franckjay

    def forward(self, x, u_cats, i_cats):
        """
        Forward pass
        :param x: Float Tensor
        :param u_cats: User index tensor
        :param i_cats: Item index tensor
        :return: Predictions for this batch
        """
        curr_batch_size = len(u_cats)
        # Take User and Item embeddings for each value

franckjay

class DictDataset(Dataset):
    def __init__(self, data_dict, norm_target=1, scaler=None):
        self.norm_target = norm_target
        self.data_df = build_pandas_ranking(data_dict)
        self.scaler = scaler

        # Build out the features that are continuous variables
        self.float_features = []

        for feat in self.data_df.columns:

franckjay

# Take our first user:
sparse.to_dense()[0] 
>> tensor([1., 0., 0., 0., 1., 1.])

# Then multiply by B:
sparse.to_dense()[0]@B
>> tensor([ 0.9660, -0.0065, -0.0065,  0.0227,  0.9660,  0.9756])

franckjay

B = P / (-1*P.diag())
B = B + torch.eye(B.shape[0])#Set diagonals to 0
tensor([
        [ 0.0000,  0.1296,  0.1296, -0.4540,  0.6806,  0.4878],
        [ 0.1296,  0.0000,  0.6806,  0.4878,  0.1296, -0.4540],
        [ 0.1296,  0.6806,  0.0000,  0.4878,  0.1296, -0.4540],
        [-0.2656,  0.2854,  0.2854,  0.0000, -0.2656,  0.9308],
        [ 0.6806,  0.1296,  0.1296, -0.4540,  0.0000,  0.4878],
        [ 0.2854, -0.2656, -0.2656,  0.9308,  0.2854,  0.0000]
])

franckjay

P = G.inverse()
tensor([
        [11.9006, -1.5420, -1.5420,  3.1611, -8.0994, -3.3963],
        [-1.5420, 11.9006, -8.0994, -3.3963, -1.5420,  3.1611],
        [-1.5420, -8.0994, 11.9006, -3.3963, -1.5420,  3.1611],
        [ 3.1611, -3.3963, -3.3963,  6.9624,  3.1611, -6.4803],
        [-8.0994, -1.5420, -1.5420,  3.1611, 11.9006, -3.3963],
        [-3.3963,  3.1611,  3.1611, -6.4803, -3.3963,  6.9624]
])

franckjay

lambda_ = 0.05
G += torch.eye(G.shape[0])*lambda_
tensor([
        [1.0500, 0.0000, 0.0000, 0.0000, 1.0000, 1.0000],
        [0.0000, 1.0500, 1.0000, 1.0000, 0.0000, 0.0000],
        [0.0000, 1.0000, 1.0500, 1.0000, 0.0000, 0.0000],
        [0.0000, 1.0000, 1.0000, 2.0500, 0.0000, 1.0000],
        [1.0000, 0.0000, 0.0000, 0.0000, 1.0500, 1.0000],
        [1.0000, 0.0000, 0.0000, 1.0000, 1.0000, 2.0500]
])

franckjay

G = sparse.to_dense().t()@sparse.to_dense()

tensor([
        [1., 0., 0., 0., 1., 1.],
        [0., 1., 1., 1., 0., 0.],
        [0., 1., 1., 1., 0., 0.],
        [0., 1., 1., 2., 0., 1.],
        [1., 0., 0., 0., 1., 1.],
        [1., 0., 0., 1., 1., 2.]
])

franckjay

indices = torch.LongTensor(
            train[[user_id_col, item_id_col]].values
        )
values = torch.ones(indices.shape[0])
torch.sparse.FloatTensor(indices.t(), values)

franckjay

import React, { useState, useEffect } from 'react';
import { Image } from "semantic-ui-react";
export const GrabBook = ()=> {
  const [outputURL, setOutputURL] = useState("");
  useEffect(() => {
   fetch("/novel_novel").then(response =>
    response.json().then(data => {
      setOutputURL(data.image_url);
      })
    );

franckjay

import React, { useState } from 'react';
import { Form, Input, Button } from 'semantic-ui-react';


export const BookEntry = ()  => {
  const [title, setTitle] = useState(''); //  Empty String
  return (
    <Form>
      <Form.Field>
        <Input