config.defaults.yaml

kowalski:
  hosts:
  # Add/remove Kowalski host names and a corresponding token below
    kowalski:
      token:
    gloria:
      token:
    melman:
      token:
  port: 443
  protocol: https
  timeout: 300
  collections:
    gaia: Gaia_EDR3
    # Current sources are from DR20 (on melman)
    sources: ZTF_sources_20240117
    # Current features generated from DR16 (on gloria)
    features: ZTF_source_features_DR16
    # Current classifications are from DR16 (on gloria)
    classifications: ZTF_source_classifications_DR16
    # Current alerts (on kowalski)
    alerts: ZTF_alerts
  # If cutting off light curve at a given data release, enter the max HJD below
  # (JD corrected for Earth/Sun light travel time)
  # By default, this is set at the end of DR16 (2459951.5)
  max_timestamp_hjd: 2459951.5
fritz:
  token:
  protocol: https
  host: fritz.science
  max_attempts: 10
  sleep_time: 5
wandb:
  entity: ztf-scope
  project: scope
  # Get your W&B token at https://wandb.ai/authorize
  token:
  sweep_config_dnn:
    method: bayes
    metric:
      goal: minimize
      name: val_loss
    run_cap: 10
    parameters:
    # For bayes method, use min/max instead of values
      lr:
        #values: [3.e-2, 3.e-3, 3.e-4]
        min: 3.e-4
        max: 3.e-2
      epsilon:
        #values: [1.e-7, 1.e-8, 1.e-9]
        min: 1.e-9
        max: 1.e-7
      amsgrad:
        #values: [3.e-3, 3.e-4, 3.e-5]
        min: 3.e-5
        max: 3.e-3
      decay:
        value: 0.0
      beta_1:
        value: 0.9
      beta_2:
        value: 0.999
      epochs:
        value: 50
      dense_branch:
        value: True
      conv_branch:
        value: True
      loss:
        value: binary_crossentropy
      optimizer:
        value: adam
      momentum:
        value: 0.9
      monitor:
        value: val_loss
      patience:
        value: 20
      callbacks:
        value:
          - reduce_lr_on_plateau
          - early_stopping
      run_eagerly:
        value: False
taxonomy:
  class: tds
  name: time domain source type
  subclasses:
    - class: phenomenological
      name: phenomenological
      description: |
        What can be said about an object given *only* ZTF data.
      subclasses:
        - class: vnv
          name: variable
          description: |
            Any ZTF source that shows detectable variability in its light curve
            due to an astrophysical origin. This excludes variability due to blended photometry,
            bright nearby stars, or any CCD artifact.
          subclasses:
            - class: pnp
              name: periodic
              description: |
                The ZTF light curve features astrophysical periodic variability.
                These are typically pulsators, rotating stars and (eclipsing) binaries.
                This does not include semi-periodic or quasi-periodic variability. This
                excludes variability due to a varying background (e.g. due to the moon),
                or spurious periodic variability due to nearby bright stars or other artifacts.
              subclasses:
                - class: sin
                  name: sinusoidal
                  subclasses:
                    - class: el
                      name: ellipsoidal
                - class: saw
                  name: sawtooth
                - class: mp
                  name: multi periodic
                - class: longt
                  name: long timescale
                  description: |
                    Any object which shows variability on timescales of ~100+ days. This can be a steady
                    increase/decrease in luminosity, e.g., AGN and CVs.
                    Long timescale period variable like Miras and the more
                    irregular semi-regular variables are generally included in this category.
                - class: e
                  name: eclipsing
                  description: |
                    Any source that shows eclipses in the light curve.
                    These are predominately eclipsing binaries.
                    Eclipsing planetisimals or planets with large rings would also
                    fall in this category.
                  subclasses:
                    - class: ea
                      name: detached binary
                    - class: eb
                      name: semi-detached binary
                    - class: ew
                      name: overcontact binary
                - class: wp
                  name: wrong period
                  subclasses:
                    - class: hp
                      name: half period
                    - class: dp
                      name: double period
            - class: i
              name: irregular
              description: |
                Objects which show irregular/stochastic variability.
                These are mostly accreting objects, AGN, CVs, and YSOs.
              subclasses:
                - class: fla
                  name: flaring
                  description: |
                    Any ZTF light curve that shows flares, rapidly rising and fading events.
                    These are mostly cataclysmic variables, some young stellar objects, M-dwarfs, and some AGN.
                - class: dip
                  name: dipping
        - class: nonvar
          name: non-variable
          subclasses:
            - class: bogus
              name: bogus
              description: |
                Any light curve that seems variable, but is not due to any astrophysical variability.
                These are galaxies that can seem to vary due to PSF variations,
                image artifacts like ghosts, blended stars, or diffraction spikes.
              subclasses:
                - class: ext
                  name: extended
                - class: blend
                  name: blend
                - class: bright
                  name: bright star
                - class: artfct
                  name: ccd artifact
    - class: ontological
      name: ontological
      description: |
        What can be said about an object given *not only* ZTF data
      subclasses:
        - class: accr
          name: Accretor
          description: |
            Any kind of accreting system.
          subclasses:
            - class: agn
              name: AGN
              description: |
                Extra-galactic objects which tend to vary irregularly. Often show slowly rising
                or fading light curves, and can also show outbursts in rare cases.
            - class: cv
              name: CV
              description: |
                A binary star that consists of two components:
                a white dwarf primary, and a mass transferring secondary.
            - class: yso
              name: YSO
              description: |
                Pre-main-sequence stars. They typically have an accretion disk and dust around
                them. This results in a light curve which shows irregular behavior,
                sometimes with outbursts or dust obscuration events.
        - class: bis
          name: Non-accreting binary
          description: |
            Any object that is a non-accreting binary system.
          subclasses:
            - class: blyr
              name: Beta Lyrae
              description: |
                Binary systems where one of the components has evolved into a subgiant or giant star and
                is filling its Roche lobe transferring mass in a disk.
                The light curve of these systems is of type EB. The period
                ranges between 0.3 days and 200 days.
                Systems with a period of >100 days contains a supergiant.
            - class: rscvn
              name: RS CVn
              description: |
                A binary star where at least one of the components
                has large stellar spots. The light curve shape is sinusoidal
                with periods of a few hours to 14 days.
                The shape of the light curve changes over timescales of months to years.
            - class: wuma
              name: W Ursae Maj
              description: |
                Eclipsing close binary stars of spectral types F, G, or K that share a common envelope
                of material and are thus in contact with one another.
            - class: hwvir
              name: HW Vir
            - class: emsms
              name: detached MS-MS
              description: |
                Detached eclipsing MS-MS binary.
            - class: peccb
              name: Peculiar compact binary
              subclasses:
                - class: nnser
                  name: NN Ser
        - class: puls
          name: Pulsator
          description: |
            Any kind of pulsating star.
          subclasses:
            - class: rrlyr
              name: RR Lyrae
              description: |
                RR Lyrae are radial pulsators on the horizontal branch; they are helium core burning and
                hydrogen shell burning. The pulsation period ranges between 0.2 and 1.0 days.
              subclasses:
                - class: rrab
                  name: RRab
                - class: rrc
                  name: RRc
                - class: rrd
                  name: RRd
                - class: rre
                  name: RRe
                - class: rrblz
                  name: Blazhko
            - class: lpv
              name: long period variable
              description: |
                Long Period Variables are cool giant stars. Nearly all stars of this type show
                some variability.
              subclasses:
                - class: mira
                  name: Mira
                  description: |
                    AGB stars which show very high amplitude (> 2.5), long period (80-1000
                    days) variability.
                - class: srv
                  name: Semiregular
                  description: |
                    Show more irregular, and lower amplitude variability than Miras.
                - class: osarg
                  name: OSARG
                  description: |
                    OGLE small-amplitude red giant.
            - class: rvtau
              name: RV Tau
            - class: dscu
              name: Delta Scuti
              description: |
                Delta Scuti are pulsating A&F main sequence stars. The pulsation period ranges between
                0.03 between 0.3 days, and the amplitude is typically 0.2 mag but can reach up to 0.8mag.
                Their light curves are asymmetric, with a rapid rise and slow decay.
            - class: ceph2
              name: Population II Cepheid
              subclasses:
                - class: blher
                  name: BL Her
                - class: wvir
                  name: W Vir
            - class: ceph
              name: Cepheid
              description: |
                Cepheids are radially pulsating giant stars in the instability strip.
                Period range between 1 and 50 days, with extreme examples of 200 days.
                Light curves shapes range from asymmetric with a steep rise
                and slow decay, to almost sinusoidal light curve shapes.
              subclasses:
                - class: f
                  name: Fundamental
                - class: o
                  name: Overtone

docs:
  field_guide:
    rr_lyr_ab:
      coordinates: [254.9327, 30.8264]
      period: 0.6299307435824886
      title: RR Lyrae ab
    rr_lyr_c:
      coordinates: [254.7213, 32.8341]
      period: 0.31087445597147706
      title: RR Lyrae c
    w_uma:
      coordinates: [270.4229, 7.1722]
      period: 0.26901030387060865
      title: W UMa
    beta_lyr:
      coordinates: [116.79866129999999, 51.4231961]
      period: 1.836431252123213
      title: Beta Lyrae
    bogus_1:
      coordinates: [328.1595911, 69.6487077]
      title: Bogus ``flaring'' from saturation ghosts
    bogus_2:
      coordinates: [209.584254, 40.4601446]
      title: Bogus ``flaring'' from a different artifact
    delta_scuti:
      coordinates: [279.9154053, 60.7140833]
      period: 0.064050575
      title: Delta Scuti
    cepheid_F:
      coordinates: [ 0.441542, 62.424306 ]
      period: 3.8785736
      title: Fundamental-mode classical Cepheid
    cepheid_F_1:
      coordinates: [ 4.260375, 60.802722 ]
      period: 6.7987667
      title: Fundamental-mode classical Cepheid with a secondary bump
    cepheid_1O:
      coordinates: [ 321.964458, 24.422528 ]
      period: 0.7703319
      title: First-overtone classical Cepheid
    cepheid_F1O:
      coordinates: [ 292.005292, 19.949889 ]
      period: 4.0442931
      title: Double-mode classical Cepheid
    flaring_dwarfnova:
      coordinates: [ 266.8566657, 63.0465916 ]
      title: Dwarf Nova (Flaring)
    flaring_mdwarf:
      coordinates: [ 103.289525, 10.194726 ]
      title: M dwarf (Flaring)
    cv_U_Gem:
      coordinates: [ 18.884181, 37.626534 ]
      title: Cataclysmic variable (U Gem dwarf nova)
    cv_Z_Cam:
      coordinates: [ 269.093260, 2.967803 ]
      title: Cataclysmic variable (Z Cam dwarf nova)
    cv_SU_UMa:
      coordinates: [ 170.883476, 43.288214 ]
      title: Cataclysmic variable (SU UMa/WZ Sge dwarf nova)
    cv_Novalike:
      coordinates: [ 322.961689, 49.233813 ]
      title: Cataclysmic variable (Novalike)
    mira:
      coordinates: [ 9.1523, 68.0221 ]
      period: 212.0066035
      title: A Mira (Long Period Variable)
    periodic:
      coordinates: [ 288.5243027, 1.51191 ]
      period: 0.350072813283344
      title: Periodic variable
    srv:
      coordinates: [ 330.302266, 66.153712 ]
      title: Semi Regular Variable
    variable:
      coordinates: [ 102.5537728, 44.1032507 ]
      title: Variable stars

features:
  info:
    period_suffix: ELS_ECE_EAOV
  phenomenological:
    ad:
      include: true
      dtype: float
      periodic: false
    ccd:
      include: false
      dtype: Int64
      periodic: false
    chi2red:
      include: true
      dtype: float
      periodic: false
    dec:
      include: false
      dtype: float
      periodic: false
    f1_BIC:
      include: true
      dtype: float
      periodic: true
    f1_a:
      include: true
      dtype: float
      periodic: true
    f1_amp:
      include: true
      dtype: float
      periodic: true
    f1_b:
      include: true
      dtype: float
      periodic: true
    f1_phi0:
      include: true
      dtype: float
      periodic: true
    f1_power:
      include: true
      dtype: float
      periodic: true
    f1_relamp1:
      include: true
      dtype: float
      periodic: true
    f1_relamp2:
      include: true
      dtype: float
      periodic: true
    f1_relamp3:
      include: true
      dtype: float
      periodic: true
    f1_relamp4:
      include: true
      dtype: float
      periodic: true
    f1_relphi1:
      include: true
      dtype: float
      periodic: true
    f1_relphi2:
      include: true
      dtype: float
      periodic: true
    f1_relphi3:
      include: true
      dtype: float
      periodic: true
    f1_relphi4:
      include: true
      dtype: float
      periodic: true
    field:
      include: false
      dtype: Int64
      periodic: false
    i60r:
      include: true
      dtype: float
      periodic: false
    i70r:
      include: true
      dtype: float
      periodic: false
    i80r:
      include: true
      dtype: float
      periodic: false
    i90r:
      include: true
      dtype: float
      periodic: false
    inv_vonneumannratio:
      include: true
      dtype: float
      periodic: false
    iqr:
      include: true
      dtype: float
      periodic: false
    mean_ztf_alert_braai:
      include: false
      dtype: float
      periodic: false
    median:
      include: true
      dtype: float
      periodic: false
    median_abs_dev:
      include: true
      dtype: float
      periodic: false
    n:
      include: false
      dtype: float
      periodic: false
    n_ztf_alerts:
      include: false
      dtype: Int64
      periodic: false
    norm_excess_var:
      include: true
      dtype: float
      periodic: false
    norm_peak_to_peak_amp:
      include: true
      dtype: float
      periodic: false
    pdot:
      include: false
      dtype: float
      periodic: true
    period:
      include: false
      dtype: float
      periodic: true
    quad:
      include: false
      dtype: Int64
      periodic: false
    ra:
      include: false
      dtype: float
      periodic: false
    roms:
      include: true
      dtype: float
      periodic: false
    significance:
      include: false
      dtype: float
      periodic: true
    skew:
      include: true
      dtype: float
      periodic: false
    smallkurt:
      include: true
      dtype: float
      periodic: false
    stetson_j:
      include: true
      dtype: float
      periodic: false
    stetson_k:
      include: true
      dtype: float
      periodic: false
    sw:
      include: true
      dtype: float
      periodic: false
    welch_i:
      include: true
      dtype: float
      periodic: false
    wmean:
      include: true
      dtype: float
      periodic: false
    wstd:
      include: true
      dtype: float
      periodic: false
  ontological:
    ad:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    ccd:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    chi2red:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    dec:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    f1_BIC:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_a:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_amp:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_b:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_phi0:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_power:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relamp1:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relamp2:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relamp3:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relamp4:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relphi1:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relphi2:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relphi3:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    f1_relphi4:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    field:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    i60r:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    i70r:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    i80r:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    i90r:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    inv_vonneumannratio:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    iqr:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    mean_ztf_alert_braai:
      include: true
      dtype: float
      impute_strategy: zero
      periodic: false
    median:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    median_abs_dev:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    n:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    n_ztf_alerts:
      include: true
      dtype: Int64
      impute_strategy: zero
      periodic: false
    norm_excess_var:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    norm_peak_to_peak_amp:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    pdot:
      include: false
      dtype: float
      impute_strategy: None
      periodic: true
    period:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    quad:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    ra:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    roms:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    significance:
      include: true
      dtype: float
      impute_strategy: None
      periodic: true
    skew:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    smallkurt:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    stetson_j:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    stetson_k:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    sw:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    welch_i:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    wmean:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    wstd:
      include: true
      dtype: float
      impute_strategy: None
      periodic: false
    AllWISE___id:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    AllWISE__ph_qual:
      include: false
      dtype: str
      impute_strategy: None
      periodic: false
    AllWISE__w1mpro:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    AllWISE__w1sigmpro:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    AllWISE__w2mpro:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    AllWISE__w2sigmpro:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    AllWISE__w3mpro:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    # The below magnitude error is missing from the majority of training sources; we therefore exclude it by default.
    AllWISE__w3sigmpro:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    AllWISE__w4mpro:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    # The below magnitude error is missing from the majority of training sources; we therefore exclude it by default.
    AllWISE__w4sigmpro:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    Gaia_EDR3___id:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    Gaia_EDR3__astrometric_excess_noise:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    Gaia_EDR3__parallax:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    Gaia_EDR3__parallax_error:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    Gaia_EDR3__phot_bp_mean_mag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    Gaia_EDR3__phot_bp_rp_excess_factor:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    Gaia_EDR3__phot_g_mean_mag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    Gaia_EDR3__phot_rp_mean_mag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    Gaia_EDR3__pmdec:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    Gaia_EDR3__pmdec_error:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    Gaia_EDR3__pmra:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    Gaia_EDR3__pmra_error:
      include: false
      dtype: float
      impute_strategy: None
      periodic: false
    PS1_DR1___id:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    PS1_DR1__gMeanPSFMag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    PS1_DR1__gMeanPSFMagErr:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    PS1_DR1__iMeanPSFMag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    PS1_DR1__iMeanPSFMagErr:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    PS1_DR1__qualityFlag:
      include: false
      dtype: Int64
      impute_strategy: None
      periodic: false
    PS1_DR1__rMeanPSFMag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    PS1_DR1__rMeanPSFMagErr:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    PS1_DR1__yMeanPSFMag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    PS1_DR1__yMeanPSFMagErr:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    PS1_DR1__zMeanPSFMag:
      include: true
      dtype: float
      impute_strategy: regress
      periodic: false
    PS1_DR1__zMeanPSFMagErr:
      include: true
      dtype: float
      impute_strategy: median
      periodic: false
    _id:
      include: false
      dtype: int
      impute_strategy: None
      periodic: false
    coordinates:
      include: false
      dtype: object
      impute_strategy: None
      periodic: false
    # This feature is included separately by nn.py, but the dtype is specified here for get_features.py
    dmdt:
      include: false
      dtype: object
      impute_strategy: None
      periodic: false

feature_stats:
  # d15-based
  ra:
    min: 0.000637
    max: 359.9959065
    median: 260.57838475
    mean: 203.8537195009122
    std: 111.29524619256385
  dec:
    min: -27.97669295
    max: 80.12096305
    median: 35.74461235
    mean: 33.8050848864133
    std: 24.53928644472575
  ccd:
    min: 1
    max: 16
    median: 10.0
    mean: 8.9215225842098
    std: 4.407593218358259
  quad:
    min: 1
    max: 4
    median: 2.0
    mean: 2.474682794956292
    std: 1.11338706658081
  field:
    min: 250
    max: 876
    median: 683.0
    mean: 658.9159381968151
    std: 146.04044551816344
  median:
    min: 11.859
    max: 21.7355
    median: 17.192999999999998
    mean: 17.198497876180767
    std: 1.984417549587372
  wmean:
    min: 0.0
    max: 21.72580234909332
    median: 17.190525885310052
    mean: 17.194469189756948
    std: 1.9724064028988926
  chi2red:
    min: 0.0
    max: 14504.867568350095
    median: 8.349023359399014
    mean: 43.90164979812083
    std: 252.59980815489058
  roms:
    min: 0.0
    max: 114.51620270036064
    median: 2.390460752111644
    mean: 3.6660180168808383
    std: 4.379744545440027
  wstd:
    min: 0.0
    max: 2.255608626306496
    median: 0.0875371400495776
    mean: 0.09811229869829607
    std: 0.08429406100213001
  norm_peak_to_peak_amp:
    min: -0.0026624340791561
    max: 0.1968704723268617
    median: 0.0067790238702771
    mean: 0.008094121375282185
    std: 0.008743865061694922
  norm_excess_var:
    min: -0.000112900619492
    max: 0.0609204201612541
    median: 1.769738951325714e-05
    mean: 7.146689144239448e-05
    std: 0.0007345666165872003
  median_abs_dev:
    min: 0.0030000000000001
    max: 1.8210000000000013
    median: 0.0659999999999989
    mean: 0.07190794059530176
    std: 0.06233962602348945
  iqr:
    min: 0.0060000000000002
    max: 4.503
    median: 0.141
    mean: 0.15836848656470792
    std: 0.14571166540496608
  i60r:
    min: 0.0085999999999994
    max: 4.783999999999999
    median: 0.1716000000000015
    mean: 0.19276104619501683
    std: 0.17420744789781734
  i70r:
    min: 0.0109999999999992
    max: 5.121999999999998
    median: 0.2028000000000034
    mean: 0.22859205687634082
    std: 0.2035872239632559
  i80r:
    min: 0.0129999999999999
    max: 5.510200000000001
    median: 0.2355999999999998
    mean: 0.26673144380824426
    std: 0.23485124406618516
  i90r:
    min: 0.0149999999999987
    max: 5.943899999999999
    median: 0.2728499999999982
    mean: 0.3086220097322302
    std: 0.26757399787961367
  skew:
    min: -2313631.3289482933
    max: 991099.7040389637
    median: 2.205022682495793
    mean: 61.31085516410248
    std: 11255.873862484796
  smallkurt:
    min: -3.11887814831396
    max: 972157474.9911993
    median: 158.06917554356693
    mean: 131691.3802343864
    std: 4342921.268698039
  inv_vonneumannratio:
    min: 0.0
    max: 9391.44532323455
    median: 0.6565422687368709
    mean: 4.93473533396986
    std: 81.77941050819155
  welch_i:
    min: -56346.08384684319
    max: 4493321.414933997
    median: 5.7813606990069095
    mean: 2267.616080217704
    std: 47873.865854501935
  stetson_j:
    min: -1609.5878099366153
    max: 43589.80955987325
    median: 5.442137093176486
    mean: 72.9671030784923
    std: 636.9630033716279
  stetson_k:
    min: 0.0
    max: 0.9900279161401804
    median: 0.8495064736265487
    mean: 0.8452080085034379
    std: 0.04080279802330599
  ad:
    min: 0.0
    max: 176.17630087537145
    median: 4.829378125197593
    mean: 11.05490766018392
    std: 16.348165501152252
  sw:
    min: 0.0
    max: 0.9927340149879456
    median: 0.9054134488105774
    mean: 0.8523579384039067
    std: 0.13993721180508925
  n_ztf_alerts:
    min: 0
    max: 4723
    median: 107.0
    mean: 303.7225303791757
    std: 463.8160278824898
  mean_ztf_alert_braai:
    min: 0.0
    max: 1.0
    median: 0.8024972106227093
    mean: 0.6186727091350507
    std: 0.399777890607977
  period:
    min: 0.0208335053175687
    max: 517.5360717773438
    median: 0.1684057712554931
    mean: 6.364124402270989
    std: 41.949603601179156
  significance:
    min: 6.118862628936768
    max: 624.5211181640625
    median: 58.54999923706055
    mean: 114.21896548587495
    std: 127.5865875752379
  f1_power:
    min: 0.0
    max: 0.9991801222326536
    median: 0.7729513576206686
    mean: 0.5856637777642174
    std: 0.39012498079263525
  f1_BIC:
    min: 0.0
    max: 2891387.504941375
    median: 230.87496827341926
    mean: 999.6595511797041
    std: 14770.393366693705
  f1_a:
    min: 11.605038108855526
    max: 35.47802516000542
    median: 17.204367321227384
    mean: 17.207453638187303
    std: 1.9754009492673177
  f1_b:
    min: -0.0081851219151183
    max: 0.0062801395065665
    median: 2.0085760689046505e-07
    mean: 3.4800597779789258e-06
    std: 7.780194814337619e-05
  f1_amp:
    min: -9.887075962261807
    max: 13.924309960344583
    median: 0.0
    mean: 0.00030681938973214007
    std: 0.13155946866638166
  f1_phi0:
    min: -11.724438770554938
    max: 29.653066320859114
    median: 0.0
    mean: -5.380212391896144e-05
    std: 0.13836656908106604
  f1_relamp1:
    min: -18.83303491356885
    max: 7.879818700280141
    median: 0.0
    mean: 0.00016760276739433792
    std: 0.07498279319084965
  f1_relphi1:
    min: -10.364638619695516
    max: 9.954031288887238
    median: 0.0
    mean: -0.00019264295360274503
    std: 0.07278466428116556
  f1_relamp2:
    min: -6.975813530902832
    max: 4.871874483725203
    median: 0.0
    mean: 0.00011113187232755609
    std: 0.043310560007686544
  f1_relphi2:
    min: -10.400297029937953
    max: 6.570057847623894
    median: 0.0
    mean: 1.0872106471319056e-05
    std: 0.05059444662093973
  f1_relamp3:
    min: -2.6981900071250675
    max: 3.781430338071792
    median: 0.0
    mean: 9.560206262050944e-06
    std: 0.029455447659825662
  f1_relphi3:
    min: -3.620249993449979
    max: 2.427014067940686
    median: 0.0
    mean: -1.4567874903282583e-05
    std: 0.03084918708847871
  f1_relamp4:
    min: -1.5591887740444768
    max: 3.3654605530559376
    median: 0.0
    mean: 2.200771291810491e-05
    std: 0.021579957892031404
  f1_relphi4:
    min: -1.3925247531097054
    max: 3.512580595735894
    median: 0.0
    mean: -3.106283403481359e-05
    std: 0.021489003829155504
  AllWISE__w1mpro:
    min: -0.64
    max: 18.55
    median: 13.375
    mean: 10.39146157663123
    std: 6.336729280993646
  AllWISE__w1sigmpro:
    min: 0.0
    max: 0.541
    median: 0.025
    mean: 0.02828977736064503
    std: 0.029680655722442908
  AllWISE__w2mpro:
    min: -1.027
    max: 18.371
    median: 13.38
    mean: 10.416712919083375
    std: 6.358968070465865
  AllWISE__w2sigmpro:
    min: 0.0
    max: 0.5429999999999999
    median: 0.0289999999999999
    mean: 0.05173096333452405
    std: 0.07441618446166971
  AllWISE__w3mpro:
    min: -2.966
    max: 13.961
    median: 12.18
    mean: 9.032498107488976
    std: 5.40822315450349
  AllWISE__w3sigmpro:
    min: 0.0
    max: 0.5429999999999999
    median: 0.0
    mean: 0.055817588047942596
    std: 0.12963007750540897
  AllWISE__w4mpro:
    min: -3.4930000000000003
    max: 10.353
    median: 8.818
    mean: 6.599634083670247
    std: 3.930799975558018
  AllWISE__w4sigmpro:
    min: 0.0
    max: 0.5429999999999999
    median: 0.0
    mean: 0.013956541473697027
    std: 0.07262771279115897
  Gaia_EDR3__astrometric_excess_noise:
    min: 0.0
    max: 159.00471000000002
    median: 0.0
    mean: 0.4413401196481314
    std: 2.654617484408687
  Gaia_EDR3__parallax:
    min: -22.923777811496677
    max: 46.29583797406394
    median: 0.3582558286094884
    mean: 0.5084082090602149
    std: 0.8450879304094892
  Gaia_EDR3__parallax_error:
    min: 0.0
    max: 3.9449198
    median: 0.06740824
    mean: 0.12593136954772724
    std: 0.17324536501697696
  Gaia_EDR3__phot_bp_mean_mag:
    min: 0.0
    max: 24.21375
    median: 17.42191
    mean: 17.10962581305227
    std: 3.113212898074037
  Gaia_EDR3__phot_bp_rp_excess_factor:
    min: 0.0
    max: 163.73576
    median: 1.244185
    mean: 1.3158426238751653
    std: 1.0058780240460925
  Gaia_EDR3__phot_g_mean_mag:
    min: 0.0
    max: 22.020529
    median: 16.873185999999997
    mean: 16.556365949973124
    std: 2.9032751923604057
  Gaia_EDR3__phot_rp_mean_mag:
    min: 0.0
    max: 22.126274
    median: 16.166319
    mean: 15.811470105575822
    std: 2.8546598354464647
  Gaia_EDR3__pmdec:
    min: -327.72186458913035
    max: 360.8625803334337
    median: -2.459304207403531
    mean: -3.4039371930688382
    std: 6.292282926685255
  Gaia_EDR3__pmdec_error:
    min: 0.0
    max: 2.9573073
    median: 0.06420316
    mean: 0.12223972343607999
    std: 0.17578232423936574
  Gaia_EDR3__pmra:
    min: -445.8502233328984
    max: 411.0655015198922
    median: -0.7855059678934171
    mean: -0.9401638846246996
    std: 6.396205073636185
  Gaia_EDR3__pmra_error:
    min: 0.0
    max: 3.0488079
    median: 0.07042039
    mean: 0.13286379004737364
    std: 0.1853519241832836
  PS1_DR1__gMeanPSFMag:
    min: 0.0
    max: 27.992001
    median: 17.5881
    mean: 17.385813641764923
    std: 2.998618558799227
  PS1_DR1__gMeanPSFMagErr:
    min: 0.0
    max: 0.4762149999999999
    median: 0.0162705005
    mean: 0.02560849714412246
    std: 0.030294569909108778
  PS1_DR1__iMeanPSFMag:
    min: 0.0
    max: 21.8801
    median: 16.6315
    mean: 16.548635769405635
    std: 2.0492561358125423
  PS1_DR1__iMeanPSFMagErr:
    min: 0.0
    max: 0.483466
    median: 0.010936
    mean: 0.016503628403760233
    std: 0.019215705218350018
  PS1_DR1__qualityFlag:
    min: 0.0
    max: 174.0
    median: 60.0
    mean: 58.39407705445202
    std: 11.330792461922785
  PS1_DR1__rMeanPSFMag:
    min: 0.0
    max: 22.238001
    median: 16.9951
    mean: 16.936967671590995
    std: 2.1596557453476377
  PS1_DR1__rMeanPSFMagErr:
    min: 0.0
    max: 0.48713499
    median: 0.012881
    mean: 0.019732657415647375
    std: 0.02306842342675604
  PS1_DR1__yMeanPSFMag:
    min: 0.0
    max: 21.304199
    median: 16.280001000000002
    mean: 16.047748625891675
    std: 2.411650378146079
  PS1_DR1__yMeanPSFMagErr:
    min: 0.0
    max: 0.48370799
    median: 0.0145479999999999
    mean: 0.022344817664936102
    std: 0.026862078598248854
  PS1_DR1__zMeanPSFMag:
    min: 0.0
    max: 21.770599
    median: 16.448
    mean: 16.325237993652525
    std: 2.086329999522018
  PS1_DR1__zMeanPSFMagErr:
    min: 0.0
    max: 0.49948201
    median: 0.011866
    mean: 0.01834675346206806
    std: 0.021816145036886694

# Below, specify algorithms, external catalogs and features to include in generated feature lists:
feature_generation:
  # Path to save generated features
  path_to_features:
  # If --doSpecificIDs is set in generate_features.py, the script will generate features for the below dataset instead of a field/ccd/quad.
  # Dataset must contain columns named "ztf_id" and "coordinates" with data in the format of these fields on Kowalski
  # Default dataset is the training set downloadable from Fritz
  dataset: tools/fritzDownload/merged_classifications_features.parquet
  # Once generate_features.py is run with --doSpecificIDs, a file will be saved with the default name below.
  # Set --skipCloseSources to load the file below and skip the idenfication of close sources:
  ids_skipGaia: specific_ids_dropCloseSources.json
  period_algorithms:
    CPU:
      - LS
      - CE
      #- AOV
    GPU:
      # If ELS_ECE_EAOV is set, period finding will be run using a nested approach which passes the top N ELS and ECE periods to EAOV.
      # Individual ELS, ECE and EAOV periods will also be saved.
      #- ELS_ECE_EAOV
      - ELS
      - ECE
      - EAOV
  fields_to_run:
    # If this list is populated (example below), generate_features_job_submission.py will only run on the specified fields.
    - 296
    # - 297
    # - 423
    # - 424
    # - 487
    # - 488
    # - 562
    # - 563
    # - 682
    # - 683
    # - 699
    # - 700
    # - 717
    # - 718
    # - 777
    # - 778
    # - 841
    # - 842
    # - 852
    # - 853
  cesium_features:
    # Some features on this list are already computed by lcstats - these are commented below
    #
    # Cadence/Error:
    #
    #- n_epochs
    - avg_err
    - med_err
    - std_err
    - total_time
    - avgt
    - cads_std
    - mean
    - cads_avg
    - cads_med
    - cad_probs_1
    - cad_probs_10
    - cad_probs_20
    - cad_probs_30
    - cad_probs_40
    - cad_probs_50
    - cad_probs_100
    - cad_probs_500
    - cad_probs_1000
    - cad_probs_5000
    - cad_probs_10000
    - cad_probs_50000
    - cad_probs_100000
    - cad_probs_500000
    - cad_probs_1000000
    - cad_probs_5000000
    - cad_probs_10000000
    - med_double_to_single_step
    - avg_double_to_single_step
    - std_double_to_single_step
    - all_times_nhist_numpeaks
    - all_times_nhist_peak_val
    - all_times_nhist_peak_1_to_2
    - all_times_nhist_peak_1_to_3
    - all_times_nhist_peak_2_to_3
    - all_times_nhist_peak_1_to_4
    - all_times_nhist_peak_2_to_4
    - all_times_nhist_peak_3_to_4
    - all_times_nhist_peak1_bin
    - all_times_nhist_peak2_bin
    - all_times_nhist_peak3_bin
    - all_times_nhist_peak4_bin
    #
    # General:
    #
    - amplitude
    - flux_percentile_ratio_mid20
    - flux_percentile_ratio_mid35
    - flux_percentile_ratio_mid50
    - flux_percentile_ratio_mid65
    - flux_percentile_ratio_mid80
    - max_slope
    - maximum
    #- median
    #- median_absolute_deviation
    - minimum
    - percent_amplitude
    - percent_beyond_1_std
    - percent_close_to_median
    - percent_difference_flux_percentile
    - period_fast
    - qso_log_chi2_qsonu
    - qso_log_chi2nuNULL_chi2nu
    - skew
    - std
    #- stetson_j
    #- stetson_k
    #- weighted_average
    #
    # Lomb-Scargle (Periodic):
    #
    # These features require a second period analysis - commented by default
    # - fold2P_slope_10percentile
    # - fold2P_slope_90percentile
    # - freq1_amplitude1
    # - freq1_amplitude2
    # - freq1_amplitude3
    # - freq1_amplitude4
    # - freq1_freq
    # - freq1_lambda
    # - freq1_rel_phase2
    # - freq1_rel_phase3
    # - freq1_rel_phase4
    # - freq1_signif
    # - freq2_amplitude1
    # - freq2_amplitude2
    # - freq2_amplitude3
    # - freq2_amplitude4
    # - freq2_freq
    # - freq2_rel_phase2
    # - freq2_rel_phase3
    # - freq2_rel_phase4
    # - freq3_amplitude1
    # - freq3_amplitude2
    # - freq3_amplitude3
    # - freq3_amplitude4
    # - freq3_freq
    # - freq3_rel_phase2
    # - freq3_rel_phase3
    # - freq3_rel_phase4
    # - freq_amplitude_ratio_21
    # - freq_amplitude_ratio_31
    # - freq_frequency_ratio_21
    # - freq_frequency_ratio_31
    # - freq_model_max_delta_mags
    # - freq_model_min_delta_mags
    # - freq_model_phi1_phi2
    # - freq_n_alias
    # - freq_signif_ratio_21
    # - freq_signif_ratio_31
    # - freq_varrat
    # - freq_y_offset
    # - linear_trend
    # - medperc90_2p_p
    # - p2p_scatter_2praw
    # - p2p_scatter_over_mad
    # - p2p_scatter_pfold_over_mad
    # - p2p_ssqr_diff_over_var
    # - scatter_res_raw
  external_catalog_features:
    AllWISE:
      filter: {}
      projection:
        _id : 1
        w1mpro : 1
        w1sigmpro : 1
        w2mpro : 1
        w2sigmpro : 1
        w3mpro : 1
        w3sigmpro : 1
        w4mpro : 1
        w4sigmpro : 1
        ph_qual : 1
    Gaia_EDR3:
      filter: {}
      projection:
        _id : 1
        phot_g_mean_mag : 1
        phot_bp_mean_mag : 1
        phot_rp_mean_mag : 1
        parallax : 1
        parallax_error : 1
        pmra : 1
        pmra_error : 1
        pmdec : 1
        pmdec_error : 1
        astrometric_excess_noise : 1
        phot_bp_rp_excess_factor : 1
    PS1_DR1:
      filter: {}
      projection:
        _id : 1
        gMeanPSFMag : 1
        gMeanPSFMagErr : 1
        rMeanPSFMag : 1
        rMeanPSFMagErr : 1
        iMeanPSFMag : 1
        iMeanPSFMagErr : 1
        zMeanPSFMag : 1
        zMeanPSFMagErr : 1
        yMeanPSFMag : 1
        yMeanPSFMagErr : 1
        qualityFlag : 1
  dmdt_ints:
    # Bin spacing for dmdt histograms
    dmints:
      - -8
      - -4.5
      - -3
      - -2.5
      - -2
      - -1.5
      - -1.25
      - -0.75
      - -0.5
      - -0.3
      - -0.2
      - -0.1
      - -0.05
      - 0
      - 0.05
      - 0.1
      - 0.2
      - 0.3
      - 0.5
      - 0.75
      - 1.25
      - 1.5
      - 2
      - 2.5
      - 3
      - 4.5
      - 8
    dtints:
      - 0.0
      - 0.02759  # (4.0 / 145)
      - 0.04  # (1.0 / 25)
      - 0.08  # (2.0 / 25)
      - 0.12  # (3.0 / 25)
      - 0.3
      - 0.75
      - 1
      - 1.5
      - 2.5
      - 3.5
      - 4.5
      - 5.5
      - 7
      - 10
      - 20
      - 30
      - 45
      - 60
      - 90
      - 120
      - 180
      - 240
      - 360
      - 500
      - 650
      - 2000

training:
  # Below, enter path to training set
  dataset: tools/fritzDownload/merged_classifications_features.parquet
  xgboost:
    # See scope.py train for descriptions of these parameters
    gridsearch_params_start_stop_step:
      max_depth: [3, 8, 2]
      min_child_weight: [1, 6, 2]
      # subsample, colsample values get divided by 10
      # (default values produce .6, .8, 1.0)
      subsample: [6, 11, 2]
      colsample_bytree: [6, 11, 2]
    other_training_params:
      eta: [0.3, 0.2, 0.1, 0.05]
      seed: 42
      nfold: 5
      metrics: ['auc']
      objective: 'binary:logistic'
      eval_metric: 'auc'
      early_stopping_rounds: 10
      num_boost_round: 999
    plot_params:
      cm_include_count: False
      cm_include_percent: True
      annotate_scores: False
  dnn:
    dense_branch: True
    conv_branch: True
    loss: 'binary_crossentropy'
    optimizer: 'adam'
  classes:
    # phenomenological classes
    vnv:
      # value of label should refer to dataset.dN.csv
      label: variable
      # conv_branch: false
      # value should refer to features section of this config
      features: phenomenological
      # our "labels" are floats [0., 0.25, 0.5, 0.75, 1.]
      threshold: 0.7
      # balance ratio for the prevalent class. leave null to use all available data
      balance:
      weight_per_class: false
      amsgrad: 0.0003724
      epsilon: 1.945e-8
      lr: 0.02229
    pnp:
      label: periodic
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.0009233
      epsilon: 4.624e-8
      lr: 0.001016
    sin:
      label: sinusoidal
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 32
      amsgrad: 0.0003503
      epsilon: 2.377e-9
      lr: 0.01362
    el:
      label: ellipsoidal
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 16
      amsgrad: 0.001602
      epsilon: 2.080e-8
      lr: 0.003313
    saw:
      label: sawtooth
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 16
      amsgrad: 0.002117
      epsilon: 2.617e-8
      lr: 0.006019
    mp:
      label: multi periodic
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.0009331
      epsilon: 7.763e-8
      lr: 0.02553
    longt:
      label: long timescale
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.002464
      epsilon: 7.828e-8
      lr: 0.0157
    e:
      label: eclipsing
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.000508
      epsilon: 9.878e-8
      lr: 0.009592
    ea:
      label: EA
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: true
      batch_size: 32
      amsgrad: 0.002106
      epsilon: 3.393e-8
      lr: 0.02956
    eb:
      label: EB
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 32
      amsgrad: 0.001482
      epsilon: 2.910e-8
      lr: 0.008546
    ew:
      label: EW
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.0007208
      epsilon: 8.521e-8
      lr: 0.01169
    wp:
      label: wrong period
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 8
      amsgrad: 0.001258
      epsilon: 3.656e-9
      lr: 0.02745
    hp:
      label: half period
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.001203
      epsilon: 2.178e-8
      lr: 0.02022
    dp:
      label: double period
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.002407
      epsilon: 3.069e-8
      lr: 0.02491
    i:
      label: irregular
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.001389
      epsilon: 6.620e-8
      lr: 0.008913
    fla:
      label: flaring
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 32
      amsgrad: 0.0005241
      epsilon: 1.414e-8
      lr: 0.01857
    dip:
      label: dipping
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 2
      amsgrad: 0.001263
      epsilon: 6.249e-8
      lr: 0.01326
    bogus:
      label: bogus
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 16
      amsgrad: 0.001224
      epsilon: 6.559e-8
      lr: 0.02439
    ext:
      label: extended
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 2
      amsgrad: 0.002583
      epsilon: 6.738e-9
      lr: 0.001791
    blend:
      label: blend
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.0006385
      epsilon: 5.348e-8
      lr: 0.02108
    bright:
      label: bright star
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.0004591
      epsilon: 3.367e-8
      lr: 0.01797
    artfct:
      label: ccd artifact
      features: phenomenological
      threshold: 0.7
      balance:
      weight_per_class: true
    # ontological classes
    agn:
      label: AGN
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.0001084
      epsilon: 2.791e-8
      lr: 0.01955
    bis:
      label: binary star
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.002084
      epsilon: 4.453e-8
      lr: 0.01773
    blher:
      label: BL Her
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 2
      amsgrad: 0.00283
      epsilon: 7.960e-8
      lr: 0.01647
    blyr: #
      label: Beta Lyr
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 32
      amsgrad: 0.002989
      epsilon: 5.096e-8
      lr: 0.0238
    cbin:
      label: compact binary
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    ceph:
      label: Cepheid
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 16
      amsgrad: 0.0003948
      epsilon: 6.115e-8
      lr: 0.01887
    ceph2:
      label: Cepheid type-II
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.002885
      epsilon: 3.463e-8
      lr: 0.0006554
    cv:
      label: CV
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.001594
      epsilon: 4.780e-8
      lr: 0.02281
    dscu:
      label: Delta Scu
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.00003541
      epsilon: 2.910e-9
      lr: 0.02856
    emsms:
      label: detached eclipsing MS-MS
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.002309
      epsilon: 7.012e-8
      lr: 0.0242
    f:
      label: F
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    hwvir:
      label: eclipsing sdB+dM (HW Vir)
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    lpv:
      label: LPV
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.0004493
      epsilon: 9.229e-8
      lr: 0.01127
    mir:
      label: Mira
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.0004852
      epsilon: 9.688e-8
      lr: 0.02416
    nnser:
      label: eclipsing WD+dM (NN Ser)
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    o:
      label: O
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    osarg:
      label: OSARG
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 32
      amsgrad: 0.001104
      epsilon: 8.725e-8
      lr: 0.01476
    puls:
      label: pulsator
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.000611
      epsilon: 3.289e-8
      lr: 0.02658
    rdbkp:
      label: Redback pulsar
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    rrlyr:
      label: RR Lyrae
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.001245
      epsilon: 4.840e-8
      lr: 0.02868
    rrab:
      label: RR Lyrae ab
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.001047
      epsilon: 4.307e-8
      lr: 0.01685
    rrc:
      label: RR Lyrae c
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.002501
      epsilon: 7.619e-8
      lr: 0.02491
    rrd:
      label: RR Lyrae d
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 4
      amsgrad: 0.001336
      epsilon: 8.316e-8
      lr: 0.02657
    rrblz:
      label: RR Lyrae Blazhko
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 2
      amsgrad: 0.002507
      epsilon: 1.744e-8
      lr: 0.01402
    rscvn:
      label: RS CVn
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 16
      amsgrad: 0.002394
      epsilon: 3.469e-8
      lr: 0.01449
    rvtau:
      label: RV Tau
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: true
    srv:
      label: SRV
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 8
      amsgrad: 0.001237
      epsilon: 7.951e-8
      lr: 0.02222
    wuma:
      label: W UMa
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.001844
      epsilon: 4.736e-8
      lr: 0.01615
    wvir:
      label: W Virginis
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      batch_size: 2
      amsgrad: 0.000135
      epsilon: 8.731e-8
      lr: 0.02813
    yso:
      label: YSO
      features: ontological
      threshold: 0.7
      balance:
      weight_per_class: false
      amsgrad: 0.0009407
      epsilon: 3.372e-8
      lr: 0.02622

# Specify inference details here when running in parallel
inference:
  fields_to_run:
    - 296
    # - 297
    # - 423
    # - 424
    # - 487
    # - 488
    # - 562
    # - 563
    # - 682
    # - 683
    # - 699
    # - 700
    # - 717
    # - 718
    # - 777
    # - 778
    # - 841
    # - 842
    # - 852
    # - 853
  path_to_preds:

# Some fields may already be completed and should be excluded from subsequent feature generation/inference
fields_to_exclude:
  # - 296