Here’s some general information about sema.berlin.pipes.
| Field | Values |
|---|---|
| Package | sema.berlin.pipes |
| Title | ML-Based Prediction of Structural Condition of Individual Sewer Pipes |
| Version | 0.0.0.9000 |
| Authors@R | c(person(given = "Mathias", family = "Riechel", role = c("aut", "cre"), email = "mathias.riechel@kompetenz-wasser.de"), person(given = "Kompetenzzentrum Wasser Berlin gGmbH (KWB)", role = "cph")) |
| Description | This package allows to predict the structural condition of individual sewer pipes. It is based on the machine learning algorithm Random Forest and contains functions for model training, test, performance evaluation and deployment for pipe priorisation. |
| License | MIT + file LICENSE |
| URL | https://github.com/KWB-R/sema.berlin.pipes |
| BugReports | https://github.com/KWB-R/sema.berlin.pipes/issues |
| Encoding | UTF-8 |
| LazyData | true |
| Imports | caret (>= 6.0-85), data.table, dplyr, doSNOW, forcats, foreach, ggplot2 (>= 3.2.1), kwb.utils (>= 0.5.0), parallel, pROC, randomForest, RColorBrewer, sema.berlin, sema.berlin.app, scales, tidyr, yaml (>= 2.2.0), verification |
| Remotes | github::kwb-r/sema.berlin.app, github::kwb-r/sema.berlin.utils |
| Suggests | covr |
| RoxygenNote | 7.1.1 |
| RemoteType | github |
| RemoteHost | api.github.com |
| RemoteRepo | sema.berlin.pipes |
| RemoteUsername | KWB-R |
| RemoteRef | HEAD |
| RemoteSha | 8edc42f7da21b8799ad655417957074dfe7ad451 |
| GithubRepo | sema.berlin.pipes |
| GithubUsername | KWB-R |
| GithubRef | HEAD |
| GithubSHA1 | 8edc42f7da21b8799ad655417957074dfe7ad451 |
| NeedsCompilation | no |
| Packaged | 2021-05-28 11:38:01 UTC; mrustl |
| Author | Mathias Riechel [aut, cre], Kompetenzzentrum Wasser Berlin gGmbH (KWB) [cph] |
| Maintainer | Mathias Riechel <mathias.riechel@kompetenz-wasser.de> |
| Built | R 4.0.4; ; 2021-05-28 11:38:02 UTC; windows |
This section analyzes the recursive package dependencies of sema.berlin.pipes.
Nodes are packages. Edges point in the direction of dependence.
| node | outDegree | inDegree | numRecursiveDeps | numRecursiveRevDeps | betweenness | pageRank |
|---|
node | outDegree | inDegree | numRecursiveDeps | numRecursiveRevDeps | betweenness | pageRank |
|---|---|---|---|---|---|---|
| CircStats | 2 | 1 | 7 | 2 | 0.000 | 0.003 |
| Formula | 1 | 1 | 4 | 3 | 0.091 | 0.003 |
| Hmisc | 18 | 1 | 68 | 2 | 43.367 | 0.003 |
| KernSmooth | 1 | 1 | 4 | 5 | 0.000 | 0.004 |
| MASS | 5 | 5 | 5 | 18 | 10.333 | 0.007 |
| Matrix | 6 | 3 | 7 | 20 | 16.893 | 0.004 |
| ModelMetrics | 2 | 1 | 7 | 2 | 1.250 | 0.003 |
| R6 | 0 | 8 | 0 | 26 | 0.000 | 0.007 |
| RColorBrewer | 0 | 4 | 0 | 16 | 0.000 | 0.004 |
| Rcpp | 2 | 10 | 5 | 19 | 21.917 | 0.016 |
| SQUAREM | 0 | 1 | 0 | 6 | 0.000 | 0.003 |
| askpass | 1 | 1 | 1 | 5 | 5.000 | 0.006 |
| backports | 0 | 1 | 0 | 5 | 0.000 | 0.005 |
| base64enc | 0 | 3 | 0 | 14 | 0.000 | 0.005 |
| boot | 2 | 2 | 4 | 3 | 1.000 | 0.005 |
| bslib | 7 | 1 | 16 | 4 | 16.500 | 0.003 |
| cachem | 2 | 1 | 3 | 4 | 0.000 | 0.003 |
| caret | 15 | 1 | 70 | 1 | 20.650 | 0.003 |
| checkmate | 2 | 1 | 2 | 4 | 4.333 | 0.003 |
| class | 3 | 1 | 6 | 4 | 0.417 | 0.004 |
| cli | 2 | 1 | 6 | 20 | 0.333 | 0.003 |
| cluster | 4 | 1 | 4 | 3 | 0.516 | 0.003 |
| codetools | 0 | 1 | 0 | 4 | 0.000 | 0.004 |
| colorspace | 4 | 1 | 5 | 16 | 2.833 | 0.005 |
| commonmark | 0 | 1 | 0 | 4 | 0.000 | 0.003 |
| compiler | 0 | 1 | 0 | 2 | 0.000 | 0.004 |
| cowplot | 7 | 1 | 38 | 2 | 0.900 | 0.003 |
| crayon | 3 | 2 | 5 | 22 | 5.900 | 0.004 |
| crosstalk | 4 | 1 | 12 | 3 | 0.200 | 0.003 |
| curl | 0 | 1 | 0 | 4 | 0.000 | 0.004 |
| data.table | 1 | 4 | 5 | 6 | 2.450 | 0.005 |
| digest | 1 | 4 | 1 | 24 | 0.417 | 0.006 |
| doSNOW | 4 | 1 | 5 | 1 | 1.700 | 0.003 |
| dotCall64 | 0 | 1 | 0 | 4 | 0.000 | 0.004 |
| dplyr | 13 | 6 | 22 | 7 | 43.335 | 0.004 |
| dtw | 5 | 1 | 5 | 2 | 2.500 | 0.003 |
| ellipsis | 1 | 9 | 2 | 24 | 0.000 | 0.008 |
| evaluate | 1 | 1 | 5 | 5 | 0.000 | 0.003 |
| fansi | 0 | 2 | 0 | 20 | 0.000 | 0.004 |
| farver | 0 | 1 | 0 | 15 | 0.000 | 0.004 |
| fastmap | 0 | 2 | 0 | 5 | 0.000 | 0.005 |
| fields | 4 | 1 | 43 | 2 | 40.500 | 0.003 |
| forcats | 4 | 1 | 18 | 1 | 1.400 | 0.003 |
| foreach | 3 | 3 | 3 | 3 | 4.700 | 0.004 |
| foreign | 3 | 1 | 5 | 3 | 0.191 | 0.003 |
| fs | 1 | 1 | 5 | 6 | 3.000 | 0.004 |
| generics | 1 | 3 | 5 | 9 | 0.143 | 0.004 |
| ggplot2 | 13 | 10 | 37 | 13 | 306.016 | 0.009 |
| glue | 1 | 10 | 5 | 31 | 19.343 | 0.012 |
| gower | 0 | 1 | 0 | 3 | 0.000 | 0.003 |
This section analyzes the dependency relationships between functions within sema.berlin.pipes.
Nodes are functions. Edges point in the direction of dependence.
| node | type | isExported | coveredLines | totalLines | coverageRatio | meanCoveragePerLine | filename | betweenness | outDegree | inDegree | numRecursiveDeps | numRecursiveRevDeps | pageRank |
|---|
node | type | isExported | coveredLines | totalLines | coverageRatio | meanCoveragePerLine | filename | betweenness | outDegree | inDegree | numRecursiveDeps | numRecursiveRevDeps | pageRank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| allocate_class_weights | function | false | 0 | 7 | 0.000 | 0.000 | R/model_training.R | 0.000 | 0 | 4 | 0 | 4 | 0.034 |
| cc_to_character | function | false | 0 | 4 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| cc_to_integer | function | true | 0 | 2 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| check_input_variables | function | true | 0 | 40 | 0.000 | 0.000 | R/model_training.R | 0.000 | 5 | 0 | 10 | 0 | 0.019 |
| compare_models_plot | function | false | 0 | 9 | 0.000 | 0.000 | R/plotting.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| confine_data | function | true | 0 | 1 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| distinguish_concrete | function | false | 0 | 16 | 0.000 | 0.000 | R/other_functions.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| downsample | function | true | 0 | 9 | 0.000 | 0.000 | R/data_preparation.R | 4.000 | 1 | 4 | 1 | 4 | 0.034 |
| get_balance_weights | function | true | 0 | 3 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| get_complete_model_performance | function | true | 0 | 5 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 3 | 0 | 3 | 0 | 0.019 |
| get_confusion_matrix | function | true | 0 | 2 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 1 | 0.025 |
| get_deviation_at_network_level | function | false | 0 | 8 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 4 | 0.026 |
| get_false_negative_rate_sb1 | function | false | 0 | 3 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 4 | 0.026 |
| get_false_positive_rate_sb1 | function | false | 0 | 3 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 4 | 0.026 |
| get_max_equal_samplesizes | function | true | 0 | 4 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| get_metrics | function | true | 0 | 18 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 4 | 0 | 4 | 0.034 |
| get_metrics_sb1 | function | false | 0 | 20 | 0.000 | 0.000 | R/performance_metrics.R | 12.000 | 4 | 3 | 4 | 3 | 0.029 |
| get_percentage_sehr_schlecht_for_perc_ranked | function | true | 0 | 6 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 1 | 0.022 |
| get_true_positive_rate_sb1 | function | false | 0 | 1 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 1 | 0 | 4 | 0.026 |
| group_to_cohorts | function | true | 0 | 26 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| load_cctv_gis_data | function | true | 0 | 1 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| make_prediction | function | true | 0 | 6 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 5 | 0 | 5 | 0.040 |
| normalize | function | true | 0 | 1 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 1 | 0 | 1 | 0.036 |
| normalize_all | function | true | 0 | 3 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 1 | 0 | 1 | 0 | 0.019 |
| one_hot_encode | function | true | 0 | 2 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| one_sim_cross_validation | function | true | 0 | 25 | 0.000 | 0.000 | R/model_training.R | 0.000 | 3 | 0 | 4 | 0 | 0.019 |
| plot_condition_vs_prediction_of_top_percent | function | true | 0 | 7 | 0.000 | 0.000 | R/plotting.R | 0.000 | 1 | 0 | 1 | 0 | 0.019 |
| plot_condition_vs_prediction_ranks | function | true | 0 | 17 | 0.000 | 0.000 | R/plotting.R | 0.000 | 1 | 0 | 1 | 0 | 0.019 |
| plot_condition_vs_prediction_ranks_hist | function | true | 0 | 4 | 0.000 | 0.000 | R/plotting.R | 0.000 | 1 | 0 | 1 | 0 | 0.019 |
| plot_roc_curve | function | true | 0 | 2 | 0.000 | 0.000 | R/plotting.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| plot_sensitivity | function | true | 0 | 11 | 0.000 | 0.000 | R/plotting.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| plot_sensitivity_2d | function | true | 0 | 9 | 0.000 | 0.000 | R/plotting.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| rank_predictions | function | true | 0 | 7 | 0.000 | 0.000 | R/performance_metrics.R | 0.000 | 0 | 3 | 0 | 3 | 0.069 |
| rect_best | function | true | 0 | 1 | 0.000 | 0.000 | R/plotting.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| save_df | function | true | 0 | 6 | 0.000 | 0.000 | R/other_functions.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| shuffle_data | function | true | 0 | 1 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 1 | 0 | 5 | 0.049 |
| split_data | function | true | 0 | 5 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| threshold_combinations | function | true | 0 | 7 | 0.000 | 0.000 | R/model_training.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| tidy_rehab_info | function | true | 0 | 11 | 0.000 | 0.000 | R/data_preparation.R | 0.000 | 0 | 0 | 0 | 0 | 0.019 |
| tune_cutoffs | function | true | 0 | 36 | 0.000 | 0.000 | R/model_training.R | 0.000 | 5 | 0 | 10 | 0 | 0.019 |
| tune_hyperparameters | function | true | 0 | 38 | 0.000 | 0.000 | R/model_training.R | 0.000 | 6 | 0 | 11 | 0 | 0.019 |
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