messy merge on carstens laptop
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							| @ -0,0 +1,206 @@ | ||||
| { | ||||
|  "cells": [ | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 13, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "import sys\n", | ||||
|     "import numpy as np\n", | ||||
|     "from sklearn.cluster import KMeans\n", | ||||
|     "sys.path.append(\"..\")\n", | ||||
|     "\n", | ||||
|     "from Tools.Emoji_Distance import sentiment_vector_to_emoji\n", | ||||
|     "from Tools.Emoji_Distance import emoji_to_sentiment_vector\n", | ||||
|     "from Tools.Emoji_Distance import dataframe_to_dictionary\n", | ||||
|     "\n", | ||||
|     "def emoji2sent(emoji_arr):\n", | ||||
|     "    return np.array([emoji_to_sentiment_vector(e) for e in emoji_arr])\n", | ||||
|     "\n", | ||||
|     "def sent2emoji(sent_arr, custom_target_emojis=None):\n", | ||||
|     "    return [sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis) for s in sent_arr]" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 8, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "data , data_only_emoticons, list_sentiment_vectors , list_emojis , list_sentiment_emoticon_vectors , list_emoticon_emojis = dataframe_to_dictionary()" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 10, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/plain": [ | ||||
|        "array([[0.46813021, 0.24716181, 0.28470797],\n", | ||||
|        "       [0.72967448, 0.05173769, 0.21858783],\n", | ||||
|        "       [0.34310532, 0.43648208, 0.2204126 ],\n", | ||||
|        "       [0.75466009, 0.0529057 , 0.19243421],\n", | ||||
|        "       [0.70401758, 0.05932203, 0.23666039],\n", | ||||
|        "       [0.57697579, 0.12699863, 0.29602558],\n", | ||||
|        "       [0.22289823, 0.59126106, 0.18584071],\n", | ||||
|        "       [0.49837557, 0.0805718 , 0.42105263],\n", | ||||
|        "       [0.44415243, 0.11169514, 0.44415243],\n", | ||||
|        "       [0.5634451 , 0.09927679, 0.33727811]])" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 10, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "array_sentiment_vectors = np.array(list_sentiment_emoticon_vectors)\n", | ||||
|     "array_sentiment_vectors[:10]" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 42, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "kmeans = KMeans(n_clusters=5, random_state=0).fit(array_sentiment_vectors)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 43, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/plain": [ | ||||
|        "array([[0.43555605, 0.2777192 , 0.28672476],\n", | ||||
|        "       [0.21254481, 0.57576584, 0.21168936],\n", | ||||
|        "       [0.56669216, 0.13017252, 0.30313532],\n", | ||||
|        "       [0.33453667, 0.45309312, 0.21237021],\n", | ||||
|        "       [0.71664806, 0.06648547, 0.21686647]])" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 43, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "centers = kmeans.cluster_centers_\n", | ||||
|     "centers" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 44, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "🙇\n", | ||||
|       "😿\n", | ||||
|       "😄\n", | ||||
|       "😭\n", | ||||
|       "😍\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "for center in centers:\n", | ||||
|     "    print(sentiment_vector_to_emoji(center))" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "* only most used emojis" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 46, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "top_emojis = [('😂', 10182),\n", | ||||
|     " ('😭', 3893),\n", | ||||
|     " ('😍', 2866),\n", | ||||
|     " ('😩', 1647),\n", | ||||
|     " ('😊', 1450),\n", | ||||
|     " ('😘', 1151),\n", | ||||
|     " ('🙏', 1089),\n", | ||||
|     " ('🙌', 1003),\n", | ||||
|     " ('😉', 752),\n", | ||||
|     " ('😁', 697),\n", | ||||
|     " ('😅', 651),\n", | ||||
|     " ('😎', 606),\n", | ||||
|     " ('😢', 544),\n", | ||||
|     " ('😒', 539),\n", | ||||
|     " ('😏', 478),\n", | ||||
|     " ('😌', 434),\n", | ||||
|     " ('😔', 415),\n", | ||||
|     " ('😋', 397),\n", | ||||
|     " ('😀', 392),\n", | ||||
|     " ('😤', 368)]" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 47, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "😂\n", | ||||
|       "😒\n", | ||||
|       "😁\n", | ||||
|       "😭\n", | ||||
|       "😍\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "for center in centers:\n", | ||||
|     "    print(sentiment_vector_to_emoji(center, custom_target_emojis=top_emojis))" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "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": "3.6.4" | ||||
|   } | ||||
|  }, | ||||
|  "nbformat": 4, | ||||
|  "nbformat_minor": 2 | ||||
| } | ||||
										
											
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							| @ -0,0 +1,688 @@ | ||||
|  | ||||
| # coding: utf-8 | ||||
|  | ||||
| # In[1]: | ||||
|  | ||||
|  | ||||
| import pandas as pd | ||||
| from IPython.display import clear_output, Markdown, Math | ||||
| import ipywidgets as widgets | ||||
| import os | ||||
| import glob | ||||
| import json | ||||
| import numpy as np | ||||
| import itertools | ||||
| import sklearn.utils as sku | ||||
| from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer | ||||
| from sklearn.model_selection import train_test_split | ||||
| from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer | ||||
| from sklearn.cluster import KMeans | ||||
| import nltk | ||||
| from keras.models import load_model | ||||
| from sklearn.externals import joblib | ||||
| import pickle | ||||
| import operator | ||||
| from sklearn.pipeline import Pipeline | ||||
| nltk.download('punkt') | ||||
| nltk.download('averaged_perceptron_tagger') | ||||
| nltk.download('wordnet') | ||||
|  | ||||
|  | ||||
| # In[2]: | ||||
|  | ||||
|  | ||||
| import sys | ||||
| sys.path.append("..") | ||||
|  | ||||
| import Tools.Emoji_Distance as edist | ||||
|  | ||||
| def emoji2sent(emoji_arr, only_emoticons=True): | ||||
|     return np.array([edist.emoji_to_sentiment_vector(e, only_emoticons=only_emoticons) for e in emoji_arr]) | ||||
|  | ||||
| def sent2emoji(sent_arr, custom_target_emojis=None, only_emoticons=True): | ||||
|     return [edist.sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis, only_emoticons=only_emoticons) for s in sent_arr] | ||||
|  | ||||
|  | ||||
| # In[3]: | ||||
|  | ||||
|  | ||||
| SINGLE_LABEL = True | ||||
|  | ||||
|  | ||||
| # ---- | ||||
| # ## classes and functions we are using later: | ||||
| # ---- | ||||
|  | ||||
| # * functions for selecting items from a set / list | ||||
|  | ||||
| # In[4]: | ||||
|  | ||||
|  | ||||
| def latest(lst): | ||||
|     return lst[-1] if len(lst) > 0 else 'X'  | ||||
| def most_common(lst): | ||||
|     # trying to find the most common used emoji in the given lst | ||||
|     return max(set(lst), key=lst.count) if len(lst) > 0 else "X" # setting label to 'X' if there is an empty emoji list | ||||
|  | ||||
|  | ||||
| # * our emoji blacklist (skin and sex modifiers) | ||||
|  | ||||
| # In[5]: | ||||
|  | ||||
|  | ||||
| # defining blacklist for modifier emojis: | ||||
| emoji_blacklist = set([ | ||||
|     chr(0x1F3FB), | ||||
|     chr(0x1F3FC), | ||||
|     chr(0x1F3FD), | ||||
|     chr(0x1F3FE), | ||||
|     chr(0x1F3FF), | ||||
|     chr(0x2642), | ||||
|     chr(0x2640) | ||||
| ]) | ||||
|  | ||||
|  | ||||
| # * lemmatization helper functions | ||||
|  | ||||
| # In[6]: | ||||
|  | ||||
|  | ||||
| from nltk.stem.snowball import SnowballStemmer | ||||
| from nltk.stem import WordNetLemmatizer | ||||
| from nltk import pos_tag | ||||
| from nltk import word_tokenize | ||||
| from nltk.corpus import wordnet | ||||
|  | ||||
| def get_wordnet_pos(treebank_tag): | ||||
|  | ||||
|     if treebank_tag.startswith('J'): | ||||
|         return wordnet.ADJ | ||||
|     elif treebank_tag.startswith('V'): | ||||
|         return wordnet.VERB | ||||
|     elif treebank_tag.startswith('N'): | ||||
|         return wordnet.NOUN | ||||
|     elif treebank_tag.startswith('R'): | ||||
|         return wordnet.ADV | ||||
|     else: | ||||
|         return wordnet.NOUN | ||||
|  | ||||
|  | ||||
| # ### sample data manager | ||||
| # the sample data manager loads and preprocesses data | ||||
| # most common way to use: | ||||
| #  | ||||
| #  | ||||
| # * `sdm = sample_data_manager.generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None)` | ||||
| #  | ||||
| #     * Generates a sample_data_manager object and preprocess data in one step | ||||
| #  | ||||
|  | ||||
| # In[7]: | ||||
|  | ||||
|  | ||||
| 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): | ||||
|         """ | ||||
|         generate, read and process train data in one step. | ||||
|          | ||||
|         @param path: folder containing json files to process | ||||
|         @param only_emoticons: if True, only messages containing emoticons (provided by Tools.Emoji_Distance) are used | ||||
|         @param apply_stemming: apply stemming and lemmatization on dataset | ||||
|         @param n_top_emojis: only use messages containing one of <`n_top_emojis`>-top emojis. set to `-1` to prevent top emoji filtering | ||||
|         @param file_range: range of file's indices to read (eg `range(3)` to read the first three files). If `None`: all files are read | ||||
|         @param n_kmeans_cluster: generating multilabeled labels with kmeans with these number of clusters. Set to -1 to use the plain sentiment space as label | ||||
|          | ||||
|         @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) | ||||
|         if apply_stemming: | ||||
|             sdm.apply_stemming_and_lemmatization() | ||||
|          | ||||
|         sdm.generate_emoji_count_and_weights() | ||||
|          | ||||
|         if n_top_emojis > 0: | ||||
|             sdm.filter_by_top_emojis(n_top=n_top_emojis) | ||||
|          | ||||
|         if n_kmeans_cluster > 0: | ||||
|             sdm.generate_kmeans_binary_label(only_emoticons=only_emoticons, n_clusters=n_kmeans_cluster) | ||||
|          | ||||
|         return sdm | ||||
|          | ||||
|      | ||||
|     def __init__(self, data_root_folder:str): | ||||
|         """ | ||||
|         constructor for manual initialization | ||||
|          | ||||
|         @param data_root_folder: folder containing json files to process | ||||
|         """ | ||||
|         self.data_root_folder = data_root_folder | ||||
|         self.json_files = sorted(glob.glob(self.data_root_folder + "/*.json")) | ||||
|         self.n_files = len(self.json_files) | ||||
|         self.raw_data = None | ||||
|         self.emojis = None | ||||
|         self.plain_text = None | ||||
|         self.labels = None | ||||
|         self.emoji_count = None | ||||
|         self.emoji_weights = None | ||||
|         self.X = None | ||||
|         self.y = None | ||||
|         self.Xt = None | ||||
|         self.yt = None | ||||
|         self.top_emojis = None | ||||
|         self.binary_labels = None | ||||
|         self.use_binary_labels = False | ||||
|         self.kmeans_cluster = None | ||||
|         self.label_binarizer = None | ||||
|      | ||||
|     def read_files(self, file_index_range:list, only_emoticons=True): | ||||
|         """ | ||||
|         reading (multiple) files to one panda table. | ||||
|          | ||||
|         @param file_index_range: range of file's indices to read (eg `range(3)` to read the first three files) | ||||
|         @param only_emoticons: if True, only messages containing emoticons (aka smileys) are used. This classification is derived from Tools.Emoji_Distance | ||||
|         """ | ||||
|         assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files | ||||
|         for i in file_index_range: | ||||
|             print("reading file: " + self.json_files[i] + "...") | ||||
|             if self.raw_data is None: | ||||
|                 self.raw_data = pd.read_json(self.json_files[i], encoding="utf-8") | ||||
|             else: | ||||
|                 self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding="utf-8")) | ||||
|          | ||||
|         self.emojis = self.raw_data['EMOJI'] | ||||
|         self.plain_text = self.raw_data['text'] | ||||
|          | ||||
|         # replacing keywords. TODO: maybe these information can be extracted and used | ||||
|         self.plain_text = self.plain_text.str.replace("(<EMOJI>|<USER>|<HASHTAG>)","").str.replace("[" + "".join(list(emoji_blacklist)) + "]","") | ||||
|          | ||||
|         # so far filtering for the latest emoji. TODO: maybe there are also better approaches | ||||
|         self.labels = emoji2sent([latest(e) for e in self.emojis], only_emoticons=only_emoticons ) | ||||
|          | ||||
|         # and filter out all samples we have no label for: | ||||
|         wrong_labels = np.isnan(np.linalg.norm(self.labels, axis=1))     | ||||
|  | ||||
|         self.labels = self.labels[np.invert(wrong_labels)] | ||||
|         self.plain_text = self.plain_text[np.invert(wrong_labels)] | ||||
|         self.emojis = self.emojis[np.invert(wrong_labels)] | ||||
|          | ||||
|         print("imported " + str(len(self.labels)) + " samples") | ||||
|      | ||||
|     def apply_stemming_and_lemmatization(self): | ||||
|         """ | ||||
|         apply stemming and lemmatization to plain text samples | ||||
|         """ | ||||
|         stemmer = SnowballStemmer("english") | ||||
|         for key in self.plain_text.keys(): | ||||
|             stemmed_sent = [] | ||||
|             for word in self.plain_text[key].split(" "): | ||||
|                 word_stemmed = stemmer.stem(word) | ||||
|                 stemmed_sent.append(word_stemmed) | ||||
|             stemmed_sent = (" ").join(stemmed_sent) | ||||
|             self.plain_text[key] = stemmed_sent | ||||
|              | ||||
|         lemmatizer = WordNetLemmatizer() | ||||
|         for key in self.plain_text.keys(): | ||||
|             lemmatized_sent = [] | ||||
|             sent_pos = pos_tag(word_tokenize(self.plain_text[key])) | ||||
|             for word in sent_pos: | ||||
|                 wordnet_pos = get_wordnet_pos(word[1].lower()) | ||||
|                 word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos) | ||||
|                 lemmatized_sent.append(word_lemmatized) | ||||
|             lemmatized_sent = (" ").join(lemmatized_sent) | ||||
|             self.plain_text[key] = lemmatized_sent | ||||
|      | ||||
|     def generate_emoji_count_and_weights(self): | ||||
|         """ | ||||
|         counting occurences of emojis | ||||
|         """ | ||||
|         self.emoji_count = {} | ||||
|         for e_list in self.emojis: | ||||
|             for e in set(e_list): | ||||
|                 if e not in self.emoji_count: | ||||
|                     self.emoji_count[e] = 0 | ||||
|                 self.emoji_count[e] += 1 | ||||
|          | ||||
|         emoji_sum = sum([self.emoji_count[e] for e in self.emoji_count]) | ||||
|  | ||||
|         self.emoji_weights = {} | ||||
|         for e in self.emoji_count: | ||||
|             # tfidf for emojis | ||||
|             self.emoji_weights[e] = np.log((emoji_sum / self.emoji_count[e])) | ||||
|  | ||||
|         weights_sum= sum([self.emoji_weights[x] for x in self.emoji_weights]) | ||||
|  | ||||
|         # normalize: | ||||
|         for e in self.emoji_weights: | ||||
|             self.emoji_weights[e] = self.emoji_weights[e] / weights_sum | ||||
|  | ||||
|         self.emoji_weights['X'] = 0  # dummy values | ||||
|         self.emoji_count['X'] = 0 | ||||
|      | ||||
|     def get_emoji_count(self): | ||||
|         """ | ||||
|         @return: descending list of tuples in form (<emoji as character>, <emoji count>)  | ||||
|         """ | ||||
|         assert self.emoji_count is not None | ||||
|          | ||||
|         sorted_emoji_count = list(reversed(sorted(self.emoji_count.items(), key=operator.itemgetter(1)))) | ||||
|         #display(sorted_emoji_count) | ||||
|         return sorted_emoji_count | ||||
|      | ||||
|     def filter_by_top_emojis(self,n_top = 20): | ||||
|         """ | ||||
|         filgter out messages not containing one of the `n_top` emojis | ||||
|          | ||||
|         @param n_top: number of top emojis used for filtering | ||||
|         """ | ||||
|         assert self.labels is not None # ← messages are already read in | ||||
|          | ||||
|         self.top_emojis = [x[0] for x in self.get_emoji_count()[:n_top]] | ||||
|         in_top = [edist.sentiment_vector_to_emoji(x) in self.top_emojis for x in self.labels] | ||||
|         self.labels = self.labels[in_top] | ||||
|         self.plain_text = self.plain_text[in_top] | ||||
|         self.emojis = self.emojis[in_top] | ||||
|         print("remaining samples after top emoji filtering: ", len(self.labels)) | ||||
|      | ||||
|     def generate_kmeans_binary_label(self, only_emoticons=True, n_clusters=5): | ||||
|         """ | ||||
|         generate binary labels using kmeans. | ||||
|          | ||||
|         @param only_emoticons: set whether we're using the full emoji set or only emoticons | ||||
|         @param n_clusters: number of cluster we're generating in emoji's sentiment space | ||||
|         """ | ||||
|         assert self.labels is not None | ||||
|         array_sentiment_vectors = edist.list_sentiment_emoticon_vectors if only_emoticons else edist.list_sentiment_vectors | ||||
|         array_sentiment_vectors = np.array(array_sentiment_vectors) | ||||
|          | ||||
|         list_emojis = edist.list_emoticon_emojis if only_emoticons else edist.list_emojis | ||||
|         self.use_binary_labels = True | ||||
|         print("clustering following emojis: " + "".join(list_emojis) + "...") | ||||
|         self.kmeans_cluster = KMeans(n_clusters=n_clusters).fit(array_sentiment_vectors) | ||||
|         print("clustering done") | ||||
|         self.label_binarizer = LabelBinarizer() | ||||
|          | ||||
|         multiclass_labels = self.kmeans_cluster.predict(self.labels) | ||||
|          | ||||
|         # FIXME: we have to guarantee that in every dataset all classes occur. | ||||
|         # otherwise batch fitting is not possible! | ||||
|         # (or we have to precompute the mlb fitting process somewhere...) | ||||
|         self.binary_labels = self.label_binarizer.fit_transform(multiclass_labels) | ||||
|          | ||||
|      | ||||
|     def create_train_test_split(self, split = 0.1, random_state = 4222): | ||||
|         assert self.plain_text is not None and self.labels is not None | ||||
|         if self.X is not None: | ||||
|             sys.stderr.write("WARNING: overwriting existing train/test split \n") | ||||
|          | ||||
|         labels = self.binary_labels if self.use_binary_labels else self.labels | ||||
|         assert labels is not None | ||||
|         self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state) | ||||
|  | ||||
|  | ||||
|  | ||||
| # * the pipeline manager saves and stores sklearn pipelines. Keras models are handled differently, so the have to be named explicitly during save and load operations | ||||
|  | ||||
| # In[8]: | ||||
|  | ||||
|  | ||||
| class pipeline_manager(object): | ||||
|     @staticmethod | ||||
|     def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []): | ||||
|         """ | ||||
|         load a pipeline from files. A pipeline should be represented by multiple model files in the form '<file_prefix>.<model_name>' | ||||
|          | ||||
|         @param file_prefix: basename of all files (without extension) | ||||
|         @param keras_models: list of keras models (keras model files, only extension name). Leave this list empty if this is not a keras pipeline | ||||
|         @param all_models: list of all models (including keras_models, only extension name). | ||||
|          | ||||
|         @return a pipeline manager object | ||||
|         """ | ||||
|          | ||||
|         pm = pipeline_manager(keras_models=keras_models) | ||||
|         pm.load(file_prefix, all_models) | ||||
|         return pm | ||||
|      | ||||
|     @staticmethod | ||||
|     def create_keras_pipeline_with_vectorizer(vectorizer, layers, sdm:sample_data_manager, loss=None, optimizer=None): | ||||
|         ''' | ||||
|         creates pipeline with vectorizer and keras classifier | ||||
|          | ||||
|         @param vectorizer: Vectorizer object. will be fitted with data provided by sdm | ||||
|         @param layers: list of keras layers. One keras layer is a tuple in form: (<#neurons:int>, <activation_func:str>) | ||||
|         @param sdm: sample data manager to get data for the vectorizer | ||||
|         @param loss: set keras loss function. Depending whether sdm use multiclass labels `categorical_crossentropy` or `mean_squared_error` is used as default | ||||
|         @param optimizer: set keras optimizer. Depending whether sdm use multiclass labels `sgd` or `adam` is used as default | ||||
|          | ||||
|         @return: a 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) | ||||
|         # creating keras model: | ||||
|         model=Sequential() | ||||
|          | ||||
|         keras_layers = [] | ||||
|         first_layer = True | ||||
|         for layer in layers: | ||||
|             if first_layer: | ||||
|                 model.add(Dense(units=layer[0], activation=layer[1], input_dim=vectorizer.transform([" "])[0]._shape[1])) | ||||
|                 first_layer = False | ||||
|             else: | ||||
|                 model.add(Dense(units=layer[0], activation=layer[1])) | ||||
|          | ||||
|         if sdm.use_binary_labels:  | ||||
|             loss_function = loss if loss is not None else 'categorical_crossentropy' | ||||
|             optimizer_function = optimizer if optimizer is not None else 'sgd' | ||||
|             model.compile(loss=loss_function, | ||||
|                           optimizer=optimizer_function, | ||||
|                           metrics=['accuracy']) | ||||
|         else: | ||||
|             loss_function = loss if loss is not None else 'mean_squared_error' | ||||
|             optimizer_function = optimizer if optimizer is not None else 'adam' | ||||
|             model.compile(loss=loss_function, | ||||
|                           optimizer=optimizer_function) | ||||
|          | ||||
|         pipeline = Pipeline([ | ||||
|             ('vectorizer',vectorizer), | ||||
|             ('keras_model', model) | ||||
|         ]) | ||||
|          | ||||
|         return pipeline_manager(pipeline=pipeline, keras_models=['keras_model']) | ||||
|      | ||||
|     @staticmethod | ||||
|     def create_pipeline_with_classifier_and_vectorizer(vectorizer, classifier, sdm:sample_data_manager = None): | ||||
|         ''' | ||||
|         creates pipeline with vectorizer and non-keras classifier | ||||
|          | ||||
|         @param vectorizer: Vectorizer object. will be fitted with data provided by sdm | ||||
|         @param classifier: unfitted classifier object (should be compatible with all sklearn classifiers) | ||||
|         @param sdm: sample data manager to get data for the vectorizer | ||||
|          | ||||
|         @return: a pipeline manager object | ||||
|         ''' | ||||
|         if sdm is not None: | ||||
|             if sdm.X is None: | ||||
|                 sdm.create_train_test_split() | ||||
|  | ||||
|             vec_train = vectorizer.fit_transform(sdm.X) | ||||
|             vec_test = vectorizer.transform(sdm.Xt) | ||||
|          | ||||
|         pipeline = Pipeline([ | ||||
|             ('vectorizer',vectorizer), | ||||
|             ('classifier', classifier) | ||||
|         ]) | ||||
|          | ||||
|         return pipeline_manager(pipeline=pipeline, keras_models=[]) | ||||
|      | ||||
|     def __init__(self, pipeline = None, keras_models = []): | ||||
|         """ | ||||
|         constructor | ||||
|          | ||||
|         @param pipeline: a sklearn pipeline | ||||
|         @param keras_models: list of keras steps in pipeline. Neccessary because saving and loading from keras models differs from the scikit ones | ||||
|         """ | ||||
|          | ||||
|         self.pipeline = pipeline | ||||
|         self.additional_objects = {} | ||||
|         self.keras_models = keras_models | ||||
|      | ||||
|     def save(self, prefix:str): | ||||
|         """ | ||||
|         saving the pipeline. It generates one file per model in the form: '<prefix>.<model_name>' | ||||
|          | ||||
|         @param prefix: file prefix for all models | ||||
|         """ | ||||
|          | ||||
|         print(self.keras_models) | ||||
|         # doing this like explained here: https://stackoverflow.com/a/43415459 | ||||
|         for step in self.pipeline.named_steps: | ||||
|             if step in self.keras_models: | ||||
|                 self.pipeline.named_steps[step].model.save(prefix + "." + step) | ||||
|             else: | ||||
|                 joblib.dump(self.pipeline.named_steps[step], prefix + "." + str(step)) | ||||
|          | ||||
|         load_command = "pipeline_manager.load_pipeline_from_files( '" | ||||
|         load_command += prefix + "', " + str(self.keras_models) + ", " | ||||
|         load_command += str(list(self.pipeline.named_steps.keys())) + ")" | ||||
|          | ||||
|         import __main__ as main | ||||
|         if not hasattr(main, '__file__'): | ||||
|             display("saved pipeline. It can be loaded the following way:") | ||||
|             display(Markdown("> ```\n"+load_command+"\n```"))              # ← if we're in jupyter, print the fancy way :) | ||||
|         else: | ||||
|             print("saved pipeline. It can be loaded the following way:") | ||||
|             print(load_command) | ||||
|          | ||||
|      | ||||
|     def load(self, prefix:str, models = []): | ||||
|         """ | ||||
|         load a pipeline. A pipeline should be represented by multiple model files in the form '<prefix>.<model_name>' | ||||
|         NOTE: keras model names (if there are some) have to be defined in self.keras_models first! | ||||
|          | ||||
|         @param prefix: the prefix for all model files | ||||
|         @param models: model_names to load | ||||
|         """ | ||||
|         self.pipeline = None | ||||
|         model_list = [] | ||||
|         for model in models: | ||||
|             if model in self.keras_models: | ||||
|                 model_list.append((model, load_model(prefix + "." + model))) | ||||
|             else: | ||||
|                 model_list.append((model, joblib.load(prefix+"." + model))) | ||||
|         self.pipeline = Pipeline(model_list) | ||||
|      | ||||
|     def fit(self,X,y): | ||||
|         """fitting the pipeline""" | ||||
|         self.pipeline.fit(X,y) | ||||
|      | ||||
|     def predict(self,X): | ||||
|         """predict""" | ||||
|         return self.pipeline.predict(X) | ||||
|      | ||||
|  | ||||
|  | ||||
| # * the trainer class passes Data from the sample manager to the pipeline manager | ||||
|  | ||||
| # In[9]: | ||||
|  | ||||
|  | ||||
| class trainer(object): | ||||
|     def __init__(self, sdm:sample_data_manager, pm:pipeline_manager): | ||||
|         """constructor""" | ||||
|         self.sdm = sdm | ||||
|         self.pm = pm | ||||
|      | ||||
|     def fit(self, max_size=10000, disabled_fit_steps=['vectorizer']): | ||||
|         """ | ||||
|         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 | ||||
|          | ||||
|         @param max_size: don't train more examples than that number | ||||
|         @param disabled_fit_steps: list of pipeline steps that we want to prevent to refit. Normally all vectorizer steps | ||||
|         """ | ||||
|         # TODO: make batch fitting available here (eg: continous waiting for data and fitting them) | ||||
|         if self.sdm.X is None: | ||||
|             self.sdm.create_train_test_split() | ||||
|         disabled_fits = {} | ||||
|         disabled_fit_transforms = {} | ||||
|          | ||||
|         named_steps = self.pm.pipeline.named_steps | ||||
|          | ||||
|         for s in disabled_fit_steps: | ||||
|             # now it gets a little bit dirty: | ||||
|             # replace fit functions we don't want to call again (e.g. for vectorizers) | ||||
|             disabled_fits[s] = named_steps[s].fit | ||||
|             disabled_fit_transforms[s] = named_steps[s].fit_transform | ||||
|             named_steps[s].fit = lambda self, X, y=None: self | ||||
|             named_steps[s].fit_transform = named_steps[s].transform | ||||
|              | ||||
|         self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size]) | ||||
|          | ||||
|         # restore replaced fit functions: | ||||
|         for s in disabled_fit_steps: | ||||
|             named_steps[s].fit = disabled_fits[s] | ||||
|             named_steps[s].fit_transform = disabled_fit_transforms[s] | ||||
|      | ||||
|     def test(self): | ||||
|         ''' | ||||
|         @return: prediction:list, teacher:list | ||||
|         ''' | ||||
|         if self.sdm.X is None: | ||||
|             self.sdm.create_train_test_split() | ||||
|         return self.pm.predict(self.sdm.Xt), self.sdm.yt | ||||
|  | ||||
|      | ||||
|  | ||||
|  | ||||
| # ---- | ||||
| # ## Train | ||||
|  | ||||
| # * when in notebook environment: run the stuff below: | ||||
|  | ||||
| # In[10]: | ||||
|  | ||||
|  | ||||
| import __main__ as main | ||||
| if not hasattr(main, '__file__'): | ||||
|     # we are in an interactive environment (probably in jupyter) | ||||
|     # load data: | ||||
|      | ||||
|     # setting n_kmeans_clusters to a value > 0 activates binarized labeling automatically!  | ||||
|     # set to -1 to disable kmeans clustering and generating labels in plain sentiment space | ||||
|      | ||||
|     #n_kmeans_cluster = 5 | ||||
|     n_kmeans_cluster = -1 | ||||
|     sdm = sample_data_manager.generate_and_read(path="./data_en/", n_top_emojis=20, file_range=range(1), n_kmeans_cluster=n_kmeans_cluster) | ||||
|     sdm.create_train_test_split() | ||||
|     #pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'),\n", | ||||
|     #                                                           layers=[(10000, 'relu'),(5000, 'relu'),(2500, 'relu'),(y1[0].shape[0],None)], sdm=sdm)\n", | ||||
|     pm = pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer=TfidfVectorizer(stop_words='english'), | ||||
|                                                            layers=[(2500, 'relu'),(sdm.y.shape[1],None)], sdm=sdm) | ||||
|     tr = trainer(sdm=sdm, pm=pm) | ||||
|     tr.fit(100) | ||||
|  | ||||
|  | ||||
| # ---- | ||||
| # ## save classifier | ||||
|  | ||||
| # In[11]: | ||||
|  | ||||
|  | ||||
| import __main__ as main | ||||
| if not hasattr(main, '__file__'): | ||||
|     pm.save('custom_classifier') | ||||
|  | ||||
|  | ||||
| # ---- | ||||
| # ## Prediction | ||||
| #  | ||||
| # * predict and save to `test.csv` | ||||
|  | ||||
| # In[12]: | ||||
|  | ||||
|  | ||||
| import __main__ as main | ||||
| if not hasattr(main, '__file__'): | ||||
|     pred, teacher = tr.test() | ||||
|      | ||||
|     display(pred) | ||||
|     display(teacher) | ||||
|      | ||||
|     print('prediction variance: ', np.linalg.norm(np.var(pred, axis=0))) | ||||
|     print('teacher variance: ', np.linalg.norm(np.var(teacher, axis=0))) | ||||
|      | ||||
|     # build a dataframe to visualize test results: | ||||
|     testlist = pd.DataFrame({'text': sdm.Xt,  | ||||
|                          'teacher': sent2emoji(sdm.yt), | ||||
|                          'teacher_sentiment': sdm.yt.tolist(), | ||||
|                          'predict': sent2emoji(pred, custom_target_emojis=sdm.top_emojis), | ||||
|                          'predicted_sentiment': pred.tolist()}) | ||||
|     # display: | ||||
|     display(testlist.head()) | ||||
|      | ||||
|     # mean squared error: | ||||
|     teacher_sentiments = np.array([sample[1]['teacher_sentiment'] for sample in testlist.iterrows()]) | ||||
|     predicted_sentiments = np.array([sample[1]['predicted_sentiment'] for sample in testlist.iterrows()]) | ||||
|  | ||||
|     mean_squared_error = ((teacher_sentiments - predicted_sentiments)**2).mean(axis=0) | ||||
|     print("Mean Squared Error: ", mean_squared_error) | ||||
|     print("Variance teacher: ", np.var(teacher_sentiments, axis=0)) | ||||
|     print("Variance prediction: ", np.var(predicted_sentiments, axis=0)) | ||||
|      | ||||
|     # save to csv: | ||||
|     testlist.to_csv('test.csv') | ||||
|  | ||||
|  | ||||
| # ---- | ||||
| # ## Load classifier | ||||
| #  | ||||
| # * loading classifier and show a test widget | ||||
|  | ||||
| # In[13]: | ||||
|  | ||||
|  | ||||
| import __main__ as main | ||||
| if not hasattr(main, '__file__'): | ||||
|     try: | ||||
|         pm | ||||
|     except NameError: | ||||
|         pass | ||||
|     else: | ||||
|         del pm # delete existing pipeline manager if ther is one | ||||
|  | ||||
|     pm = pipeline_manager.load_pipeline_from_files( 'custom_classifier', ['keras_model'], ['vectorizer', 'keras_model']) | ||||
|     lookup_emojis = [#'😂', | ||||
|          '😭', | ||||
|          '😍', | ||||
|          '😩', | ||||
|          '😊', | ||||
|          '😘', | ||||
|          '🙏', | ||||
|          '🙌', | ||||
|          '😉', | ||||
|          '😁', | ||||
|          '😅', | ||||
|          '😎', | ||||
|          '😢', | ||||
|          '😒', | ||||
|          '😏', | ||||
|          '😌', | ||||
|          '😔', | ||||
|          '😋', | ||||
|          '😀', | ||||
|          '😤'] | ||||
|     out = widgets.Output() | ||||
|  | ||||
|     t = widgets.Text() | ||||
|     b = widgets.Button( | ||||
|         description='get emoji', | ||||
|         disabled=False, | ||||
|         button_style='', # 'success', 'info', 'warning', 'danger' or '' | ||||
|         tooltip='Click me', | ||||
|         icon='check' | ||||
|     ) | ||||
|  | ||||
|  | ||||
|  | ||||
|     def handle_submit(sender): | ||||
|         with out: | ||||
|             clear_output() | ||||
|         with out: | ||||
|             pred = pm.predict([t.value]) | ||||
|  | ||||
|             display(Markdown("# Predicted Emoji " + str(sent2emoji(pred, lookup_emojis)[0]))) | ||||
|             display(Markdown("# Sentiment Vector: $$ \pmatrix{" + str(pred[0,0]) + | ||||
|                              "\\\\" + str(pred[0,1]) + "\\\\" + str(pred[0,2]) + "}$$")) | ||||
|  | ||||
|     b.on_click(handle_submit) | ||||
|  | ||||
|     display(t) | ||||
|     display(widgets.VBox([b, out]))   | ||||
|  | ||||
							
								
								
									
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