self.nsp[\"this\"][used_conf]))\n", "\n", - "g.add((nsp[\"this\"].jonas_weinz_task_2, nsp[\"rdf\"].type, nsp[\"mexcore\"].Experiment))\n", "\n", - "g.add((nsp[\"this\"].ds1, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", - "g.add((nsp[\"this\"].ds1, nsp[\"rdfs\"].label, Literal(\"Dataset01\")))\n", - "\n", - "g.add((nsp[\"this\"].ds2, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", - "g.add((nsp[\"this\"].ds2, nsp[\"rdfs\"].label, Literal(\"Dataset02\")))\n", - "\n", - "g.add((nsp[\"this\"].ds3, nsp[\"rdf\"].type, nsp[\"mexcore\"].dataset))\n", - "g.add((nsp[\"this\"].ds3, nsp[\"rdfs\"].label, Literal(\"Dataset03\")))\n", - "\n", - "g.add((nsp[\"this\"].modela,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", - "g.add((nsp[\"this\"].modela,nsp[\"rdfs\"].label,Literal('model a')))\n", - "g.add((nsp[\"this\"].modela,nsp[\"dct\"].identifier,Literal('RandomForestClassifier')))\n", - "\n", - "g.add((nsp[\"this\"].modelb,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", - "g.add((nsp[\"this\"].modelb,nsp[\"rdfs\"].label,Literal('model b')))\n", - "g.add((nsp[\"this\"].modelb,nsp[\"dct\"].identifier,Literal('MLPClassifier')))\n", - "\n", - "g.add((nsp[\"this\"].modelc,nsp[\"rdf\"].type,nsp[\"mexalgo\"].Algorithm))\n", - "g.add((nsp[\"this\"].modelc,nsp[\"rdfs\"].label,Literal('model c')))\n", - "g.add((nsp[\"this\"].modelc,nsp[\"dct\"].identifier,Literal('MLPClassifier')))\n", - "\n", - "g.add((nsp[\"this\"].conf1,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", - "g.add((nsp[\"this\"].conf1,nsp[\"rdfs\"].label,Literal('configuration 1')))\n", - "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].used, nsp[\"this\"].modela))\n", - "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].used, nsp[\"this\"].ds1))\n", - "g.add((nsp[\"this\"].conf1,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", - "\n", - "g.add((nsp[\"this\"].conf2,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", - "g.add((nsp[\"this\"].conf2,nsp[\"rdfs\"].label,Literal('configuration 2')))\n", - "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].used, nsp[\"this\"].modelb))\n", - "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].used, nsp[\"this\"].ds2))\n", - "g.add((nsp[\"this\"].conf2,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", - "\n", - "g.add((nsp[\"this\"].conf3a,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", - "g.add((nsp[\"this\"].conf3a,nsp[\"rdfs\"].label,Literal('configuration 3a')))\n", - "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].used, nsp[\"this\"].modelb))\n", - "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].used, nsp[\"this\"].ds1))\n", - "g.add((nsp[\"this\"].conf3a,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", - "\n", - "g.add((nsp[\"this\"].conf3b,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", - "g.add((nsp[\"this\"].conf3b,nsp[\"rdfs\"].label,Literal('configuration 3b')))\n", - "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].used, nsp[\"this\"].modela))\n", - "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].used, nsp[\"this\"].ds2))\n", - "g.add((nsp[\"this\"].conf3b,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", - " \n", - "g.add((nsp[\"this\"].conf4,nsp[\"rdf\"].type,nsp[\"mexcore\"].ExperimentConfiguration))\n", - "g.add((nsp[\"this\"].conf4,nsp[\"rdfs\"].label,Literal('configuration 4')))\n", - "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].used, nsp[\"this\"].modelc))\n", - "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].used, nsp[\"this\"].ds3))\n", - "g.add((nsp[\"this\"].conf4,nsp[\"prov\"].wasStartedBy, nsp[\"this\"].jonas_weinz_task_2))\n", - "\n", - "g.add((nsp[\"this\"].measure1,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", - "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].precision,Literal(str(precision(cm_1)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].recall,Literal(str(recall(cm_1)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure1,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_1)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure1,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf1))\n", - "\n", - "g.add((nsp[\"this\"].measure2,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", - "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].precision,Literal(str(precision(cm_2)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].recall,Literal(str(recall(cm_2)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure2,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_2)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure2,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf2))\n", - "\n", - "g.add((nsp[\"this\"].measure3a,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", - "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].precision,Literal(str(precision(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].recall,Literal(str(recall(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3a,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_3a)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3a,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf3a))\n", - "\n", - "g.add((nsp[\"this\"].measure3b,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", - "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].precision,Literal(str(precision(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].recall,Literal(str(recall(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3b,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_3b)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure3b,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf3b))\n", - "\n", - "g.add((nsp[\"this\"].measure4,nsp[\"rdf\"].type,nsp[\"mexcore\"].PerformanceMeasure))\n", - "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].precision,Literal(str(precision(cm_4)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].recall,Literal(str(recall(cm_4)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure4,nsp[\"mexperf\"].accuracy,Literal(str(accuracy(cm_4)),datatype=nsp[\"xsd\"].float)))\n", - "g.add((nsp[\"this\"].measure4,nsp[\"prov\"].wasGeneratedBy,nsp[\"this\"].conf4))\n" + " " ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "g.serialize(destination='output.ttl', format='turtle')" + "mg = mexcore_graph(\"jonas_weinz_task_2\")\n", + "mg.add_datasets([\"Dataset01\",\"Dataset02\",\"Dataset03\"])\n", + "\n", + "mg.add_model(\"model_a\", \"RandomForestClassifier\")\n", + "mg.add_model(\"model_b\", \"MLPClassifier\")\n", + "mg.add_model(\"model_c\", \"MLPClassifier\")\n", + "\n", + "mg.add_configuration(\"conf1\",\"model_a\",\"Dataset01\")\n", + "mg.add_configuration(\"conf2\",\"model_b\", \"Dataset02\")\n", + "mg.add_configuration(\"conf3a\",\"model_b\", \"Dataset01\")\n", + "mg.add_configuration(\"conf3b\", \"model_a\", \"Dataset02\")\n", + "mg.add_configuration(\"conf4\", \"model_c\", \"Dataset03\")\n", + "\n", + "mg.add_measurement(\"measure1\", \"conf1\", cm_1)\n", + "mg.add_measurement(\"measure2\", \"conf2\", cm_2)\n", + "mg.add_measurement(\"measure3a\", \"conf3a\", cm_3a)\n", + "mg.add_measurement(\"measure3b\", \"conf3b\", cm_3b)\n", + "mg.add_measurement(\"measure4\", \"conf4\", cm_4)\n", + "\n", + "mg.g.serialize(destination='output.ttl', format='turtle')" ] }, { diff --git a/Jonas_Solutions/Task_03.ipynb b/Jonas_Solutions/Task_03.ipynb index 49e0899..af6ec4e 100644 --- a/Jonas_Solutions/Task_03.ipynb +++ b/Jonas_Solutions/Task_03.ipynb @@ -2,9 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jonas/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n" + ] + } + ], "source": [ "import numpy as np \n", "import pandas as pd \n", @@ -18,9 +28,22 @@ "import re\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">**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" + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -65,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -94,12 +117,12 @@ "df = df[df.sentiment != \"Neutral\"]\n", "df['text'] = df['text'].apply(lambda x: x.lower())\n", "df['text'] = df['text'].apply(lambda x: x.replace('rt',' '))\n", - "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-z0-9\\s]','',x)))" + "df['text'] = df['text'].apply((lambda x: re.sub('[^a-zA-Z0-9\\s]','',x)))" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -121,13 +144,13 @@ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "embedding_1 (Embedding) (None, 26, 128) 64000 \n", + "embedding_2 (Embedding) (None, 26, 128) 64000 \n", "_________________________________________________________________\n", - "spatial_dropout1d_1 (Spatial (None, 26, 128) 0 \n", + "spatial_dropout1d_2 (Spatial (None, 26, 128) 0 \n", "_________________________________________________________________\n", - "lstm_1 (LSTM) (None, 196) 254800 \n", + "lstm_2 (LSTM) (None, 196) 254800 \n", "_________________________________________________________________\n", - "dense_1 (Dense) (None, 2) 394 \n", + "dense_2 (Dense) (None, 2) 394 \n", "=================================================================\n", "Total params: 319,194\n", "Trainable params: 319,194\n", @@ -157,15 +180,15 @@ "output_type": "stream", "text": [ "Epoch 1/2\n", - " - 30s - loss: 0.4374 - acc: 0.8161\n", + " - 21s - loss: 0.4322 - acc: 0.8196\n", "Epoch 2/2\n", - " - 30s - loss: 0.3614 - acc: 0.8487\n" + " - 25s - loss: 0.3612 - acc: 0.8509\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -181,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -189,7 +212,7 @@ "output_type": "stream", "text": [ "score: 0.37\n", - "acc: 0.85\n" + "acc: 0.84\n" ] } ], @@ -205,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -221,15 +244,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "pos_acc 31.770833333333332 %\n", - "neg_acc 97.27722772277228 %\n" + "pos_acc 39.58333333333333 %\n", + "neg_acc 95.29702970297029 %\n" ] } ], @@ -240,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -249,7 +272,7 @@ "text": [ "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 37\n", " 311 189 4 144 22 16 1 281]]\n", - "[0.93431044 0.06568963]\n" + "[0.8364928 0.16350722]\n" ] } ], diff --git a/Jonas_Solutions/output.ttl b/Jonas_Solutions/output.ttl index 104e95b..41dfcb2 100644 --- a/Jonas_Solutions/output.ttl +++ b/Jonas_Solutions/output.ttl @@ -39,55 +39,55 @@ this:measure4 a mexcore:PerformanceMeasure ; mexperf:recall "0.7680865449628127"^^xsd:float ; prov:wasGeneratedBy this:conf4 . +this:Dataset03 a mexcore:dataset ; + rdfs:label "Dataset03" . + this:conf1 a mexcore:ExperimentConfiguration ; - rdfs:label "configuration 1" ; - prov:used this:ds1, - this:modela ; + rdfs:label "conf1" ; + prov:used this:Dataset01, + this:model_a ; prov:wasStartedBy this:jonas_weinz_task_2 . this:conf2 a mexcore:ExperimentConfiguration ; - rdfs:label "configuration 2" ; - prov:used this:ds2, - this:modelb ; + rdfs:label "conf2" ; + prov:used this:Dataset02, + this:model_b ; prov:wasStartedBy this:jonas_weinz_task_2 . this:conf3a a mexcore:ExperimentConfiguration ; - rdfs:label "configuration 3a" ; - prov:used this:ds1, - this:modelb ; + rdfs:label "conf3a" ; + prov:used this:Dataset01, + this:model_b ; prov:wasStartedBy this:jonas_weinz_task_2 . this:conf3b a mexcore:ExperimentConfiguration ; - rdfs:label "configuration 3b" ; - prov:used this:ds2, - this:modela ; + rdfs:label "conf3b" ; + prov:used this:Dataset02, + this:model_a ; prov:wasStartedBy this:jonas_weinz_task_2 . this:conf4 a mexcore:ExperimentConfiguration ; - rdfs:label "configuration 4" ; - prov:used this:ds3, - this:modelc ; + rdfs:label "conf4" ; + prov:used this:Dataset03, + this:model_c ; prov:wasStartedBy this:jonas_weinz_task_2 . -this:ds3 a mexcore:dataset ; - rdfs:label "Dataset03" . - -this:modelc a mexalgo:Algorithm ; - rdfs:label "model c" ; +this:model_c a mexalgo:Algorithm ; + rdfs:label "model_c" ; dct:identifier "MLPClassifier" . -this:ds1 a mexcore:dataset ; +this:Dataset01 a mexcore:dataset ; rdfs:label "Dataset01" . -this:ds2 a mexcore:dataset ; +this:Dataset02 a mexcore:dataset ; rdfs:label "Dataset02" . -this:modela a mexalgo:Algorithm ; - rdfs:label "model a" ; +this:model_a a mexalgo:Algorithm ; + rdfs:label "model_a" ; dct:identifier "RandomForestClassifier" . -this:modelb a mexalgo:Algorithm ; - rdfs:label "model b" ; +this:model_b a mexalgo:Algorithm ; + rdfs:label "model_b" ; dct:identifier "MLPClassifier" . this:jonas_weinz_task_2 a mexcore:Experiment .