Instructions to use molkab/dashboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use molkab/dashboard with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://molkab/dashboard") - Notebooks
- Google Colab
- Kaggle
Upload 6 files
Browse files- autoencoder.keras +0 -0
- df_avec_anomalies.parquet +3 -0
- modeles_anomalie.joblib +3 -0
- performance_qualitative_modeles.csv +8 -0
- resultats_anomalie.json +58 -0
- score_stations.parquet +3 -0
autoencoder.keras
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df_avec_anomalies.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:b436a13abb961a069dae16c763446e47107602feffebd6ef8041545d5a62ab69
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size 228341443
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modeles_anomalie.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:d164fa464ef78e115f26de4f646709a816fe358aba88da4b90fd670fbd09ee36
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size 233150156
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performance_qualitative_modeles.csv
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,pct_detection_%,valid_metier_%,instabilite,pval_stabilite,score_stabilite,overlap_ensemble_%,type_dominant,score_composite
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Isolation Forest,4.28,33.5,0.46,0.0,95.4,5.7,t4_%,55.33
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Elliptic Envelope,4.44,30.2,0.06,0.1075,100.0,5.8,t4_%,55.17
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DBSCAN,3.25,36.2,0.11,0.0006,98.9,5.5,t4_%,54.89
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One-Class SVM,4.47,28.7,0.03,0.4384,100.0,5.8,t4_%,54.67
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GMM,4.45,27.4,0.53,0.0,94.7,5.8,t4_%,52.98
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Autoencoder,4.09,17.0,0.57,0.0,94.3,4.0,t4_%,49.29
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LOF,3.99,9.2,0.12,0.0011,98.8,2.9,t4_%,47.62
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resultats_anomalie.json
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{
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"Isolation Forest": {
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"n_anomalies": "56239",
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"pct_anomalies": "4.28",
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"pct_train": "4.13",
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"pct_test": "4.59",
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"instabilite": "0.46",
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"accord_metier_%": "98.0"
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},
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"LOF": {
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"n_anomalies": "52425",
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"pct_anomalies": "3.99",
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"pct_train": "4.03",
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"pct_test": "3.91",
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"instabilite": "0.12",
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"accord_metier_%": "93.5"
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},
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"One-Class SVM": {
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"n_anomalies": "58707",
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"pct_anomalies": "4.47",
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"pct_train": "4.46",
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"pct_test": "4.49",
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"instabilite": "0.03",
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"accord_metier_%": "96.7"
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},
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"DBSCAN": {
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"n_anomalies": "42688",
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"pct_anomalies": "3.25",
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"pct_train": "3.21",
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"pct_test": "3.32",
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"instabilite": "0.11",
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"accord_metier_%": "98.4"
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},
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"Elliptic Envelope": {
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"n_anomalies": "58378",
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"pct_anomalies": "4.44",
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"pct_train": "4.42",
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"pct_test": "4.48",
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"instabilite": "0.06",
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"accord_metier_%": "98.4"
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},
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"GMM": {
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"n_anomalies": "58519",
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"pct_anomalies": "4.45",
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"pct_train": "4.28",
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"pct_test": "4.81",
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"instabilite": "0.53",
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"accord_metier_%": "96.8"
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},
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"Autoencoder": {
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"n_anomalies": "53688",
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"pct_anomalies": "4.09",
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"pct_train": "4.28",
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"pct_test": "3.7",
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"instabilite": "0.57",
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"accord_metier_%": "95.5"
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}
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}
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score_stations.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:5a75a8073dd491405e1ed8a09dce77f73163dbb0807a0e68d6e485846803acf1
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size 13926
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