Usage: 2d_sat.py [OPTIONS] IN_FILE Reads data from IN_FILE with the following format: comment_tree_id, commenter_id, voter_id, vote_type 1, 200, 3000, 1 1, 201, 3000, -1 ... or (in case of real data): r.reply_to r.message_id r.uid_alias r.vote_type 1 200 3000 UP 1 201 3000 DOWN ... Outputs a CSV which contains whether the comment/article-tree had a sign- rank of 2 or not. Redirect output to a file to save it. Options: --dims INTEGER What dimensional embedding to test for? --cpus INTEGER How many CPUs to use. --timeout INTEGER Time after which to give up (ms). --real / --no-real Assume format of real-data. --improve PATH Improve the results from the provided file. Will only run for `unknown` ids in the file. --context-id / --no-context-id Use the context_id instead of comment_tree_id to group comments into matrices. --nrows INTEGER Number of rows from the CSV to read. --help Show this message and exit. 

Usage: SR_fill.py [OPTIONS] IN_MAT_FILE OP_MAT_FILE OP_SC_FILE Read M_partial from IN_MAT_FILE and fill in the matrix using SR. The vote at (i, j) will be removed before filling in the matrix. The resulting matrix will be saved at OP_MAT_FILE and the given LOO entry will be placed along with the original vote in OP_LOO_FILE. Additionally, the best guess for the Sign Rank will be placed in OP_SC_FILE along with the source node which results in that. Options: -i INTEGER LOO i -j INTEGER LOO j --op-loo PATH Output path for the LOO. --seed INTEGER Seed which was used to create this test-case. --min-avg / --no-min-avg This flag will cause minimization of the average SC instead of worst case SC. Is much faster. --transpose / --no-transpose Whether to transpose the matrix or not. --help Show this message and exit. 

Usage: z3_fill.py [OPTIONS] IN_MAT_FILE OP_MAT_FILE OP_LOO_FILE Read M_partial from IN_MAT_FILE and fill in the matrix using Z3. The vote at (i, j) will be removed before filling in the matrix. The resulting matrix will be saved at OP_MAT_FILE and the given LOO entry will be placed along with the original vote in OP_LOO_FILE. Options: -i INTEGER LOO i -j INTEGER LOO j --sat_2d TEXT Is the matrix 2D-SAT w/o LOO? --sat_1d TEXT Is the matrix 1D-SAT w/o LOO? --seed INTEGER Seed which was used to create this test-cast. --guess INTEGER Whether to use the given guess {-1, +1} for doing LOO prediction; 0 means no guessing. --help Show this message and exit. 

Usage: low-rank-completion.py [OPTIONS] IN_MAT_FILE Read M_partial from IN_MAT_FILE and optimize the embeddings to maximize the likelihood under the logit model. Options: --dims INTEGER The dimensionality of the embedding. --seed INTEGER The random seed to use for initializing matrices, in case initial values are not given. --suffix TEXT Suffix to add before saving the embeddings. --init-c-vecs TEXT File which contains initial embedding of c_vecs. --init-v-vecs TEXT File which contains initial embedding of v_vecs. -i INTEGER Which i index to LOO. -j INTEGER Which j index to LOO. --alpha FLOAT Bound on the spikiness of M. --sigma FLOAT What is the variance of (logistic) noise to add. --lbfgs / --no-lbfgs Whether to use LBFGS instead of BFGS. --loo-output TEXT Where to save the LOO output. --loo-only / --no-loo-only Whether to only save the LOO output or whether to save the complete recovered matrix. --uv / --no-uv Whether to impose the alpha constraint on both U and V or on U.V^T. --verbose / --no-verbose Verbose output. --help Show this message and exit. 

Usage: soft-impute.py [OPTIONS] IN_MAT_FILE Read M_partial from IN_MAT_FILE and complete the matrix using soft-impute method. Options: --dims INTEGER The dimensionality of the embedding. --seed INTEGER The random seed to use for initializing matrices, in case initial values are not given. --suffix TEXT Suffix to add before saving the embeddings. -i INTEGER Which i index to LOO. -j INTEGER Which j index to LOO. --loo-output TEXT Where to save the LOO output. --loo-only / --no-loo-only Whether to only save the LOO output or whether to save the complete recovered matrix. --verbose / --no-verbose Verbose output. --help Show this message and exit. 

Usage: matrix-Z3-embed.py [OPTIONS] MAT_FILE Read the partial matrix in MAT_FILE and save embeddings for the file to `mat_file.commenters` and `mat_file.voters` file. Options: --dim INTEGER What dimension to use while splitting matrix. --timeout INTEGER What timeout to use (minutes). --help Show this message and exit.