704 lines
23 KiB
Plaintext
704 lines
23 KiB
Plaintext
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Self made simplified I-KNN"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import helpers\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import scipy.sparse as sparse\n",
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"from collections import defaultdict\n",
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"from itertools import chain\n",
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"import random\n",
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"\n",
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"train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None)\n",
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"test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
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"train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code = helpers.data_to_csr(train_read, test_read)"
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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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"class IKNN():\n",
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" \n",
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" def fit(self, train_ui):\n",
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" self.train_ui=train_ui\n",
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" \n",
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" train_iu=train_ui.transpose()\n",
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" norms=np.linalg.norm(train_iu.A, axis=1) # here we compute lenth of each item ratings vector\n",
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" norms=np.vectorize(lambda x: max(x,1))(norms[:,None]) # to avoid dividing by zero\n",
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"\n",
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" normalized_train_iu=sparse.csr_matrix(train_iu/norms)\n",
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"\n",
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" self.similarity_matrix_ii=normalized_train_iu*normalized_train_iu.transpose()\n",
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" \n",
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" self.estimations=np.array(train_ui*self.similarity_matrix_ii/((train_ui>0)*self.similarity_matrix_ii))\n",
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" \n",
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" def recommend(self, user_code_id, item_code_id, topK=10):\n",
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" \n",
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" top_k = defaultdict(list)\n",
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" for nb_user, user in enumerate(self.estimations):\n",
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" \n",
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" user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n",
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" for item, score in enumerate(user):\n",
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" if item not in user_rated and not np.isnan(score):\n",
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" top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n",
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" result=[]\n",
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" # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n",
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" for uid, item_scores in top_k.items():\n",
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" item_scores.sort(key=lambda x: x[1], reverse=True)\n",
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" result.append([uid]+list(chain(*item_scores[:topK])))\n",
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" return result\n",
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" \n",
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" def estimate(self, user_code_id, item_code_id, test_ui):\n",
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" result=[]\n",
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" for user, item in zip(*test_ui.nonzero()):\n",
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" result.append([user_code_id[user], item_code_id[item], \n",
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" self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])\n",
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" return result"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"toy train ui:\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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"array([[3, 4, 0, 0, 5, 0, 0, 4],\n",
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" [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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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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"similarity matrix:\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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" 0. , 0. , 1. ],\n",
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" [0.9701425 , 1. , 0.24253563, 0.12478355, 0.9701425 ,\n",
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" 0. , 0. , 0.9701425 ],\n",
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" 0. , 0. , 0. ],\n",
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" [0. , 0.12478355, 0.51449576, 1. , 0. ,\n",
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" 0.85749293, 0.85749293, 0. ],\n",
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" [1. , 0.9701425 , 0. , 0. , 1. ,\n",
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" 0. , 0. , 1. ],\n",
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" [0. , 0. , 0. , 0.85749293, 0. ,\n",
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" 1. , 1. , 0. ],\n",
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" [0. , 0. , 0. , 0.85749293, 0. ,\n",
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" 1. , 1. , 0. ],\n",
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" [1. , 0.9701425 , 0. , 0. , 1. ,\n",
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" 0. , 0. , 1. ]])"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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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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"estimations matrix:\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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"array([[4. , 4. , 4. , 4. , 4. ,\n",
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" nan, nan, 4. ],\n",
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" [1. , 1.35990333, 2.15478388, 2.53390319, 1. ,\n",
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" 3. , 3. , 1. ],\n",
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" [ nan, 5. , 5. , 4.05248907, nan,\n",
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" 3.95012863, 3.95012863, nan]])"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[[0, 20, 4.0, 30, 4.0],\n",
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" [10, 50, 3.0, 60, 3.0, 0, 1.0, 40, 1.0, 70, 1.0],\n",
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" [20, 10, 5.0, 20, 5.0]]"
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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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"# toy example\n",
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"toy_train_read=pd.read_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
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"toy_test_read=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
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"\n",
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"toy_train_ui, toy_test_ui, toy_user_code_id, toy_user_id_code, \\\n",
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"toy_item_code_id, toy_item_id_code = helpers.data_to_csr(toy_train_read, toy_test_read)\n",
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"\n",
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"\n",
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"model=IKNN()\n",
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"model.fit(toy_train_ui)\n",
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"\n",
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"print('toy train ui:')\n",
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"display(toy_train_ui.A)\n",
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"\n",
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"print('similarity matrix:')\n",
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"display(model.similarity_matrix_ii.A)\n",
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"\n",
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"print('estimations matrix:')\n",
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"display(model.estimations)\n",
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"\n",
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"model.recommend(toy_user_code_id, toy_item_code_id)"
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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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"model=IKNN()\n",
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"model.fit(train_ui)\n",
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"\n",
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"top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))\n",
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"\n",
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"top_n.to_csv('Recommendations generated/ml-100k/Self_IKNN_reco.csv', index=False, header=False)\n",
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"\n",
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"estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))\n",
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"estimations.to_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', index=False, header=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": 5,
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"943it [00:00, 8068.73it/s]\n"
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>RMSE</th>\n",
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" <th>MAE</th>\n",
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" <th>precision</th>\n",
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" <th>recall</th>\n",
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" <th>F_1</th>\n",
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" <th>F_05</th>\n",
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" <th>precision_super</th>\n",
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" <th>recall_super</th>\n",
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" <th>NDCG</th>\n",
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" <th>mAP</th>\n",
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" <th>MRR</th>\n",
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" <th>LAUC</th>\n",
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" <th>HR</th>\n",
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" <th>Reco in test</th>\n",
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" <th>Test coverage</th>\n",
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" <th>Shannon</th>\n",
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" <th>Gini</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1.018363</td>\n",
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" <td>0.808793</td>\n",
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" <td>0.000318</td>\n",
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" <td>0.000108</td>\n",
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" <td>0.00014</td>\n",
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" <td>0.000189</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.000214</td>\n",
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" <td>0.000037</td>\n",
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" <td>0.000368</td>\n",
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" <td>0.496391</td>\n",
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" <td>0.003181</td>\n",
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" <td>0.392153</td>\n",
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" <td>0.11544</td>\n",
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" <td>4.174741</td>\n",
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" <td>0.965327</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" RMSE MAE precision recall F_1 F_05 \\\n",
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"0 1.018363 0.808793 0.000318 0.000108 0.00014 0.000189 \n",
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"\n",
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" precision_super recall_super NDCG mAP MRR LAUC \\\n",
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"0 0.0 0.0 0.000214 0.000037 0.000368 0.496391 \n",
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"\n",
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" HR Reco in test Test coverage Shannon Gini \n",
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"0 0.003181 0.392153 0.11544 4.174741 0.965327 "
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]
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},
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"execution_count": 5,
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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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"import evaluation_measures as ev\n",
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"estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', header=None)\n",
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"reco=np.loadtxt('Recommendations generated/ml-100k/Self_IKNN_reco.csv', delimiter=',')\n",
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"\n",
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"ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None),\n",
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" estimations_df=estimations_df, \n",
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" reco=reco,\n",
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" super_reactions=[4,5])"
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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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" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Model</th>\n",
|
||
|
|
" <th>RMSE</th>\n",
|
||
|
|
" <th>MAE</th>\n",
|
||
|
|
" <th>precision</th>\n",
|
||
|
|
" <th>recall</th>\n",
|
||
|
|
" <th>F_1</th>\n",
|
||
|
|
" <th>F_05</th>\n",
|
||
|
|
" <th>precision_super</th>\n",
|
||
|
|
" <th>recall_super</th>\n",
|
||
|
|
" <th>NDCG</th>\n",
|
||
|
|
" <th>mAP</th>\n",
|
||
|
|
" <th>MRR</th>\n",
|
||
|
|
" <th>LAUC</th>\n",
|
||
|
|
" <th>HR</th>\n",
|
||
|
|
" <th>Reco in test</th>\n",
|
||
|
|
" <th>Test coverage</th>\n",
|
||
|
|
" <th>Shannon</th>\n",
|
||
|
|
" <th>Gini</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Self_TopPop</td>\n",
|
||
|
|
" <td>2.508258</td>\n",
|
||
|
|
" <td>2.217909</td>\n",
|
||
|
|
" <td>0.188865</td>\n",
|
||
|
|
" <td>0.116919</td>\n",
|
||
|
|
" <td>0.118732</td>\n",
|
||
|
|
" <td>0.141584</td>\n",
|
||
|
|
" <td>0.130472</td>\n",
|
||
|
|
" <td>0.137473</td>\n",
|
||
|
|
" <td>0.214651</td>\n",
|
||
|
|
" <td>0.111707</td>\n",
|
||
|
|
" <td>0.400939</td>\n",
|
||
|
|
" <td>0.555546</td>\n",
|
||
|
|
" <td>0.765642</td>\n",
|
||
|
|
" <td>1.000000</td>\n",
|
||
|
|
" <td>0.038961</td>\n",
|
||
|
|
" <td>3.159079</td>\n",
|
||
|
|
" <td>0.987317</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Ready_Baseline</td>\n",
|
||
|
|
" <td>0.949459</td>\n",
|
||
|
|
" <td>0.752487</td>\n",
|
||
|
|
" <td>0.091410</td>\n",
|
||
|
|
" <td>0.037652</td>\n",
|
||
|
|
" <td>0.046030</td>\n",
|
||
|
|
" <td>0.061286</td>\n",
|
||
|
|
" <td>0.079614</td>\n",
|
||
|
|
" <td>0.056463</td>\n",
|
||
|
|
" <td>0.095957</td>\n",
|
||
|
|
" <td>0.043178</td>\n",
|
||
|
|
" <td>0.198193</td>\n",
|
||
|
|
" <td>0.515501</td>\n",
|
||
|
|
" <td>0.437964</td>\n",
|
||
|
|
" <td>1.000000</td>\n",
|
||
|
|
" <td>0.033911</td>\n",
|
||
|
|
" <td>2.836513</td>\n",
|
||
|
|
" <td>0.991139</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Self_GlobalAvg</td>\n",
|
||
|
|
" <td>1.125760</td>\n",
|
||
|
|
" <td>0.943534</td>\n",
|
||
|
|
" <td>0.061188</td>\n",
|
||
|
|
" <td>0.025968</td>\n",
|
||
|
|
" <td>0.031383</td>\n",
|
||
|
|
" <td>0.041343</td>\n",
|
||
|
|
" <td>0.040558</td>\n",
|
||
|
|
" <td>0.032107</td>\n",
|
||
|
|
" <td>0.067695</td>\n",
|
||
|
|
" <td>0.027470</td>\n",
|
||
|
|
" <td>0.171187</td>\n",
|
||
|
|
" <td>0.509546</td>\n",
|
||
|
|
" <td>0.384942</td>\n",
|
||
|
|
" <td>1.000000</td>\n",
|
||
|
|
" <td>0.025974</td>\n",
|
||
|
|
" <td>2.711772</td>\n",
|
||
|
|
" <td>0.992003</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Ready_Random</td>\n",
|
||
|
|
" <td>1.517787</td>\n",
|
||
|
|
" <td>1.217953</td>\n",
|
||
|
|
" <td>0.047826</td>\n",
|
||
|
|
" <td>0.017861</td>\n",
|
||
|
|
" <td>0.022711</td>\n",
|
||
|
|
" <td>0.031080</td>\n",
|
||
|
|
" <td>0.028219</td>\n",
|
||
|
|
" <td>0.016982</td>\n",
|
||
|
|
" <td>0.051154</td>\n",
|
||
|
|
" <td>0.019551</td>\n",
|
||
|
|
" <td>0.125693</td>\n",
|
||
|
|
" <td>0.505448</td>\n",
|
||
|
|
" <td>0.318134</td>\n",
|
||
|
|
" <td>0.986426</td>\n",
|
||
|
|
" <td>0.186869</td>\n",
|
||
|
|
" <td>5.091730</td>\n",
|
||
|
|
" <td>0.908288</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Self_BaselineUI</td>\n",
|
||
|
|
" <td>0.967585</td>\n",
|
||
|
|
" <td>0.762740</td>\n",
|
||
|
|
" <td>0.000954</td>\n",
|
||
|
|
" <td>0.000170</td>\n",
|
||
|
|
" <td>0.000278</td>\n",
|
||
|
|
" <td>0.000463</td>\n",
|
||
|
|
" <td>0.000644</td>\n",
|
||
|
|
" <td>0.000189</td>\n",
|
||
|
|
" <td>0.000752</td>\n",
|
||
|
|
" <td>0.000168</td>\n",
|
||
|
|
" <td>0.001677</td>\n",
|
||
|
|
" <td>0.496424</td>\n",
|
||
|
|
" <td>0.009544</td>\n",
|
||
|
|
" <td>0.600530</td>\n",
|
||
|
|
" <td>0.005051</td>\n",
|
||
|
|
" <td>1.803126</td>\n",
|
||
|
|
" <td>0.996380</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Self_IKNN</td>\n",
|
||
|
|
" <td>1.018363</td>\n",
|
||
|
|
" <td>0.808793</td>\n",
|
||
|
|
" <td>0.000318</td>\n",
|
||
|
|
" <td>0.000108</td>\n",
|
||
|
|
" <td>0.000140</td>\n",
|
||
|
|
" <td>0.000189</td>\n",
|
||
|
|
" <td>0.000000</td>\n",
|
||
|
|
" <td>0.000000</td>\n",
|
||
|
|
" <td>0.000214</td>\n",
|
||
|
|
" <td>0.000037</td>\n",
|
||
|
|
" <td>0.000368</td>\n",
|
||
|
|
" <td>0.496391</td>\n",
|
||
|
|
" <td>0.003181</td>\n",
|
||
|
|
" <td>0.392153</td>\n",
|
||
|
|
" <td>0.115440</td>\n",
|
||
|
|
" <td>4.174741</td>\n",
|
||
|
|
" <td>0.965327</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Model RMSE MAE precision recall F_1 \\\n",
|
||
|
|
"0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n",
|
||
|
|
"0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n",
|
||
|
|
"0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n",
|
||
|
|
"0 Ready_Random 1.517787 1.217953 0.047826 0.017861 0.022711 \n",
|
||
|
|
"0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n",
|
||
|
|
"0 Self_IKNN 1.018363 0.808793 0.000318 0.000108 0.000140 \n",
|
||
|
|
"\n",
|
||
|
|
" F_05 precision_super recall_super NDCG mAP MRR \\\n",
|
||
|
|
"0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n",
|
||
|
|
"0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n",
|
||
|
|
"0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n",
|
||
|
|
"0 0.031080 0.028219 0.016982 0.051154 0.019551 0.125693 \n",
|
||
|
|
"0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n",
|
||
|
|
"0 0.000189 0.000000 0.000000 0.000214 0.000037 0.000368 \n",
|
||
|
|
"\n",
|
||
|
|
" LAUC HR Reco in test Test coverage Shannon Gini \n",
|
||
|
|
"0 0.555546 0.765642 1.000000 0.038961 3.159079 0.987317 \n",
|
||
|
|
"0 0.515501 0.437964 1.000000 0.033911 2.836513 0.991139 \n",
|
||
|
|
"0 0.509546 0.384942 1.000000 0.025974 2.711772 0.992003 \n",
|
||
|
|
"0 0.505448 0.318134 0.986426 0.186869 5.091730 0.908288 \n",
|
||
|
|
"0 0.496424 0.009544 0.600530 0.005051 1.803126 0.996380 \n",
|
||
|
|
"0 0.496391 0.003181 0.392153 0.115440 4.174741 0.965327 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import imp\n",
|
||
|
|
"imp.reload(ev)\n",
|
||
|
|
"\n",
|
||
|
|
"import evaluation_measures as ev\n",
|
||
|
|
"dir_path=\"Recommendations generated/ml-100k/\"\n",
|
||
|
|
"super_reactions=[4,5]\n",
|
||
|
|
"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
|
||
|
|
"\n",
|
||
|
|
"ev.evaluate_all(test, dir_path, super_reactions)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Ready-made KNNs - Surprise implementation"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### I-KNN - basic"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Computing the cosine similarity matrix...\n",
|
||
|
|
"Done computing similarity matrix.\n",
|
||
|
|
"Generating predictions...\n",
|
||
|
|
"Generating top N recommendations...\n",
|
||
|
|
"Generating predictions...\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import helpers\n",
|
||
|
|
"import surprise as sp\n",
|
||
|
|
"import imp\n",
|
||
|
|
"imp.reload(helpers)\n",
|
||
|
|
"\n",
|
||
|
|
"sim_options = {'name': 'cosine',\n",
|
||
|
|
" 'user_based': False} # compute similarities between items\n",
|
||
|
|
"algo = sp.KNNBasic(sim_options=sim_options)\n",
|
||
|
|
"\n",
|
||
|
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNN_reco.csv',\n",
|
||
|
|
" estimations_path='Recommendations generated/ml-100k/Ready_I-KNN_estimations.csv')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### U-KNN - basic"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Computing the cosine similarity matrix...\n",
|
||
|
|
"Done computing similarity matrix.\n",
|
||
|
|
"Generating predictions...\n",
|
||
|
|
"Generating top N recommendations...\n",
|
||
|
|
"Generating predictions...\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import helpers\n",
|
||
|
|
"import surprise as sp\n",
|
||
|
|
"import imp\n",
|
||
|
|
"imp.reload(helpers)\n",
|
||
|
|
"\n",
|
||
|
|
"sim_options = {'name': 'cosine',\n",
|
||
|
|
" 'user_based': True} # compute similarities between users\n",
|
||
|
|
"algo = sp.KNNBasic(sim_options=sim_options)\n",
|
||
|
|
"\n",
|
||
|
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_U-KNN_reco.csv',\n",
|
||
|
|
" estimations_path='Recommendations generated/ml-100k/Ready_U-KNN_estimations.csv')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### I-KNN - on top baseline"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Estimating biases using als...\n",
|
||
|
|
"Computing the msd similarity matrix...\n",
|
||
|
|
"Done computing similarity matrix.\n",
|
||
|
|
"Generating predictions...\n",
|
||
|
|
"Generating top N recommendations...\n",
|
||
|
|
"Generating predictions...\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import helpers\n",
|
||
|
|
"import surprise as sp\n",
|
||
|
|
"import imp\n",
|
||
|
|
"imp.reload(helpers)\n",
|
||
|
|
"\n",
|
||
|
|
"sim_options = {'name': 'cosine',\n",
|
||
|
|
" 'user_based': False} # compute similarities between items\n",
|
||
|
|
"algo = sp.KNNBaseline()\n",
|
||
|
|
"\n",
|
||
|
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_reco.csv',\n",
|
||
|
|
" estimations_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_estimations.csv')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# project task 4: use a version of your choice of Surprise KNNalgorithm"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# read the docs and try to find best parameter configuration (let say in terms of RMSE)\n",
|
||
|
|
"# https://surprise.readthedocs.io/en/stable/knn_inspired.html##surprise.prediction_algorithms.knns.KNNBaseline\n",
|
||
|
|
"# the solution here can be similar to examples above\n",
|
||
|
|
"# please save the output in 'Recommendations generated/ml-100k/Self_KNNSurprisetask_reco.csv' and\n",
|
||
|
|
"# 'Recommendations generated/ml-100k/Self_KNNSurprisetask_estimations.csv'"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.6.9"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|