291 lines
6.4 KiB
Plaintext
291 lines
6.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"import sys\n",
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"\n",
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"from conllu import parse\n",
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"\n",
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"sys.path.insert(0,'..')\n",
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"import settings\n",
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"\n",
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"from tagging_tools import print_visualized_tags\n",
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"\n",
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"from train_sample_generator import ConlluReader, ConlluDataProvider\n",
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"\n",
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"from gensim.test.utils import common_texts, get_tmpfile\n",
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"from gensim.models import Word2Vec\n",
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"from nltk import PorterStemmer\n",
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"import numpy as np\n",
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"from sklearn import preprocessing\n",
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"porter = PorterStemmer()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"conllu_reader = ConlluReader(\"recipes0.conllu\", iter_documents=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[TokenList<Set, oven, to, 350, degrees, F, .>]"
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]
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},
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"execution_count": 3,
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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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"conllu_reader.__iter__().__next__()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"conllu_data_provider = ConlluDataProvider(\"recipes0.conllu\", \n",
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" word2vec_model=None,\n",
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" batchsize=100,\n",
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" window_size=3,\n",
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" iter_documents=False,\n",
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" food_type=\"ingredient\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"x,y = conllu_data_provider.getNextDataBatch(y_food_type_label=\"ingredient\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1148"
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]
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},
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"execution_count": 6,
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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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"len(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"sum_tokens = 0\n",
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"i = 0\n",
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"for x,y in conllu_data_provider:\n",
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" sum_tokens += len(x)\n",
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" i += 1\n",
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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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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"649423"
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]
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},
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"execution_count": 12,
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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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"sum_tokens"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"576"
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]
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},
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"execution_count": 13,
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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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"i"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## decision tree classifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.model_selection import train_test_split"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"conllu_data_provider = ConlluDataProvider(\"recipes0.conllu\", \n",
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" word2vec_model=None,\n",
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" batchsize=100,\n",
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" window_size=3,\n",
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" iter_documents=False,\n",
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" food_type=\"ingredient\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"clf = RandomForestClassifier(n_estimators=100 ,random_state=0, warm_start=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"for x,y in conllu_data_provider:\n",
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" break\n",
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" X_train, X_test, y_train, y_test = train_test_split(x,y, random_state=0)\n",
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" clf.fit(X_train, y_train)\n",
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" pred = tree.predict(X_test)\n",
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" print(\"loss: \", np.sum((pred - y_test)**2) / len(x))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" ...,\n",
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" [0., 0., 1., ..., 0., 0., 0.],\n",
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" [0., 0., 0., ..., 0., 0., 0.],\n",
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" [0., 1., 0., ..., 0., 0., 0.]])"
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]
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},
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"execution_count": 29,
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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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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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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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"loss: 0.041811846689895474\n"
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]
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},
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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/python3.7/site-packages/sklearn/ensemble/forest.py:307: UserWarning: Warm-start fitting without increasing n_estimators does not fit new trees.\n",
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" warn(\"Warm-start fitting without increasing n_estimators does not \"\n"
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]
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}
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],
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"source": [
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"clf.fit(X_train, y_train)\n",
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"pred = tree.predict(X_test)\n",
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"print(\"loss: \", np.sum((pred - y_test)**2) / len(x))"
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]
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},
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{
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"cell_type": "code",
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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