From bc941073537f32a57ba40bd06e4c23d66c6f77df Mon Sep 17 00:00:00 2001 From: aikira7 Date: Sat, 7 Jul 2018 18:25:17 +0200 Subject: [PATCH 1/6] Naive approach mit word2Vec similarities --- Project/naive_approach/naive_approach.py | 47 ++++++++++------------- Project/naive_approach/word2vec.model | Bin 0 -> 1480644 bytes 2 files changed, 21 insertions(+), 26 deletions(-) create mode 100644 Project/naive_approach/word2vec.model diff --git a/Project/naive_approach/naive_approach.py b/Project/naive_approach/naive_approach.py index 809a2f2..3c911a5 100644 --- a/Project/naive_approach/naive_approach.py +++ b/Project/naive_approach/naive_approach.py @@ -15,6 +15,7 @@ from nltk.corpus import wordnet import math import pprint +from gensim.models import Word2Vec, KeyedVectors # # Naive Approach table = pd.read_csv('../Tools/emoji_descriptions.csv') @@ -29,25 +30,25 @@ for index, row in table.iterrows(): # Helper functions ####################### -def stemming(messages): - stemmed_messages = [] +def stemming(message): ps = PorterStemmer() - for m in messages: - words = word_tokenize(m) - sm = [] - for w in words: - sm.append(ps.stem(w)) - m = (" ").join(sm) - stemmed_messages.append(m) - return stemmed_messages + words = word_tokenize(message) + sm = [] + for w in words: + sm.append(ps.stem(w)) + stemmed_message = (" ").join(sm) + return stemmed_message # * compare words to emoji descriptions -def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', emojis_to_consider="all"): +def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', emojis_to_consider="all", stem=True): + # assumes there is a trained w2v model stored in the same directory! + wv = KeyedVectors.load("word2vec.model", mmap='r') + if (stem): + sentence = stemming(sentence) tokenized_sentence = word_tokenize(sentence) n = len(tokenized_sentence) - l = table.shape[0] matrix_list = [] for index in tableDict.keys(): @@ -57,20 +58,11 @@ def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', e mat = np.zeros(shape=(m,n)) for i in range(len(emoji_tokens)): for j in range(len(tokenized_sentence)): - syn1 = wordnet.synsets(emoji_tokens[i],lang=lang) - if len(syn1) == 0: - continue - w1 = syn1[0] - #print(j, tokenized_sentence) - syn2 = wordnet.synsets(tokenized_sentence[j], lang=lang) - if len(syn2) == 0: - continue - w2 = syn2[0] - val = w1.wup_similarity(w2) - if val is None: + try: + val = wv.similarity(emoji_tokens[i], tokenized_sentence[j]) + except KeyError: continue mat[i,j] = val - #print(row['character'], mat) matrix_list.append(mat) return matrix_list @@ -83,10 +75,13 @@ def evaluate_sentence(sentence, description_key = 'description', lang = 'eng', e # load and preprocess data # emojis_to_consider can be either a list or "all" -def prepareData(stemming=False): - if(stemming): +def prepareData(stem=True, lower=True): + if(stem): for index in tableDict.keys(): tableDict[index][1] = stemming(tableDict[index][1]) + if(lower): + for index in tableDict.keys(): + tableDict[index][1] = tableDict[index][1].lower() #collect the emojis lookup = {} diff --git a/Project/naive_approach/word2vec.model b/Project/naive_approach/word2vec.model new file mode 100644 index 0000000000000000000000000000000000000000..9ce773d5bc617f69eb9891c4feb8df182ddcbf1d GIT binary patch literal 1480644 zcmX`U_g59q()Mix6mw2s7BPzf_Ot;LehnBfVL}ljA_^uHQF0PV0un`nB1%$F*t0>_ zs`~x?m)_^o-1qaGvyQ;tGdcRLVQxvt@wn8Pg5p_#!ljcq{s2MQzrf2VV*yG zko0t_z39f{UDU)L-aP`Xk${<5sk=!_=xy+)-TuYO3z9^Aic`v?A(k=B2(y=4JT-FvH5%=&RDKV0%nUX8M zQ%B{CvsgN%P<%!|LWZ0>bGX*4eNrCfga%z< z@iAwOROzUE^t@ZJ+upfMd&Si(&A)G?QaZKV(VzPDfYEP9SiO5w{_1aWWmNlbi1(Qc z-QemP`K4c+MENM$Jn3)Xf8uk_1&D8se2C(M;w6ubR7b^-M!sqD*dq#OYPGXP>SwVUMA63Me*4AnuXndLJF740dq--K$tNQPTDRMr zezIHKNsrX)%oguHqjh(61mV51yS8=tO%oG3+sN>8X%*kpclX8T7mpC6Y3UB}O_T<< z(A}WL=Z*Y|v)g%VgCCEo(zbO5)oOWd7EAq7vqUme#dqnxy5cTCo^@vP`i&+yK}uME z&9?h&#;}fD;m*35OT`x|U!~gUCsd<=NoXxP?bFh8ug!U`s}*;j({8(# 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zc$ix9L*=zaHojlZh4r4x$eiFpxS(kOo<<>9-erI<|5S3;ZmmGIh_^()R}KZmH$a_1 zIa&r*!`73vVB3F{O4W)%A5|dZ?{et;U6*OQkS$N;sU%gOF&9!|eZVN}Dt^Cn6ZW0B z3_Z6LaZ~jXu#ZTm-)x!hFs2H|Rad~1b*DkUwT|pxx)IZlf29RM{&-$mgc`JF;C*7l z@~P+1E^RTo?YsloGyR}`yot~$edMb|)&s4qDjI(Sg8S#gtW9O~gWh$xQF#MLR1;C0 zb#6ObrqR7Ol|aR&n&h4J#SZt?AosA0_)Arxy`LUv(HMCBRf`y0Oow$T)A6-3kJKo4 z!$$5ivP&(HWlUS~O#CwVCq56pbC=WO>*P?XBoOcYu7hh2LKv5Mlr(-0<9Qz9!S>~^ zNq}()CMAxMHoksPOWq2Zhe|o|xvQafbpa+FI)jySI*3+gC6p$+kxui=@c3aHm<_hU zk&u_`RdVt zO+p+x`7H!XruO5?jlBV-<2MJbPyQg=Fhacl zvxRdXx6*A!4KO;q2fi-t0palwT%(r=Q`WWkVTU@Ld+&1YgVkZ-Y15`T9CNg>wYRo( kJmzxh+{b;Vp6oksby$GCar~@R literal 0 HcmV?d00001 From b4ae0b033e3181c6e525fa6273072b5d52d7254c Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 10 Jul 2018 13:48:51 +0200 Subject: [PATCH 3/6] added possibility to load pretrained doc2vecD --- .../simple_approach/Continous_Learner.ipynb | 15 ++++++++++----- .../simple_approach/simple_twitter_learning.py | 18 +++++++++--------- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb index 59996ec..2cafe29 100644 --- a/Project/simple_approach/Continous_Learner.ipynb +++ b/Project/simple_approach/Continous_Learner.ipynb @@ -40,7 +40,8 @@ "import simple_twitter_learning as stl\n", "import glob\n", "import sys\n", - "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer" + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", + "import pickle" ] }, { @@ -144,7 +145,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d304cda50752491da1637b292a9367e8", + "model_id": "5a488abefd074719adb15425714a076f", "version_major": 2, "version_minor": 0 }, @@ -205,6 +206,7 @@ " ],\n", " [\n", " (widgets.Checkbox(value=True),\"use_doc2vec\"),\n", + " (widgets.Checkbox(value=True),\"d2v_use_pretrained\"),\n", " (widgets.IntText(value=100),\"d2v_size\"),\n", " (widgets.IntText(value=8), \"d2v_window\"),\n", " (widgets.IntSlider(value=5, min=0, max=32), \"d2v_min_count\")\n", @@ -558,9 +560,12 @@ " # creating the vectorizer\n", " vectorizer = None\n", " if shown_widgets[\"use_doc2vec\"].value:\n", - " vectorizer = stl.skd2v.Doc2VecTransformer(size=shown_widgets[\"d2v_size\"].value,\n", - " window=shown_widgets[\"d2v_window\"].value,\n", - " min_count=shown_widgets[\"d2v_min_count\"].value)\n", + " if shown_widgets[\"d2v_use_pretrained\"].value:\n", + " vectorizer = pickle.load( open( \"doc2VecModel.p\", \"rb\" ) )\n", + " else:\n", + " vectorizer = stl.skd2v.Doc2VecTransformer(size=shown_widgets[\"d2v_size\"].value,\n", + " window=shown_widgets[\"d2v_window\"].value,\n", + " min_count=shown_widgets[\"d2v_min_count\"].value)\n", " else:\n", " vectorizer=TfidfVectorizer(stop_words='english')\n", " \n", diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py index 88267b3..c9b82c7 100644 --- a/Project/simple_approach/simple_twitter_learning.py +++ b/Project/simple_approach/simple_twitter_learning.py @@ -52,7 +52,6 @@ def sent2emoji(sent_arr, custom_target_emojis=None, only_emoticons=True): # In[3]: - SINGLE_LABEL = True @@ -431,7 +430,7 @@ class pipeline_manager(object): return pm @staticmethod - def create_keras_pipeline_with_vectorizer(vectorizer, layers, sdm:sample_data_manager, loss=None, optimizer=None): + def create_keras_pipeline_with_vectorizer(vectorizer, layers, sdm:sample_data_manager, loss=None, optimizer=None, fit_vectorizer=True): ''' creates pipeline with vectorizer and keras classifier @@ -447,11 +446,12 @@ class pipeline_manager(object): from keras.models import Sequential from keras.layers import Dense - if sdm.X is None: - sdm.create_train_test_split() - - vec_train = vectorizer.fit_transform(sdm.X) - vec_test = vectorizer.transform(sdm.Xt) + if fit_vectorizer: + if sdm.X is None: + sdm.create_train_test_split() + + vec_train = vectorizer.fit_transform(sdm.X) + vec_test = vectorizer.transform(sdm.Xt) # creating keras model: model=Sequential() @@ -578,7 +578,7 @@ class pipeline_manager(object): """fitting the pipeline""" self.pipeline.fit(X,y) - def predict(self,X, use_stemming=True, use_lemmatization=True): + def predict(self,X, use_stemming=False, use_lemmatization=False): """predict""" if use_stemming: X = np.array(batch_stem(X)) @@ -608,7 +608,7 @@ class trainer(object): self.sdm = sdm self.pm = pm - def fit(self, max_size=10000, disabled_fit_steps=['vectorizer'], keras_batch_fitting_layer=['keras_model'], batch_size=None, n_epochs=1, progress_callback=None): + def fit(self, max_size=1000000, disabled_fit_steps=['vectorizer'], keras_batch_fitting_layer=['keras_model'], batch_size=None, n_epochs=1, progress_callback=None): """ fitting data in the pipeline. Because we don't want to refit the vectorizer, the pipeline models containing the vectorizer have to be named explicitly From 7f6c9791ae98d2c48bc6b6da82e44983d7b0cba2 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 10 Jul 2018 15:59:28 +0200 Subject: [PATCH 4/6] simple improvements --- Project/simple_approach/Continous_Learner.ipynb | 10 +++++++--- Project/simple_approach/simple_twitter_learning.py | 13 ++++++++++--- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb index 2cafe29..454d176 100644 --- a/Project/simple_approach/Continous_Learner.ipynb +++ b/Project/simple_approach/Continous_Learner.ipynb @@ -145,7 +145,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a488abefd074719adb15425714a076f", + "model_id": "d00ff918ad4d473499b1e91d4dcb8702", "version_major": 2, "version_minor": 0 }, @@ -174,7 +174,8 @@ " ],\n", " [\n", " (widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\"),\n", - " (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\")\n", + " (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\"),\n", + " (widgets.Dropdown(options=[\"latest\", \"mean\"], value=\"latest\"), \"label_criteria\")\n", " ],\n", " [\n", " (widgets.Button(disabled=True),\"load_data\")\n", @@ -446,13 +447,16 @@ " if lemm_and_stemm:\n", " p_s = progress_indicator(\"stemming progress\")\n", " \n", + " emoji_mean = shown_widgets[\"label_criteria\"].value == \"mean\"\n", + " \n", " sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n", " n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n", " file_range=range(r[0], r[1]),\n", " n_kmeans_cluster=shown_widgets[\"k_means_cluster\"].value,\n", " read_progress_callback=p_r.update,\n", " stem_progress_callback=p_s.update if lemm_and_stemm else None,\n", - " apply_stemming = lemm_and_stemm)\n", + " apply_stemming = lemm_and_stemm,\n", + " emoji_mean=emoji_mean)\n", " shown_widgets[\"batch_size\"].max = len(sdm.labels)\n", " \n", " \n", diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py index c9b82c7..a193819 100644 --- a/Project/simple_approach/simple_twitter_learning.py +++ b/Project/simple_approach/simple_twitter_learning.py @@ -28,6 +28,8 @@ nltk.download('punkt') nltk.download('averaged_perceptron_tagger') nltk.download('wordnet') +from keras import losses + # check whether the display function exists: try: display @@ -160,7 +162,7 @@ def batch_lemm(sentences): class sample_data_manager(object): @staticmethod - def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, read_progress_callback=None, stem_progress_callback=None): + def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, read_progress_callback=None, stem_progress_callback=None, emoji_mean=False): """ generate, read and process train data in one step. @@ -174,7 +176,7 @@ class sample_data_manager(object): @return: sample_data_manager object """ sdm = sample_data_manager(path) - sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=read_progress_callback) + sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=read_progress_callback, emoji_mean=emoji_mean) if apply_stemming: sdm.apply_stemming_and_lemmatization(progress_callback=stem_progress_callback) @@ -641,7 +643,12 @@ class trainer(object): named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(to_dense_if_sparse(X), y) # ← why has keras no sparse support on batch progressing!?!?! if batch_size is None: - self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size]) + for e in range(n_epochs): + print("epoch", e) + self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size]) + pred, yt = self.test() + mean_squared_error = ((pred - yt)**2).mean(axis=0) + print("#" + str(e) + ": validation loss: ", mean_squared_error, "scalar: ", np.mean(mean_squared_error)) else: n = len(self.sdm.X) // batch_size for i in range(n_epochs): From 16551c64f6eaea9ba4517c8da427e0abfea31d79 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 10 Jul 2018 16:23:54 +0200 Subject: [PATCH 5/6] simple fix --- Project/simple_approach/simple_twitter_learning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py index a193819..102b790 100644 --- a/Project/simple_approach/simple_twitter_learning.py +++ b/Project/simple_approach/simple_twitter_learning.py @@ -240,7 +240,7 @@ class sample_data_manager(object): # so far filtering for the latest emoji. TODO: maybe there are also better approaches labels_i = emoji2sent([latest(e) for e in emojis_i], only_emoticons=only_emoticons ) else: - labels_i = np.array([np.mean(emoji2sent(e, only_emoticons=only_emoticons), axis=0) for e in emojis_i]) + labels_i = np.array([np.mean(emoji2sent(e, only_emoticons=only_emoticons), axis=0).tolist() for e in emojis_i]) # and filter out all samples we have no label for: wrong_labels = np.isnan(np.linalg.norm(labels_i, axis=1)) From 22f92da3fdcf6f71432f920d5a96b9a7a90005d5 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Mon, 16 Jul 2018 11:54:56 +0200 Subject: [PATCH 6/6] emoji plotting test2 2 --- Project/Tools/emoji_plotting.ipynb | 169 +++++++++++++++++++++++++++++ 1 file changed, 169 insertions(+) create mode 100644 Project/Tools/emoji_plotting.ipynb diff --git a/Project/Tools/emoji_plotting.ipynb b/Project/Tools/emoji_plotting.ipynb new file mode 100644 index 0000000..80c8d9c --- /dev/null +++ b/Project/Tools/emoji_plotting.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from __future__ import unicode_literals\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#plt.rc('font', family='Arial') \n", + "\n", + "#plt.rc('font', **{'sans-serif' : 'Arial', 'family' : 'sans-serif'}) \n", + "\n", + "plt.title('Développés et fabriqués')\n", + "plt.xlabel(\"réactivité nous permettent d'être sélectionnés et adoptés\")\n", + "plt.ylabel('André was here!')\n", + "plt.text(0.2, 0.8, '😂')\n", + "#plt.text(0.4, 0.2, 'AVA (check kerning)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def sentiment_score(s):\n", + " #(pos, neg, neu)^T\n", + " return s[0] - s[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import Emoji_Distance as edist\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "list_emoticon_scores = np.array([sentiment_score(x) for x in edist.list_sentiment_emoticon_vectors])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_emoji_list(sentiment_vectors, unicode_repr, title=\"Emoji Space\"):\n", + " \n", + " # sentiment score axis\n", + " X = np.array([sentiment_score(x) for x in sentiment_vectors])\n", + " \n", + " # neutral axis:\n", + " Y = np.array([x[2] for x in sentiment_vectors])\n", + " \n", + " plt.title(title)\n", + " plt.xlabel(\"sentiment score\")\n", + " plt.ylabel(\"neutrality\")\n", + " plt.xlim([-1,1])\n", + " for i in range(len(X)):\n", + " plt.text(X[i], Y[i], unicode_repr[i])\n", + " plt.show()\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_emoji_list(sentiment_vectors=edist.list_sentiment_emoticon_vectors, unicode_repr=edist.list_emoticon_emojis, title=\"Emoticon Emoji space\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_emoji_list(sentiment_vectors=edist.list_sentiment_vectors, unicode_repr=edist.list_emojis, title=\"Full Emoji space\")" + ] + }, + { + "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 +}