updated notebook according to changes in the python file
This commit is contained in:
Jonas Weinz
parent
df811196e5
commit
10fd5817e7
@ -2,43 +2,9 @@
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"cells": [
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"cells": [
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/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",
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" from ._conv import register_converters as _register_converters\n",
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"Using TensorFlow backend.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
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"[nltk_data] Package punkt is already up-to-date!\n",
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"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
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"[nltk_data] /home/jonas/nltk_data...\n",
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"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
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"[nltk_data] date!\n",
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"[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n",
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"[nltk_data] Package wordnet is already up-to-date!\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"source": [
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"import pandas as pd\n",
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"import pandas as pd\n",
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"from IPython.display import clear_output, Markdown, Math\n",
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"from IPython.display import clear_output, Markdown, Math\n",
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@ -59,6 +25,7 @@
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"import pickle\n",
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"import pickle\n",
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"import operator\n",
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"import operator\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.pipeline import Pipeline\n",
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"import json\n",
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"nltk.download('punkt')\n",
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"nltk.download('punkt')\n",
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"nltk.download('averaged_perceptron_tagger')\n",
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"nltk.download('averaged_perceptron_tagger')\n",
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"nltk.download('wordnet')"
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"nltk.download('wordnet')"
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@ -66,7 +33,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -84,7 +51,7 @@
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -109,7 +76,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -129,7 +96,7 @@
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -154,7 +121,7 @@
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"source": [
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@ -194,13 +161,13 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"class sample_data_manager(object):\n",
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"class sample_data_manager(object):\n",
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" @staticmethod\n",
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" @staticmethod\n",
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" def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1):\n",
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" def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, progress_callback=None):\n",
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" \"\"\"\n",
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" \"\"\"\n",
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" generate, read and process train data in one step.\n",
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" generate, read and process train data in one step.\n",
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" \n",
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" \n",
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@ -214,7 +181,7 @@
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" @return: sample_data_manager object\n",
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" @return: sample_data_manager object\n",
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" \"\"\"\n",
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" \"\"\"\n",
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" sdm = sample_data_manager(path)\n",
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" sdm = sample_data_manager(path)\n",
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" sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons)\n",
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" sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=progress_callback)\n",
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" if apply_stemming:\n",
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" if apply_stemming:\n",
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" sdm.apply_stemming_and_lemmatization()\n",
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" sdm.apply_stemming_and_lemmatization()\n",
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" \n",
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" \n",
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@ -254,7 +221,7 @@
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" self.kmeans_cluster = None\n",
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" self.kmeans_cluster = None\n",
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" self.label_binarizer = None\n",
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" self.label_binarizer = None\n",
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" \n",
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" \n",
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" def read_files(self, file_index_range:list, only_emoticons=True):\n",
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" def read_files(self, file_index_range:list, only_emoticons=True, progress_callback=None):\n",
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" \"\"\"\n",
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" \"\"\"\n",
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" reading (multiple) files to one panda table.\n",
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" reading (multiple) files to one panda table.\n",
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" \n",
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" \n",
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@ -268,7 +235,8 @@
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" self.raw_data = pd.read_json(self.json_files[i], encoding=\"utf-8\")\n",
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" self.raw_data = pd.read_json(self.json_files[i], encoding=\"utf-8\")\n",
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" else:\n",
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" else:\n",
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" self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding=\"utf-8\"))\n",
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" self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding=\"utf-8\"))\n",
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" \n",
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" if progress_callback is not None:\n",
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" progress_callback()\n",
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" self.emojis = self.raw_data['EMOJI']\n",
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" self.emojis = self.raw_data['EMOJI']\n",
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" self.plain_text = self.raw_data['text']\n",
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" self.plain_text = self.raw_data['text']\n",
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" \n",
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" \n",
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" \n",
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" \n",
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" labels = self.binary_labels if self.use_binary_labels else self.labels\n",
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" labels = self.binary_labels if self.use_binary_labels else self.labels\n",
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" assert labels is not None\n",
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" assert labels is not None\n",
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" self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state)\n",
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" self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state)\n"
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"\n"
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]
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]
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},
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},
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{
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"class pipeline_manager(object):\n",
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"class pipeline_manager(object):\n",
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" @staticmethod\n",
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" @staticmethod\n",
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" def load_from_pipeline_file(pipeline_file:str):\n",
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" \"\"\"\n",
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" loading a json configuration file and using it's paramters to call 'load_pipeline_from_files'\n",
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" \"\"\"\n",
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" with open(pipeline_file, 'r') as f:\n",
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" d = json.load(f)\n",
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" \n",
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" keras_models = d['keras_models']\n",
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" all_models = d['all_models']\n",
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" \n",
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" return pipeline_manager.load_pipeline_from_files(pipeline_file.rsplit('.',1)[0], keras_models, all_models)\n",
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"\n",
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"\n",
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" @staticmethod\n",
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" def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []):\n",
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" def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []):\n",
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" \"\"\"\n",
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" \"\"\"\n",
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" load a pipeline from files. A pipeline should be represented by multiple model files in the form '<file_prefix>.<model_name>'\n",
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" load a pipeline from files. A pipeline should be represented by multiple model files in the form '<file_prefix>.<model_name>'\n",
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" @param prefix: file prefix for all models\n",
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" @param prefix: file prefix for all models\n",
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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",
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" print(self.keras_models)\n",
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" print(self.keras_models)\n",
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" # doing this like explained here: https://stackoverflow.com/a/43415459\n",
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" # doing this like explained here: https://stackoverflow.com/a/43415459\n",
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" for step in self.pipeline.named_steps:\n",
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" for step in self.pipeline.named_steps:\n",
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" load_command = \"pipeline_manager.load_pipeline_from_files( '\"\n",
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" load_command = \"pipeline_manager.load_pipeline_from_files( '\"\n",
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" load_command += prefix + \"', \" + str(self.keras_models) + \", \"\n",
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" load_command += prefix + \"', \" + str(self.keras_models) + \", \"\n",
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" load_command += str(list(self.pipeline.named_steps.keys())) + \")\"\n",
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" load_command += str(list(self.pipeline.named_steps.keys())) + \")\"\n",
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"\n",
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" with open(prefix + '.pipeline', 'w') as outfile:\n",
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" json.dump({'keras_models': self.keras_models, 'all_models': [step for step in self.pipeline.named_steps]}, outfile)\n",
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" \n",
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" \n",
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" import __main__ as main\n",
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" import __main__ as main\n",
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" if not hasattr(main, '__file__'):\n",
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" if not hasattr(main, '__file__'):\n",
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},
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},
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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" self.sdm = sdm\n",
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" self.sdm = sdm\n",
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" self.pm = pm\n",
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" self.pm = pm\n",
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" \n",
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" \n",
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" def fit(self, max_size=10000, disabled_fit_steps=['vectorizer']):\n",
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" 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):\n",
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" \"\"\"\n",
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" \"\"\"\n",
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" 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\n",
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" 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\n",
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" \n",
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" \n",
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" disabled_fits = {}\n",
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" disabled_fits = {}\n",
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" disabled_fit_transforms = {}\n",
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" disabled_fit_transforms = {}\n",
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" \n",
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" \n",
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" disabled_keras_fits = {}\n",
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" \n",
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" named_steps = self.pm.pipeline.named_steps\n",
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" named_steps = self.pm.pipeline.named_steps\n",
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" \n",
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" \n",
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" for s in disabled_fit_steps:\n",
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" for s in disabled_fit_steps:\n",
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" # now it gets a little bit dirty:\n",
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" # now it gets really dirty:\n",
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" # replace fit functions we don't want to call again (e.g. for vectorizers)\n",
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" # replace fit functions we don't want to call again (e.g. for vectorizers)\n",
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" disabled_fits[s] = named_steps[s].fit\n",
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" disabled_fits[s] = named_steps[s].fit\n",
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" disabled_fit_transforms[s] = named_steps[s].fit_transform\n",
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" disabled_fit_transforms[s] = named_steps[s].fit_transform\n",
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" named_steps[s].fit = lambda self, X, y=None: self\n",
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" named_steps[s].fit = lambda self, X, y=None: self\n",
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" named_steps[s].fit_transform = named_steps[s].transform\n",
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" named_steps[s].fit_transform = named_steps[s].transform\n",
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" \n",
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" \n",
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" self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])\n",
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" for k in keras_batch_fitting_layer:\n",
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" # forcing batch fitting on keras\n",
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" disabled_keras_fits[k]=named_steps[k].fit\n",
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" named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(X.todense(), y) # ← why has keras no sparse support on batch progressing!?!?!\n",
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" \n",
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" if batch_size is None:\n",
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" self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])\n",
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" else:\n",
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" n = len(self.sdm.X) // batch_size\n",
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" for i in range(n_epochs):\n",
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" for j in range(n):\n",
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" self.pm.fit(X = np.array(self.sdm.X[j*batch_size:(j+1)*batch_size]), y = np.array(self.sdm.y[j*batch_size:(j+1)*batch_size]))\n",
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" if progress_callback is not None:\n",
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" progress_callback()\n",
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" pred, yt = self.test()\n",
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" mean_squared_error = ((pred - yt)**2).mean(axis=0)\n",
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" print(\"#\" + str(j) + \": loss: \", mean_squared_error)\n",
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"\n",
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" \n",
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" \n",
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" # restore replaced fit functions:\n",
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" # restore replaced fit functions:\n",
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" for s in disabled_fit_steps:\n",
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" for s in disabled_fit_steps:\n",
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" named_steps[s].fit = disabled_fits[s]\n",
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" named_steps[s].fit = disabled_fits[s]\n",
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" named_steps[s].fit_transform = disabled_fit_transforms[s]\n",
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" named_steps[s].fit_transform = disabled_fit_transforms[s]\n",
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" \n",
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" for k in keras_batch_fitting_layer:\n",
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" named_steps[k].fit = disabled_keras_fits[k]\n",
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" \n",
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" \n",
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" def test(self):\n",
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" def test(self):\n",
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" '''\n",
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" '''\n",
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" '''\n",
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" '''\n",
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" if self.sdm.X is None:\n",
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" if self.sdm.X is None:\n",
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" self.sdm.create_train_test_split()\n",
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" self.sdm.create_train_test_split()\n",
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" return self.pm.predict(self.sdm.Xt), self.sdm.yt\n",
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" return self.pm.predict(self.sdm.Xt), self.sdm.yt\n"
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"\n",
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" "
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]
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]
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},
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 10,
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"> ```\n",
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"array([[0.16062996, 0.08324276, 0.09433182],\n",
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"text": [
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"prediction variance: 0.0005431187\n",
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||||||
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||||||
" <th>35671</th>\n",
|
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||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.16062995791435242, 0.0832427591085434, 0.09...</td>\n",
|
|
||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
|
|
||||||
" <td>i feel like i care so much more in everi situat</td>\n",
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||||||
" </tr>\n",
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||||||
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|
|
||||||
" <th>25683</th>\n",
|
|
||||||
" <td>😢</td>\n",
|
|
||||||
" <td>[0.16413000226020813, 0.0942138284444809, 0.07...</td>\n",
|
|
||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
|
|
||||||
" <td>i did not meat to add that 2 there ... hav see...</td>\n",
|
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||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>8985</th>\n",
|
|
||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.11994962394237518, 0.05705730617046356, 0.0...</td>\n",
|
|
||||||
" <td>😊</td>\n",
|
|
||||||
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
|
|
||||||
" <td>never…</td>\n",
|
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||||||
" </tr>\n",
|
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||||||
" <tr>\n",
|
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||||||
" <th>5410</th>\n",
|
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||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.18114930391311646, 0.10199417173862457, 0.1...</td>\n",
|
|
||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
|
|
||||||
" <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" <tr>\n",
|
|
||||||
" <th>62611</th>\n",
|
|
||||||
" <td>😂</td>\n",
|
|
||||||
" <td>[0.16997836530208588, 0.08633847534656525, 0.0...</td>\n",
|
|
||||||
" <td>😊</td>\n",
|
|
||||||
" <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
|
|
||||||
" <td>this dude alway help me get through my school ...</td>\n",
|
|
||||||
" </tr>\n",
|
|
||||||
" </tbody>\n",
|
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||||||
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|
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|
|
||||||
" predict predicted_sentiment teacher \\\n",
|
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||||||
"35671 😂 [0.16062995791435242, 0.0832427591085434, 0.09... 😂 \n",
|
|
||||||
"25683 😢 [0.16413000226020813, 0.0942138284444809, 0.07... 😂 \n",
|
|
||||||
"8985 😂 [0.11994962394237518, 0.05705730617046356, 0.0... 😊 \n",
|
|
||||||
"5410 😂 [0.18114930391311646, 0.10199417173862457, 0.1... 😂 \n",
|
|
||||||
"62611 😂 [0.16997836530208588, 0.08633847534656525, 0.0... 😊 \n",
|
|
||||||
"\n",
|
|
||||||
" teacher_sentiment \\\n",
|
|
||||||
"35671 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
|
|
||||||
"25683 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
|
|
||||||
"8985 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
|
|
||||||
"5410 [0.46813021474490496, 0.24716181096977158, 0.2... \n",
|
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||||||
"62611 [0.7040175768989329, 0.059322033898305086, 0.2... \n",
|
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||||||
"\n",
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||||||
" text \n",
|
|
||||||
"35671 i feel like i care so much more in everi situat \n",
|
|
||||||
"25683 i did not meat to add that 2 there ... hav see... \n",
|
|
||||||
"8985 never… \n",
|
|
||||||
"5410 lmao on me ! ! ! wtf wa he suppos to say \n",
|
|
||||||
"62611 this dude alway help me get through my school ... "
|
|
||||||
]
|
|
||||||
},
|
|
||||||
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||||||
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|
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||||||
"text": [
|
|
||||||
"Mean Squared Error: [0.13877691 0.04682433 0.02937794]\n",
|
|
||||||
"Variance teacher: [0.02183094 0.02513847 0.00285735]\n",
|
|
||||||
"Variance prediction: [0.00046378 0.00019441 0.00020516]\n"
|
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||||||
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}
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||||||
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||||||
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|
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|
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|
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|
||||||
@ -945,68 +760,9 @@
|
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|
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|
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|
|
||||||
" Widgets Documentation</a> for setup instructions.\n",
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|
||||||
"</p>\n",
|
|
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"<p>\n",
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|
||||||
" If you're reading this message in another frontend (for example, a static\n",
|
|
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" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
|
|
||||||
" it may mean that your frontend doesn't currently support widgets.\n",
|
|
||||||
"</p>\n"
|
|
||||||
],
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|
||||||
"text/plain": [
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|
||||||
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|
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|
||||||
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
|
|
||||||
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|
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|
|
||||||
" Widgets Documentation</a> for setup instructions.\n",
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|
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"</p>\n",
|
|
||||||
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|
|
||||||
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|
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|
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|
||||||
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|
|
||||||
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|
||||||
"text/plain": [
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|
||||||
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|
||||||
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||||||
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|
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|
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],
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|
||||||
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|
||||||
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|
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|
||||||
"if not hasattr(main, '__file__'):\n",
|
"if not hasattr(main, '__file__'):\n",
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user