Building a Recommender System from Scratch¶
Step 1: Import Dependencies¶
We are using pandas.DataFrame to represent our data. We will visualize our data with matplotlib and seaborn.
What is a DataFrame?
- a two-dimensional Pandas data structure
- columns represent features, rows represent items
- analogous to an Excel spreadsheet or SQL table
- documentation can be found here
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
Step 2: Load Data¶
Let’s download a small version of the MovieLens dataset. See here for zip file url, or directly download here. We’re working with data in ml-latest-small.zip and will need to add the following files to our repository:
- ratings.csv
- movies.csv
Alternatively, you can access the data here:
- https://s3-us-west-2.amazonaws.com/recommender-tutorial/movies.csv
- https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv
In [2]:
ratings = pd.read_csv('data/ratings.csv')
In [3]:
movies = pd.read_csv('data/movies.csv')
Step 3: Exploratory Data Analysis¶
In [4]:
n_ratings = len(ratings)
n_movies = ratings['movieId'].nunique()
n_users = ratings['userId'].nunique()
print(f"Number of ratings: {n_ratings}")
print(f"Number of unique movieId's: {n_movies}")
print(f"Number of unique users: {n_users}")
print(f"Average number of ratings per user: {round(n_ratings/n_users, 2)}")
print(f"Average number of ratings per movie: {round(n_ratings/n_movies, 2)}")
Number of ratings: 100836
Number of unique movieId's: 9724
Number of unique users: 610
Average number of ratings per user: 165.3
Average number of ratings per movie: 10.37
In [5]:
sns.countplot(x="rating", data=ratings, palette="viridis")
plt.title("Distribution of movie ratings", fontsize=14)
plt.show()
In [6]:
print(f"Mean global rating: {round(ratings['rating'].mean(),2)}.")
mean_ratings = ratings.groupby('userId')['rating'].mean()
print(f"Mean rating per user: {round(mean_ratings.mean(),2)}.")
Mean global rating: 3.5.
Mean rating per user: 3.66.
Best vs. Worst Movie¶
Which movie has the lowest and highest average rating?
In [7]:
mean_ratings = ratings.groupby('movieId')[['rating']].mean()
lowest_rated = mean_ratings['rating'].idxmin()
movies[movies['movieId'] == lowest_rated]
Out[7]:
| movieId | title | genres | |
|---|---|---|---|
| 2689 | 3604 | Gypsy (1962) | Musical |
Gypsy
has the lowest average rating.
In [8]:
highest_rated = mean_ratings['rating'].idxmax()
movies[movies['movieId'] == highest_rated]
Out[8]:
| movieId | title | genres | |
|---|---|---|---|
| 48 | 53 | Lamerica (1994) | Adventure|Drama |
Lamerica may be the “highest” rated movie, but it only has one rating. A better approach for evaluating movie popularity is to do look at the Bayesian average.
Bayesian Average¶
Bayesian Average is defined as:
\(r_{i} = \frac{C \times m + \Sigma{\text{reviews}}}{C+N}\)
where \(C\) represents our confidence, \(m\) represents our prior, and \(N\) is the total number of reviews for movie \(i\). In this case, our prior will be the average rating across all movies. By defintion, C represents “the typical data set size”. Let’s make \(C\) be the average number of ratings for a given movie.
In [9]:
movie_stats = ratings.groupby('movieId')[['rating']].agg(['count', 'mean'])
movie_stats.columns = movie_stats.columns.droplevel()
In [10]:
C = movie_stats['count'].mean()
m = movie_stats['mean'].mean()
def bayesian_avg(ratings):
bayesian_avg = (C*m+ratings.sum())/(C+ratings.count())
return bayesian_avg
bayesian_avg_ratings = ratings.groupby('movieId')['rating'].agg(bayesian_avg).reset_index()
bayesian_avg_ratings.columns = ['movieId', 'bayesian_avg']
movie_stats = movie_stats.merge(bayesian_avg_ratings, on='movieId')
In [11]:
movie_stats = movie_stats.merge(movies[['movieId', 'title']])
movie_stats.sort_values('bayesian_avg', ascending=False).head()
Out[11]:
| movieId | count | mean | bayesian_avg | title | |
|---|---|---|---|---|---|
| 277 | 318 | 317 | 4.429022 | 4.392070 | Shawshank Redemption, The (1994) |
| 659 | 858 | 192 | 4.289062 | 4.236457 | Godfather, The (1972) |
| 2224 | 2959 | 218 | 4.272936 | 4.227052 | Fight Club (1999) |
| 224 | 260 | 251 | 4.231076 | 4.192646 | Star Wars: Episode IV - A New Hope (1977) |
| 46 | 50 | 204 | 4.237745 | 4.190567 | Usual Suspects, The (1995) |
Using the Bayesian average, we see that Shawshank Redemption
,
The Godfather
, and The Usual Suspects
are the most highly rated
movies. This result makes much more sense since these movies are
critically acclaimed films.
In [12]:
movie_stats.sort_values('bayesian_avg', ascending=True).head()
Out[12]:
| movieId | count | mean | bayesian_avg | title | |
|---|---|---|---|---|---|
| 1172 | 1556 | 19 | 1.605263 | 2.190377 | Speed 2: Cruise Control (1997) |
| 2679 | 3593 | 19 | 1.657895 | 2.224426 | Battlefield Earth (2000) |
| 1372 | 1882 | 33 | 1.954545 | 2.267268 | Godzilla (1998) |
| 1144 | 1499 | 27 | 1.925926 | 2.296800 | Anaconda (1997) |
| 1988 | 2643 | 16 | 1.687500 | 2.306841 | Superman IV: The Quest for Peace (1987) |
With Bayesian averaging, it looks like Speed 2: Cruise Control
,
Battlefield Earth
, and Godzilla
are the worst rated movies.
Gypsy
isn’t so bad after all.
A Glimpse at Movie Genres¶
The movies dataset needs to be cleaned in two ways:
genres
is expressed as a string with a pipe|
separating each genre. We will manipulate this string into a list, which will make it much easier to analyze.title
currently has (year) appended at the end. We will extract year from each title string and create a new column for it.
In [13]:
movies['genres'] = movies['genres'].apply(lambda x: x.split("|"))
movies.head()
Out[13]:
| movieId | title | genres | |
|---|---|---|---|
| 0 | 1 | Toy Story (1995) | [Adventure, Animation, Children, Comedy, Fantasy] |
| 1 | 2 | Jumanji (1995) | [Adventure, Children, Fantasy] |
| 2 | 3 | Grumpier Old Men (1995) | [Comedy, Romance] |
| 3 | 4 | Waiting to Exhale (1995) | [Comedy, Drama, Romance] |
| 4 | 5 | Father of the Bride Part II (1995) | [Comedy] |
How many movie genres are there?
In [14]:
from collections import Counter
genre_frequency = Counter(g for genres in movies['genres'] for g in genres)
print(f"There are {len(genre_frequency)} genres.")
genre_frequency
There are 20 genres.
Out[14]:
Counter({'Adventure': 1263,
'Animation': 611,
'Children': 664,
'Comedy': 3756,
'Fantasy': 779,
'Romance': 1596,
'Drama': 4361,
'Action': 1828,
'Crime': 1199,
'Thriller': 1894,
'Horror': 978,
'Mystery': 573,
'Sci-Fi': 980,
'War': 382,
'Musical': 334,
'Documentary': 440,
'IMAX': 158,
'Western': 167,
'Film-Noir': 87,
'(no genres listed)': 34})
In [15]:
print("The 5 most common genres: \n", genre_frequency.most_common(5))
The 5 most common genres:
[('Drama', 4361), ('Comedy', 3756), ('Thriller', 1894), ('Action', 1828), ('Romance', 1596)]
Step 4: Data Pre-processing¶
We are going to use a technique called colaborative filtering to generate recommendations for users. This technique is based on the premise that similar people like similar things.
The first step is to transform our data into a user-item matrix, also known as a “utility” matrix. In this matrix, rows represent users and columns represent movies. The beauty of collaborative filtering is that it doesn’t require any information about the users or the movies user to generate recommendations.
In [16]:
from IPython.display import Image
Image("imgs/user-movie-matrix.png")
Out[16]:
The create_X()
function outputs a sparse matrix X with four mapper
dictionaries:
- user_mapper: maps user id to user index
- movie_mapper: maps movie id to movie index
- user_inv_mapper: maps user index to user id
- movie_inv_mapper: maps movie index to movie id
We need these dictionaries because they map which row/column of the utility matrix corresponds to which user/movie id.
Our X (user-item) matrix is a scipy.sparse.csr_matrix which stores the data sparsely.
In [17]:
from scipy.sparse import csr_matrix
def create_X(df):
"""
Generates a sparse matrix from ratings dataframe.
Args:
df: pandas dataframe containing 3 columns (userId, movieId, rating)
Returns:
X: sparse matrix
user_mapper: dict that maps user id's to user indices
user_inv_mapper: dict that maps user indices to user id's
movie_mapper: dict that maps movie id's to movie indices
movie_inv_mapper: dict that maps movie indices to movie id's
"""
M = df['userId'].nunique()
N = df['movieId'].nunique()
user_mapper = dict(zip(np.unique(df["userId"]), list(range(M))))
movie_mapper = dict(zip(np.unique(df["movieId"]), list(range(N))))
user_inv_mapper = dict(zip(list(range(M)), np.unique(df["userId"])))
movie_inv_mapper = dict(zip(list(range(N)), np.unique(df["movieId"])))
user_index = [user_mapper[i] for i in df['userId']]
item_index = [movie_mapper[i] for i in df['movieId']]
X = csr_matrix((df["rating"], (user_index,item_index)), shape=(M,N))
return X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper
X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_X(ratings)
Evaluating sparsity¶
Here, we calculate sparsity by dividing the number of stored elements by total number of elements. The number of stored (non-empty) elements in our matrix is equivalent to the number of ratings in our dataset.
In [18]:
n_total = X.shape[0]*X.shape[1]
n_ratings = X.nnz
sparsity = n_ratings/n_total
print(f"Matrix sparsity: {round(sparsity*100,2)}%")
Matrix sparsity: 1.7%
csr_matrix.nnz
counts the stored values in our sparse matrix. The
rest of our cells are empty.
The cold start problem is when there are new users and movies in our matrix that do not have any ratings. In our Movielens dataset, all users and movies have at least one rating but in general, it’s useful to check which users and movies have few interactions.
In [19]:
n_ratings_per_user = X.getnnz(axis=1)
print(f"Most active user rated {n_ratings_per_user.max()} movies.")
print(f"Least active user rated {n_ratings_per_user.min()} movies.")
Most active user rated 2698 movies.
Least active user rated 20 movies.
In [20]:
n_ratings_per_movie = X.getnnz(axis=0)
print(f"Most rated movie has {n_ratings_per_movie.max()} ratings.")
print(f"Least rated movie has {n_ratings_per_movie.min()} ratings.")
Most rated movie has 329 ratings.
Least rated movie has 1 ratings.
In [21]:
plt.figure(figsize=(16,4))
plt.subplot(1,2,1)
sns.kdeplot(n_ratings_per_user, shade=True)
plt.xlim(0)
plt.title("Number of Ratings Per User", fontsize=14)
plt.xlabel("number of ratings per user")
plt.ylabel("density")
plt.subplot(1,2,2)
sns.kdeplot(n_ratings_per_movie, shade=True)
plt.xlim(0)
plt.title("Number of Ratings Per Movie", fontsize=14)
plt.xlabel("number of ratings per movie")
plt.ylabel("density")
plt.show()
Normalizing our data¶
User-item bias is defined as: \(b_{ui} = \mu + b_{u} + b_{i}\)
We will perform mean normalization by subtracting \(b_{i}\) from each movie’s rating.
In [22]:
sum_ratings_per_movie = X.sum(axis=0)
mean_rating_per_movie = sum_ratings_per_movie/n_ratings_per_movie
Now that we have the mean rating for each movie, we are going to
subtract this from our utility matrix. In order to do this, we need to
expand our 1D mean_rating_per_movie
array into a matrix with the
same shape as our utility matrix (n_users, n_movies)
. Numpy’s
tile
method is useful for this task.
In [23]:
X_mean_movie = np.tile(mean_rating_per_movie, (X.shape[0],1))
In [24]:
X_mean_movie.shape
Out[24]:
(610, 9724)
X_mean_movie
is a matrix of shape (n_users, n_movies) in which each
column is populated with a given movie’s mean rating.
We can easily normalize our X matrix by subtracting X_mean_movie
from original X
.
In [25]:
X_norm = X - csr_matrix(X_mean_movie)
As expected, X_norm
’s values are different from X
s. Cells
that are empty in the original matrix are now replaced with negative
values representing the mean rating of a given movie.
In [26]:
print("Original X:", X[0].todense())
print("Normalized X:", X_norm[0].todense())
Original X: [[4. 0. 4. ... 0. 0. 0.]]
Normalized X: [[ 0.07906977 -3.43181818 0.74038462 ... -3.5 -3.5
-4. ]]
Step 5: Item-item Recommendations with k-Nearest Neighbors¶
We are going to find the \(k\) movies that have the most similar user engagement vectors for movie \(i\).
In [27]:
from sklearn.neighbors import NearestNeighbors
def find_similar_movies(movie_id, X, movie_mapper, movie_inv_mapper, k, metric='cosine'):
"""
Finds k-nearest neighbours for a given movie id.
Args:
movie_id: id of the movie of interest
X: user-item utility matrix
k: number of similar movies to retrieve
metric: distance metric for kNN calculations
Output: returns list of k similar movie ID's
"""
X = X.T
neighbour_ids = []
movie_ind = movie_mapper[movie_id]
movie_vec = X[movie_ind]
if isinstance(movie_vec, (np.ndarray)):
movie_vec = movie_vec.reshape(1,-1)
# use k+1 since kNN output includes the movieId of interest
kNN = NearestNeighbors(n_neighbors=k+1, algorithm="brute", metric=metric)
kNN.fit(X)
neighbour = kNN.kneighbors(movie_vec, return_distance=False)
for i in range(0,k):
n = neighbour.item(i)
neighbour_ids.append(movie_inv_mapper[n])
neighbour_ids.pop(0)
return neighbour_ids
find_similar_movies()
takes in a movieId
and X
matrix, and
outputs a list of \(k\) movies that are similar to the movieId
of interest.
Let’s see how it works in action. We will first create another mapper that maps movieId to title so that our results are interpretable.
In [28]:
similar_movies = find_similar_movies(1, X_norm, movie_mapper, movie_inv_mapper, k=10)
similar_movies
Out[28]:
[3114, 78499, 2054, 788, 2355, 2018, 596, 2078, 673]
find_similar_movies()
returns a list of movieId
’s that are
most similar to your movie of interest. Let’s convert these id’s to
titles so that we can interpret our results. To make things easier, we
will create a dictionary that maps movieId
to title
.
In [29]:
movie_titles = dict(zip(movies['movieId'], movies['title']))
movie_id = 1
similar_movies = find_similar_movies(movie_id, X_norm, movie_mapper, movie_inv_mapper, metric='cosine', k=10)
movie_title = movie_titles[movie_id]
print(f"Because you watched {movie_title}:")
for i in similar_movies:
print(movie_titles[i])
Because you watched Toy Story (1995):
Toy Story 2 (1999)
Toy Story 3 (2010)
Honey, I Shrunk the Kids (1989)
Nutty Professor, The (1996)
Bug's Life, A (1998)
Bambi (1942)
Pinocchio (1940)
Jungle Book, The (1967)
Space Jam (1996)
The results above show the 10 movies that are most similar to Toy Story. Most movies in this list are family movies from the 1990s, which seems pretty reasonable. Note that these recommendations are based solely on user-item ratings. Movie features such as genres are not used in this approach.
You can also play around with the kNN distance metric and see what results you would get if you use “manhattan” or “euclidean” instead of “cosine”.
In [30]:
movie_id = 1
similar_movies = find_similar_movies(movie_id, X_norm, movie_mapper, movie_inv_mapper, metric='euclidean', k=10)
movie_title = movie_titles[movie_id]
print(f"Because you watched {movie_title}:")
for i in similar_movies:
print(movie_titles[i])
Because you watched Toy Story (1995):
Honey, I Shrunk the Kids (1989)
Nutty Professor, The (1996)
Space Jam (1996)
Mighty Ducks, The (1992)
The Amazing Spider-Man 2 (2014)
Popeye (1980)
Hocus Pocus (1993)
Lost World: Jurassic Park, The (1997)
Snakes on a Plane (2006)
Step 6: Handling the cold-start problem¶
Collaborative filtering relies solely on user-item interactions within the utility matrix. The issue with this approach is that brand new users or items with no iteractions get excluded from the recommendation system. This is called the cold start problem. Content-based filtering is a way to handle this problem by generating recommendations based on user and item features.
First, we need to convert the genres
column into binary features.
Each genre will have its own column in the dataframe, and will be
populated with 0 or 1.
In [31]:
n_movies = movies['movieId'].nunique()
print(f"There are {n_movies} unique movies in our movies dataset.")
There are 9742 unique movies in our movies dataset.
In [32]:
genres = set(g for G in movies['genres'] for g in G)
for g in genres:
movies[g] = movies.genres.transform(lambda x: int(g in x))
movie_genres = movies.drop(columns=['movieId', 'title','genres'])
In [33]:
movie_genres.head()
Out[33]:
| Action | Film-Noir | Adventure | War | Children | Drama | (no genres listed) | Mystery | Animation | Musical | Horror | Documentary | Western | Romance | Sci-Fi | Fantasy | Crime | Thriller | Comedy | IMAX | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
| 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
In [34]:
from sklearn.metrics.pairwise import cosine_similarity
cosine_sim = cosine_similarity(movie_genres, movie_genres)
print(f"Dimensions of our genres cosine similarity matrix: {cosine_sim.shape}")
Dimensions of our genres cosine similarity matrix: (9742, 9742)
The dimensions of our cosine similarity matrix are \(M \times M\) where \(M\) represents the total number of movies.
In [35]:
def movie_finder(title):
return movies[movies['title'].str.contains(title)]['title'].tolist()
movie_finder('Toy Story')
Out[35]:
['Toy Story (1995)', 'Toy Story 2 (1999)', 'Toy Story 3 (2010)']
In [36]:
movie_idx = dict(zip(movies['title'], list(movies.index)))
title = movie_finder('Toy Story')[0]
n_recommendations = 10
idx = movie_idx[title]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:(n_recommendations+1)]
similar_movies = [i[0] for i in sim_scores]
print(f"Recommendations for {title}:")
movies['title'].iloc[similar_movies]
Recommendations for Toy Story (1995):
Out[36]:
1706 Antz (1998)
2355 Toy Story 2 (1999)
2809 Adventures of Rocky and Bullwinkle, The (2000)
3000 Emperor's New Groove, The (2000)
3568 Monsters, Inc. (2001)
6194 Wild, The (2006)
6486 Shrek the Third (2007)
6948 Tale of Despereaux, The (2008)
7760 Asterix and the Vikings (Astérix et les Viking...
8219 Turbo (2013)
Name: title, dtype: object
With this approach, we are assuming that two movies are “similar” if they have similar genres. These “similar movie” recommendations are based solely on the metadata from our movies dataset. Feel free to test out different movies.
Step 7: Dimensionality Reduction with Matrix Factorization (advanced)¶
Matrix factorization (MF) is a linear algebra technique that can help us discover latent features underlying the interactions between users and movies. These latent features give a more compact representation of user tastes and item descriptions. MF is particularly useful for very sparse data and can enhance the quality of recommendations. The algorithm works by factorizing the original user-item matrix into two factor matrices:
- user-factor matrix (n_users, k)
- item-factor matrix (k, n_items)
We are reducing the dimensions of our original matrix into “taste” dimensions. We cannot interpret what each latent feature \(k\) represents. However, we could imagine that one latent feature may represent users who like romantic comedies from the 1990s, while another latent feature may represent movies which are independent foreign language films.
In [37]:
Image("imgs/matrix-factorization.png")
Out[37]:
In [38]:
from sklearn.decomposition import TruncatedSVD
In [39]:
svd = TruncatedSVD(n_components=20, n_iter=10)
Z = svd.fit_transform(X.T)
In [40]:
movie_id = 1
similar_movies = find_similar_movies(movie_id, Z.T, movie_mapper, movie_inv_mapper, metric='cosine', k=10)
movie_title = movie_titles[movie_id]
print(f"Because you watched {movie_title}:")
for i in similar_movies:
print(movie_titles[i])
Because you watched Toy Story (1995):
Home Alone (1990)
Jurassic Park (1993)
Aladdin (1992)
Willy Wonka & the Chocolate Factory (1971)
Forrest Gump (1994)
Back to the Future (1985)
Groundhog Day (1993)
Star Wars: Episode IV - A New Hope (1977)
Babe (1995)
The results above are the most similar movies to Toy Story using kNN on our “compressed” movie-factor matrix. We reduced the dimensions down to n_components=30. We can think of each component representing a latent feature such as movie genre.
Predicting missing ratings to generate top N recommendations¶
With matrix factorization, we can also predict missing ratings in our
original user-item matrix. We can do this by reconstructing our matrix
by getting the inner product of our user-factor matrix and movie-factor
matrix. We can use the inverse_transform
on our movie-factor matrix
Z to get our reconstructed user-item matrix.
In [41]:
new_X = svd.inverse_transform(Z).T
Let’s look at userId=100
’s recommendations. In the table below, we
can see which movies that have been rated highly:
In [42]:
userId = 100
user_preferences = ratings[(ratings['userId']==userId)&(ratings['rating']>=4)]
user_preferences = user_preferences.merge(movies[['movieId', 'title']])
user_preferences.sort_values('rating', ascending=False).head(10)
Out[42]:
| userId | movieId | rating | timestamp | title | |
|---|---|---|---|---|---|
| 97 | 100 | 5620 | 5.0 |<PHONE_NUMBER> | Sweet Home Alabama (2002) |
| 89 | 100 | 4041 | 5.0 |<PHONE_NUMBER> | Officer and a Gentleman, An (1982) |
| 64 | 100 | 1958 | 5.0 |<PHONE_NUMBER> | Terms of Endearment (1983) |
| 45 | 100 | 1101 | 5.0 |<PHONE_NUMBER> | Top Gun (1986) |
| 75 | 100 | 2423 | 5.0 |<PHONE_NUMBER> | Christmas Vacation (National Lampoon's Christm... |
| 1 | 100 | 16 | 4.5 |<PHONE_NUMBER> | Casino (1995) |
| 36 | 100 | 919 | 4.5 |<PHONE_NUMBER> | Wizard of Oz, The (1939) |
| 82 | 100 | 3259 | 4.5 |<PHONE_NUMBER> | Far and Away (1992) |
| 40 | 100 | 934 | 4.5 |<PHONE_NUMBER> | Father of the Bride (1950) |
| 41 | 100 | 1028 | 4.5 |<PHONE_NUMBER> | Mary Poppins (1964) |
Now, let’s take a look at which movies we predict userId=100
will
like based on their previous ratings. We will need get the row which
represents userId=100
and sort their predicted ratings in descending
order. np.argsort()
is a useful method for getting the top N indices
(i.e., movies with highest rating predictions) for userId=100
.
In [43]:
top_N = 10
movie_titles = dict(zip(movies['movieId'], movies['title']))
top_N_indices = new_X[user_mapper[userId]].argsort()[-top_N:][::-1]
print(f"Top {top_N} Recommendations for UserId {userId}:")
for i in top_N_indices:
movie_id = movie_inv_mapper[i]
print(movie_titles[movie_id])
Top 10 Recommendations for UserId 100:
Forrest Gump (1994)
Pretty Woman (1990)
When Harry Met Sally... (1989)
Apollo 13 (1995)
Toy Story (1995)
Shakespeare in Love (1998)
Aladdin (1992)
Clueless (1995)
Princess Bride, The (1987)
Four Weddings and a Funeral (1994)
Step 8: Evaluation¶
How do we know if our recommendations are good? The best way to evaluate our system is to test it in the wild with A/B testing or usability tests, where we can get direct feedback from users. However, this option is not always available which means that we need to use offline metrics.
In traditional machine learning, we evaluate a model by splitting our
data into two sets: 1) training set
and 2) validation set
. We
train our model using the training set
, then test our model on the
validation set
. In recommender systems, we can’t split our data the
traditional way because our model won’t work if our training set
represents a completely different user population than the validation
set. Instead, we generate our training set
by masking random
interactions in our user-item matrix. In other words, we pretend that we
don’t if a user liked or hated Movie X, but we actually do. Once we
train our model and generate our rating predictions, we can then compare
our predicted rating with the actual rating.
In [44]:
Image("imgs/splitting.png")
Out[44]:
There are various ways to compare our predicted vs. actual ratings. The most common metrics include:
- Root Mean Squared Error: \(\text{RMSE} = \sqrt{\frac{\Sigma^N_{i=1}(y-\hat{y})^2}{N}}\)
- Precision: \(\text{Precision} = \frac{TP}{TP+FP}\)
- Recall: \(\text{Recall} = \frac{TP}{TP+FN}\)


Father of three, husband of one, and former bartender and banquet captain of 16 years. Now, I add web developer to that list.
Our final project. A social media app for your cat. This will really tug on your felines. Users can login, add their cat, vote on other cats, as well as find pets at local adoption centers.
My second group made a fantastic app in which you could search for your favorite music, comment on different songs, and read the comments of other users. It's vision would be to be the social media of music.
In my first application using React, I created a clicky game with art inspired by the band Tool.
My first backend only application, I created a mock health food store, where users can purchase items and supervisors can manage inventory.
When I'm not improving my web development skills, prepping meals, changing a diaper or wiping a butt, I am an avid and terrible disc golfer. In recent years, I have also found a love for nutrition and running outdoors.
I also enjoy anything that makes me laugh and that can include watching on television anything from live C-SPAN events, to movies and shows, to stand-up comedy. I also cry during every episode of This is Us.


Where Does Your Tea Take You?
"Drink your tea slowly and reverently, as if it is the axis on which the world earth revolves." Thich Nhat Hanh


7
Class Orientation!
187
TRs expected this week
18
Classes Scheduled, Yet to be Confirmed
56
New Registrations!


Adidas Account Creator Token Harvester
For each account you want to create you will have to solve a captcha
No accounts will save until all captchas are solved
The Submit button doesnt create tokens DO NOT PRESS IT
Captcha Sitekey: {{ sitekey}}


“We are the most preferred and leading private educational institution offering IT-enhanced programs transforming students to globally competitive individuals who can be innovators of the world.”
The Informatics Group is established in Singapore.
Informatics was established by entrepreneur Leo Riingen. First campus is at Informatics SM Megamall.
Informatics is named among the world’s 100 Best Managed Small Companies outside the USA by Forbes Magazine.
Opens its first College campuses in Manila and Caloocan.
International Master Franchise of the Year Top Computer Learning Center Two-time Singapore Brand Awardee.
Trained about 15,000 TESDA scholars nationwide in a period of six months.
It is lauded by TESDA as the best IT school for employment for garnering the highest post-training employment rate of 70% per TESDA Survey.
Senator Cayetano together with TESDA,BJMP, and Informatics launched BILIB IT wherein more than 200 inmates were provided training with Job-Enabling skills
We focus in understanding our students and putting them first in everything we do.
We empower our people to be professionals demonstrating the highest standards of ethics and integrity.
We manifest creativity and innovation in our development, delivery of programs and services.
We strive to be responsible corporate citizens in every society that we operate.
We endeavor to provide sustainable returns to our stakeholders.
Leonardo Riingen is an entrepreneur who has already contributed immensely to improving information technology (IT) training and education in the Philippines.
Riingen came up with the idea that an Information Technology training should be made available to people. When you think of Informatics, "the computer school in the mall ", Riingen is the man behind it. That is the innovative and creative, if not a proactive way of making business and fulfilling his dream of making IT accessible to the people.
Utilizing his education in U.K., he applied the ladderized program in IT, patterned after the British education system, to allow students to learn skills and make them immediately employable. Also, he designed and customized IT training programs and curriculum for companies and government offices to give learners a comprehensive, organization-wide IT training. To make the IT education and training that his schools offer at par with international standards, his schools boast of international courses validated by the world's leading IT qualification awarding bodies; and maintains affiliations with IT industry technology frontrunners.


cocoAspell copyright © 2001-##YEAR## by Anton Leuski Aspell code copyright © 2000-2005 by Kevin Atkinson
cocoAspell graphics by Igor Krizhanovsky


Size: 300x30 Interpolation: basis Animation: true Transition: 1000ms Update Frequency: 1000ms
Size: 150x15 Interpolation: basis Animation: true Transition: 1000ms Update Frequency: 1000ms
Size: 150x15 Interpolation: linear Animation: true Transition: 1000ms Update Frequency: 1000ms
Size: 150x15 Interpolation: linear Animation: false Transition: 1000ms Update Frequency: 1000ms
Size: 300x30 Interpolation: basis Animation: true Transition: 750ms Update Frequency: 1500ms
Size: 300x30 Interpolation: basis Animation: false Transition: 750ms Update Frequency: 1500ms


param:
random
fixed (switch between positive/negative)
easing:
cubicInOut
linear
rotate
rotate and scale
scale
Rotate remain:
-
ms Scale remain:
-
ms


Accept Invitation?
You've been invited to join {{ invitation.team.name }}
by {{ invitation.invited_by.get_display_name }}.
{% if request.user.is_authenticated and request.user == invitation.email %}
Accept
{% elif request.user.is_authenticated %}
This invitation was sent to {{ invitation.email }} but you're logged in as {{ request.user.email }}.
What would you like to do?
Accept as {{ request.user.email }}
Sign out
{% else %}
Create Account to Accept
{% endif %}


--- layout: post title: AmoebaDB 32 Released category: [AmoebaDB] ---
We are pleased to announce the release of AmoebaDB 32


2022 ACM MM Multi-Modal Experience Inspired AI Creation Qian Cao, Xu Chen, Ruihua Song, Hao Jiang, Guang Yang, and Zhao Cao In Proceedings of the 30th ACM international conference on Multimedia, Oct 2022 Bib HTML PDF Code @inproceedings{Cao2022MultiModalEI, title = {Multi-Modal Experience Inspired AI Creation}, author = {Cao, Qian and Chen, Xu and Song, Ruihua and Jiang, Hao and Yang, Guang and Cao, Zhao}, booktitle = {Proceedings of the 30th ACM international conference on Multimedia,}, year = {2022}, month = oct, doi = {10.1145/3503161.3548189}, }


File cleaner will clean up unused files in hosts hourly. You can define some policies and
apply a combination of them to target directories in a config.
Policies
{{ policy.name }}
{{ policy.operation.type | titlecase }} {{ policy.target | lowercase }}
based on
{{ criterion.type | lowercase }}


I have worked at Hy-vee for almost five years. I started in the Italian department making pizza. Soon after moved over to the Asain department. Loved it there, stayed for 2 years until I saw an opportunity when they opened up the Market Cafes to make more and have been doing that up until now.
For fun I enjoy playing soccer with my highschool friends when they are in town. I run as well when i can doing long distance while the wheather is nice.
Amongst my family dreams and occupational dreams I have a deep love of travel. I would love to have the opportunity to travel the globe visiting Europe again or maybe South America. Ive gotten the chance to travel to Vancouver, Canada once. On top of this in my senior year in highschool I took the spanish class trip to Spain for two weeks visiting castles and the cities and museums. It was so much fun!


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Cortana drops the jokes (and most of the world), gets all business in Windows 10 20H1
by SururWhile Microsoft has drawn Cortana back from most of the world, the company has continued to develop Cortana as a productivity aid on Windows 10.
In a blog post, Microsoft explained the latest developments to the digital assistant on Windows 10 20H1.
Here’s what’s new:
- To give you an improved companion experience that’s more seamless with your workflows, Microsoft has undocked the app from the taskbar, so now you can move or re-size Cortana like any other app.
- Microsoft has made it easy for you to configure your default input to Cortana via Settings > Talk to Cortana, so now you can choose if you prefer typing or speaking.
-
The improved email skill lets you use Cortana to create and query emails:
- “Send an email to <name> letting them know I’m running late”
- “Show me emails from <name>”
-
The improved calendar skill lets you create and query meetings:
- “Set up a 1:1 with <name> at <time>”
- “What’s my next meeting?”
- “Join my meeting” (Note: This only works for Teams meetings at this time.)
-
The improved Windows skill lets you use Cortana to open apps and adjust settings:
- “Open Paint 3D”
- “Change the brightness”
- Microsoft has made significant accessibility improvements, so Cortana is easier for everyone to use.
Microsoft has also removed some functionality from Cortana:
- Microsoft has removed Cortana support for ‘connect accounts,’ so now your work and personal accounts are two separate experiences based on whichever account you sign in with. If you sign in with a Microsoft Account, you’ll interact with your personal information. If you sign in with a work or school account, Cortana will work with that one. Your chat history will be retained on your local device, and if you use a personal Microsoft account your data will still be visible in the Microsoft Privacy Dashboard.
-
Microsoft has also temporarily removed a few Cortana skills that were previously released to Windows Insiders (which may be returned later). These include:
- Bing Instant Answers
- Jokes and other assistant conversations.
- Timers
Microsoft says the new Cortana will only be available on US-EN (ie US English) with other languages to be added at a later date.
Will any of our readers miss the fun features, or do you love Cortana as a productivity aid? Let us know below.
via Neowin


A B C D E G I M N O P R S T U V W Y
All Classes All Packages
All Classes All Packages
All Classes All Packages
A
- addAuthToken(String, String) - Method in class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Adds the provided Site name and token to the Map.
- addAuthToken(Site, String) - Method in class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Adds the provided
Site name
and token to the Map. - ARCANE_CENTER_XYZ - org.botblock.javabotblockapi.Site
B
- BotBlockAPI - Class in org.botblock.javabotblockapi
-
Class used to define the auth-tokens used for the different sites.
- BotBlockAPI.Builder - Class in org.botblock.javabotblockapi
-
Builder class to create an instance of
BotBlockAPI
- BOTLIST_SPACE - org.botblock.javabotblockapi.Site
- BOTS_ONDISCORD_XYZ - org.botblock.javabotblockapi.Site
- BOTSDATABASE_COM - org.botblock.javabotblockapi.Site
- BOTSFORDISCORD_COM - org.botblock.javabotblockapi.Site
- build() - Method in class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Builds the instance of
BotBlockAPI
. - Builder() - Constructor for class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Empty constructor to get the class.
C
- CheckUtil - Class in org.botblock.javabotblockapi.requests
- CheckUtil() - Constructor for class org.botblock.javabotblockapi.requests.CheckUtil
- condition(boolean, String) - Static method in class org.botblock.javabotblockapi.requests.CheckUtil
D
- DBLISTA_PL - org.botblock.javabotblockapi.Site
- DEFAULT_DELAY - Static variable in class org.botblock.javabotblockapi.BotBlockAPI
- DeprecatedSince - Annotation Type in org.botblock.javabotblockapi.annotations
-
Annotation used to indicate since when an Object is deprecated.
- disableAutoPost() - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Shuts down the scheduler, to stop the automatic posting.
- DISCORD_BOATS - org.botblock.javabotblockapi.Site
- DISCORD_BOTS_GG - org.botblock.javabotblockapi.Site
- DISCORDAPPS_DEV - org.botblock.javabotblockapi.Site
- DISCORDBOTLIST_COM - org.botblock.javabotblockapi.Site
- DISCORDBOTS_CO - org.botblock.javabotblockapi.Site
- DISCORDBOTS_FUN - org.botblock.javabotblockapi.Site
- DISCORDEXTREMELIST_XYZ - org.botblock.javabotblockapi.Site
- DISCORDLIST_CO - org.botblock.javabotblockapi.Site
- DISCORDLISTOLOGY_COM - org.botblock.javabotblockapi.Site
E
- enableAutoPost(Long, int, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Starts posting of the guild count each n minutes.
- enableAutoPost(String, int, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Starts posting of the guild count each n minutes.
- enableAutoPost(JDA, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Starts posting of the guild count each n minutes.
- enableAutoPost(ShardManager, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Starts posting of the guild count each n minutes.
G
- getApiField() - Method in enum org.botblock.javabotblockapi.requests.GetListAction.ApiField
- getApiField(String, String, GetListAction.ApiField) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the name of the specified field.
- getApiField(String, Site, GetListAction.ApiField) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the name of the specified field.
- GetBotAction - Class in org.botblock.javabotblockapi.requests
-
Class used to perform GET actions on the
/api/bots/:id
endpoint. - GetBotAction(boolean, String) - Constructor for class org.botblock.javabotblockapi.requests.GetBotAction
-
Constructor to get an instance of GetBotAction.
- GetBotAction(boolean, String, String) - Constructor for class org.botblock.javabotblockapi.requests.GetBotAction
-
Constructor to get the instance of GetBotAction.
- GetBotAction(String) - Constructor for class org.botblock.javabotblockapi.requests.GetBotAction
-
Constructor to get an instance of GetBotAction.
- getBotId() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns the bot id that was rate limited.
- getBotInfo(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the full information of a bot.
- getBotInfo(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the full information of a bot.
- getBotListInfo(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the various bot lists.
- getBotListInfo(Long, String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the specified bot list.
- getBotListInfo(Long, Site) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the specified bot list.
- getBotListInfo(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the various bot lists.
- getBotListInfo(String, String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the specified bot list.
- getBotListInfo(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the information from the specified bot list.
- getBotWidgetUrl(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL used to display a widget (custom image) of the bot.
- getBotWidgetUrl(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL used to display a widget (custom image) of the bot.
- getDelay() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns the delay - in milliseconds - you have to wait to perform a request again.
- getDescription(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the description of the bot list.
- getDescription(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the description of the bot list.
- getDiscordInvite(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the invite to the Discord of the bot list.
- getDiscordInvite(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the invite to the Discord of the bot list.
- getDiscriminator(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the discriminator (The 4 numbers after the # in the username) of the bot.
- getDiscriminator(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the discriminator (The 4 numbers after the # in the username) of the bot.
- getFeatures(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the features of the bot list.
- getFeatures(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the features of the bot list.
- getFilteredLists(String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Returns the filtered JSON of bot lists.
- getGitHub(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the GitHub link of the bot.
- getGitHub(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the GitHub link of the bot.
- getIcon(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL displaying the current Icon of the bot list.
- getIcon(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL displaying the current Icon of the bot list.
- getId(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the id of the bot list.
- getId(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the id of the bot list.
- getIp() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns the ip that was rate limited.
- getLanguage(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the primary language of the bot list.
- getLanguage(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the primary language of the bot list.
- getLibrary(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the currently used library of the bot.
- getLibrary(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the currently used library of the bot.
- getList(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the information of a specific bot list.
- getList(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the information of a specific bot list.
- GetListAction - Class in org.botblock.javabotblockapi.requests
-
Class used to perform GET actions on the
/api/lists
and/api/lists/:id
endpoints. - GetListAction(boolean, String) - Constructor for class org.botblock.javabotblockapi.requests.GetListAction
-
Constructor to get an instance of GetListAction.
- GetListAction(boolean, String, String) - Constructor for class org.botblock.javabotblockapi.requests.GetListAction
-
Constructor to get the instance of GetListAction.
- GetListAction(String) - Constructor for class org.botblock.javabotblockapi.requests.GetListAction
-
Constructor to get an instance of GetListAction.
- GetListAction.ApiField - Enum in org.botblock.javabotblockapi.requests
-
Enum containing the different API fields a bot list may have.
- getLists(String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the information about all the currently listed bot lists.
- getMessage() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns a formatted message displaying the various information returned in this exception.
- getName(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the name of the bot.
- getName(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the name of the bot.
- getName(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the name of the bot list.
- getName(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the name of the bot list.
- getOAuthInvite(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the OAuth invite link of a bot.
- getOAuthInvite(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the OAuth invite link of a bot.
- getOwners(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets an ArrayList with the owner ids of the bot.
- getOwners(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets an ArrayList with the owner ids of the bot.
- getOwners(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the owners of a bot list.
- getOwners(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the owners of a bot list.
- getPrefix(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the prefix of the bot.
- getPrefix(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the prefix of the bot.
- getRoute() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns the route on which the bot was rate limited.
- getServerCount(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the server count of the bot.
- getServerCount(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the server count of the bot.
- getSite() - Method in enum org.botblock.javabotblockapi.Site
-
Gives the ID of the selected site, which is used as ID in the BotBlock.org API.
- getSupportLink(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the support link (i.e.
- getSupportLink(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the support link (i.e.
- getTimeAdded(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the UNIX timestamp of when the bot list was added to BotBlock as Integer.
- getTimeAdded(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the UNIX timestamp of when the bot list was added to BotBlock as Integer.
- getTokens() - Method in class org.botblock.javabotblockapi.BotBlockAPI
- getUpdateDelay() - Method in class org.botblock.javabotblockapi.BotBlockAPI
- getUrl(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL for the bot list's website.
- getUrl(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Gets the URL for the bot list's website.
- getWebsite(Long) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the website of the bot.
- getWebsite(String) - Method in class org.botblock.javabotblockapi.requests.GetBotAction
-
Gets the website of the bot.
- GLENNBOTLIST_XYZ - org.botblock.javabotblockapi.Site
I
- isDefunct(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Returns if the bot list is defunct.
- isDefunct(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Returns if the bot list is defunct.
- isDiscordOnly(String, String) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Returns if the bot list is only for Discord bots.
- isDiscordOnly(String, Site) - Method in class org.botblock.javabotblockapi.requests.GetListAction
-
Returns if the bot list is only for Discord bots.
M
- MYTHICALBOTS_XYZ - org.botblock.javabotblockapi.Site
N
- notEmpty(String, String) - Static method in class org.botblock.javabotblockapi.requests.CheckUtil
- notEmpty(Map<?, ?>, String) - Static method in class org.botblock.javabotblockapi.requests.CheckUtil
O
- org.botblock.javabotblockapi - package org.botblock.javabotblockapi
-
Contains the
BotBlockAPI
class for setting up bot tokens and sites. - org.botblock.javabotblockapi.annotations - package org.botblock.javabotblockapi.annotations
-
The different annotations used to mark various Objects with it.
- org.botblock.javabotblockapi.exceptions - package org.botblock.javabotblockapi.exceptions
-
Contsins custom exceptions this API may throw.
- org.botblock.javabotblockapi.requests - package org.botblock.javabotblockapi.requests
-
Contains classes used for various GET and POST requests.
P
- PlannedRemoval - Annotation Type in org.botblock.javabotblockapi.annotations
-
Annotation to mark an Object to be planned for removal.
- PostAction - Class in org.botblock.javabotblockapi.requests
-
Class used to perform POST requests towards the
/api/count
endpoint. - PostAction(String) - Constructor for class org.botblock.javabotblockapi.requests.PostAction
-
Constructor to get an instance of PostAction.
- PostAction(String, String) - Constructor for class org.botblock.javabotblockapi.requests.PostAction
-
Constructor to get an instance of PostAction.
- postGuilds(Long, Integer, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Posts the guild count with the provided bot id.
- postGuilds(String, Integer, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Posts the guild count with the provided bot id.
- postGuilds(JDA, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Posts the guild count provided through the
JDA instance
. - postGuilds(ShardManager, BotBlockAPI) - Method in class org.botblock.javabotblockapi.requests.PostAction
-
Posts the guild count from the provided
ShardManager instance
.
R
- RatelimitedException - Exception in org.botblock.javabotblockapi.exceptions
-
Indicates that the Wrapper got rate limited by the BotBlock API.
- RatelimitedException(String) - Constructor for exception org.botblock.javabotblockapi.exceptions.RatelimitedException
- replacements() - Method in annotation type org.botblock.javabotblockapi.annotations.DeprecatedSince
-
Optional String indicating a possible replacement method or field to use.
S
- setAuthTokens(Map<String, String>) - Method in class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Sets the provided Map as the new Map.
- setUpdateDelay(Integer) - Method in class org.botblock.javabotblockapi.BotBlockAPI.Builder
-
Sets the update delay (in minutes) for the auto-posting.
- Site - Enum in org.botblock.javabotblockapi
-
Enum class containing all sites currently supported by BotBlock.org.
- SPACE_BOT_LIST_ORG - org.botblock.javabotblockapi.Site
- STRING_SERVER_COUNT - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Name of the field for the server count.
- STRING_SHARD_COUNT - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Name of the field for the shard count.
- STRING_SHARD_ID - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Name of the field for the shard id.
- STRING_SHARDS - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Name of the field for the shards.
T
- toString() - Method in exception org.botblock.javabotblockapi.exceptions.RatelimitedException
-
Returns this class formatted to a String.
- toString() - Method in enum org.botblock.javabotblockapi.requests.GetListAction.ApiField
U
- URL_ALL - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
URL to GET all listed bots on a bot list.
- URL_DOCS - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
URL to view the API documentation of the bot list
- URL_GET - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
URL to GET information about a single bot listed on the bot list.
- URL_POST - org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
URL to POST the server count to a bot list.
V
- valueOf(String) - Static method in enum org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum org.botblock.javabotblockapi.Site
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum org.botblock.javabotblockapi.requests.GetListAction.ApiField
-
Returns an array containing the constants of this enum type, in the order they are declared.
- values() - Static method in enum org.botblock.javabotblockapi.Site
-
Returns an array containing the constants of this enum type, in the order they are declared.
- version() - Method in annotation type org.botblock.javabotblockapi.annotations.DeprecatedSince
-
Since what version this method or field is deprecated.
- version() - Method in annotation type org.botblock.javabotblockapi.annotations.PlannedRemoval
-
The version for when the annotated object will be removed.
- VULTREX_IO - org.botblock.javabotblockapi.Site
-
Deprecated.Renamed to
DISCORDBOTS_CO
Planned removal in v5.2.5
W
- WONDERBOTLIST_COM - org.botblock.javabotblockapi.Site
Y
A B C D E G I M N O P R S T U V W YAll Classes All Packages


11
1 A reed was given to me to use like a measuring rod. I was told, "Get up and measure the temple of God and the altar, and those who worship in it.
2 But do not measure the courtyard outside the temple, for it has been given over to the Gentiles. They will trample the holy city for forty-two months.
3 I will give my two witnesses authority to prophesy for 1,260 days, clothed in sackcloth."
4 These witnesses are the two olive trees and the two lampstands that have stood before the Lord of the earth.
5 If anyone chooses to harm them, fire comes out of their mouth and devours their enemies. Anyone who wishes to harm must be killed in this way.
6 These witnesses have authority to close up the sky so no rain will fall during the time when they prophesy. They have power to turn the waters to blood and to strike the earth with every kind of plague whenever they wish.
7 When they will have finished their testimony, the beast that comes up from the deep and endless pit will make war against them.  Great fear will fall on those who see them.
12 Then they will hear a loud voice from heaven say to them, "Come up here!" And they will go up into heaven in a cloud, while their enemies look on.
13 At that hour there will be a major earthquake and a tenth part of the city will collapse. Seven thousand people will be killed in the earthquake and the survivors will be terrified and give glory to the God of heaven.
14 The second woe is past. Watch! The third woe is coming quickly.
15 Then the seventh angel sounded his trumpet, and loud voices spoke in heaven and said,
"The kingdom of the world has become the kingdom of our Lord and of his Christ.
He will reign forever and ever."

The time has come for the dead to be judged
and for you to reward your servants the prophets,
the saints, and those who feared your name,
both the unimportant and the mighty.

19 Then God's temple in heaven was opened and the ark of his covenant was seen within his temple. There were flashes of lightning, rumblings, crashes of thunder, an earthquake, and a great hailstorm.


Whiteboard
This is a shared whiteboard with everyone who is visiting this site. It is fully P2P so nothing is stored on the server. Content is saved as long as someone keeps a browser window open. Colors and names are randomly generated. You can also drop images and video onto the whiteboard. Powered by Webtorrent.io.


--- layout: page title: About header-img: "img/about-bg.png" ---
I like to learn, make music and art as well as research interesting topics.


Weekly Meeting: 2013-06-04
6th June 2013: EC2, shared ring performance and outreach
Attendees: Jonathan Ludlam, Vincent Bernardoff, Dave Scott, Anil Madhavapeddy, Amir Chaudhry, Mike McClurg
Mirari
Excellent progress on Mirari from Vincent, who will have patches available soon for merging.
Anil: main last change we need to make is to stop Mirari from requiring OPAM switch. This is because it forces you to compile a new compiler (takes a long time) just to build a Xen kernel. Instead, we can use the OPAM constraint solver to do the selection of packages rather than environment variables.
All agreed (Thomas is on vacation but agreed this would work earlier on a cl-mirage mail thread). This should be the last breaking change in the build infrastructure before the release, so we’ll aim to integrate by next week.
Testing and EC2 integration
Vincent: There is no automatic way to set up a free EC2 for Mirage with micro. You have to do it by hand each time you want to upload a kernel.
Anil: We also need reliable shutdown hooks for EC2, or risk spending lots of cash by mistake!
Balraj has been playing with performance to optimise the network stack. Some questions about perf and Xen: there doesn’t appear to be a perf-xen mode although there is a perf-kvm mode. OCaml 4.1-trunk has really good CFI stubs that make using perf almost pleasant.
nil: Tom Ridge is just starting work on building a verified filesystem in OCaml, so we had a meeting about that with Thomas Gazagnaire last week. Very exciting work, as it should be easy to hook into Mirage.
Some discussion of the RPC mechanism for Vincent’s testing infrastructure. Consensus is that instead of using Xenstore as an RPC mechanism, we should use an explicit libvchan communication channel instead. This would let us build command-line tools for vchan that make it easier to manipulate the kernel.
Balraj also raised the question of TCP and intended behaviour of the ring interface with fragmentation. His tests run very small packets (below TCP minimum size) and deadlock if the ring doesn’t have enough free slots. Dave/Anil think that we need to lock the shared rings to queue up producers (so that we dont end up with out-of-order packets on the wire) and also provide a new function to request a reservation of multiple contiguous ring slots. Dave will look into this further.
Outreach
Anil: Preparing for the release in July, we need to think about what blog posts people would be interested in.
Anil: A really good blog post for Dave would be how to build a userspace block device in pure OCaml that provides storage to VMs. For a memory device, we could pretty much build this from the OCaml toplevel in dom0.
Jon: I like the idea of getting people to do screen-casts.
Anil: Aim for one high quality blog post from everyone every two months. Should be able to get a post up every two weeks then. Every post should think about its target (level of Xen familiarity, etc). Lars from Xen.org could advise on this.
Merging to trunk
Vincent: Can someone look at Unix back-end and code-review the shift to multiple processes for UNIX.
Anil: on the queue for this week, and will definitely have it merged by next week’s meeting. This will very likely break trunk for a little while since I’ll do the OPAM rearrangement at the same time.
SSL libraries
Anil: Now that ctypes is released, we should look at SSL alternative bindings again. Prashanth pointed out axTLS, which is a BSD licensed alternative to MatrixSSL and therefore worth looking into more (but it’s API isn’t quite as agnostic to sockets as MatrixSSL, sadly, but should still work fine as sockets can be nonblocking in the axTLS world).


Physics 7B is the second of the two physics classes required for chemistry and chemical engineering majors. This class covers a lot of material and focuses on three major physics concepts: thermodynamics, electricity, and magnetism. There are two, two-hour discussions each week and a total of five labs in the course that need to be completed. Homework is done on MasteringPhysics.
Physics 7A, Math 1A, Math 1B, and Math 53 (can be taken concurrently)
3 hours of lecture per week and 4 hours of lab section per week.
This class is required for chemical engineering and chemistry majors. It is offered both spring and fall semesters. This is the second physics course in the 7 series. It is usually taken in the first semester of sophomore year, but can be taken anytime beforehand as well.
Physics 7B is taught similarly to 7A with very similar discussion and lab structure and the use of MasteringPhysics. Students who skipped out of 7A may struggle slightly to adjust to the course, however it is totally doable.
The labs are relatively easy and done completely during the period. The difficulty of the quizzes depends on the GSI. MasterPhysics solutions can be easily accessible online, but I highly recommend doing the problems first to ensure understanding of the material. Be prepared to apply calculus to your problems.
Students generally find the thermodynamics portion of the course easier than the electricity and magnetism portion.
Written by: Justin Yeung
Last edited: Fall 2018


This guide will help you migrate from 1.x to 2.x. If you are first time using nya-bootstrap-select , just ignore this section.
The collection which are used to populate options and the model which are used to work with selection results don't changed.
Both array and object collection are supported by the new directive nya-bs-option
. See the document to learn usage.
model of multiple selection is array and model of single selection is object or string as.
The most changes are the template. <select>
is totally deprecated. you need to use new directive nya-bs-select
to build your select.
If you are using static options you should use nya-bs-option
class and value
attribute to identify your option.
otherwise. the nya-bs-option
directive must be used to populate options.
In the 1.x version:
<select class="nya-selectpicker" ng-model="model" ng-options="option in options">
...
</select>
In the 2.x version:

{{ option }}

Note that the element of
nya-bs-select
directive can be any block level element. To make your html semantically. we suggest use some list container tag, like ul or ol.Anyway you can use
<nya-bs-select>
or

and other form as well.The
nya-bs-option
is a different case. Which its element should aways be

element, in order to utilize the bootstrap class. So is its children
, it's also a required direct child ofnya-bs-option
.
For group and other usage changes, see the examples.
If you have used some features of Bootstrap-select in the 1.x version. Those features may not be supported or have a different implementation at current version.
dropup this feature, in fact, can be supported in current version. But I found the implementation in Bootstrap-select is also not very functional. It need user to click
the dropdown-toggle button at least one time to calculate the height of the dropdown-menu. Then a scroll event listener is attached to calculate the distance between dropdown-toggle button and the edge of viewport.
automatically add dropup
class to dropdown-menu-container. This implementation may impact the performance when too many controls in a page.
to use this feature. manually add dropup
class to the nya-bs-select
directive if you want a drop-up menu.
data-header this feature may be supported in future version.
data-max-options this feature may be supported in future version.
data-width this feature may be supported in future version.
data-style
In current version, if you want to make a different look of the dropdown-toggle button, just add the corresponding class to the nya-bs-select
directive.
You can add these classes: btn-primary
btn-info
btn-success
btn-warning
btn-danger
. btn-inverse
are not supported.
show-tick
You don't need this class any more, because the select options are built by yourself. you can get full control of what content a select should have. so you need to add tick by yourself.
Add
inside
tag which is a direct child of nya-bs-option
directive. make sure the content of option is before the check-mark
element.
...

{{ option }}

style an option, custom HTML of option, data-icon, showSubText These features are made by yourself. See the examples.
disable an option, disable a group. just add a class to nya-bs-option which you want to disable. if you need to disable a group, you have to disable its every element.
disable a select control
unlike Bootstrap-select to just add an attribute. you need to give disabled
a expression which can be evaluated to true or false. when it's true, the control will be disabled.
otherwise it will be enabled again.


FoodNoms for iOS offers robust food, calorie, macros tracking with an Apple-like UI
by Michael PotuckA new food, calorie, and macros tracking app for iPhone and iPad has arrived with a compelling design, great Apple integration, and more. FoodNoms is available now and customers can even try it out for free without signing up.
FoodNoms includes rich features like HealthKit support, iCloud syncing, intermittent fasting tracking, Siri and Siri Shortcuts support, and much more (h/t Kyle Seth Gray). It also features a wonderfully clean and simple Apple-like user interface.
Another great aspect is FoodNoms allows new customers to try out the app for free without signup. For the full feature set, FoodNoms runs $2/month or $17/year.
FoodNoms highlights:
- Log what you eat with a clean, beautiful interface
- Track your progress with customizable goals for calories, macros (protein, fat, carbohydrates), cholesterol, fiber, sugars, water, alcohol, and caffeine
- Log rich nutritional data (including calories, macros, vitamins, minerals, and more) to the Health app
- Log food and check on your goals with Siri and Siri Shortcuts
- A food database with hundreds of thousands of food items with rich nutritional data from trustworthy sources
- Find your food fast with an as-you-type search interface
- Scan nutrition labels to quickly log accurate data for packaged foods
- Leave the food scale at home and instead record the accuracy of your recordings
- Track intermittent fasts automatically
- Use Active Energy data from the Health app (i.e. the Move ring on the Apple Watch) to automatically adjust your calorie goal
- See the breakdown of your day in terms or which meals and foods had the most impact
- Save time using Smart Suggestions, which recommends foods to log based on your food habits
- iPad support
- Dark mode
- Sync your data to multiple devices securely with iCloud
FoodNoms is a free download from the App Store for iPhone and iPad.
FTC: We use income earning auto affiliate links. More.


# -*- coding: utf-8 -*-
API call to
Sections
API call to obtain a list of silos owned by the user, create new silos, delete silo, manage users and alter silo metadata
Controller: datasets action: siloview
GET: Obtain a list of datasets in a silo
POST: Create new dataset
| id | {id to create} |
| embargoed | {true|false} (optional). If the parameter is not supplied, a default value of false will be used. |
| embargoed_until | {ISO8601 date} (optional). If embargoed = true and no date has been supplied, a default time delta of 70 years will be used |
| title | {(Optional)} |
PUT, DELETE: NOOP
API call to obtain information about the dataset, create a dataset, modify the datasets embargo information, post files to the dataset and delete the dataset. All of this is performed on the latest version of the dataset.
Controller: datasets action: datasetview
GET: Obtain information about the dataset
POST: Create new dataset. Dataset id doesn't exist. (As POST /{silo}/datasets id=...)
| embargoed | {true|false} (optional). If the parameter is not supplied, a default value of false will be used. |
| embargoed_until | {ISO8601 date} (optional). If embargoed = true and no date has been supplied, a default time delta of 70 years will be used |
| title | {(Optional)} |
POST : change embargo information in dataset. On success, version is incremented.
| embargo_change | true (used as a convenience parameter for html forms) |
| embargoed | {true|false} |
| embargoed_until | {ISO8601 date} (optional). If embargoed = true and no date has been supplied, a default time delta of 70 years will be used |
POST: Upload file to root directory. On success, version is incremented.
| file | Multipart-encoded (HTML) file upload |
| filename | {Optional filename for upload} |
POST: Text upload. Convenience function for HTML to update/create text files. On success, version is incremented.
| text | {UTF text to store} |
| filename | {desired filename} |
DELETE Deletes the dataset id
PUT NOOP
API call to obtain information about a particular version of the dataset
Controller: datasets action: datasetview_vnum
GET: Obtain information about a particular version of the dataset
API call to view files / contents of a folder and add, update or delete a file. All of these actions are performed on the latest version of the dataset.
Controller: datasets action: itemview
GET: view the contents of subpath (subpath could point to a file or a folder)
PUT: used to add content to a file.
If the subpath points to manifest.rdf, the content is munged with exisitng metadata.
If the subpath does not exist, a file is created in the same name as the filename in path including all the intermediate directories in the subpath (using os.makedirs) and the contents are added to that file.
On success, version is incremented.
POST Upload a file within the subpath
If the subpath + filename parameter points to manifest.rdf, the content is munged with exisitng metadata.
If the subpath + filename parameter does not exist, the file is added as filename into subpath. The intermediate directories in the subpath (using os.makedirs) are created if they do not exist.
On success, version is incremented.
| file | Multipart-encoded (HTML) file upload |
| filename | {Optional filename for upload}. When provided, it is only used if subpath points to an existing directory. |
DELETE: deletes files or directories within the dataset. It also performs recursive deletes (directories with sub-directories within them).
On success, version is incremented.
API call to obtain a file or view the contents of a folder, for a particular version of the dataset
Controller: datasets action: itemview
GET: Obtain files or view the contents of a directory for a particular version of the dataset.
POST, PUT, DELETE: NOOP


HOPE FLOATS 1998 A film review by Timothy Voon Copyright 1998 Timothy Voon 2 :-) :-) out of 4 for the abandoned woman
Cast: Sandra Bullock, Harry Connick Jr., Gena Rowlands, Mae Whitman, Michael Pare, Cameron Finley Director: Forest Whitaker Producer: Linda Obst Screenplay: Steven Rogers
Sandra has finally found a movie of substance, after such disappointing efforts like SPEED 2 and IN LOVE AND WAR. These epic disasters were rather unfortunate in view of previous successes like SPEED and WHILE YOU WERE SLEEPING. This otherwise, likeable, always charming, bankable star, has decided to dig deeper this time round, and found something that is unlikely to stink or sink. From this aspect itself, we can literally say that yes, HOPE does FLOAT, even though your last two movies were big box office duds. In an age when divorce and separation is on the rise, comes a movie about surviving the holocaust of family break-ups. This is a story of an ex-beauty queen, Birdee Pruit (Bullock), who is dumped by her spineless husband for her best friend. The point hits home when all is revealed on a talk show, unbeknownst to the victim. Carrying wounded pride, Birdee wanders home to her mother to nurse her wounds. This is a journey of a woman who initially wallows in self-pity and depression, only to survive the crisis by reclaiming her dignity and more importantly, retaining the love of her daughter.
Yes, this deep and mushy stuff, but at least it makes a valiant attempt to address the issues at hand. Sandra will not lose fans by smiling, crying and laughing through problems relating to difficult mothers, angry daughters and unfaithful husbands.  Whilst Michael Pare takes a break from C grade pics and appears likeable as the unfaithful husband.
Overall, a thoughtful movie, that says that HOPE FLOATS when you are holding on to the last straw. Things tend to get better even when you're hurting badly on the inside. Don't mope about, don't blame yourself and don't keep hoping your unfaithful husband will come back to you. Take the remains of the day and do something worthwhile. Somehow you will survive the awful mess of a marriage breakup.
Timothy Voon e-mail<EMAIL_ADDRESS>Movie Archives http://us.imdb.com/M/reviews_by?Tim+Voon Movies In Melbourne http://www.labyrinth.net.au/~pfowler/week/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Definition: One or more people participate in a competition where the primary activity is riding a horse
Explication: There are many types of competitive horseback riding activities, which may be practiced by people of almost any age, from very young children to adults. The objective of the competition may be to be the fastest, as in horse racing or barrel racing, or to complete activities or horse movements with proper form from horse and rider, as in dressage and reining, or complete activities with other animals, as in roping and cutting.
In competitions involving form and movements of the horse, riders may perform a planned routine one at a time, or riders may have their horses perform movements as they are called out by an announcer. In show jumping, riders compete one at a time and direct their horse to jump over each hurdle in a course, following a pre-determined order.
During all the relevant types of competitions, the horse wears a leather seat on its back with a strap going underneath its belly (the saddle), which is where the rider sits; a harness on its head (the bridle), which connects to either long straps or a long loop of rope or leather (the reins) held by the rider and used to help control the horse's behavior. The category of objects such as the saddle and bridle that are worn by the horse are called "tack". Horses are trained to respond to verbal cues, (e.g. clicking the tongue, saying "whoa"), and non-verbal cues (e.g. squeezing or kicking the horse's sides to make it move forward, pulling on (or moving) one or both reins to stop or steer). During horse racing, the jockey (the rider), often hits the horse's sides with a riding crop or whip to encourage it to go faster. Besides racing style, there are two main styles of riding/rider dress/horse tack: Western and English. Western style for the rider is dressing in jeans, cowboy boots, and often a cowboy hat. The saddle used in Western style riding normally has a saddle horn, to which a rope (such as a lasso) can be attached and the back of the seat is normally fairly high. The saddle used in English style riding never has a saddle horn and the seat is much flatter.
Any kind of horse racing would be relevant for this event, for if there are at least two horse and rider pairs racing it is automatically a competition. For other types of riding events such as jumping and barrel racing, it can be sometimes hard to determine if the riding is being done as part of competition or practice. Some important indicators of riding in competition include the rider's dress (e.g. if the rider is wearing an English riding helmet with jeans and a t-shirt, it is probably not a competition), whether there are spectators, whether the person is wearing a competitor number on their back and/or arm, whether there is an announcer, and whether the rider seems to be performing a full routine (e.g. if the rider comes out, goes over two of many jumps and then stops the horse and walks, it is probably not a competition).


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SHADRACH
Reviewed by Harvey Karten, Ph.D. Columbia Pictures Director: Susanna Styron Writer: Susanna Styron, William Styron, Bridget Terry Cast: Harvey Keitel, Andie MacDowell, John Franklin Sawyer, Scott Terra, Daniel Treat, Monica Bugajski, Darrell Larson, Deborah Hedwall, Tom Bower, John Sawyer, Martin Sheen
If this story's narrator were alive today, he'd be 73 years old, a certainty that's difficult to accept since we see him as an adorable, sensitive lad just ten years old in the Depression-torn America of 1935. "Shadrach," told from the point of view of little Paul Whitehurst (Scott Terra), deals with the boy's encounter with a 99-year-old black man who has walked all the way from Alabama to rural Tidewater, Virginia, to request burial in the land of his birth. The tale is a quietly moving one based on a short story by William Styron which was published in Esquire magazine in 1978 and which has been carefully and decorously adapted for the screen by his daughter, Susanna, with the help of Bridget Terry. Though this is a simple Depression story, plainly told and without the complexities of John Steinbeck's "The Grapes of Wrath," it has a universal resonance that goes beyond its unadorned details. The title figure, Shadrach (John Franklin Sawyer), is going home to die, and by communicating his burial wishes provides for the young people a graphic lesson: death is a part of life and is not to be feared.
The tale opens on the Whitehurst household in the Tidewater, Virginia of 1935, one which appears untouched by the Depression. Paul dad (Darrell Larson) and mom (Deborah Hedwall) enjoy the services of a cook, they all dress for dinner, and use their shiny, new motorcar to enjoy regular vacations. Yet they are not snobs, allowing their youngster to play with his best friend, Little Mole (Daniel Treat), who lives in ramshackle quarters on the other side of the tracks. Little Mole's father, Vernon Dabney (Harvey Keitel) and mom, Trixie (Andie MacDowell), earn their scant living by selling bootleg liquor, and Vernon blames his financial problems, strangely enough, on President Franklin D. Roosevelt whose New Deal does nothing for him. As he describes his woes, cursing intermittently, photographer Hiro Narita's camera pans the warren, formerly part of a lush plantation, to observe several pregnant young women; a quick, distant look at a couple making love in a beat-up car; and the adult inhabitants guzzling bottled beer by the case. When the 99-year-old Shadrach appears, he does not make his presence felt with a melodramatic flourish, but seems to materialize out of nowhere. In barely audible tones, he informs the Dabney family of his desire to be buried on their land and is looked after sympathetically by Trixie, Vernon, Paul, and by the Dabney youngsters. Informed by the sheriff that burying a black man on Dabney's own land is illegal in Virginia, the Dabneys employ the services of a black undertaker in a nearby county whose $35 fee is considered exorbitant. The family must resort to imaginative tactics to fulfill Shadrach's wish.
"Shadrach" is wonderfully acted, particularly by Andie MacDowell as a beer-swilling, earthy wife who loves her family, tolerates her poverty, and shows particular sensitivity to the plight of the stranger in their midst. The story is directed with no sense of urgency by Susanna Styron, who had thought about making this movie since her dad published the tale in 1978. Her dialogue is anything but pressing: Shadrach: "I'd like to see the mill pond." Trixie: "How come you want to see the mill pond?" Shadrach: "I want to see the mill pond."
Paul takes in this dialogue, cleverly figuring that Shadrach does not long for his slave days but wants only to recapture his innocence, the one untroubled part of his life. In one compelling scene, the Dabney children are skinny-dipping in the pond when they turn into Shadrach as a young boy, swimming likewise with his friends in the very same water, giving the whole narrative a feeling for history.
While the targeted audience for "Shadrach" is probably children of the ages of 8-15, the film is a heart-rending, even stirring story for mature adults as well. The movie is in no way marred by literary devices, particularly Martin Sheen's occasional narration. At times it runs the risk of seeming a defense of slavery, in that Shadrach longs to be buried on the land in which he was born to servitude and insists that he considers himself a Dabney, a part of the plantation-owning family. When Vernon Dabney exclaims that some black people were better off before they were emancipated in 1865, his statement is far from laughable. After all, he points out, the slaves were taken care of better than they were after they took on paid jobs as sharecroppers: they were buried right on the plantation after their death under a system which became ironically illegal after the liberation. That family members could be wrenched apart by being separately sold off is indicated only briefly, in passing. By the conclusion of the tale, we can see why ten-year-old Paul's rendezvous with Shadrach became the most consequential experience of his life.
Rated PG-13. Running time: 88 minutes. (C) Harvey Karten 1998
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Babe: A Pig in the City (1998)
A Film review by Mark O'Hara
A Pig in the City picks up where Babe left off, with Arthur Hoggett (James Cromwell) quietly accepting a trophy for his champion sheepdog, er, pig. He hugs his wife, turns down myriad offers for public appearances, and falls down a well, sustaining injuries that put him out of most of the movie.
This is the first risk taken by the sequel, and it's one of many reasons why A Pig in the City is so successful.
The storybook qualities are still here: along with the Hoggetts' generously thatched cottage, we see a quirky hotel housing two humans and a menagerie of animals. The landscapes are still beautiful and cozy, skies glowing like canvases by N. C. Wyeth. Of course the animals talk. And the city of the title is "just to the left of the twentieth century," its landmarks icons from real cities -- the Seattle Space Needle, the "Hollywood" letters, the Eiffel Tower, the Sydney Opera House (the film was shot in Australia, after all). Accordingly, the sets, costumes and lighting accentuate the magical effects.
What is so remarkable about this sequel is that it differs so much from Babe. Yes, many of the same characters are here - indeed, I wanted more of the taciturn Hoggett, the sheepdogs Fly and Rex, and others - but this new zoo of a cast is just as charming, and even more uproarious in its adventures. This film proves that strong sequels, and installments of series films, rely only briefly on their original incarnations, and stand on their own as solid and engaging stories.
Because Mr, Hoggett is laid up and the bank is threatening foreclosure, Mrs. Hoggett brainstorms and decides to travel to the city for a grand state fair. There she will display "Pig," which is Babe's name to humans, and receive enough money to save the farm. As the two board a plane that heads into the sky, there are captivating scenes of Ferdinand the duck (voice of Danny Mann) in hot pursuit, asking advice from a flock of wise pelicans. When she is detained at the airport, Mrs. Hoggett not only misses the state fair but fails to locate a hotel that will accept her particular type of pet. In the face of a stranger who tells her of a lodging where she and Babe/Pig will be safe, we see a motif that contributes again and again to the humor of the story. Another artifice surfaces when Mrs. Hoggett stumbles a second time into trouble with the law, leaving Babe alone with animals that at first seem menacing but turn out charming. The bulk of the plot involves Babe's relationships with the animals that inhabit, in violation of city ordinances, this storybook hotel. Will the creatures be saved from the animal control agency that evicts them? Will Babe ever reunite with Esme Cordelia Hoggett, let alone with Arthur?
As Mrs. Hoggett, Magda Szubanski is outstanding. In the original she was so much set decoration, a prudish Mrs. Sprat who talks two miles a minute and concerns herself with fattening the protagonist for Christmas. In this story we see her determination developed and focused on those keeping her apart from Babe and from her husband. She is wonderfully game in going after the endangered animals, paired with the drink-of-water woman (Mary Stein) who owns the hotel. And when she dons an inflatable clown suit and bounces all over a ballroom in pursuit of Babe, we enjoy a series of belly laughs. This was my son's favorite scene.
My daughter's favorite part involves a chimp named Zootie (Glenne Headley). She is the mate of a punkish chimp voiced by the comedian Steven Wright. Both lend their characters quirky and likable personalities. Moreover, the animal actors perform memorably. Along with the chimps there are multitudinous dogs (one with wheels for back legs, a creature that nearly made me fall into the aisle of the theater!), cats, a goldfish, a modest orangutan, and the familiar chorus of mice. Congratulations to director George Miller, as well as to the many animal trainers, handlers and animatronics engineers. They assist the animals in telling large sections of the narrative; perhaps the animals are more important than even the humans.
Mickey Rooney has a cameo as the hotel owner's uncle, a strange little man named Fugly Flooms. He's a clown who entertains hospitalized children, and when he conscripts Babe to help in the magic act, very funny chaos follows. In a small reprise of his role as Farmer Hoggett, James Cromwell shows his experience; he's tolerant and venerable, and after watching the first movie, you just know the words with which Hoggett will end this one.
It's refreshing to see such a promising lineup of family entertainment in theaters for the holidays. My ten year-old oftens scans ratings and discovers no G- or PG-rated films playing anywhere. For the next several weeks we will be able to take her to several. Babe: A Pig in the City holds its own with all audiences, as suggested by several senior citizens sitting around us, also laughing with abandon. It may even garner for the franchise a second Academy Award nomination for Best Picture.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


The State of the State of the States
Name: Marcus Stucki; E-Mail<EMAIL_ADDRESS>UID: u0420655
Grouped by Topic


Reset password in Open Even API Server
Reset password in Open Even API Server
The addition of reset password API in the Open Event API Server enables the user to send a forgot password request to the server so that user can reset the password.
Reset Password API is a two step process. The first endpoint allows you to request a token to reset the password and this token is sent to the user via email. The second process is making a PATCH request with the token and new password to set the new password on user’s account.
Creating a Reset token
This endpoint is non JSON spec based API. A reset token is simply a hash of random bits which is stored in a specific column of user’s table.
hash_ = random.getrandbits(128)
self.reset_password = str(hash_)
Once the user completed the resetting of the password using the specific token, the old token is flushed and the new token is generated. These tokens are all one time use only.
Requesting a token
A token can be request on a specific endpoint POST /v1/auth/reset-password
The token with the direct link will be sent to registered email.
link = make_frontend_url(‘/reset-password’, {‘token’: user.reset_password})
send_email_with_action(user, PASSWORD_RESET,
app_name=get_settings()['app_name'], link=link)
Flow with frontend
The flow is broken into 2 steps with front end is serving to the backend. The user when click on forget password will be redirected to reset password page in front end which will call the API endpoint in the backend with an email to send the token.
The email received will contain the link for the front end URL which when clicked will redirect the user to the front end page of providing the new password. The new password entered with the token will be sent to API server by front end and reset password will complete.
Updating Password
Once clicked on the link in the email, the user will be asked to provide the new password. This password will be sent to the endpoint PATCH /v1/auth/reset-password
The body will receive the token and the new password to update. The user will be identified using the token and password is updated for the respective user.
try:
user = User.query.filter_by(reset_password=token).one()
except NoResultFound:
return abort(
make_response(jsonify(error="User not found"), 404)
)
else:
user.password = password
save_to_db(user)
References
-
Understand Self-service reset password https://en.wikipedia.org/wiki/Self-service_password_reset
-
Python - getrandbits() https://docs.python.org/2/library/random.html


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